CN111193268B - Method and device for processing electric heating equipment - Google Patents

Method and device for processing electric heating equipment Download PDF

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CN111193268B
CN111193268B CN201911399339.2A CN201911399339A CN111193268B CN 111193268 B CN111193268 B CN 111193268B CN 201911399339 A CN201911399339 A CN 201911399339A CN 111193268 B CN111193268 B CN 111193268B
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distribution network
day
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model
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CN111193268A (en
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曾爽
陈平
梁安琪
张宝群
王钊
赵宇彤
丁屹峰
李干
宫成
杨烁
孙钦斐
马凯
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention discloses a processing method and a device of electric heating equipment. Wherein, the method comprises the following steps: acquiring the load of the power distribution network; establishing a target model, wherein the target model is used for adjusting the electric heating equipment based on a first parameter of the power distribution network under the condition that the power distribution network meets the load; establishing an in-day rolling model, wherein the in-day rolling model is used for adjusting the electric heating equipment based on a second parameter of the power distribution network under the condition that the power distribution network meets the load; inputting at least one constraint condition into the target model and/or the day rolling model respectively, wherein the constraint condition is used for indicating the range within which the first parameter and/or the second parameter of the power distribution network are limited under the condition that the load requirement is met; and obtaining a mode for adjusting the electric heating equipment according to the target model and/or the rolling model in the day. The invention solves the technical problem that the influence of uncertain prediction on starting and stopping of the electric heating equipment is not considered in the related technology, so that the adjusting effect is poor.

Description

Method and device for processing electric heating equipment
Technical Field
The invention relates to the field of electric heating equipment control, in particular to a processing method and a processing device for electric heating equipment.
Background
In recent years, an air source heat pump becomes a main heating device for changing coal into electricity in Jingjin Ji areas, the load is greatly increased due to the access of a large-area air source heat pump, if the electric heating device is used in a load peak period, the peak-valley difference of a power grid is further increased, the non-economic operation mode that the power grid device runs under heavy load at the load peak and runs under light load at the valley load is caused, and even the safe operation of the power grid device can be influenced. Meanwhile, as the load of a user is mostly single-phase access, the problem of unbalanced three phases is increasingly prominent after the heat pump is accessed in a large scale.
In the application scenario of the air source heat pump, for example, in the prior art, when the day-ahead scheduling optimization is adopted, the prediction error is not sufficiently estimated, so that some situations that the user comfort is damaged appear after a period of time exist, and in such an operation mode, the peak-valley difference of the operation of the power distribution network and the three-phase imbalance degree can not achieve the expected effect.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a processing method and a processing device for electric heating equipment, which are used for at least solving the technical problem of poor adjusting effect caused by the fact that the influence of uncertain prediction on starting and stopping of the electric heating equipment is not considered in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a method for processing electric heating equipment, including: acquiring the load of the power distribution network; establishing a target model, wherein the target model is used for adjusting the electric heating equipment based on a first parameter of the power distribution network when the power distribution network meets the load, wherein the first parameter comprises at least one of the following parameters: the daily peak power of the nodes and the three-phase unbalanced power; establishing an in-day rolling model, wherein the in-day rolling model is used for adjusting the electric heating equipment based on second parameters of the power distribution network under the condition that the power distribution network meets the load, and the second parameters comprise at least one of the following parameters: outdoor temperature, active power, reactive power; inputting at least one constraint condition to the target model and/or the intra-day rolling model, respectively, wherein the constraint condition is used for indicating a range within which the first parameter and/or the second parameter of the power distribution network is limited if the load requirement is met; and obtaining a mode for adjusting the electric heating equipment according to the target model and/or the day rolling model.
