CN116031886B - Method for controlling two-stage flexible climbing capacity of heat pump virtual power plant day-day before day - Google Patents
Method for controlling two-stage flexible climbing capacity of heat pump virtual power plant day-day before day Download PDFInfo
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Abstract
The invention discloses a method for controlling the dual-stage flexible climbing capacity of a heat pump virtual power plant in the day-ahead and day, which comprises the following steps: acquiring parameters required by the flexible climbing capability of the single heat pump based on a cloud-side-end heat pump virtual power plant architecture, and establishing a flexible climbing capability assessment model of the single heat pump by utilizing an affine mathematical theory and considering the uncertainty of outdoor temperature and illumination intensity; on the basis of a single heat pump flexible climbing capacity evaluation model, a heat pump cluster virtual power plant day-ahead flexible climbing capacity evaluation model is constructed, and a climbing capacity boundary is provided for the next day of issuing flexible climbing demands of the power system; and establishing a virtual power plant flexible climbing capacity correction model in the daily stage, correcting the daily flexible climbing capacity of the virtual power plant in a rolling manner, and distributing climbing demands among the heat pumps based on a maximum-minimum fairness algorithm.
Description
Technical Field
The invention relates to the field of comprehensive energy and electric power systems, in particular to a method for controlling the daily-daily two-stage flexible climbing capacity of a heat pump virtual power plant.
Background
To achieve the goal of "peak carbon, neutral carbon", the installed capacity of renewable energy generation in the world has been 30% of the total installed capacity of electricity by the end of 2021 [1] . With the deep advancement of the construction of a novel power system, the proportion of fluctuating renewable energy sources is continuously improved, and the net load curve of the power system is more frequently climbed or slides in a short time, so that the problem of shortage of flexible resources is needed to be solved. For this reason, governments and enterprises at home and abroad have corresponding policies for going out or mining flexible resources through market mechanisms. The national energy office issues 'electric auxiliary service management method' at the end of 2021 to propose novel auxiliary services such as climbing, quick frequency response, moment of inertia and the like [2] . The U.S. midwestern electric Market (MISO), california electric market (caso), and british national electric market (NGESO) also motivate users to provide flexible climbing capabilities to participate in climbing assistance services by marketizing means [3]-[4] 。
The flexible climbing capability refers to the capability of the power system to reserve a certain ascending or descending climbing margin in the current period for coping with the net load fluctuation and uncertainty under the scheduling period of 5-15min time scale and meeting the supply requirement of the next period, and the unit is MW/h or MW/min [5] . Under the existing market mechanism, the flexible climbing capability mainly depends on the traditional generator set to adjust the real-time output supply, and comprises the steps of reserving certain unit capacity to meet the climbing requirement in actual operation [6] And the future market prepares the flexible climbing capability in advance to meet the flexible climbing requirement of the next day [7] Two ways. However, as defined by the flexible climbing capacity, the flexible climbing capacity can be similarly determined by the stored energy [8] Electric automobile [9] Electric heating equipment [10] The equal load side flexibly adjusts the resource supply.
As a novel electric heating type, frequency conversionThe heat pump heating can be widely involved in the multi-element flexibility auxiliary service such as frequency modulation, peak regulation and the like of the power system by utilizing the characteristic of flexible and adjustable power on the premise of meeting the comfort level of user heating [11]-[12] . However, because the adjustable capacity of the heat pump monomer is smaller, a certain scale of heat pump is often polymerized into a heat pump virtual power plant, and interaction with the power system is realized under unified regulation and control [13] . In this regard, document [14]The building virtual energy storage model is constructed, the flexibility of the building participating in the climbing auxiliary service is evaluated, and the feasibility of providing the climbing auxiliary service by adopting the heat pump is verified; document [15]The flexibility climbing capacity of the heat pump clusters is quantized, and load power tracking is realized by combining a heat pump cluster control method based on priority stacks; document [16]On the premise of guaranteeing user comfort, a multi-type flexibility assessment method comprising the participation of a heat pump cluster in climbing auxiliary service is provided; document [17]The climbing capacity available by the virtual power plant is used as an evaluation index, and the daily flexibility of the heat pump virtual power plant is evaluated; document [18]A three-level regulation strategy comprising a user-aggregator-power grid is designed for providing flexibility for the heat pump, and the distribution of flexibility demands among different users is developed based on demand priorities.
However, existing research remains to be in depth in providing flexible climbing capability with heat pump virtual power plants:
1) In the day-ahead and day-ahead two-stage assessment method, uncertain factors such as outdoor temperature, illumination intensity and the like are not sufficiently considered, and the accuracy of the day-ahead reporting and the day-ahead rolling correction climbing capacity assessment is to be improved;
2) The centralized assessment method is large in data demand and calculation amount, is unfavorable for privacy protection of users, and is applicable to a flexible climbing demand distribution method among different heat pumps in a virtual power plant due to the fact that different climbing capacities of different users are different.
