Two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation

Abstract

The invention belongs to the technical field of primary frequency modulation of a power system, and particularly relates to a two-layer optimization quantitative evaluation method for load-side distributed resources to participate in primary frequency modulation. Comprising the following steps that: a virtual control unit classifies various load side resources according to the capacity characteristics of the load side resources by utilizing tagging, and aggregates the various load side resources by utilizing a two-layer structure; the virtual control unit selects various load side resources to participate in primary frequency modulation; and the virtual control unit evaluates the power capacity of each load side resource, and reports the total capacity to the power grid by taking the maximum total income of the virtual control unit as a target. According to the method, the aggregation of distributed resources is realized, the aggregation difficulty of the virtual control unit is simplified, the income of the virtual control unit is maximized under the condition that the time scale requirement of participating in the primary frequency modulation service of the power grid is met, the efficiency of estimating the credible capacity of the virtual control unit is improved, and the reliability of the virtual control unit is improved by taking maximization of the income of the virtual control unit as a target. And a theoretical basis is provided for load-side resources to participate in primary frequency modulation of the whole power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of primary frequency regulation of electric power systems, and in particular relates to a two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency regulation. Background Art

[0002] The world today is facing serious energy crisis and environmental pollution problems, so the penetration rate of new energy in the power system is gradually increasing, but new energy is intermittent, volatile, and random, and replaces the conventional units with the minimum technical output as the main resource of primary frequency regulation, resulting in weak frequency regulation capacity of the power system; while the investment in supplementary source side resources is large, and at the same time, there are a large number of flexible resources on the load side of the power system, which contains rich frequency regulation potential, so it is necessary to study the participation of load side resources in primary frequency regulation. Since the capacity scale of a single load side resource is insufficient and it is difficult to be directly called by the power system, it is necessary to use a virtual control unit to aggregate the load side resources so that their capacity is sufficient to participate in the primary frequency regulation service for the entire power system. However, due to the physical characteristics and user factors of each load side resource, its capacity to participate in primary frequency regulation is uncertain, which makes the total capacity of the virtual control unit that aggregates a large number of load side resources also uncertain, so it is necessary to quantitatively evaluate the credible capacity of the virtual control unit.

[0003] The main grid assigns a frequency regulation task to the virtual control center and puts forward the "three elements" requirements for power, namely, response time, response capacity, and sustainable duration. Among them, the response time is composed of lag time and ramp time; the response capacity is the power change when the virtual control unit fully responds; the sustainable duration is the time for the virtual control unit to maintain the maximum power change. Meeting the grid's requirements for response time and sustainable duration in terms of power output is the premise for the virtual control unit to participate in the grid's primary frequency regulation. Only on this basis can the size of the virtual control unit's response capacity be evaluated. While ensuring that the virtual control unit's capacity meets the grid's requirements, the probabilistic characteristics of the capacity of each load-side resource should be mastered. Then, considering the maximum benefit of the virtual control unit, the first round of risk analysis is carried out to determine the optimal reporting amount. After the main grid selects the reporting amount of the virtual control unit, the virtual control unit conducts a second round of risk analysis for which load-side resources are selected to participate in the primary frequency regulation. Therefore, it is necessary to provide an effective method for the virtual control unit to determine the reporting amount and select the load-side resources. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention provides a two-layer optimization and quantitative evaluation method for the participation of load-side distributed resources in primary frequency regulation. The purpose is to achieve the invention purpose of quickly and accurately evaluating the credible capacity of load-side resources participating in the primary frequency regulation of the power system and reporting the optimal capacity to the power grid.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0006] The two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation includes the following steps:

[0007] The virtual control unit uses labeling to classify various load-side resources according to their capacity characteristics, and then uses a two-layer structure to aggregate multiple load-side resources;

[0008] The virtual control unit selects multiple load-side resources to participate in primary frequency modulation;

[0009] The virtual control unit evaluates the power capacity of each load-side resource, and reports the total capacity to the grid with the goal of maximizing the total revenue of the virtual control unit.

[0010] Furthermore, the virtual control unit utilizes labeling to classify various load-side resources according to their capacity characteristics, that is, the load-side resources are divided into rectangular and trapezoidal labels according to their power output dynamic characteristics, wherein the ramp-up time of rectangular label resources is in milliseconds, and the ramp-up time of trapezoidal label resources is in seconds; the virtual control unit then aggregates the two types of label resources.

[0011] Furthermore, the virtual control unit selects multiple load-side resources to participate in the primary frequency regulation, which means that after labeling, the quantity, output size and time period of each type of load-side resource participating in the primary frequency regulation are taken into consideration, so that the load-side resources are "superimposed" and each resource is "relayed" to participate in the regulation, so as to meet the grid's requirements on response time and sustainable duration, make it eligible to participate in the primary frequency regulation, and conduct a credible quantitative study on the capacity of the virtual control unit.

[0012] Furthermore, the rectangular label includes electric vehicles and battery energy storage; the trapezoidal label includes electric water heater load.