Optionally, the objective model uses the following objective function:
Figure BDA0002347108320000021
where t is time, i is node, Pi maxIs the peak power of the node day, n is the number of nodes of the distribution network, lambdaiThree-phase imbalance value weight of i node, X1(t) is the difference between the first three-phase unbalanced power and the second three-phase unbalanced power, X2(t) is the difference between the second three-phase unbalanced power and the third three-phase unbalanced power, X3And (t) is the difference value of the first three-phase unbalanced power and the third three-phase unbalanced power.
Optionally, the objective function satisfying the constraint condition includes at least one of: x1(t)≥Pi A(t)-Pi B(t) wherein Pi A(t)≥Pi B(t)；X1(t)≥Pi B(t)-Pi A(t) wherein Pi B(t)≥Pi A(t)；X2(t)≥Pi B(t)-Pi C(t) wherein Pi B(t)≥Pi C(t)；X2(t)≥Pi C(t)-Pi B(t) wherein Pi C(t)≥Pi B(t)；X3(t)≥Pi C(t)-Pi A(t) wherein Pi C(t)≥Pi A(t)；X3(t)≥Pi A(t)-Pi C(t) wherein Pi A(t)≥Pi C(t); wherein, Pi A(t) a first three-phase unbalanced power, P, of the i node at time ti B(t) a second three-phase unbalanced power, P, of node i at time ti CAnd (t) is the third three-phase unbalanced power of the node i at the time t.
Optionally, the intra-day rolling model uses the following objective function:
Figure BDA0002347108320000022
Figure BDA0002347108320000023
wherein the content of the first and second substances,
Figure BDA0002347108320000024
the actual value of the outdoor temperature being t,
Figure BDA0002347108320000025
for the actual value of the active power of the inode at time t,
Figure BDA0002347108320000026
is the actual value of reactive power at the i node at time T, To f(t) predicted value of outdoor temperature, P, which is a prediction of ti f(t) is a predicted value of the active power of the inode at the time t,
Figure BDA00023471083200000214
for the predicted value of reactive power at the i-node at time t,
Figure BDA0002347108320000027
is a prediction error of the outdoor temperature at time t,
Figure BDA0002347108320000028
the prediction error of the active power of the i node at the time t,
Figure BDA0002347108320000029
Respectively represent the prediction error of the reactive power of the i node at the t moment.
Optionally, the prediction error satisfies an interval constraint including at least one of:
Figure BDA00023471083200000210
Figure BDA00023471083200000211
optionally, the ratio of the robust interval boundary of the intra-day rolling model to the maximum error in the historical dataρAt least one of the following is satisfied:
Figure BDA00023471083200000212
wherein the content of the first and second substances,
Figure BDA00023471083200000213
the absolute values of the maximum values in the historical error data of outdoor temperature, active power and reactive power are respectively.
Optionally, the constraint condition input comprises at least one of: the system comprises an electric heating equipment model, a house and heat storage model, a power distribution network flow model and thermal boundary constraint of rolling in the day.
According to another aspect of the embodiments of the present invention, there is also provided a processing apparatus of an electric heating device, including: the acquisition module is used for acquiring the load of the power distribution network; a first modeling module configured to establish a target model, wherein the target model is configured to adjust the electric heating equipment based on a first parameter of the power distribution network when the power distribution network satisfies the load, wherein the first parameter includes at least one of: the daily peak power of the nodes and the three-phase unbalanced power; a second modeling module, configured to establish a rolling day model, where the rolling day model is used to adjust the electric heating equipment based on a second parameter of the power distribution network when the power distribution network satisfies the load, where the second parameter includes at least one of: outdoor temperature, active power, reactive power; an input module, configured to input at least one constraint condition to the target model and/or the intra-day rolling model, respectively, where the constraint condition is used to indicate a range within which the first parameter and/or the second parameter of the power distribution network is limited if the load requirement is met; and the adjusting module is used for obtaining a mode for adjusting the electric heating equipment according to the target model and/or the day rolling model.
According to another aspect of the embodiment of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, the device where the storage medium is located is controlled to execute the processing method of the electric heating device.