Reference to the literature
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Disclosure of Invention
The invention provides a control method for the dual-stage flexible climbing capacity of a heat pump virtual power plant in the day front-day, which can evaluate the dual-stage climbing capacity of the heat pump virtual power plant in the day front-day and distribute climbing demands under the condition of uncertain illumination intensity and outdoor temperature, and can effectively give consideration to user energy comfort at the same time, as described in detail below:
a method for controlling a dual-stage flexible climbing capacity of a heat pump virtual power plant day-day, the method comprising:
acquiring parameters required by the flexible climbing capability of the single heat pump based on a cloud-side-end heat pump virtual power plant architecture, and establishing a flexible climbing capability assessment model of the single heat pump by utilizing an affine mathematical theory and considering the uncertainty of outdoor temperature and illumination intensity;
on the basis of a single heat pump flexible climbing capacity evaluation model, a heat pump cluster virtual power plant day-ahead flexible climbing capacity evaluation model is constructed, and a climbing capacity boundary is provided for the next day of issuing flexible climbing demands of the power system;
and establishing a virtual power plant flexible climbing capacity correction model in the daily stage, correcting the daily flexible climbing capacity of the virtual power plant in a rolling manner, and distributing climbing demands among the heat pumps based on a maximum-minimum fairness algorithm.
The cloud-side-end heat pump virtual power plant architecture specifically comprises:
the upward and downward climbing capacity of the virtual power plant is obtained based on the upward and downward climbing capacity of the single heat pump at the moment t uploaded by the cloud layer polymerization end layer; the cloud layer receives and decomposes the issued upward and downward climbing demands; reporting the climbing capacity of the virtual power plant for 24 hours in the future to an upper-level scheduling in the day-ahead stage, and providing a reference standard for responding to the next-day climbing demand; in the intra-day stage, rolling and evaluating the flexible climbing capacity of the virtual power plant at the moment t+1 at the moment t by taking deltat as an interval, and receiving and distributing climbing demand regulation and control instructions;
the side layer stores parameters such as a user house, an upper limit/lower limit of a heat pump climbing rate, a heat pump efficiency ratio, user thermal comfort level and the like, acquires outdoor temperature and illumination intensity prediction data according to data uploaded by the side layer and the outside, and receives climbing demand instructions issued by a cloud;
the terminal layer acquires temperature data of the heat pump and the house node through the micropower intelligent sensing and narrowband internet of things equipment controller, uploads the temperature data to the side layer, and responds to the issued instruction to control the heat pump output.
The virtual power plant flexible climbing capacity correction model is as follows:
when t=1, obtaining the maximum upward/downward flexibility climbing capacity boundary at the moment according to the day-ahead flexibility climbing capacity evaluation result; when t is not equal to 1, calculating the state of the virtual power plant at the moment t according to the moment t-1; calculating the flexible climbing capacity at the time t+1 through a daily correction model, and reporting to a power dispatching center; determining the running power of each heat pump in the virtual power plant according to the issued climbing demand;
for heat pump i in the virtual power plant, it is assumed that at time t heat pump i is engaged in an up/down flexible hill climbing service,consider the t+1 period maximum predicted power P i,t+1 Is:
wherein the daily flexibility of the virtual power plant is taken up by the upward climbing capacityIs to take +.>Is defined by the lower boundary of (c).
Further, the allocation of the climbing requirement among the heat pumps based on the max-min fairness algorithm is specifically as follows:
for n users in the virtual power plant, which can participate in upward or downward flexible climbing response at the time t, sorting according to the upward or downward flexible climbing capacity of the n users;
the average value of the climbing demands to be distributed is distributed to a user i, and the flexible climbing capacity actually participated by the user i is obtained, wherein the value of the flexible climbing capacity is equal to the smaller value of the average value of the climbing demands and the flexible climbing capacity participatable by the user i; and judging whether all users have distributed climbing demands.
The technical scheme provided by the invention has the beneficial effects that:
1) Under the uncertainty of illumination intensity and outdoor temperature, the affine form-based heat pump virtual power plant day-ahead flexibility climbing capacity assessment model can provide a reference for the heat pump virtual power plant reporting day-ahead flexibility climbing capacity in the day-ahead stage;
2) On the premise of not influencing the comfort level of users, the invention can correct the flexible climbing capacity of the virtual power plant in the day, respond to the climbing demand issued by the power system, and realize the climbing demand distribution by a maximum-minimum fairness distribution method;
3) The invention can quantify the flexible climbing capacity of the virtual power plants which are clustered by the heat pump clusters, provide flexible climbing capacity resources for the heat pump virtual power plants to participate in the climbing auxiliary service of the power system, and reduce the influence caused by the shortage of the flexibility of the power system.