[0013] Furthermore, the constraints of the electric vehicle include: charging and discharging power constraints, state of charge constraints and time coupling constraints; wherein the charging and discharging power constraints of the electric vehicle are: in, It is the opposite of the maximum discharge power allowed for a single electric vehicle. Plan the charging power for a single electric vehicle, is the adjustable power capacity of a single electric vehicle, is the maximum charging power allowed for a single electric vehicle; the state of charge constraint is: in, and are the minimum and maximum states of charge of electric vehicles, respectively;

[0014] The constraints of the battery energy storage system include: charging and discharging power constraints, state of charge constraints and time coupling constraints;

[0015] The battery energy storage charging and discharging power constraints are: in, It is the opposite of the maximum discharge power allowed by the battery energy storage unit. Plan the charging power for the battery energy storage unit; the state of charge constraint is: in, and are the minimum and maximum states of charge of the battery energy storage system, respectively;

[0016] The constraints of the electric water heater load include: operating state constraints and temperature change constraints;

[0017] The operating state constraints are as follows: the operating cycle of the electric water heater is 30 minutes, and there is a working state every 2 minutes in one working cycle, and there are 15 working states in one working cycle; among them, 1-5 are heating states, at this time the electric water heater is adjusted upward by reducing the load, denoted as m=1, m is the switch state of the electric water heater load, and its probability is 1 / 3; 6-15 are non-heating states, at this time the electric water heater cannot be adjusted upward by reducing the load, denoted as m=0, and the probability is 2 / 3;

[0018] The temperature change constraint is: meet user demand ΔT n ≤θ n .

[0019] Furthermore, the virtual control unit evaluates the power capacity of each load-side resource, takes the maximum total revenue of the virtual control unit as the goal, and reports the total capacity to the power grid. The total revenue of the virtual control unit takes the maximum total revenue as the goal, reports the total capacity to the power grid, and uses optimization to solve the optimal reported power capacity of the virtual control unit; wherein, the total revenue of the virtual control unit is maximized as the goal, and the total revenue of the virtual control unit takes the revenue and cost into consideration. The expression of the objective function is: maxInc total =max(Inc1+Inc2-C pay -C fine );

[0020] The revenue includes: the capacity fee Inc1 determined by the capacity reported by the virtual control center and the adjustment fee Inc2 paid by the power system dispatching center to the virtual control unit; the cost includes: the resource cost C paid by the virtual control unit to the load-side resources pay and the possible penalty cost C for failing to complete the regulation task issued by the power grid fine .

[0021] Furthermore, the total revenue of the virtual control unit is: Inc total =Inc1+Inc2-C pay -C fineThe revenue includes the capacity fee Inc1 determined by the capacity reported by the virtual control unit and the regulation fee Inc2 for the virtual control unit to participate in the primary frequency regulation service; the cost includes the resource cost C that the virtual control unit should pay to each load-side resource participating in the primary frequency regulation. pay ; and possible penalty costs C for failure to complete the regulation tasks issued by the power grid fine ;

[0022] Resource cost C pay It consists of rectangular load cluster cost and trapezoidal load cluster cost: C pay =C tangs +C lads , where the rectangular load cluster cost is C tangs , the cost of the trapezoidal load cluster is C lads ;

[0023] The rectangular load cluster cost consists of the electric vehicle cost and the battery energy storage system cost:

[0024]

[0025] In the above formula, k i is the unit price of the rectangular tag resource, i is 1 for all electric vehicles and 2 for all battery energy storage systems; M i is the number of individuals with electric vehicles or battery energy storage, P i,mi For each electric vehicle or battery energy storage unit, t i,mi,o The duration for each EV or BESS to participate in frequency response;

[0026] The ladder load cluster cost consists of the electric water heater cost:

[0027]

[0028] In the above formula, k lads is the unit price of the trapezoidal label resource, that is, the unit price of the electric water heater load, N is the number of individuals of the electric water heater load, P B，n (t) is the regulation output of each electric water heater load, t out,n The duration for each electric water heater load individual to participate in frequency response;

[0029] Substitute the available resource cost C pay The expression is:

[0030]

[0031] In the above formula, M i is the number of individuals of electric vehicles or battery energy storage, and N is the number of individuals of electric water heater load;

[0032] Capacity fee is expressed as: Inc1 = k rep P rep , where k rep is the capacity unit price of the virtual control unit, P rep It is the reported quantity of the virtual control unit;

[0033] The adjustment fee is expressed as: Among them, k reg is the adjustment unit price of the virtual control unit, and H is the number of adjustments;

[0034] When the virtual control unit participates in the primary frequency regulation of the power system, there is a situation where the capacity of the virtual control unit cannot meet the task volume. At this time, the power grid imposes a penalty on the virtual control unit, and the value is:

[0035]

[0036] Among them, P fact,h is the capacity value when the virtual control unit fully responds (t start ≤t≤t G,h ), H is the number of disturbances, k fine is the penalty unit price, t G,h The duration of each frequency regulation auxiliary service provided by the virtual control unit;

[0037] Considering the physical characteristics of electric vehicles and battery energy storage, the constraints are obtained, including power constraints, upper and lower limits of charge and discharge power, in: represents the maximum discharge power, Represents the maximum charging power, P 1，m1,charge Plan the charging power for a single electric vehicle, P 1，m1 is the adjustable power capacity of a single electric vehicle; power constraints, upper and lower limits of the state of charge, S m1 (t) is the state of charge of the electric vehicle, and are the minimum and maximum charge states of electric vehicles respectively; When the electric vehicle is in the discharging state, there is in is the charge state of the electric vehicle at the initial moment, Represents the charge and discharge efficiency, Indicates the rated capacity of the battery of the electric vehicle;

[0038] when When the electric vehicle is in the discharging state, there is

[0039]

[0040] The constraints of the battery energy storage system include power constraints, upper and lower limits of charge and discharge power, in represents the maximum discharge power, Represents the maximum charging power P 2，m2,charge is the planned charging power of a single battery energy storage unit, P 2，m2 The adjustable power capacity of a single battery storage; power constraints, upper and lower limits of the state of charge, Among them, when When the battery energy storage system is in the discharge state, there is S m2 (t) is the state of charge of the battery energy storage system, where is the charge state of the battery energy storage system at the initial moment, Represents the charge and discharge efficiency, Indicates the rated capacity of the battery in the battery energy storage system;

[0041] when When the battery energy storage system is in the discharge state, there is When participating in a frequency modulation, the temperature change constraint 0≤ΔT≤θ is satisfied, where ΔT is the water temperature change and θ is the maximum water temperature change value acceptable to the user.