According to another aspect of the embodiment of the present invention, there is further provided a processor, where the processor is configured to execute a program, where the program executes the processing method of the electric heating equipment in any one of the above.
In the embodiment of the invention, the load of the power distribution network is obtained; establishing a target model, wherein the target model is used for adjusting the electric heating equipment based on a first parameter of the power distribution network when the power distribution network meets the load, wherein the first parameter comprises at least one of the following parameters: the daily peak power of the nodes and the three-phase unbalanced power; establishing an in-day rolling model, wherein the in-day rolling model is used for adjusting the electric heating equipment based on second parameters of the power distribution network under the condition that the power distribution network meets the load, and the second parameters comprise at least one of the following parameters: outdoor temperature, active power, reactive power; inputting at least one constraint condition to the target model and/or the intra-day rolling model, respectively, wherein the constraint condition is used for indicating a range within which the first parameter and/or the second parameter of the power distribution network is limited if the load requirement is met; the method for adjusting the electric heating equipment is obtained according to the target model and/or the rolling model in the day, the electric heating equipment is adjusted based on the target model and the rolling model in the day by using constraint conditions, the purpose of reducing prediction errors is achieved, the running peak-valley difference and the three-phase imbalance degree of the power distribution network are reduced, the technical effect of improving the comfort degree qualification rate is achieved, and the technical problem that the adjustment effect is poor due to the fact that the influence of uncertain prediction on starting and stopping of the electric heating equipment is not considered in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a processing method of an electric heating apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a predicted value and an actual value of an outdoor air temperature for a scene according to an alternative embodiment of the present invention;
FIG. 3 is a schematic representation of the room air temperature and the tank temperature in a day roll mode of operation in accordance with an alternative embodiment of the present invention;
FIG. 4 is a schematic illustration of indoor air temperature and water tank temperature under a day-ahead scheduling strategy in accordance with an alternative embodiment of the present invention;
FIG. 5 is a schematic diagram of the start and stop of 10 heat pumps in node 4 under a rolling scheduling strategy according to an alternative embodiment of the present invention;
FIG. 6 is a schematic diagram of the start and stop of 10 heat pumps at node 4 under a day-ahead scheduling strategy according to an alternative embodiment of the present invention;
FIG. 7 is a schematic diagram of a node 4 load curve under a rolling and day-ahead scheduling strategy in accordance with an alternative embodiment of the present invention;
fig. 8 is a processing unit of an electric heating apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a processing method for an electric heating apparatus, it should be noted that the steps illustrated in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that described herein.
Fig. 1 is a flowchart of a processing method of an electric heating apparatus according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, acquiring the load of the power distribution network;
step S104, establishing a target model, wherein the target model is used for adjusting the electric heating equipment based on a first parameter of the power distribution network under the condition that the power distribution network meets the load, and the first parameter comprises at least one of the following parameters: the daily peak power of the nodes and the three-phase unbalanced power;
step S106, establishing a rolling model in the day, wherein the rolling model in the day is used for adjusting the electric heating equipment based on a second parameter of the power distribution network under the condition that the power distribution network meets the load, and the second parameter comprises at least one of the following parameters: outdoor temperature, active power, reactive power;
step S108, at least one constraint condition is input into the target model and/or the day rolling model respectively, wherein the constraint condition is used for indicating the limited range of the first parameter and/or the second parameter of the power distribution network under the condition that the load requirement is met;
and step S110, obtaining a mode for adjusting the electric heating equipment according to the target model and/or the rolling model in the day.
Optionally, the electric heating device includes, but is not limited to, an air source heat pump.
