CN116207772A - Energy scheduling method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses an energy scheduling method, an energy scheduling device, electronic equipment and a storage medium. Wherein the method comprises the following steps: determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network; and determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, and adjusting network output parameters of the target power network on the current day according to the target power change range and the target climbing range of each cluster. According to the technical scheme provided by the embodiment of the invention, the flexibility and the accuracy of energy scheduling are improved through the adjustment of the network output parameters in the current day of adjustment of the target power change range and the target climbing range in the distributed renewable energy system of the target power network.
Description
Technical Field
The present invention relates to the field of energy clustering technologies, and in particular, to an energy scheduling method, an energy scheduling device, an electronic device, and a storage medium.
Background
At present, as development and utilization of renewable energy sources are continuously promoted, a power grid starts to be connected into low-voltage distributed renewable energy sources in a large scale, the low-voltage distributed renewable energy sources have the characteristics of small monomer capacity, low access voltage level and the like, cluster division can be carried out so as to facilitate control and management, and the uncertainty of the capability of different clusters for providing renewable energy sources is high. Therefore, a method with flexible schedulability is needed to accurately schedule the clusters, and ensure effective utilization of various resources.
The existing energy scheduling method is poor in flexibility and accuracy, and cannot meet the requirements of accurate and flexible scheduling of renewable energy.
Disclosure of Invention
The invention provides an energy scheduling method, an energy scheduling device, electronic equipment and a storage medium, which are used for solving the problems of poor flexibility and poor accuracy of energy scheduling.
According to an aspect of the present invention, there is provided an energy scheduling method, wherein the method includes:
determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network, wherein the cluster comprises at least one of a wind power generation set, a photovoltaic set, a cogeneration set, a generator set and a battery set;
Determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day;
and for each cluster, adjusting the network output parameters of the current day of the target power network according to the target power change range and the target climbing range of the cluster.
According to another aspect of the present invention, there is provided a wind power energy storage calling device, wherein the device includes:
a cluster determination module for determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network, wherein the cluster comprises at least one of a wind power generation set, a photovoltaic set, a cogeneration set, a generator set, and a battery set;
the range determining module is used for determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day;
And the parameter adjustment module is used for adjusting the network output parameters of the target power network on the current day according to the target power change range and the target climbing range of each cluster.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the energy scheduling method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the energy scheduling method according to any one of the embodiments of the present invention.
According to the technical scheme, the cluster corresponding to the target power network is determined based on the distributed renewable energy system established for the target power network; determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day; the target power change range and the target climbing range are accurately determined, and an accurate parameter range is provided for accurate scheduling of energy. And for each cluster, adjusting the network output parameters of the current day of the target power network according to the target power change range and the target climbing range of the cluster. And the adjustment of network output parameters on the same day is adjusted through the target power change range and the target climbing range of the day-ahead cluster, so that the flexibility and the accuracy of energy scheduling are improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an energy scheduling method according to a first embodiment of the present invention;
fig. 2 is a flowchart of an energy scheduling method according to a second embodiment of the present invention;
FIG. 3 is an overall flow chart of an energy scheduling method provided in accordance with an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an energy scheduling device according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing an energy scheduling method according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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
Fig. 1 is a flowchart of an energy scheduling method according to an embodiment of the present invention, where the method may be applied to the case of energy scheduling, and the method may be performed by an energy scheduling device, where the energy scheduling device may be implemented in a form of hardware and/or software, and the energy scheduling device may be configured in a computer. As shown in fig. 1, the method includes:
s110, determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network.
The target power network is understood to be a network which consists of a substation and transmission lines with different voltage levels and can transmit, control and distribute electric energy. The distributed renewable energy system may be understood as a system that controls and manages the distributed renewable energy accessing the target power network.
The cluster may be understood as a cluster in the distributed renewable energy system having the function of capturing renewable energy. In embodiments of the present invention, the distributed renewable energy system may comprise one or more clusters. Optionally, the cluster may include at least one of a wind power generation set, a photovoltaic set, a cogeneration set, a generator set, and a battery pack.