Drawings
FIG. 1 is a schematic diagram of a heat pump virtual power plant architecture;
FIG. 2 is a schematic diagram of a house RC model;
FIG. 3 is a schematic diagram of the flexible climbing capacity of a single heat pump;
FIG. 4 is a flow chart of a two-stage flexible climbing capacity control of a heat pump virtual power plant day-day;
FIG. 5 is a schematic diagram of predicted values of illumination intensity and outdoor temperature;
FIG. 6 is a schematic diagram of a flexible hill climbing requirement;
FIG. 7 is a schematic diagram of a virtual power plant's day-ahead flexible climbing capability;
FIG. 8 is a schematic diagram of the daily flexible climbing capacity and actual response results of a heat pump virtual power plant;
FIG. 9 is a schematic diagram of a virtual power plant flexible hill climbing demand allocation result;
FIG. 10 is a schematic illustration of the day-ahead flexible climbing capability of a virtual power plant at different comfort levels;
FIG. 11 is a schematic diagram of the indoor temperature and heat pump operating conditions of the user 10;
FIG. 12 is a schematic diagram of the day-ahead flexible climbing capability of a virtual power plant with confidence in prediction errors of different outdoor temperatures and illumination intensities.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below.
Example 1
With the participation of heat pump and other demand side resources in flexible climbing auxiliary service, the assessment of the flexible climbing capability of the heat pump under uncertain conditions is a key problem to be solved urgently. The embodiment of the invention provides a method for controlling the daily-daily two-stage flexible climbing capacity of a heat pump virtual power plant, which analyzes the daily and daily two-stage flexible climbing capacity, and is shown in fig. 1 to 4, and the method comprises the following steps:
101: establishing a heat pump virtual power plant architecture based on cloud-side-end, and providing an information architecture for flexible climbing capability assessment of the heat pump virtual power plant;
102: based on a virtual power plant architecture, parameters required by the flexible climbing capacity of the single heat pump are obtained, and an affine mathematical theory is utilized to establish a flexible climbing capacity assessment model of the single heat pump, which considers the uncertainty of outdoor temperature and illumination intensity;
103: on the basis of a single heat pump flexible climbing capacity evaluation model, a heat pump cluster virtual power plant day-ahead flexible climbing capacity evaluation model is constructed, and a climbing capacity boundary is provided for the next day of issuing flexible climbing demands of the power system;
104: and establishing a virtual power plant flexible climbing capacity correction model in the daily stage, correcting the daily flexible climbing capacity of the virtual power plant in a rolling manner, and distributing climbing demands among the heat pumps based on a maximum-minimum fairness algorithm.
In summary, the embodiment of the invention provides a flexible climbing capacity assessment method for a heat pump virtual power plant constructed by air source heat pump clusters, which can assess the climbing capacity of the heat pump virtual power plant in two stages of day-day and perform climbing demand distribution under the condition of uncertain illumination intensity and outdoor temperature, and can effectively give consideration to user energy comfort; and the verification of an example shows that the method effectively evaluates the flexibility climbing capability of the heat pump virtual power plant on the premise of ensuring the indoor comfort level of the user.
Example 2
The scheme of example 1 is further described below with reference to fig. 1-7, and specific calculation formulas are described in detail below:
202: the heat pump virtual power plant based on the cloud-side-end architecture is constructed, the climbing capacity of the heat pump is uploaded to the cloud after being calculated locally, the data volume of cloud calculation is reduced, and meanwhile, the user data privacy is protected;
202: in the day-ahead stage, providing an affine form-based heat pump virtual power plant day-ahead flexible climbing capacity assessment model, and providing a reference for reporting the day-ahead flexible climbing capacity of the heat pump virtual power plant under the uncertainty of illumination intensity and outdoor temperature;
in the daily stage, the virtual power plant corrects the flexible climbing capability, responds to the climbing requirement issued by the power system on the premise of not affecting the comfort level of the user, and realizes the distribution of the climbing requirement through a maximum-minimum fairness distribution method.
203: the confidence coefficient of prediction errors of different outdoor temperatures and illumination intensities and the boundary of flexible climbing capacity of the virtual power plant are affected by different indoor comfortableness, reasonable confidence coefficient and comfort degree parameters are set, and the influence of the two aspects should be considered in actual evaluation.
1. Heat pump virtual power plant architecture
As shown in fig. 1, taking a heat pump virtual power plant composed of n variable frequency heat pumps as an example, the virtual power plant is based on a 'cloud-side-end' architecture. The architecture supports the heat pump virtual power plant to participate in flexible climbing capacity auxiliary service by utilizing unified regulation and control of cloud layer virtual power plant operators, side layer data storage and local calculation and end layer perception and interaction response devices.