[0042] The two-layer optimization quantitative evaluation device for the load-side distributed resources participating in primary frequency modulation includes:

[0043] The classification and aggregation module is used by the virtual control unit to classify various load-side resources according to their capacity characteristics by using labels, and then aggregate various load-side resources using a two-layer structure;

[0044] A selection module is used for the virtual control unit to select multiple load-side resources to participate in a frequency modulation;

[0045] An evaluation and reporting module is used by the virtual control unit to evaluate the power capacity of each load-side resource;

[0046] The two-layer optimization and quantitative evaluation of the load-side distributed resources participating in a frequency modulation is used to implement the steps of the two-layer optimization and quantitative evaluation method for the load-side distributed resources participating in a frequency modulation as described in any one of claims 1-7.

[0047] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the two-layer optimization quantitative evaluation methods for load-side distributed resources participating in a frequency modulation are implemented.

[0048] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the two-layer optimization quantitative evaluation methods for load-side distributed resources participating in a frequency modulation are implemented.

[0049] The present invention has the following beneficial effects and advantages:

[0050] The present invention provides a two-layer optimization and quantitative evaluation method for load-side distributed resources participating in primary frequency modulation, in which the load-side distributed resources are labeled and classified according to capacity characteristics, each label cluster aggregates similar label resources, and the virtual control unit aggregates each label cluster, constructing a multi-layer structure in which distributed resources are aggregated into virtual control units, thereby realizing the aggregation of distributed resources.

[0051] Virtual control units are used to aggregate a variety of load-side resources. On this basis, a two-layer aggregation structure for load-side resources participating in the primary frequency regulation capacity of the power system is proposed, which simplifies the aggregation difficulty of the virtual control units. The goal is to maximize the income of the virtual control units while meeting the time scale requirements of participating in the primary frequency regulation service of the power grid.

[0052] The present invention adopts a model of a two-layer optimized virtual control unit that classifies load-side resources by labeling and aggregates various distributed resources to participate in primary frequency modulation. The model can make full use of the ability of various load-side resources to perform primary frequency modulation, and can improve the efficiency of estimating the credible capacity of the virtual control unit. The virtual control unit classifies various load-side resources according to their capacity characteristics by labeling, and then aggregates a large number of load-side resources by a two-layer structure to ensure that the total capacity of the virtual control unit meets the requirements of the power grid for delay time, ramp time and sustainable duration; since the virtual control unit that aggregates various load-side resources needs to report the capacity to the power grid, it is necessary to conduct a credible capacity assessment on it. Under the premise of mastering the probabilistic characteristics of the capacity size of each load-side resource, the goal is to maximize the benefit of the virtual control unit. The credible capacity assessment method of the virtual control unit adopted by the present invention simplifies the capacity quantification process of the load-side resources, and can provide a theoretical basis for the participation of load-side resources in the primary frequency modulation of the entire power system, and also provides strong support for the development of distributed resources on the load side.

[0053] The present invention solves the problem that the number of load-side resources is large, the capacity is small, and it is difficult to be directly called by the power grid. The aggregated capacity is sufficient to participate in the primary frequency regulation service for the power system and is suitable for load-side resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0055] Figure 1 It is a schematic diagram of a two-layer structure of a virtual control unit aggregating load-side resources of the present invention;

[0056] Figure 2 This is a schematic diagram of the virtual control unit of the present invention;

[0057] Figure 3 It is a schematic diagram of the capacity of a rectangular tag cluster of the present invention;

[0058] Figure 4 It is a schematic diagram of the capacity of the trapezoidal tag cluster of the present invention;

[0059] Figure 5 It is a schematic diagram of the total capacity of the virtual control unit of the present invention;

[0060] Figure 6 It is a schematic diagram of the probability distribution of the total capacity of the virtual control unit of the present invention. DETAILED DESCRIPTION

[0061] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0063] Refer to the following Figure 1-Figure 6 The technical solutions of some embodiments of the present invention are described.

[0064] Example 1

[0065] The present invention provides an embodiment, which is a two-layer optimization quantitative evaluation method for the participation of load-side distributed resources in primary frequency regulation. Since the number of load-side distributed resources is large and the capacity is small, it is difficult to be directly regulated by the power grid. Therefore, a virtual control unit is used to aggregate multiple load-side resources so that their capacity is sufficient to participate in the primary frequency regulation service for the power system. The virtual control unit is equivalent to an intermediary, and the load-side distributed resources participating in the primary frequency regulation are selected by negotiation, market, etc. The virtual control unit needs to evaluate the power capacity of each load-side resource, and then report the total capacity to the power grid. According to the capacity characteristics of the load-side resources, each resource is labeled and classified, with the premise of meeting the capacity requirements of the power grid for primary frequency regulation. Under the premise of mastering the probabilistic characteristics of the capacity of each load-side resource, risk analysis is used to maximize the total income of the virtual control unit and determine the capacity reporting amount of the virtual control unit.

[0066] A two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation includes the following steps:

[0067] Step 1. The virtual control unit uses labeling to classify various load-side resources according to their capacity characteristics, and then uses a two-layer structure to aggregate multiple load-side resources. The load-side resources are divided into rectangular and trapezoidal labels according to their power output dynamic characteristics. The ramp-up time of rectangular label resources is milliseconds, and the ramp-up time of trapezoidal label resources is seconds; the virtual control unit then aggregates the two types of label resources.

[0068] The virtual control unit uses labeling to classify various load-side resources according to their capacity characteristics, including: rectangular load clusters and trapezoidal load clusters; then uses a two-layer structure to aggregate multiple load-side resources, clarify the inherent physical characteristics of the response time, climbing time, and sustainable duration of each load-side resource, and classify them by labeling according to the different climbing times.