Through the steps, the load of the power distribution network can be acquired; establishing a target model, wherein the target model is used for adjusting the electric heating equipment based on a first parameter of the power distribution network under the condition that the power distribution network meets the load, and the first parameter comprises at least one of the following parameters: the daily peak power of the nodes and the three-phase unbalanced power; establishing an in-day rolling model, wherein the in-day rolling model is used for adjusting the electric heating equipment based on a second parameter of the power distribution network under the condition that the power distribution network meets the load, and the second parameter comprises at least one of the following parameters: outdoor temperature, active power, reactive power; inputting at least one constraint condition into the target model and/or the day rolling model respectively, wherein the constraint condition is used for indicating the range within which the first parameter and/or the second parameter of the power distribution network are limited under the condition that the load requirement is met; the method for adjusting the electric heating equipment is obtained according to the target model and/or the rolling model in the day, the electric heating equipment is adjusted based on the target model and the rolling model in the day by using the constraint condition, the purpose of reducing the prediction error is achieved, the running peak-valley difference and the three-phase unbalance degree of the power distribution network are reduced, the technical effect of the comfort level qualification rate is improved, and the technical problem that the adjusting effect is poor due to the fact that the influence of starting and stopping of the electric heating equipment in the uncertain aspect of prediction is not considered in the related technology is solved.
As an optional embodiment, due to energy storage benefits of the house and the heat storage device, the start-stop time of each user electric heating device can be optimized through the method, peak clipping and valley filling are achieved, three-phase imbalance is reduced, the controlled object is the start-stop of the electric heating device, and the constraint conditions include but are not limited to network constraint, indoor temperature constraint, device constraint and the like.
As an alternative embodiment, the objective model uses the following objective function, wherein the objective function is to minimize the weighted sum of the full-day peak power and the three-phase imbalance flag of each node, as shown in the following formula:
Figure BDA0002347108320000051
it should be noted that the above objective function may be converted into another objective function and its corresponding constraint.
Optionally, the objective model uses the following objective function:
Figure BDA0002347108320000061
where t is time, i is node, Pi maxIs the peak power of the node day, n is the number of nodes of the distribution network, lambdaiThree-phase imbalance value weight of i node, X1(t) is the difference between the first three-phase unbalanced power and the second three-phase unbalanced power, X2(t) is the difference between the second three-phase unbalanced power and the third three-phase unbalanced power, X3And (t) is the difference value of the first three-phase unbalanced power and the third three-phase unbalanced power.
As an alternative example, 24 hours per day may be divided into 96 scheduled times, each of 15 minutes. And in each time period, the starting and stopping states of the electric heating equipment are kept constant. The model inputs are power grid structure and parameters, electric heating equipment attributes, various house and heat storage parameters, next day environmental temperature prediction and load prediction, and the output is a next day electric heating equipment start-stop plan.
Optionally, the objective function satisfying the constraint condition includes at least one of: x1(t)≥Pi A(t)-Pi B(t) wherein Pi A(t)≥Pi B(t)；X1(t)≥Pi B(t)-Pi A(t) wherein Pi B(t)≥Pi A(t)；X2(t)≥Pi B(t)-Pi C(t) wherein Pi B(t)≥Pi C(t)；X2(t)≥Pi C(t)-Pi B(t) wherein Pi C(t)≥Pi B(t)；X3(t)≥Pi C(t)-Pi A(t) wherein Pi C(t)≥Pi A(t)；X3(t)≥Pi A(t)-Pi C(t) wherein Pi A(t)≥Pi C(t); wherein, Pi A(t) a first three-phase unbalanced power, P, of the i node at time ti B(t) a second three-phase unbalanced power, P, of node i at time ti CAnd (t) is the third three-phase unbalanced power of the node i at the time t.
Optionally, the intra-day rolling model uses the following objective function:
Figure BDA0002347108320000062
Figure BDA0002347108320000063
wherein the content of the first and second substances,
Figure BDA0002347108320000064
the actual value of the outdoor temperature being t,
Figure BDA0002347108320000065
for the actual value of the active power of the inode at time t,
Figure BDA0002347108320000066
is the actual value of reactive power at the i node at time T, To f(t) predicted value of outdoor temperature, P, which is a prediction of ti f(t) is a predicted value of the active power of the inode at the time t,
Figure BDA00023471083200000611
for the predicted value of reactive power at the i-node at time t,
Figure BDA0002347108320000067
is a prediction error of the outdoor temperature at time t,
Figure BDA00023471083200000612
the prediction error of the active power of the i node at the time t,
Figure BDA0002347108320000068
Respectively represent the prediction error of the reactive power of the i node at the t moment.