Cloud layer: on one hand, the flexibility of the single heat pump at the moment t uploaded by the cloud layer polymerization end layer can climb up and down (R up,i,t And R is down,i,t I=1, 2 … n) to obtain the upward and downward climbing capability of the virtual power plantAnd->) The method comprises the steps of carrying out a first treatment on the surface of the On the other hand, the cloud layer receives and decomposes the issued upward and downward climbing demands. Reporting the climbing capacity of the virtual power plant for 24 hours in the future to an upper-level scheduling in the day-ahead stage, and providing a reference standard for responding to the next-day climbing demand; and in the daily stage, rolling and evaluating the flexible climbing capacity of the virtual power plant at the time t+1 at the time t by taking deltat as an interval, and receiving and distributing a climbing demand regulation command.
Edge layer: side layer storage user house (building envelope heat capacity C, building envelope heat resistance R, area A, height)h) Heat pump (Power upper and lower limit)And->) Upper and lower limits (R) of climbing rate i,max And D i,max ) Coefficient of potency ratio (Coefficient of Performance, COP) (alpha COP 、β COP 、χ COP ) User thermal comfort lambda) PMV Equal parameters, according to the data uploaded by the terminal layer, obtaining the outdoor temperature theta from outside out,t And illumination intensity I solar,t And predicting data, realizing localized calculation of the flexible climbing capacity of the single heat pump on the basis of calculating the house heat load and the heat pump output, uploading the data to cloud layer virtual power plant operators, and receiving climbing demand instructions issued by a cloud. For a user, the climbing capacity uploaded to an operator is time sequence related data obtained after local calculation, and the user behavior cannot be directly analyzed from the time sequence related data, so that the privacy of the user can be effectively protected.
End layer: on the one hand, a heat pump (operating power P) is acquired through micro-power intelligent sensing and narrowband internet of things (Narrow Band Internet of Things, NB-IoT) device controller t ) House node (wall inner surface theta) w,in,t Outer surface theta of wall body w,out,t Inner surface θ of floor f,in,t Roof inner surface theta r,in,t Outer surface theta of roof r,out,t Indoor theta in,t ) And the temperature data is uploaded to the side layer, and on the other hand, the heat pump output is controlled in response to the issued command.
2. Single heat pump flexibility climbing capacity assessment model
1. Heat pump and house thermal load model
1) Heat pump model
The embodiment of the invention adopts the variable-frequency air source heat pump as heating equipment, and the output is continuously adjustable [19] . Heat pump electric power P at time t t And heating power P COP,t The relation of (2) is shown in the formula (1):
P COP,t ＝P t λ COP,t (1)
wherein: lambda (lambda) COP Is the heat pump efficiency ratio (Coefficient of Performance, COP) and the value of the heat pump efficiency ratio is equal to the outdoor temperature theta at the moment t out,t In a related manner, the embodiment of the invention adopts the coefficient alpha provided by the manufacturer COP ,β COP And χ (x) COP The COP of the heat pump is determined, and the calculation formula is shown as formula (2):
2) House thermal load model
The embodiment of the invention introduces an RC network physical model to predict the house heat load so as to describe the relation between the indoor heat load demand and the indoor temperature, the illumination intensity and the outdoor temperature, as shown in figure 2 [20] 。
Based on the RC model, the house thermodynamic equation is shown as formula (3):
wherein: x is the temperature state matrix of each node of the house, as shown in formula (4); u is a heat pump heating power input matrix, as shown in formula (5); A. b, C, D, F is a coefficient matrix of the thermodynamic equation, and is shown in equations (6) - (10), respectively.
x t ＝[θ in,t θ r,out,t θ r,in,t θ w,out,t θ w,in,t θ f,in,t ] T (4)
In the middle of
F＝[1 0 0 0 0 0] (10)
Wherein: alpha is the radiation absorptivity;is the solar altitude; lambda (lambda) win Is the glass transmission coefficient; q (Q) vent Heat consumption for air permeation, kW; q (Q) man Thermal power, kW, Q, generated for user behavior vent And Q man And taking a fixed value and not inputting the fixed value as a measurement value.