[0069] First, the inherent physical characteristics of each load-side resource, such as response time, ramp time, and duration, are clarified, and they are categorized by labeling according to the different ramp times. Resources with millisecond-level ramp times are categorized as rectangular labels, and resources with second-level ramp times are categorized as trapezoidal labels. The virtual control unit ensures that the total capacity of the virtual control unit meets the response time and duration requirements of the power grid. Only under this premise can the virtual control unit be qualified to participate in the primary frequency regulation service of the power system.

[0070] like Figure 1 As shown, Figure 1 It is a schematic diagram of the two-layer structure of the virtual control unit of the present invention for aggregating load-side resources. The two-layer structure of the virtual control unit for aggregating load-side resources is to classify by labeling according to the operating characteristics of the load-side resources, and to classify them into different labels according to the shape of the output curve, that is, to perform "capability-oriented labeling" processing on each resource. According to a large number of surveys, the power curves of most resources are rectangular or curved-edge trapezoidal, so resources with rectangular output characteristics, that is, resources with milliseconds from the start of ramping to full response, are classified as rectangular labels; resources with trapezoidal output characteristics, that is, resources with seconds from the start of ramping to full response, are classified as trapezoidal labels. After each label cluster aggregates the load-side resources belonging to the label, the virtual control unit then aggregates each label cluster using a two-layer structure.

[0071] Step 2. The virtual control unit selects multiple load-side resources to participate in a frequency modulation;

[0072] In the present invention, three typical distributed resources are selected to participate in the primary frequency regulation service for the entire power system. These load-side resources are common in life and play a greater role in people's production and life. They are electric vehicles, battery energy storage devices and electric water heater loads. After investigation, it is known that the ramp time of electric vehicles and battery energy storage systems is in milliseconds, which are classified as rectangular labels; while the ramp time of electric water heater loads is in seconds, which is classified as trapezoidal labels.

[0073] After "labeling", the number of various load-side resources, the output size and the time period for participating in the primary frequency regulation should be considered. In order to meet the requirements of the power grid, in addition to "superimposing" the load-side resources, each resource can also participate in the regulation in a "relay". Only when the response time and duration requirements given by the power grid are met, it is eligible to participate in the primary frequency regulation, and only then can a credible quantitative study on the capacity of the virtual control unit be conducted.

[0074] The frequency modulation capabilities of many distributed resources under the virtual control center are uncertain. In order to obtain the credibility of the capacity of each load-side resource, the probabilistic characteristics of the mean and variance of each distributed resource about the output should be mastered, which is the premise for the reliable quantification of the total capacity of the virtual control unit.

[0075] Step 3. The virtual control unit evaluates the power capacity of each load-side resource, with the goal of maximizing the total revenue of the virtual control unit, and reports the total capacity to the power grid; with the goal of maximizing the total revenue of the virtual control unit, the total capacity is reported to the power grid. The total revenue of the virtual control unit takes into account the benefits and costs, and uses optimization to solve the optimal reported power capacity of the virtual control unit.

[0076] The goal is to maximize the total revenue of the virtual control unit. The total revenue of the virtual control unit takes into account the benefits and costs. The revenue includes: the capacity benefits and regulation benefits paid by the power system to the virtual control unit; the costs include: the resource costs paid by the virtual control unit to the resources and the penalty costs paid to the power grid for possible failure to complete the regulation task. Figure 2 As shown, Figure 2 It is a schematic diagram of the virtual control unit of the present invention.

[0077] In addition, since the primary frequency regulation capacity of each load-side resource is an uncertain quantity, in order to evaluate its credibility, the probabilistic characteristics of the mean, variance and other probabilistic characteristics of the capacity of each load-side resource should be mastered, and the coupling relationship between resource capacity and time should be considered.

[0078] On the basis of the above, risk analysis is carried out with the goal of maximizing total revenue. The number of various load-side resources is determined, and the probability distribution of the virtual control unit capacity of various output combinations is obtained. In order to maximize the benefits, select which load-side resources to aggregate, and determine the output size of each load-side resource, the time to start participating in a frequency regulation, the duration of participating in a frequency regulation, and determine the reported value. Then the power grid selects the capacity from the reported value of the virtual control unit capacity.

[0079] Aiming at the benefits and costs of load-side resources participating in the primary frequency regulation of the power system, the total revenue expression of the virtual control unit is listed through the principles of market economics, and considering various equations and inequality constraints under actual operation, a mathematical optimization model of the total revenue of the virtual control unit can be obtained.

[0080] The power grid makes requirements on the power capacity of the virtual control unit for participating in the primary frequency regulation, namely, delay time, ramp time, response capacity, and sustainable duration. Among them, the response time includes: delay time and ramp time. The response time includes the lag time of the power grid issuing the regulation task and the ramp time of the virtual control unit; the response capacity is the power change when the virtual control unit fully responds; the sustainable duration is the time for the virtual control unit to maintain the response capacity. Only when the power grid's requirements for response time and sustainable duration are met, the virtual control unit is eligible to participate in the primary frequency regulation service of the power system.

[0081] The benefits of the virtual control unit include: income and cost. The income includes the capacity fee Inc1 determined by the capacity reported by the virtual control unit and the regulation fee Inc2 for the virtual control unit to participate in the primary frequency regulation service. The cost includes the resource cost C that the virtual control unit should pay to each load-side resource participating in the primary frequency regulation. pay ; and possible penalty costs C for failure to complete the regulation tasks issued by the power grid fine ;

[0082] The expression of the objective function is: maxInc total =max(Inc1+Inc2-C pay -C fine ).