The wind power plant is understood to be a cluster that converts the kinetic energy of wind into electrical energy. The photovoltaic unit is understood to be a cluster that converts light energy directly into electrical energy. The cogeneration unit can be understood as a cluster for producing electric energy and heat energy by steam which is acted by a steam turbine generator. The generator set may be understood as a cluster that converts other forms of energy into electrical energy. The battery can be understood as a cluster of electrical energy supplied by means of series and parallel batteries.
In the embodiment of the invention, a distributed renewable energy system can be established in advance for a target power network, and then, a cluster corresponding to the target power network is determined based on the distributed renewable energy system established for the target power network.
And S120, determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day before the current day.
The day-ahead aggregation model can be understood as an energy aggregation model corresponding to a target power network on the day before the current day. The daily aggregation model may be specifically used to represent the sum of the difference between the maximum output power and the minimum output power of each cluster in different time periods and the difference between the maximum climbing value and the minimum climbing value in the previous day of the current day.
The energy constraint condition can be understood as an energy constraint condition corresponding to the cluster. Optionally, the energy constraint condition may be a constraint condition of a wind generating set, a constraint condition of a photovoltaic set, a constraint condition of a cogeneration set, a constraint condition of a generating set, a constraint condition of a battery pack, and the like.
Wherein the target power variation range may be determined based on a maximum output power and a minimum output power. Specifically, the maximum output power and the minimum output power corresponding to each cluster on the same day may be determined based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network.
The target climbing range may be determined based on a maximum climbing value and a minimum climbing value corresponding to each cluster. Specifically, a maximum climbing value and a minimum climbing value corresponding to the current day of each cluster can be determined based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network.
And S130, for each cluster, adjusting the network output parameters of the target power network on the current day according to the target power change range and the target climbing range of the cluster.
The network output parameter may be understood as an energy adjustment parameter output for the target power network. It can be appreciated that the day-ahead aggregation model can be adjusted according to the energy adjustment parameters to improve the accuracy of the aggregation model.
Optionally, the energy scheduling method further includes: and acquiring a pre-constructed cost function corresponding to the target power network, wherein the cost function is constructed based on the power generation power of each cluster and a preset power generation cost coefficient. Furthermore, the adjusting the network output parameter of the current day of the target power network according to the target power variation range and the climbing range of the cluster includes: and according to the target power change range and the climbing range of the cluster and the cost function corresponding to the target power network, adjusting the network output parameters of the target power network on the current day.
Wherein the cost function may be understood as a function for calculating the energy scheduling cost. Alternatively, the cost function may be a functional relationship constructed based on the generated power of each cluster and a preset generated cost coefficient. Further, the cost function may be used to adjust network output parameters of the target power network on the current day.
The electricity generation cost coefficient can be understood as a ratio between the cost after processing and the cost before processing for the same renewable energy source raw material.
Alternatively, the cost function may be:
wherein,,
representing the output power of cluster i, a i Representing the power generation cost coefficients in cluster i, b i Representing the power generation cost coefficients in cluster i, c i Representing the power generation cost coefficients in cluster i.
Further, specifically, the minimum operation cost of the target power network corresponding to the cost function may be obtained, and the calculation formula may be:
wherein,,
representing the output power of cluster i, a i Representing the power generation cost coefficients in cluster i, b i Representing the power generation cost coefficients in cluster i, c i Representing the power generation cost coefficients in cluster i.
Specifically, the minimum operation cost of the target power network corresponding to the cost function is obtained as one of optimization targets, and the network output parameter of the target power network on the same day is adjusted.
Optionally, according to the target power change range and the climbing range of the cluster and the cost function corresponding to the target power network, the adjusting the network output parameter of the current day of the target power network includes: and adjusting the operation parameters of at least one cluster on the same day of the target power network so that the output power of the cluster is positioned in a target power variation range, the climbing value of the cluster is positioned in a target climbing range, and the cost of the target power network calculated based on a cost function corresponding to the target power network does not exceed a preset cost.