3) Indoor thermal comfort calculation
Embodiments of the present invention introduce a thermal sensation average scale prediction (Predicted Mean Vote, PMV) for quantitatively determining a user indoor comfort temperature interval [21] As shown in formula (11):
wherein: lambda (lambda) PMV Is PMV index; θ s Is the skin temperature of the human body in a comfortable state and is at the temperature of DEG C; m is the human energy metabolism rate, W/M 2 ；I cl Is the thermal resistance of the clothing, (m) 2 Temperature · W. The indoor set comfortable temperature can be obtained through mathematical transformationAs shown in formula (12):
PMV index is classified into 7 grades, lambda PMV Is the optimal comfort state of human body when 0, lambda PMV Is +1, +2, +3, which correspond to slightly warm, warm and hot, lambda respectively PMV Is-1, -2 and-3 respectively corresponding to a little cool, cool and cold [22] 。
2. Heat pump flexibility climbing capability assessment
Taking the ith heat pump (i=1, 2, …, n) in the heat pump virtual power plant as an example, the electric power can be as followsAnd (3) internal continuous adjustment, wherein the heat pump adjusts power at the time t, and the scheduling interval is delta t. The flexible climbing capacity of the heat pump at the time t and the indoor temperature change of the heat supply house are shown in fig. 3, and the calculation formula of the flexible climbing capacity of the heat pump i is shown in (13):
wherein:andP′ i,t+1 the maximum up/down regulating power of the heat pump i at the time t+1 is respectively limited by the indoor comfort temperature of the heat pump i heating house and the power of the heat pump self equipment, as shown in a formula (14):
wherein:and->Upper boundary of indoor temperature comfort level of house for supplying heat to heat pump i respectively>And lower border->Maximum electric power of +.>And->The solving mode is as follows:
when the heat pump output increases, the indoor temperature increases, and the difference processing is carried out on the heat pump output taking the state equation (formula (3)) of the house heat load into consideration [23] Can calculate and make the indoor temperature reachThe required electric power value +.>As shown in formula (15):
wherein:
similarly, when the heat pump output decreases and the indoor temperature decreases, it can be found that the indoor temperature reaches the lower boundary of comfortThe required electric power value +.>As shown in formula (16):
wherein:
3. modeling of illumination intensity and outdoor temperature uncertainty
From equations (13) and (14), it can be seen that the flexible climbing capacity of heat pump i at time t is determined by the electric power of heat pump i at time t and the upper/lower limit of electric power achievable at time t+1. According to the constructed house heat load model, the outdoor temperature and the illumination intensity are main influencing factors influencing the house heat load, so that the flexibility climbing capability provided by the heat pump is influenced [24] . For this reason, the embodiment of the invention introduces affine mathematical theory for describing the flexible climbing capability of the heat pump under the uncertain factors of the illumination intensity and the outdoor temperature [25] 。
The prediction error compliance mean value of the illumination intensity is 0 standard deviation sigma s Normal distribution of (2) [26] The probability density function is shown in formula (17):
the prediction error compliance mean value of the outdoor temperature is 0 standard deviation sigma θ Normal distribution of (2) [27] The probability density function is shown in formula (18):
based on the relationship between probability density and confidence interval [28] At the nominal coverage probabilityLight intensity at time tPrediction error interval +.>The relationship to the probability density function can be expressed as:
wherein: pr (·) is the probability that the event (·) is true.
And the same can be obtained at the nominal coverage probabilityUnder, t moment outdoor temperature prediction error interval +.>The relationship to the probability density function can be expressed as:
the embodiment of the invention adopts an affine form of which the parameters are represented by 'inverted V', and the illumination intensity predicted value is expressed in the affine form at the time tFor predicting the central value I solar,0,t And prediction error DeltaI solar,t The sum is represented by formula (21). Similarly, the predicted value of the outdoor temperature at time t +.>For predicting the central value theta out,0,t And prediction error delta theta out,t The sum is represented by formula (22).
Due to the intervalSum zone->Are all central prediction intervals, and are according to the interconversion relationship of affine mathematics and interval mathematics [29] The illumination intensity and the outdoor temperature actual values at time t are as shown in formulas (23) - (24):
wherein: epsilon solar,t And epsilon out,t Noise elements of illumination intensity and outdoor temperature, respectively.
Further, the heat pump operating power affine form taking into account the illumination intensity and the outdoor temperature uncertainty can be expressed as:
wherein: p (P) i,0,t Operating a power center value for the heat pump;and->The heat pump operation power deviation caused by uncertainty of illumination intensity and outdoor temperature is respectively. />
And then substituting the formula (25) into the formula (13) to obtain the i day front flexibility climbing capacity of the heat pump under the condition that the illumination intensity at the moment t and the outdoor temperature are uncertain:
3. method for evaluating flexible climbing capacity of heat pump virtual power plant in two stages of day before and day in
1. Day-ahead flexible climbing capacity assessment model
According to the embodiment of the invention, the daily flexible climbing capacity of the virtual power plant is evaluated through the daily flexible climbing capacity of the single heat pump in the cloud layer aggregation virtual power plant.
2. Heat pump flexibility climbing capability assessment
And each heat pump monomer in the virtual power plant calculates the daily front climbing capacity of the heat pump monomer at the side layer through a flexible climbing capacity calculation method and uploads the daily front climbing capacity to a cloud end aggregator, and the aggregator aggregates the daily front flexible climbing capacities of each heat pump to obtain the daily front climbing capacity of the virtual power plant, so as to provide a capacity boundary for the next daily flexible climbing requirement of a dispatching center.
The future flexible ramp up capability of the virtual plant in affine form is shown in equation (27).
Wherein:and->Up/down flexible climbing capability for the virtual power plant t moment.