The uncertainty variables in the intra-day rolling model mainly include the outdoor temperature and the node injection power, and the real values thereof can be expressed in the form of predicted values thereof plus prediction errors, for example, by the objective function referred to in the above embodiments. Therefore, the prediction error is fully considered in such a way, so that the prediction result is more accurate.
Optionally, the prediction error satisfying the interval constraint comprises at least one of:
Figure BDA0002347108320000069
Figure BDA00023471083200000610
the prediction error is controlled within a certain range through the interval constraint, and the inaccuracy of the prediction result caused by the uncontrollable error is avoided.
Optionally, the ratio of the robust interval boundary of the intra-day rolling model to the maximum error in the historical dataρAt least one of the following is satisfied:
Figure BDA0002347108320000071
wherein the content of the first and second substances,
Figure BDA0002347108320000072
the absolute values of the maximum values in the historical error data of outdoor temperature, active power and reactive power are respectively.
Optionally, the input constraint comprises at least one of: the system comprises an electric heating equipment model, a house and heat storage model, a power distribution network flow model and thermal boundary constraint of rolling in the day.
In a specific implementation process, at least one of the built electric heating equipment model, the house and heat storage model, the power distribution network flow model and the thermal boundary constraint of the day rolling can be used as a constraint condition, and the constraint condition is matched with the target model and/or the day rolling model for use, so that the optimization of the electric heating equipment is realized, for example, the operation state of the electric heating equipment is adjusted.
An alternative embodiment of the present application is described below.
As an optional processing method for the electric heating equipment, compared with a day-ahead scheduling strategy, the intra-day rolling robust scheduling strategy (equivalent to the intra-day rolling model) for the electric heating equipment has the advantage of coping with uncertain prediction. The prediction errors of the photovoltaic, the load and the outdoor air temperature are random variables, and the errors are randomly generated according to probability distribution. Fig. 2 is a schematic diagram of a predicted value and an actual value of the outdoor air temperature in a certain scene according to an alternative embodiment of the present invention, where as shown in fig. 2, a smooth line is the predicted value of the outdoor temperature, and a non-smooth line is the actual measured value of the outdoor temperature.
And according to the rolling optimization robust solving model, generating a heat pump ordered power utilization scheduling strategy 4 hours after the current moment according to the current prediction and state every 15 minutes, and only adopting a start-stop strategy at the current moment. Comparing the scheduling strategy with a scheduling strategy which only adopts day before all the day, wherein the chance constraint damage probability is 0.05. The first 9 hours were taken for the analysis and the results are shown in fig. 3 to 6 and table 1. Fig. 3 is a schematic diagram of an indoor air temperature and a water tank temperature in a rolling operation mode in day according to an alternative embodiment of the present invention, fig. 4 is a schematic diagram of an indoor air temperature and a water tank temperature in a scheduling policy in day-ahead according to an alternative embodiment of the present invention, fig. 5 is a schematic diagram of the start and stop of 10 heat pumps under a node 4 under a rolling scheduling policy in an alternative embodiment of the present invention, and fig. 6 is a schematic diagram of the start and stop of 10 heat pumps under a node 4 under a scheduling policy in day-ahead according to an alternative embodiment of the present invention.
The indoor air temperature and water tank temperature and heat pump on-off conditions in the rolling and day-ahead scheduling modes are shown in fig. 3-6. In fig. 3 and 4, the upper curve represents the water temperature of the water tanks in 10 rooms, and the lower curve represents the indoor air temperature of 10 rooms. The points in fig. 5 and 6 indicate that the heat pump is in operation at the present moment and is off otherwise.