The preset cost can be understood as an upper cost limit when energy scheduling is performed. In the embodiment of the present invention, the preset cost may be preset according to the scene requirement, which is not specifically limited herein. It is understood that the target power network cost calculated based on the cost function corresponding to the target power network may be a minimum operation cost, and may not exceed a preset cost.
According to the technical scheme, a cluster corresponding to a target power network is determined based on a distributed renewable energy system established for the target power network, wherein the cluster comprises at least one of a wind power generation unit, a photovoltaic unit, a cogeneration unit, a generator set and a battery pack; determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day; the target power change range and the target climbing range are accurately determined, and an accurate parameter range is provided for accurate scheduling of energy. And for each cluster, adjusting the network output parameters of the current day of the target power network according to the target power change range and the target climbing range of the cluster. And the flexibility and the accuracy of energy scheduling are improved through the adjustment of network output parameters.
Example two
Fig. 2 is a flowchart of an energy scheduling method according to a second embodiment of the present invention, where the target power variation range and the target climbing range of each cluster are determined for refinement based on a pre-established daily aggregation model and an energy constraint condition corresponding to the target power network in the foregoing embodiment. As shown in fig. 2, the method includes:
s210, determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network.
S220, determining the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in each time period in the previous day of the current day respectively.
The maximum ramp value may be understood as a maximum rate of change of the output power for each cluster in each time period. The minimum ramp value may be understood as the minimum rate of change of the output power for each cluster over the respective time period.
In the embodiment of the present invention, each time period in the day before the current day may be preset according to the scene requirement, which is not specifically limited herein. Alternatively, each time period in the day immediately before the current day may be a time period divided for the day immediately before the current day in units of one hour.
Optionally, the energy constraint condition corresponding to the target power network at least includes an output power constraint condition and a climbing range constraint condition corresponding to each cluster, the maximum output power and the minimum output power of the clusters satisfy the output power constraint condition corresponding to the clusters, and the maximum climbing value and the minimum climbing value of the clusters satisfy the climbing range constraint condition corresponding to the clusters.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a wind generating set, the maximum output power and the minimum output power of the wind generating set satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the wind generating set satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the wind generating set may be:
P W，min ≤P t W ≤P W，max
wherein P is i w Representing the output power of the wind generating set at time t, P W，max Represents the maximum output power, P, of the wind generating set W，min Representing the minimum output power of the wind generating set, R W，max Representing a wind power generatorMaximum hill climbing value of group R W，min Representing the minimum hill climbing value of the wind generating set.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a photovoltaic unit, the maximum output power and the minimum output power of the photovoltaic unit satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the photovoltaic unit satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the photovoltaic unit may be:
wherein P is t PV Representing the output power of the photovoltaic unit at time t, P PV，max Represents the maximum output power of the photovoltaic unit, P PV，min Representing the minimum output power of the photovoltaic unit, R PV，max Represents the maximum climbing value of the photovoltaic unit, R PV，min Representing the minimum ramp value of the photovoltaic unit.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a cogeneration unit, the maximum output power and the minimum output power of the cogeneration unit satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the cogeneration unit satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the cogeneration unit may be:
Wherein P is t CHP The electric quantity output power of the cogeneration unit at time t is represented,
representing heat output power, P of cogeneration unit at time t CHP，max Represents the maximum output power of the electric quantity of the cogeneration unit, P CHP，min Represents the minimum output power of the electric quantity of the cogeneration unit, H CH P ，max Represents the maximum output power of heat of the cogeneration unit, H CHP，min Represents the minimum output power of heat of the cogeneration unit, R CHP，max Represents the maximum climbing value of the cogeneration unit, R CHP，min And the minimum climbing value of the cogeneration unit is indicated.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a generator set, the maximum output power and the minimum output power of the generator set satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the generator set satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the generator set may be:
wherein P is t G Representing the output power of the generator set at time t, P G，max Represents the maximum output power of the generator set, P G ，min Represents the minimum output power of the generator set, R G，max Represents the maximum climbing value of the generator set, R G，min And representing the minimum climbing value of the generator set.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a battery pack, the maximum output power and the minimum output power of the battery pack satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the battery pack satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the battery pack may be:
P ESS-dc，min ≤≤P i ESS-dc ≤P ESS-dc，max
P ESS-c，min ≤P t ESS-c ≤P ESS-c，max
wherein P is t ESS-c Representing the charge power of the battery pack at time t, P t ESS-dc Represents the discharge power of the battery pack at time t, P ESS-dc,max Represents the maximum discharge power of the battery pack, P ESS-dc,min Represents the minimum discharge power of the battery pack, P ESS-c,max Represents the maximum charging power of the battery pack, P ESS-c,min Indicating the maximum charge power of the battery pack,
representing the maximum remaining battery power at time t, < >>
Indicating the maximum remaining battery power at time t.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a gas boiler unit, the maximum output power and the minimum output power of the gas boiler unit satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the gas boiler unit satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the gas boiler unit may be:
Wherein P is t B Representing the loss power of the gas boiler unit at time t, P B,max Represents the maximum loss power of the gas boiler unit, P B,min Represents the minimum loss power of the gas boiler unit,
represents the output power of the gas boiler unit at time t, H B,max Represents the maximum output power of the gas boiler unit, H B,min Representing the minimum output power of the gas boiler unit, R B,max Represents the maximum climbing value of the gas boiler unit, R B,min Representing the minimum climbing value of the gas boiler unit.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to a boiler unit, the maximum output power and the minimum output power of the boiler unit satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the boiler unit satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the boiler unit may be:
wherein P is t BG Representing the output power of the boiler unit at time t, P BG，max Represents the maximum output power of the boiler unit, P BG，min Representing the minimum output power of the boiler unit, R BG，max Represents the maximum climbing value of the boiler unit, R BG，min Representing the minimum ramp value of the boiler unit.
The energy constraint condition corresponding to the target power network may be an output power constraint condition and a climbing range constraint condition corresponding to an electric boiler unit, the maximum output power and the minimum output power of the electric boiler unit satisfy the output power constraint condition corresponding to the cluster, the maximum climbing value and the minimum climbing value of the electric boiler unit satisfy the climbing range constraint condition corresponding to the cluster, and specifically, the constraint condition of the electric boiler unit may be:
P EB，min ≤P i EB ≤P EB，max
wherein P is t EB Represents the consumption power of the electric boiler unit at the time t, H EB，max Represents the maximum output power of the electric boiler unit, H EB，min Representing the minimum output power of the electric boiler unit,
indicating the output power of the electric boiler unit at time t, R EB ，max Indicating maximum power consumption of electric boiler unit, R EB，min Representing the minimum power consumption of the electric boiler unit.
Optionally, the energy constraint condition corresponding to the target power network further includes a power balance constraint condition and a thermal balance constraint condition, wherein the power balance constraint condition is constructed based on the active power of the power load of the target power network, and the thermal balance constraint condition is constructed based on the thermal load power of the target power network.
Specifically, the electric power balance constraint condition and the thermal balance constraint condition may be:
wherein P is t W Representing the output power of the wind generating set at time t, P t PV Representing the output power of the photovoltaic unit at time t, P t CHP Representing the electric quantity output power, P of the cogeneration unit at time t t G Representing the output power of the generator set at time t, P t ESS Indicating the output power of the battery pack at time t,
representing the active power of the power load of said target power network at time t,
Indicating the heat output of the cogeneration unit at time t,
Indicating the output of the gas boiler unit at time t, < >>
Indicating the output of the electric boiler unit at time t, < >>
And representing the thermal load power of the target power network at the time t.
S230, for each time period, calculating a power difference value of the maximum output power and the minimum output power of each cluster and a climbing difference value of the maximum climbing value and the minimum climbing value respectively, and calculating a sum value of the power difference value and the climbing difference value of each cluster in the time period.