Since the result of equation (27) is a flexible climbing capacity interval at a certain confidence level, the future flexibility of the virtual power plant is taken up the climbing capacityIs the lower border of (1) the day-ahead flexibility down-hill climbing capacity +.>Is not included in the upper boundary of (a).
2. Daily flexible climbing capacity correction model
In the daytime, the flexible climbing capacity of the virtual power plant can be changed along with the change of the indoor temperature of a house heated by a subordinate monomer heat pump and the change of the running state of the heat pump, and meanwhile, the flexible climbing requirement issued by the power system at the moment t and the heat pump response process can influence the running state of the heat pump at the moment t, so that the flexible climbing capacity of the heat pump at the moment t+1 is influenced. Therefore, the flexible climbing capacity of the virtual power plant needs to be corrected at the time t in the day, and the corrected flexible climbing capacity of the virtual power plant at the time t+1 is provided for the upper-level scheduling.
In order to solve the problems, the embodiment of the invention provides an assessment model for the daily correction flexibility climbing capacity of a heat pump virtual power plant. First, when t=1, it is assumed that the maximum upward/downward flexible climbing ability boundary at this time is obtained from the day-ahead flexible climbing ability evaluation result; when t is not equal to 1, calculating the state of the virtual power plant at the moment t according to the moment t-1; further calculating the flexible climbing capacity at the time t+1 through a daily correction model, and reporting to a power dispatching center; and determining the running power of each heat pump in the virtual power plant according to the issued climbing demand, wherein a specific allocation method is provided in the fourth part.
For heat pump i in the virtual power plant, it is assumed that at time t heat pump i participates in an up/down flexible hill climbing service. Due to P in real-time scheduling i,t There is no uncertainty in the actual operating power, so in the intra-day correction model, only the t+1 period maximum predicted power P needs to be considered i,t+1 The uncertainty of formula (13) may be rewritten as:
/>
further, the intra-day flexible climbing capacity of the single heat pump is polymerized at the t moment to obtain the intra-day flexible climbing capacity of the virtual power plant. Similarly, virtual power plants due to aggregation climb in daily flexibilityThe slope capacity is an interval containing uncertain information, and the daily flexibility of the virtual power plant is taken out by the upward slope climbing capacityIs to take +.>Is defined by the lower boundary of (c).
3. Flexible climbing demand distribution method based on maximum-minimum fairness algorithm
When the flexible climbing capacity that can be provided by the virtual power plant is greater than the actual flexible climbing demand issued by the power system, if the climbing demand allocation is not considered, some users will provide their maximum climbing capacity while other users provide less or no climbing capacity [30] . Therefore, in order to solve the problem of providing climbing capacity for different users, a flexible climbing demand distribution method based on a max-min fairness algorithm is provided [31] The algorithm implementation flow is as follows:
step 1: for n users in the virtual power plant which can participate in the up/down flexible climbing response at the time t, the up (down) flexible climbing capacity R is used up(down),i,t Sequencing the sizes to meet R up,1,t ≤R up,2,t ≤...≤R up,n,t Or R is down,1,t ≥R down,2,t ≥...≥R down,n,t The upward/downward flexible climbing requirement issued at the t moment is that
Step 2: average value of climbing demands to be distributedAssigned to user i (i=1, 2, n), user i actually participates in a flexible climbing capacity +.>
Step 3: and (3) judging whether all users have distributed climbing demands, and if not, returning to the step (2).
4. Day-ahead-day-in-day two-stage flexible climbing capability assessment method
The method for evaluating the flexible climbing capacity of the heat pump virtual power plant at two stages of day front and day inner comprises a virtual power plant day front flexible climbing capacity evaluation model and a day inner flexible climbing capacity correction model, and the flow is shown in fig. 4, and the specific steps are as follows:
step 1: model parameters are input. Initializing each heat pump parameter, house envelope parameter and house area probability distribution model in the virtual power plant, obtaining outdoor temperature, illumination intensity and user comfort probability distribution function, calculating house heat load (formulas (3) - (10)), user comfort (formulas (11) - (12)), outdoor temperature before day and illumination intensity interval distribution (formulas (17) - (24));
step 2: solving the daily climbing capacity of the heat pump monomer (formulas (13) - (16), (26)) based on the calculation result of the step 1;
step 3: solving the flexible daily climbing capacity of the virtual power plant (27);
step 4: judging whether a flexible climbing requirement exists at the time t+1, if so, jumping to the step 5, otherwise, jumping to the step 6;
step 5: correcting the flexible climbing capacity boundary (28) of the virtual power plant at the time t+1, referring to the corrected upward/downward climbing capacity boundary, ifThen distributing climbing demands participated by each heat pump in the virtual power plant according to a maximum-minimum fairness algorithm; if->The total up/down hill climbing capacity of all heat pumps in the virtual power plant responds to the hill climbing demand;
step 6: let t=t+1 and return to step 3.