As can be seen from fig. 3 and 4, in the rolling operation mode adopting robust scheduling, the temperature fluctuations of the indoor air and the water temperature of the water tank are smaller than those in the day-ahead scheduling mode, which indicates that the rolling operation mode has stronger anti-interference capability, and the current state information and the prediction information are used to correct the scheduling error of the previous strategy, so that the indoor operation is ensured within the temperature constraint range, and the comfort requirement of the user is met. The mode of operation employing only the day-ahead scheduling strategy presents some cases of impaired user comfort after a period of time, which results from underestimation of prediction error during day-ahead scheduling optimization. Simulations show that the above mentioned adverse effects can be eliminated to some extent by using a robust optimization strategy with continuous rolling operation.
Fig. 7 is a schematic diagram of a load curve of a node 4 under a rolling and day-ahead scheduling strategy according to an alternative embodiment of the present invention, and as shown in fig. 7, a heat pump scheduling strategy adopting rolling scheduling can significantly reduce local peak-valley difference, and the peak-valley difference in the first 9 hours is smaller than that in the day-ahead scheduling mode. The maximum peak values of the photovoltaic prediction error and the load prediction error are compared, the load peak value in the day-ahead scheduling mode is smaller, and the excellent tolerance capability of the rolling scheduling mode relative to the day-ahead scheduling model on the photovoltaic prediction error and the load prediction error is reflected.
TABLE 1 Monte Carlo simulation results
Day-ahead scheduling mode Robust scheduling mode
Distribution network peak valley difference (kW) 616.8 606.5
Node 4 Peak valley difference (kW) 8.3 7.6
Three-phase unbalance of whole network 0.014 0.011
Comfort pass rate 96.5％ 99.2％
Table 1 further shows the monte carlo simulation results, which are randomly generated according to the normal distribution with the same parameters as in chapter ii. Compared with the day-ahead scheduling mode, the robust scheduling mode not only reduces the peak-valley difference of the power distribution network, but also improves the comfort qualification rate and reduces the unbalanced degree of three phases.
Therefore, compared with a day-ahead scheduling mode, the air source heat pump day-in rolling strategy based on the robust interval has stronger anti-jamming capability, the current state information and the prediction information are utilized to correct the scheduling error of the previous strategy, the indoor operation is guaranteed to be within the temperature constraint range, and the comfort requirement of a user is met. Not only reduced the distribution network operation peak valley poor, still promoted the comfort level qualification rate and reduced the unbalanced three phase degree.
Example 2
According to another aspect of the embodiment of the present invention, there is also provided a processing device for electric heating equipment, and fig. 8 is the processing device for electric heating equipment according to the embodiment of the present invention, as shown in fig. 8, the processing device for electric heating equipment includes: an acquisition module 802, a first modeling module 804, a second modeling module 806, an input module 808, and an adjustment module 810. The processing means of the electric heating apparatus will be explained in detail below.
An obtaining module 802, configured to obtain a load of a power distribution network;
a first modeling module 804, connected to the obtaining module 802, for establishing a target model, where the target model is used to adjust the electric heating equipment based on a first parameter of the power distribution network when the power distribution network satisfies a load, where the first parameter includes at least one of: the daily peak power of the nodes and the three-phase unbalanced power;
a second modeling module 806, connected to the first modeling module 804, configured to establish an intra-day rolling model, where the intra-day rolling model is used to adjust the electric heating equipment based on a second parameter of the power distribution network when the power distribution network satisfies a load, where the second parameter includes at least one of: outdoor temperature, active power, reactive power;
an input module 808, connected to the second modeling module 806, for inputting at least one constraint condition to the target model and/or the intra-day rolling model, respectively, wherein the constraint condition is used for indicating a range within which the first parameter and/or the second parameter of the power distribution network is limited in case of meeting the load requirement;
and the adjusting module 810 is connected to the input module 808 and is used for obtaining a mode for adjusting the electric heating equipment according to the target model and/or the day-in rolling model.