Specifically, for each time period, calculating a power difference value between the maximum output power and the minimum output power of each cluster and a climbing difference value between the maximum climbing value and the minimum climbing value, and calculating a sum value of the power difference value and the climbing difference value of each cluster in the time period, where the algebraic formula may be:
Where N represents the total number of clusters,
represents the maximum output power of cluster i at time t, < >>
Representing the minimum output power of cluster i at time t, < >>
Representing the maximum ramp value of cluster i at time t, +.>
Representing the minimum ramp value of cluster i at time t.
S240, determining a target power change range and a target climbing range of each cluster according to the sum value corresponding to each time period.
In the embodiment of the invention, the accurate determination of the target power change range and the target climbing range of each cluster can be a key for improving the accurate energy scheduling.
Optionally, the determining the target power change range and the target climbing range of each cluster according to the sum value corresponding to each time period includes: and taking the power difference value and the climbing difference value of each cluster in the time period corresponding to the maximum sum value as a target power change range and a target climbing range of the cluster.
Specifically, determining the power difference value and the climbing difference value of each cluster in the time period corresponding to the maximum sum value, where the algebraic expression may be:
where N represents the total number of clusters,
represents the maximum output power of cluster i at time t, < > >
Representing the minimum output power of cluster i at time t, < >>
Representing the maximum climbing value of cluster i at time tmax +.>
Representing the minimum ramp value of cluster i at time t.
And finally, taking the power difference value and the climbing difference value of each cluster in the time period corresponding to the maximum sum value as a target power change range and a target climbing range of the cluster.
And S250, aiming at each cluster, adjusting the network output parameters of the target power network on the current day according to the target power change range and the target climbing range of the cluster.
According to the technical scheme, the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in each time period in the previous day of the current day are respectively determined; for each time period, calculating a power difference value of the maximum output power and the minimum output power of each cluster and a climbing difference value of the maximum climbing value and the minimum climbing value respectively, and calculating a sum value of the power difference value and the climbing difference value of each cluster in the time period; and determining a target power change range and a target climbing range of each cluster according to the sum value corresponding to each time period. And determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, and providing an accurate parameter range for accurate energy scheduling.
FIG. 3 is an overall flow chart of an energy scheduling method provided in accordance with an embodiment of the present invention; as shown in fig. 3, the overall flow of the energy scheduling method may be:
1. and (5) establishing a distributed renewable energy system. A distributed renewable energy system is established for a target power network, and a cluster corresponding to the target power network is determined.
2. And establishing a daily aggregation model and a cost function. And constructing a day-ahead aggregation model based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day before the current day, and constructing a cost function based on the power generation power of each cluster and a preset power generation cost coefficient.
3. And determining the energy constraint condition corresponding to the target power network. The output power constraint condition and the climbing range constraint condition corresponding to each cluster.
4. And determining a target power variation range and a target climbing range of each cluster. And taking the power difference value and the climbing difference value of each cluster in the time period corresponding to the maximum sum value as a target power change range and a target climbing range of the cluster.
5. And adjusting the network output parameters of the current day of the target power network. And according to the target power change range and the climbing range of the cluster and the cost function corresponding to the target power network, adjusting the network output parameters of the target power network on the current day.
In the embodiment of the present invention, the time periods may be represented by a character T, and it is understood that each of the time periods may be divided into 24 time periods per day in units of one hour. Then at t=1, a first time period per day may be characterized and the network output parameters are adjusted in units of each time period; further, the day-ahead aggregation model may be adjusted in daily units at T > 24.
The energy scheduling method provided by the invention is aimed at obtaining clusters with similar output characteristics by dividing the distributed renewable energy sources with small single-point capacity and dispersed geographic positions, so that the effective utilization of various resources is ensured; aggregating distributed renewable energy sources in a target power network into a distributed renewable energy source system; and determining clusters corresponding to the target power network, a pre-established daily aggregation model, obtaining a cost function corresponding to the target power network, determining the maximum sum of the power difference value and the climbing difference value of each cluster in the time period, and obtaining a target power change range and a target climbing range of each cluster.