Step 7: and when t is more than or equal to 24/delta t, ending the flow.
Example 3
The schemes in examples 1 and 2 were validated in conjunction with specific examples, as described in detail below:
1. setting of calculation examples
Taking a typical winter heating scene in North China as an example, 50 household users heated by adopting a variable-frequency air source heat pump are selected, the house and user parameters are shown in a table 1, and the house enclosure structure and the parameters are shown in an attached table 1 [32] . Setting lambda PMV And when the temperature is plus or minus 1, the upper and lower boundaries of indoor thermal comfort are defined. The predicted values of the illumination intensity and the outdoor temperature are shown in FIG. 5 [33] . The illumination intensity and the outdoor temperature prediction error obey N (0, 1) and N (0,0.18), respectively 2 ) Is a normal distribution with a confidence of 95% [26] . Scheduling interval Δt=10 min. Climbing demand issued by power system selects climbing demand curve in CAISO [34] As shown in fig. 6.
Table 1 house and user parameters Table 1Parameters of building and user
Table 2 heat pump parameters Table 2Parameters of heat pump
2. Calculation example results
The future flexible climbing capability of the virtual power plant is shown in fig. 7. The maximum upward flexible climbing capacity of the virtual power plant before the day occurs at 8:10 with a value of 227.13kW/h, and the maximum downward flexible climbing capacity occurs at 5:20 with a value of-196.00 kW/h. At 8:00-18: the electric power required by maintaining the indoor comfortable temperature in the 00 time period is smaller, so that the climbing capacity of the heat pump virtual power plant is smaller, and meanwhile, the climbing capacity of the virtual power plant is reduced compared with other time periods due to the boundary constraint of the indoor temperature comfort level.
When the virtual power plant participates in the response, the daily flexible climbing capacity of the heat pump virtual power plant and the actual response result are shown in fig. 8. The virtual power plant can accurately track the climbing demand curve issued by the power system in the daytime. And since the downward climbing demand is larger at four scheduling moments (blocks in fig. 8) of 4:10, 5:10, 6:10 and 7:10, the daily climbing capacity boundary is obviously increased.
FIG. 9 shows the flexible hill climbing demand allocation results for virtual power plants at 12:00, 17:10 and 20:00. Wherein, at 12:00 and 20:00 virtual power plants participate in uphill responses, providing an upward flexible uphill capability, and at 17:10 participate in downhill responses, providing a downward flexible uphill capability. As shown in fig. 9 (a) and (c), there is a difference in the flexible climbing capability provided between users, and the method provided herein can enable all users in the virtual power plant to provide the climbing capability at the same scheduling time. In fig. 9 (b), at this point the virtual power plant participates in a downhill climb response, and the full climbing capacity of the heat pump in the virtual power plant participates in the response.
FIG. 10 is a graph of the virtual power plant flexibility climbing capacity boundary for a heat pump considering indoor comfort boundaries of + -0.5, + -1, + -1.5 and + -2, respectively. It can be seen that with the increase of the comfort level boundary, the upward flexibility climbing capability boundary of the virtual power plant increases, because the upper/lower boundary of the indoor comfort temperature respectively increases and decreases, the maximum electric power of the house indoor temperature heated by the heat pump reaches the upper boundary of the comfort level, but due to the self-output limit of the heat pump, the expansion amplitude of the flexibility climbing capability boundary gradually decreases, when the comfort level boundary is within the + -2 period of time, 2:00-8:00 and 16:00-22:00, the upward flexibility climbing capability of the virtual power plant is basically consistent at each moment, at this time, the upward flexibility climbing capability of each user in the virtual power plant has reached the maximum value, and further increasing the user comfort level boundary will not promote the flexibility climbing capability of the virtual power plant any more. However, the virtual power plant down-flexibility climbing capacity boundary only increases when the indoor thermal comfort boundary changes from + -0.5 to + -1, and when the comfort boundary further increases, the virtual power plant down-flexibility climbing capacity boundary does not increase because the heat pump in the virtual power plant has been fully turned off, and the down-flexibility climbing capacity reaches an upper limit.