Each module combines in the above-mentioned device, can realize realizing the adjustment to electric heating equipment based on target model and day interior roll model through using the constraint condition, has reached the purpose that reduces prediction error to realized reducing the poor and unbalanced degree of three-phase of distribution network operation peak valley, promoted the technological effect of comfort level qualification rate, and then solved not consider among the correlation technique and predict the uncertain influence of aspect to opening and stopping of electric heating equipment, lead to the not good technical problem of regulating effect.
It should be noted here that the obtaining module 802, the first modeling module 804, the second modeling module 806, the input module 808, and the adjusting module 810 correspond to steps S102 to S110 in embodiment 1, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, the device where the storage medium is located is controlled to execute the processing method of the electric heating device in any one of the above.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a processor, where the processor is configured to execute a program, where the program executes a processing method of an electric heating device in any one of the above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (8)

1. A processing method of electric heating equipment is characterized by comprising the following steps:
acquiring the load of the power distribution network;
establishing a target model, wherein the target model is used for adjusting the electric heating equipment based on a first parameter of the power distribution network when the power distribution network meets the load, wherein the first parameter comprises at least one of the following parameters: the daily peak power of the nodes and the three-phase unbalanced power;
establishing an in-day rolling model, wherein the in-day rolling model is used for adjusting the electric heating equipment based on second parameters of the power distribution network under the condition that the power distribution network meets the load, and the second parameters comprise at least one of the following parameters: outdoor temperature, active power, reactive power;
inputting at least one constraint condition to the target model and/or the intra-day rolling model, respectively, wherein the constraint condition is used for indicating a range within which the first parameter and/or the second parameter of the power distribution network is limited if the load requirement is met;
obtaining a mode for adjusting the electric heating equipment according to the target model and/or the day rolling model;
the target model uses the following target function, and the target function is the weighted sum of the minimum all-day peak power and the three-phase imbalance flag of each node:
Figure FDA0003284701220000011
where t is time, i is node, Pi maxIs the peak power of the node day, and n is the distributionNumber of network nodes, λiThree-phase imbalance value weight of i node, X1(t) is the difference between the first three-phase unbalanced power and the second three-phase unbalanced power, X2(t) is the difference between the second three-phase unbalanced power and the third three-phase unbalanced power, X3(t) is the difference between the first three-phase unbalanced power and the third three-phase unbalanced power;
the intra-day rolling model uses the following objective function:
Figure FDA0003284701220000012
Figure FDA0003284701220000013
Figure FDA0003284701220000014
wherein, To(t) an actual value of outdoor temperature t,
Figure FDA0003284701220000015
for the actual value of the active power of the inode at time t,
Figure FDA0003284701220000016
is the actual value of reactive power at the i node at time T, To f(t) predicted value of outdoor temperature, P, which is a prediction of ti f(t) is a predicted value of the active power of the inode at the time t,
Figure FDA0003284701220000021
for the predicted value of reactive power at the i-node at time t,
Figure FDA0003284701220000022
is a prediction error of the outdoor temperature at time t,
Figure FDA0003284701220000023
For the prediction error of the active power of the i node at the time t,
Figure FDA0003284701220000024
Respectively represent the prediction error of the reactive power of the i node at the t moment.
2. The method of claim 1, wherein the objective function satisfying a constraint comprises at least one of:
X1(t)≥Pi A(t)-Pi B(t) wherein Pi A(t)≥Pi B(t)；
X1(t)≥Pi B(t)-Pi A(t) wherein Pi B(t)≥Pi A(t)；
X2(t)≥Pi B(t)-Pi C(t) wherein Pi B(t)≥Pi C(t)；
X2(t)≥Pi C(t)-Pi B(t) wherein Pi C(t)≥Pi B(t)；
X3(t)≥Pi C(t)-Pi A(t) wherein Pi C(t)≥Pi A(t)；
X3(t)≥Pi A(t)-Pi C(t) wherein Pi A(t)≥Pi C(t)；
Wherein, Pi A(t) a first three-phase unbalanced power, P, of the i node at time ti B(t) a second three-phase unbalanced power, P, of node i at time ti CAnd (t) is the third three-phase unbalanced power of the node i at the time t.