[0083] The income includes: the capacity income Inc1 determined by the capacity reported by the virtual control center and the adjustment fee Inc2 paid by the power system dispatching center to the virtual control unit, that is, the power purchase fee. The cost includes: the resource cost C paid by the virtual control unit to the load-side resources pay And possible failure to complete the regulation task issued by the power grid C fine .

[0084] The classification of virtual control units includes: rectangular load set clusters and trapezoidal load set clusters. The subordinate load-side resources aggregated by the virtual control unit include three types of loads: battery energy storage systems, electric vehicles, and electric water heaters. These three common types of load-side resources are more typical in people's lives. Among them, electric vehicles and battery energy storage systems are classified as rectangular label resources because their ramp-up time is in the millisecond level, and belong to the rectangular load set cluster; while the electric water heater load has a ramp-up time of seconds, which is classified as a trapezoidal label resource and belongs to the trapezoidal load set cluster. Therefore, in the process of estimating the credible capacity of the virtual control unit, the operating characteristics and constraints of each load-side resource should be considered.

[0085] The constraints of electric vehicles include: charging and discharging power constraints, state of charge constraints and time coupling constraints.

[0086] The constraints of the battery energy storage system include: charging and discharging power constraints, state of charge constraints and time coupling constraints.

[0087] The constraints of the electric water heater load include operating state constraints and temperature change constraints.

[0088] Electric vehicles include power constraints, and their charging and discharging power should be maintained within a certain range. The charging and discharging power constraints of electric vehicles are: in, is the opposite of the maximum discharge power allowed for a single electric vehicle, and the planned charging power for a single electric vehicle. is the adjustable power capacity of a single electric vehicle, is the maximum charging power allowed for a single electric vehicle; the power constraint means that the state of charge should be maintained within a certain range, i.e. and are the minimum and maximum state of charge of an electric vehicle, respectively.

[0089] Battery energy storage includes power constraints. Its charging and discharging power should be maintained within a certain range. The battery energy storage charging and discharging power constraints are: in, The opposite of the maximum discharge power allowed by the battery energy storage unit The charging power is planned for the battery energy storage unit; the power constraint is that the state of charge should be maintained within a certain range, that is, and are the minimum and maximum states of charge of the battery energy storage system, respectively.

[0090] The operation cycle of the electric water heater is 30 minutes, and every 2 minutes in one working cycle is a working state, that is, there are 15 working states in one working cycle. Among them, 1-5 are heating states, at this time, the electric water heater can be adjusted upward by reducing the load, and this is recorded as m=1, and its probability is 1 / 3; 6-15 are non-heating states, at this time, the electric water heater cannot be adjusted upward by reducing the load, and this is recorded as m=0, and its probability is 2 / 3. In addition, the temperature change range of the electric water heater should meet the user's needs, that is, ΔT n ≤θ n .

[0091] The method for determining the reporting capacity of load-side resources participating in primary frequency modulation of the present invention determines the selection of load-side resources and the capacity reporting value with the goal of maximizing the virtual control unit income, and the optimal load-side resource combination and reporting amount can be obtained by solving the mathematical optimization model composed of the objective function and the constraint conditions.

[0092] Example 2

[0093] The present invention further provides an embodiment, which is a two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation.

[0094] like Figure 1 As shown, Figure 1 It is a schematic diagram of the two-layer structure of the virtual control unit of the present invention for aggregating load-side resources. Label classification is performed according to the operating characteristics of the load-side resources, and different labels are classified according to the shape of the output curve, that is, each resource is subjected to "capability-oriented labeling". According to a large number of surveys, it is concluded that the power curves of most resources are rectangular or curved-edge trapezoidal. Therefore, resources with rectangular output characteristics, that is, the time from the beginning of ramping to full response is in milliseconds, are classified as rectangular labels; resources with trapezoidal output characteristics, that is, the time from the beginning of ramping to full response is in seconds, are classified as trapezoidal labels. After each label cluster aggregates the load-side resources belonging to the label, the virtual control unit aggregates each label cluster again.

[0095] In the present invention, three typical distributed resources are selected to participate in the primary frequency regulation service for the entire power system. These load-side resources are common in life and play a greater role in people's production and life. They are electric vehicles, battery energy storage devices and electric water heater loads. After investigation, it is known that the ramp time of electric vehicles and battery energy storage systems is in milliseconds, which are classified as rectangular labels; while the ramp time of electric water heater loads is in seconds, which is classified as trapezoidal labels.

[0096] After "labeling", the number of various load-side resources, the output size and the time period for participating in the primary frequency regulation should be considered. In order to meet the requirements of the power grid, in addition to "superimposing" the load-side resources, each resource can also participate in the regulation in a "relay". Only when the response time and duration requirements given by the power grid are met, it is eligible to participate in the primary frequency regulation, and only then can a credible quantitative study on the capacity of the virtual control unit be conducted.

[0097] The frequency modulation capabilities of many distributed resources under the virtual control center are uncertain. In order to obtain the credibility of the capacity of each load-side resource, the probabilistic characteristics of the mean and variance of each distributed resource about the output should be mastered, which is the premise for the reliable quantification of the total capacity of the virtual control unit.

[0098] like Figure 2 As shown, Figure 2 The virtual control unit income structure of the present invention is a schematic diagram of the virtual control unit income. The virtual control unit income structure is to maximize the total income of the virtual control unit and determine the capacity reporting value. The total income of the virtual control unit needs to consider the benefits and costs. total =Inc1+Inc2-C pay -C fine The revenue includes the capacity revenue Inc1 determined by the capacity reported by the virtual control unit; and the regulation revenue Inc2 of the virtual control unit participating in the primary frequency regulation service; the cost includes the resource cost C that the virtual control unit should pay to each load-side resource participating in the primary frequency regulation. pay; and possible penalty costs C for failure to complete the regulation tasks issued by the power grid fine .