And according to the target power change range and the climbing range of the cluster and the cost function corresponding to the target power network, adjusting the network output parameters of the target power network on the current day.
The energy scheduling method provided by the invention can divide the distributed renewable resources with small single-point capacity and dispersed geographic positions, and ensure the effective utilization of various renewable resources; the proposed daily dynamic adjustment of the daily aggregation model can ensure the accuracy of the network output parameters, avoid the problems of increased scheduling cost, safe system operation and the like in the energy scheduling process, provide accurate results for a target power network and ensure the feasibility of a target power network scheduling plan; the integration of distributed renewable source energy into one distributed renewable energy system can increase the flexibility and economy of the target power network.
Example III
Fig. 4 is a schematic structural diagram of an energy scheduling device according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: cluster determination module 310, range determination module 320, and parameter adjustment module 330.
Wherein the cluster determining module 310 is configured to determine a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network, where the cluster includes at least one of a wind power generation set, a photovoltaic set, a cogeneration set, a generator set, and a battery set; the range determining module 320 is configured to determine a target power variation range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, where the day-ahead aggregation model is constructed based on a maximum output power, a minimum output power, a maximum climbing value, and a minimum climbing value of each cluster in different time periods in a day before the current day; and the parameter adjustment module 330 is configured to adjust, for each cluster, a network output parameter of the current day of the target power network according to a target power variation range and a target climbing range of the cluster.
According to the technical scheme, a cluster corresponding to a target power network is determined based on a distributed renewable energy system established for the target power network, wherein the cluster comprises at least one of a wind power generation unit, a photovoltaic unit, a cogeneration unit, a generator set and a battery pack; determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day; the target power change range and the target climbing range are accurately determined, and an accurate parameter range is provided for accurate scheduling of energy. And for each cluster, adjusting the network output parameters of the current day of the target power network according to the target power change range and the target climbing range of the cluster. The flexibility and the accuracy of energy scheduling are improved and poor through the adjustment of network output parameters.
Optionally, the range determining module 320 includes: the device comprises a data determination sub-module, a value calculation sub-module and a range determination sub-module.
The data determining module is used for determining the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in each time period in the previous day of the current day respectively;
the sum value calculation sub-module is used for respectively calculating a power difference value of the maximum output power and the minimum output power of each cluster and a climbing difference value of a maximum climbing value and a minimum climbing value according to each time period, and calculating a sum value of the power difference value and the climbing difference value of each cluster in the time period;
and the range determination submodule is used for determining the target power change range and the target climbing range of each cluster according to the sum value corresponding to each time period.
Optionally, the range determination submodule is configured to:
and taking the power difference value and the climbing difference value of each cluster in the time period corresponding to the maximum sum value as a target power change range and a target climbing range of the cluster.
Optionally, the energy constraint condition corresponding to the target power network at least includes an output power constraint condition and a climbing range constraint condition corresponding to each cluster, the maximum output power and the minimum output power of the clusters satisfy the output power constraint condition corresponding to the clusters, and the maximum climbing value and the minimum climbing value of the clusters satisfy the climbing range constraint condition corresponding to the clusters.
Optionally, the energy constraint condition corresponding to the target power network further includes a power balance constraint condition and a thermal balance constraint condition, wherein the power balance constraint condition is constructed based on the active power of the power load of the target power network, and the thermal balance constraint condition is constructed based on the thermal load power of the target power network.
Optionally, the energy scheduling method further includes: and a cost function acquisition module.
The cost function acquisition module is used for acquiring a pre-constructed cost function corresponding to the target power network, wherein the cost function is constructed based on the power generation power of each cluster and a preset power generation cost coefficient;
the parameter adjustment module 330 includes: and a parameter adjustment sub-module.
And the parameter adjustment sub-module is used for adjusting the network output parameters of the target power network on the current day according to the target power change range and the climbing range of the cluster and the cost function corresponding to the target power network.