3. The method of claim 1, wherein the prediction error satisfying an interval constraint comprises at least one of:
Figure FDA0003284701220000025
4. the method of claim 3, wherein a ratio p of a robust interval boundary of the intra-day rolling model to a maximum error in historical data satisfies at least one of:
Figure FDA0003284701220000026
Figure FDA0003284701220000027
wherein the content of the first and second substances,
Figure FDA0003284701220000028
the absolute values of the maximum values in the historical error data of outdoor temperature, active power and reactive power are respectively.
5. The method according to any one of claims 1 to 4, wherein the constraint condition input comprises at least one of: the system comprises an electric heating equipment model, a house and heat storage model, a power distribution network flow model and thermal boundary constraint of rolling in the day.
6. A processing unit of electric heating equipment is characterized by comprising:
the acquisition module is used for acquiring the load of the power distribution network;
a first modeling module configured to establish a target model, wherein the target model is configured to adjust the electric heating equipment based on a first parameter of the power distribution network when the power distribution network satisfies the load, wherein the first parameter includes at least one of: the daily peak power of the nodes and the three-phase unbalanced power;
a second modeling module, configured to establish a rolling day model, where the rolling day model is used to adjust the electric heating equipment based on a second parameter of the power distribution network when the power distribution network satisfies the load, where the second parameter includes at least one of: outdoor temperature, active power, reactive power;
an input module, configured to input at least one constraint condition to the target model and/or the intra-day rolling model, respectively, where the constraint condition is used to indicate a range within which the first parameter and/or the second parameter of the power distribution network is limited if the load requirement is met;
the adjusting module is used for obtaining a mode for adjusting the electric heating equipment according to the target model and/or the day rolling model;
the target model uses the following target function, and the target function is the weighted sum of the minimum all-day peak power and the three-phase imbalance flag of each node:
Figure FDA0003284701220000031
where t is time, i is node, Pi maxIs the peak power of the node day, n is the number of nodes of the distribution network, lambdaiThree-phase imbalance value weight of i node, X1(t) is the difference between the first three-phase unbalanced power and the second three-phase unbalanced power, X2(t) is the difference between the second three-phase unbalanced power and the third three-phase unbalanced power, X3(t) is the difference between the first three-phase unbalanced power and the third three-phase unbalanced power;
the intra-day rolling model uses the following objective function:
Figure FDA0003284701220000032
Figure FDA0003284701220000033
Figure FDA0003284701220000034
wherein, To(t) an actual value of outdoor temperature t,
Figure FDA0003284701220000035
for the actual value of the active power of the inode at time t,
Figure FDA0003284701220000036
is the actual value of reactive power at the i node at time T, To f(t) predicted value of outdoor temperature, P, which is a prediction of ti f(t) is a predicted value of the active power of the inode at the time t,
Figure FDA0003284701220000037
for the predicted value of reactive power at the i-node at time t,
Figure FDA0003284701220000038
is a prediction error of the outdoor temperature at time t,
Figure FDA0003284701220000039
For the prediction error of the active power of the i node at the time t,
Figure FDA00032847012200000310
Respectively represent the prediction error of the reactive power of the i node at the t moment.
7. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the processing method of the electric heating device according to any one of claims 1 to 5.
8. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the processing method of an electric heating apparatus according to any one of claims 1 to 5 when running.
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Publication number Priority date Publication date Assignee Title
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CN107665384A (en) * 2017-10-27 2018-02-06 天津大学 A kind of electric power heating power integrated energy system dispatching method of the energy source station containing multizone
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