[0099] Resource cost C pay It consists of the rectangular load cluster cost and the trapezoidal load cluster cost, namely C pay =C tangs +C lads , where the rectangular load cluster cost is C tangs , the cost of the trapezoidal load cluster is C lads .

[0100] The rectangular load cluster cost is composed of the electric vehicle cost and the battery energy storage system cost:

[0101]

[0102] In the above formula, k i is the unit price of the rectangular tag resource, i is 1 for all electric vehicles and 2 for all battery energy storage systems; M i is the number of individuals with electric vehicles or battery energy storage, P i,mi For each electric vehicle or battery energy storage unit, t i,mi,o The duration for each EV or BESS to participate in frequency response;

[0103] The trapezoidal load cluster cost consists of the electric water heater cost:

[0104]

[0105] In the above formula, k lads is the unit price of the trapezoidal label resource, that is, the unit price of the electric water heater load, N is the number of individuals of the electric water heater load, P B，n (t) is the regulation output of each electric water heater load, t out,n The duration for each electric water heater load individual to participate in frequency response;

[0106] Substitute the available resource cost C pay The expression is:

[0107]

[0108] In the above formula, M i is the number of individuals of electric vehicles or battery energy storage, and N is the number of individuals of electric water heater load;

[0109] The capacity benefit depends on the portion of the reported value of the virtual control unit to the grid that is selected by the grid. In the present invention, it is assumed that the reported value of the virtual control unit will be selected by the grid, so the capacity benefit can be expressed as: Inc1 = k rep Prep , where k rep is the capacity unit price of the virtual control unit, P rep This is the reported amount of the virtual control unit.

[0110] The regulation benefit is determined by the amount of tasks issued by the power system to the virtual control unit. The amount of tasks has two elements, namely the output of the virtual control unit and the regulation time. Therefore, the regulation benefit can be expressed as: In the formula, k reg is the adjustment unit price of the virtual control unit, and H is the number of adjustments.

[0111] When the virtual control unit participates in the primary frequency regulation of the power system, there is a certain probability that the capacity of the virtual control unit will not meet the task volume. At this time, the power grid will impose a fine on the virtual control unit. Its value is:

[0112]

[0113] Among them, P fact,h is the capacity value when the virtual control unit fully responds (t start ≤t≤t G,h ), H is the number of disturbances, k fine is the penalty unit price, t G,h The duration of each frequency regulation auxiliary service provided by the virtual control unit;

[0114] Considering the physical characteristics of electric vehicles and battery energy storage, their constraints can be obtained. For electric vehicles, their constraints should include power constraints, that is, the upper and lower limits of charging and discharging power. in: represents the maximum discharge power, Represents the maximum charging power, P 1，m1,charge Plan the charging power for a single electric vehicle, P 1，m1 is the adjustable power capacity of a single electric vehicle; the power constraint, i.e. the upper and lower limits of the state of charge, S m1 (t) is the state of charge of the electric vehicle, and are the minimum and maximum charge states of electric vehicles, respectively. When the electric vehicle is in the discharging state, there is in is the charge state of the electric vehicle at the initial moment, Represents the charge and discharge efficiency, Indicates the rated capacity of the battery of the electric vehicle.

[0115] when When the electric vehicle is in the discharging state, there is For battery energy storage systems, the constraints should include power constraints, i.e. the upper and lower limits of charge and discharge power. in represents the maximum discharge power, Represents the maximum charging power P 2，m2,charge is the planned charging power of a single battery energy storage unit, P 2，m2 is the adjustable power capacity of a single battery energy storage; the power constraint, that is, the upper and lower limits of the state of charge, Among them, when When the battery energy storage system is in the discharge state, there is S m2 (t) is the state of charge of the battery energy storage system, where is the charge state of the battery energy storage system at the initial moment, Represents the charge and discharge efficiency, Indicates the rated capacity of the battery in the battery energy storage system.

[0116] when When the battery energy storage system is in the discharge state, there is Taking into account the physical characteristics of the electric water heater and the user's comfort, it should meet the temperature change constraint 0≤ΔT≤θ when participating in primary frequency modulation, where ΔT is the water temperature change and θ is the maximum water temperature change value acceptable to the user.

[0117] In order to be practical, the present invention selects 200 electric vehicles, 100 battery energy storage systems and 1,000 electric water heater loads as the load-side resources under the virtual control unit. According to their capacity characteristics participating in primary frequency modulation, they are classified by labeling. As mentioned above, electric vehicles and battery energy storage systems are rectangular label resources, and electric water heater loads are trapezoidal label resources, and then each label is aggregated separately. And according to the output probability distribution of each load-side resource, the probability distribution of the total capacity of the virtual control unit is obtained. And a risk analysis is performed from the perspective of the virtual control unit. When evaluating the maximum profit, what should be the capacity reporting value of the virtual control unit.

[0118] In order to be practical, the rated charging power of each electric vehicle is set to 1kW, and the maximum charging and discharging power is Both are 5kW, charging and discharging efficiency Subject to U(0.9,0.93), minimum state of charge is 0.1, the maximum state of charge Subject to U(0.95,1), initial state of charge Subject to U(0.1,0.9), battery rated capacity 30kWh; the rated charging power of the battery energy storage system The maximum charge and discharge power is 2kW Both are 8kW, charging and discharging efficiency Subject to U(0.92,0.94), minimum state of charge is 0.1, the maximum state of charge Subject to U(0.98,1), initial state of charge Subject to U(0.1,0.9), battery rated capacity The maximum water temperature change value θ acceptable to users of each electric water heater load is set to 10K, and the rated power is 2kW.