Optionally, the parameter adjustment submodule is configured to:
and adjusting the operation parameters of at least one cluster on the same day of the target power network so that the output power of the cluster is positioned in a target power variation range, the climbing value of the cluster is positioned in a target climbing range, and the cost of the target power network calculated based on a cost function corresponding to the target power network does not exceed a preset cost.
The energy scheduling device provided by the embodiment of the invention can execute the energy scheduling method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the energy scheduling method.
In some embodiments, the energy scheduling method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the energy scheduling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the energy scheduling method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. An energy scheduling method, comprising:
determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network, wherein the cluster comprises at least one of a wind power generation unit, a photovoltaic unit, a cogeneration unit, a generator unit, and a battery pack;
determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day;
And for each cluster, adjusting the network output parameters of the current day of the target power network according to the target power change range and the target climbing range of the cluster.
2. The method of claim 1, wherein determining the target power variation range and the target ramp range for each cluster based on the pre-established day-ahead aggregation model and the constraint condition corresponding to each cluster comprises:
determining the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in each time period in the previous day of the current day respectively;
for each time period, calculating a power difference value of the maximum output power and the minimum output power of each cluster and a climbing difference value of the maximum climbing value and the minimum climbing value respectively, and calculating a sum value of the power difference value and the climbing difference value of each cluster in the time period;
and determining a target power change range and a target climbing range of each cluster according to the sum value corresponding to each time period.
3. The method of claim 2, wherein the determining the target power variation range and the target climbing range of each cluster according to the sum value corresponding to each time period comprises:
And taking the power difference value and the climbing difference value of each cluster in the time period corresponding to the maximum sum value as a target power change range and a target climbing range of the cluster.
4. The method of claim 2, wherein the energy constraints corresponding to the target power network include at least an output power constraint and a climb range constraint corresponding to each cluster, a maximum output power and a minimum output power of the cluster satisfy the output power constraint corresponding to the cluster, and a maximum ramp value and a minimum ramp value of the cluster satisfy the climb range constraint corresponding to the cluster.
5. The method of claim 4, wherein the energy constraints corresponding to the target power network further comprise a power balance constraint that is built based on an active power of a power load of the target power network and a thermal balance constraint that is built based on a thermal load power of the target power network.
6. The method as recited in claim 1, further comprising:
acquiring a pre-constructed cost function corresponding to the target power network, wherein the cost function is constructed based on the power generation power of each cluster and a preset power generation cost coefficient;
The adjusting the network output parameter of the current day of the target power network according to the target power variation range and the climbing range of the cluster includes:
and according to the target power change range and the climbing range of the cluster and the cost function corresponding to the target power network, adjusting the network output parameters of the target power network on the current day.
7. The method of claim 6, wherein the adjusting the network output parameter of the current day of the target power network according to the target power variation range and the climbing range of the cluster and the cost function corresponding to the target power network comprises:
and adjusting the operation parameters of at least one cluster on the same day of the target power network so that the output power of the cluster is positioned in a target power variation range, the climbing value of the cluster is positioned in a target climbing range, and the cost of the target power network calculated based on a cost function corresponding to the target power network does not exceed a preset cost.
8. A wind power energy storage calling device, comprising:
a cluster determination module for determining a cluster corresponding to a target power network based on a distributed renewable energy system established for the target power network, wherein the cluster comprises at least one of a wind power generation set, a photovoltaic set, a cogeneration set, a generator set, and a battery set;
The range determining module is used for determining a target power change range and a target climbing range of each cluster based on a pre-established day-ahead aggregation model and an energy constraint condition corresponding to the target power network, wherein the day-ahead aggregation model is constructed based on the maximum output power, the minimum output power, the maximum climbing value and the minimum climbing value of each cluster in different time periods in the day ahead of the current day;
and the parameter adjustment module is used for adjusting the network output parameters of the target power network on the current day according to the target power change range and the target climbing range of each cluster.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the wind power storage invocation method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the wind power energy storage invocation method of any of claims 1-7 when executed.
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