The users in the virtual power plant (number 10) are randomly drawn, and their indoor temperatures and heat pump operating states are shown in fig. 11. After the heat pump virtual power plant participates in the climbing response, the heat pump power is increased, the indoor temperature of the corresponding heat supply house is increased, the upward flexibility climbing capacity is reduced corresponding to the next period, the downward flexibility climbing capacity is increased, and the climbing capacity boundary moves downwards. In contrast, after the heat pump virtual power plant participates in the downhill climbing response, the climbing capacity boundary moves up. Compared with the optimal indoor temperature of 23.31 ℃, the heat pump provides upward/downward flexibility climbing capability, the indoor temperature is up to 25.47 ℃ and up to 9.27%, the indoor temperature is down to 21.50 ℃ and down to 7.76%, but the indoor temperature is kept within a set comfortable temperature interval (obtained according to formulas (11) - (12)), and the comfort of users is not affected
3. Influence of prediction error confidence of different outdoor temperatures and illumination intensities on flexible climbing capacity
Considering the influence of different prediction error confidence levels of outdoor temperature and illumination on the flexibility climbing capability, the flexibility climbing capability of the virtual power plant under the confidence levels of 95%, 90%, 80%, 60% and 50% is evaluated respectively, and the result is shown in fig. 12. It can be seen that as the confidence level is continuously increased, the up/down flexible climbing capacity estimated in the day before is increased, because when the climbing capacity is estimated in the day before stage, the predicted values of the outdoor temperature and the illumination are both expressed in the form of a section, and after solving to obtain the climbing capacity boundary in the form of a section, the up flexible climbing capacity takes the lower boundary of the climbing capacity section, and the down flexible climbing capacity takes the upper boundary of the climbing capacity section.
Those skilled in the art will appreciate that the drawings are schematic representations of only one preferred embodiment, and that the above-described embodiment numbers are merely for illustration purposes and do not represent advantages or disadvantages of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (1)
1. A method for controlling the dual-stage flexible climbing capacity of a heat pump virtual power plant in front of day-in-day is characterized by comprising the following steps:
acquiring parameters required by the flexible climbing capability of the single heat pump based on a cloud-side-end heat pump virtual power plant architecture, and establishing a flexible climbing capability assessment model of the single heat pump by utilizing an affine mathematical theory and considering the uncertainty of outdoor temperature and illumination intensity;
on the basis of a single heat pump flexible climbing capacity evaluation model, a heat pump cluster virtual power plant day-ahead flexible climbing capacity evaluation model is constructed, and a climbing capacity boundary is provided for the next day of issuing flexible climbing demands of the power system;
establishing a virtual power plant flexible climbing capacity correction model in a daily stage, correcting the daily flexible climbing capacity of the virtual power plant in a rolling manner, and distributing climbing demands among heat pumps based on a maximum-minimum fairness algorithm;
the cloud-side-end heat pump virtual power plant architecture specifically comprises:
the upward and downward climbing capacity of the virtual power plant is obtained based on the upward and downward climbing capacity of the single heat pump at the moment t uploaded by the cloud layer polymerization end layer; the cloud layer receives and decomposes the issued upward and downward climbing demands; reporting the climbing capacity of the virtual power plant for 24 hours in the future to an upper-level scheduling in the day-ahead stage, and providing a reference standard for responding to the next-day climbing demand; in the intra-day stage, rolling and evaluating the flexible climbing capacity of the virtual power plant at the moment t+1 at the moment t by taking deltat as an interval, and receiving and distributing climbing demand regulation and control instructions;
the side layer stores user houses, upper/lower limits of heat pump climbing rate, heat pump efficiency ratio and user thermal comfort level, acquires outdoor temperature and illumination intensity prediction data according to data uploaded by the side layer and the outside, and receives climbing demand instructions issued by a cloud;
the terminal layer acquires temperature data of the heat pump and the house node through the micro-power intelligent sensing and narrowband internet of things equipment controller, uploads the temperature data to the side layer, and responds to the issued instruction to control the output of the heat pump;
the virtual power plant flexible climbing capacity correction model is as follows:
when t=1, obtaining the maximum upward/downward flexibility climbing capacity boundary at the moment according to the day-ahead flexibility climbing capacity evaluation result; when t is not equal to 1, calculating the state of the virtual power plant at the moment t according to the moment t-1; calculating the flexible climbing capacity at the time t+1 through a daily correction model, and reporting to a power dispatching center; determining the running power of each heat pump in the virtual power plant according to the issued climbing demand;
for heat pump i in a virtual power plant, assume that at time t heat pump i participates in an up/down flexible climbing service, consider maximum predicted power P at time t+1 i,t+1 Is:
wherein the daily flexibility of the virtual power plant is taken up by the upward climbing capacityIs to take +.>Lower boundary of (2); Δt is a scheduling interval; r is R i,max Is the upward climbing speed; d (D) i,max Is the downward climbing rate; />The upward flexibility climbing capacity is realized for the moment t of the virtual power plant; />The climbing capacity is the downward flexibility of the virtual power plant at the moment t;
the method for distributing climbing demands among heat pumps based on the maximum-minimum fairness algorithm comprises the following steps:
for n users in the virtual power plant, which can participate in upward or downward flexible climbing response at the time t, sorting according to the upward or downward flexible climbing capacity of the n users;
the average value of the climbing demands to be distributed is distributed to a user i, and the flexible climbing capacity actually participated by the user i is obtained, wherein the value of the flexible climbing capacity is equal to the smaller value of the average value of the climbing demands and the flexible climbing capacity participatable by the user i; and judging whether all users have distributed climbing demands.
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