[0119] like Figure 3 , Figure 4 and Figure 5 The aggregation results shown are Figure 3 is a schematic diagram of the capacity of a rectangular tag cluster of the present invention, Figure 4 is a schematic diagram of the capacity of a trapezoidal tag cluster of the present invention, Figure 5 Schematic diagram of the total capacity of the virtual control unit of the present invention. Figure 3 is the result of rectangle label aggregation, Figure 4 It is the result of trapezoidal label aggregation. Figure 5 It is the aggregated result of the total capacity of the virtual control unit with each tag aggregated.

[0120] like Figure 6 As shown, Figure 6 : is a schematic diagram of the probability distribution of the total capacity of the virtual control unit of the present invention. : is the probability density of the total capacity of the virtual control unit obtained by solving. It can be seen that the capacity of the aggregated virtual control unit is sufficient to participate in the primary frequency regulation service for the entire power system.

[0121] Through 10,000 Monte Carlo simulations, we solved the reported amount when the expected benefit is maximized, and found that when the reported amount is 1570.380kW, the expected benefit is maximized.

[0122] Example 3

[0123] The present invention further provides an embodiment, which is a two-layer optimization quantitative evaluation device for load-side distributed resources participating in primary frequency modulation, comprising:

[0124] The classification and aggregation module is used for the virtual control unit to classify various load-side resources according to their capacity characteristics by labeling, and then aggregate various load-side resources using a two-layer structure; the load-side resources are divided into rectangular and trapezoidal labels according to their power output dynamic characteristics, where the ramp-up time of rectangular label resources is milliseconds, and the ramp-up time of trapezoidal label resources is seconds; the virtual control unit then aggregates the two types of label resources.

[0125] The selection module is used for the virtual control unit to select multiple load-side resources to participate in the primary frequency regulation; after labeling, the number of various load-side resources, output size and time period participating in the primary frequency regulation are considered, so that the load-side resources are "superimposed" and each resource is "relayed" to participate in the regulation, meeting the grid's requirements on response time and duration, making it eligible to participate in the primary frequency regulation, and conducting a credible quantitative study on the capacity of the virtual control unit.

[0126] The evaluation and reporting module is used for the virtual control unit to evaluate the power capacity of each load-side resource, and report the total capacity to the power grid with the goal of maximizing the total income of the virtual control unit; the total capacity is reported to the power grid with the goal of maximizing the total income of the virtual control unit. The total income of the virtual control unit takes into account the benefits and costs, and uses optimization to solve the optimal reported power capacity of the virtual control unit.

[0127] The two-layer optimization and quantitative evaluation of the load-side distributed resources participating in a frequency modulation is used to implement the steps of the two-layer optimization and quantitative evaluation method for the load-side distributed resources participating in a frequency modulation described in any one of embodiments 1-2.

[0128] Example 4

[0129] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation described in any one of Embodiments 1-2 are implemented.

[0130] Example 5

[0131] Based on the same inventive concept, an embodiment of the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the two-layer optimization quantitative evaluation method for load-side distributed resources participating in a frequency modulation described in any one of embodiments 1-2 are implemented.

[0132] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] 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. Any modification or equivalent replacement that does 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 two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation is characterized by: The following steps are involved: The virtual control unit uses labeling to classify various load-side resources according to their capacity characteristics, and then uses a two-layer structure to aggregate multiple load-side resources; The virtual control unit selects multiple load-side resources to participate in primary frequency modulation; The virtual control unit evaluates the power capacity of each load-side resource, and reports the total capacity to the grid with the goal of maximizing the total revenue of the virtual control unit.

2. According to claim 1, the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation is characterized by: The virtual control unit uses labeling to classify various load-side resources according to their capacity characteristics, that is, the load-side resources are divided into rectangular and trapezoidal labels according to their power output dynamic characteristics, where the climbing time of rectangular label resources is milliseconds and the climbing time of trapezoidal label resources is seconds; the virtual control unit then aggregates the two types of label resources.

3. According to claim 1, the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation is characterized by: The virtual control unit selects multiple load-side resources to participate in primary frequency regulation, which means that after labeling, the number of various load-side resources, the output size and the time period participating in the primary frequency regulation are considered, so that the load-side resources are "superimposed" and each resource is "relayed" to participate in the regulation, so as to meet the requirements of the power grid on response time and sustainable duration, make it eligible to participate in the primary frequency regulation, and conduct a credible quantitative study on the capacity of the virtual control unit.

4. According to claim 1, the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation is characterized by: The rectangular label includes electric vehicles and battery energy storage; the trapezoidal label includes electric water heater load.

5. According to claim 4, the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation is characterized by: The constraints of the electric vehicle include: charge and discharge power constraints, charge state constraints and time coupling constraints; wherein the charge and discharge power constraints of the electric vehicle are: in, It is the opposite of the maximum discharge power allowed for a single electric vehicle. Plan the charging power for a single electric vehicle, is the adjustable power capacity of a single electric vehicle, is the maximum charging power allowed for a single electric vehicle; the state of charge constraint is: in, and are the minimum and maximum states of charge of electric vehicles, respectively; The constraints of the battery energy storage system include: charging and discharging power constraints, state of charge constraints and time coupling constraints; The battery energy storage charging and discharging power constraints are: in, It is the opposite of the maximum discharge power allowed by the battery energy storage unit. Plan the charging power for the battery energy storage unit; the state of charge constraint is: in, and are the minimum and maximum states of charge of the battery energy storage system, respectively; The constraints of the electric water heater load include: operating state constraints and temperature change constraints; The operating state constraints are as follows: the operating cycle of the electric water heater is 30 minutes, and there is a working state every 2 minutes in one working cycle, and there are 15 working states in one working cycle; among them, 1-5 are heating states, at this time the electric water heater is adjusted upward by reducing the load, denoted as m=1, m is the switch state of the electric water heater load, and its probability is 1 / 3; 6-15 are non-heating states, at this time the electric water heater cannot be adjusted upward by reducing the load, denoted as m=0, and the probability is 2 / 3; The temperature change constraint is: meet user demand ΔT n ≤θ n .

6. According to claim 1, the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation is characterized by: The virtual control unit evaluates the power capacity of each load-side resource, takes the maximum total revenue of the virtual control unit as the goal, and reports the total capacity to the power grid. The virtual control unit takes the maximum total revenue as the goal, reports the total capacity to the power grid, and the total revenue of the virtual control unit considers the benefits and costs. The optimization is used to solve the optimal reported power capacity of the virtual control unit; wherein, the total revenue of the virtual control unit is maximized as the goal, and the total revenue of the virtual control unit considers the benefits and costs. The expression of the objective function is: maxInc total =max(Inc1+Inc2-C pay -C fine ); The revenue includes: the capacity fee Inc1 determined by the capacity reported by the virtual control center and the adjustment fee Inc2 paid by the power system dispatching center to the virtual control unit; the cost includes: the resource cost C paid by the virtual control unit to the load-side resources pay and the possible penalty cost C for failing to complete the regulation task issued by the power grid fine .

7. The two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation according to claim 6 is characterized by: The total revenue of the virtual control unit is: Inc total =Inc1+Inc2-C pay -C fine The revenue includes the capacity fee Inc1 determined by the capacity reported by the virtual control unit and the regulation fee Inc2 for the virtual control unit to participate in the primary frequency regulation service; the cost includes the resource cost C that the virtual control unit should pay to each load-side resource participating in the primary frequency regulation. pay ; and possible penalty costs C for failure to complete the regulation tasks issued by the power grid fine ; Resource cost C pay It consists of rectangular load cluster cost and trapezoidal load cluster cost: C pay =C tangs +C lads , where the rectangular load cluster cost is C tangs , the cost of the trapezoidal load cluster is C lads ; The rectangular load cluster cost consists of the electric vehicle cost and the battery energy storage system cost: In the above formula, k i is the unit price of the rectangular tag resource. For all electric vehicles, i is 1, and for all battery energy storage systems, i is 2; M i The number of individuals who store energy for electric vehicles or batteries, To adjust the output of each electric vehicle or battery energy storage unit, The duration for each EV or BESS to participate in frequency response; The ladder load cluster cost consists of the electric water heater cost: In the above formula, k lads is the unit price of the trapezoidal label resource, that is, the unit price of the electric water heater load, N is the number of individuals of the electric water heater load, P B，n (t) is the regulation output of each electric water heater load, t out,n The duration for each electric water heater load individual to participate in frequency response; Substitute the available resource cost C pay The expression is: In the above formula, M i is the number of individuals of electric vehicles or battery energy storage, and N is the number of individuals of electric water heater load; Capacity fee is expressed as: Inc1 = k rep P rep , where k rep is the capacity unit price of the virtual control unit, P rep It is the reported quantity of the virtual control unit; The adjustment fee is expressed as: Among them, k reg is the adjustment unit price of the virtual control unit, and H is the number of adjustments; When the virtual control unit participates in the primary frequency regulation of the power system, there is a situation where the capacity of the virtual control unit cannot meet the task volume. At this time, the power grid imposes a penalty on the virtual control unit, and the value is: Among them, P fact,h is the capacity value when the virtual control unit fully responds (t start ≤t≤t G,h ), H is the number of disturbances, k fine is the penalty unit price, t G,h The duration of each frequency regulation auxiliary service provided by the virtual control unit; Considering the physical characteristics of electric vehicles and battery energy storage, the constraints are obtained, including power constraints, upper and lower limits of charge and discharge power, in: represents the maximum discharge power, Represents the maximum charging power, Plan the charging power for a single electric vehicle, is the adjustable power capacity of a single electric vehicle; power constraints, upper and lower limits of the state of charge, is the state of charge of the electric vehicle, and are the minimum and maximum charge states of electric vehicles respectively; When the electric vehicle is in the discharging state, there is in is the charge state of the electric vehicle at the initial moment, Represents the charge and discharge efficiency, Indicates the rated capacity of the battery of the electric vehicle; when When the electric vehicle is in the discharging state, there is The constraints of the battery energy storage system include power constraints, upper and lower limits of charge and discharge power, in represents the maximum discharge power, Represents the maximum charging power Plan charging power for a single battery energy storage unit, The adjustable power capacity of a single battery storage; power constraints, upper and lower limits of the state of charge, Among them, when When the battery energy storage system is in the discharge state, there is is the state of charge of the battery energy storage system, where is the charge state of the battery energy storage system at the initial moment, Represents the charge and discharge efficiency, Indicates the rated capacity of the battery in the battery energy storage system; when When the battery energy storage system is in the discharge state, there is When participating in a frequency modulation, the temperature change constraint 0≤ΔT≤θ is satisfied, where ΔT is the water temperature change and θ is the maximum water temperature change value acceptable to the user.

8. A two-layer optimization quantitative evaluation device for load-side distributed resources participating in primary frequency modulation, characterized by: The classification and aggregation module is used by the virtual control unit to classify various load-side resources according to their capacity characteristics by using labels, and then aggregate various load-side resources using a two-layer structure; A selection module is used for the virtual control unit to select multiple load-side resources to participate in a frequency modulation; An evaluation and reporting module is used by the virtual control unit to evaluate the power capacity of each load-side resource; The two-layer optimization and quantitative evaluation of the load-side distributed resources participating in primary frequency modulation is used to implement the steps of the two-layer optimization and quantitative evaluation method for the load-side distributed resources participating in primary frequency modulation as described in any one of claims 1-7.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation described in any one of claims 1 to 7 are implemented.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the two-layer optimization quantitative evaluation method for load-side distributed resources participating in primary frequency modulation as described in any one of claims 1 to 7.

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