CN110783963A - Power system optimal scheduling method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to an optimized scheduling method, an optimized scheduling device, computer equipment and a storage medium for an electric power system, wherein an optimized scheduling method for the electric power system is characterized in that a target function and a basic constraint condition are obtained by processing coal burner group data and renewable energy unit data, a safety constraint condition is established based on a power transmission distribution factor, coal-fired historical output data, renewable energy historical output data and the load rate of each line, and the target function is solved by utilizing the safety constraint condition and the basic constraint condition to obtain a scheduling result, so that the influence of source load uncertainty fluctuation on the operation of the electric power system can be determined; meanwhile, the safety and the economical efficiency of the operation of the power system are also realized.
Description
Technical Field
The present application relates to the field of power system scheduling technologies, and in particular, to a power system optimal scheduling method, apparatus, computer device, and storage medium.
Background
With the increase of the Power consumption scale, the electrical load is continuously increased, the probability that the electrical Power System is in the limit operation state is greatly increased, and in order to ensure the safe and stable operation of the electrical Power System, the operation mode of the electrical Power System needs to be flexibly controlled in a self-adaptive manner under the disturbance conditions of circuit breaking, short circuit and the like, so that the electrical Power System can still properly respond to the credible expected accident, namely, an elastic Power System (Resilient Power System) needs to be realized.
However, in the implementation process, the inventor finds that at least the following problems exist in the conventional technology: most of the existing power system scheduling methods only consider single type of uncertainty when determining uncertainty factors such as intermittent renewable energy output and load fluctuation, and scheduling flexibility is poor when a credible expected accident occurs.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a power system optimal scheduling method, device, computer device, and storage medium with good flexibility.
In order to achieve the above object, an embodiment of the present application provides an optimal scheduling method for an electric power system, including the following steps:
acquiring coal burner group data, renewable energy unit data, historical load data of an electric power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
establishing a safety constraint condition according to the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint condition comprises a standby availability constraint condition, a loss load proportion constraint condition and a load severity constraint condition;
solving the objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
The embodiment of the application provides a power system optimizes scheduling device, and the device includes:
the data acquisition module is used for acquiring coal burner group data, renewable energy unit data, historical load data of the power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
the deterministic model establishing module is used for processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
the safety constraint condition establishing module is used for establishing a safety constraint condition according to a power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint condition comprises a standby availability constraint condition, a loss load proportion constraint condition and a load severity constraint condition;
the scheduling result acquisition module is used for solving the objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
The embodiment of the application provides computer equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of any one of the above power system optimal scheduling methods.
An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of any one of the foregoing power system optimal scheduling methods.
One of the above technical solutions has the following advantages and beneficial effects:
the method comprises the steps of obtaining a target function and a basic constraint condition by processing coal burner group data and renewable energy source unit data, establishing a safety constraint condition based on a power transmission distribution factor, coal-fired historical output data, renewable energy source historical output data and the load rate of each line, and solving the target function by using the safety constraint condition and the basic constraint condition to obtain a scheduling result, so that the influence of source load uncertainty fluctuation on the operation of the power system can be determined, when a credible expected accident occurs, the power system can carry out optimized scheduling according to the scheduling result, the scheduling flexibility is improved, and the power system can adjust a scheduling strategy according to the scheduling result to meet the system load requirement as much as possible; meanwhile, the safety and the economical efficiency of the operation of the power system are also realized.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a diagram of an exemplary embodiment of an application environment for a method for optimal scheduling of an electrical power system;
FIG. 2 is a schematic flow chart diagram of a power system optimal scheduling method in one embodiment;
FIG. 3 is a schematic diagram of a wind power output scenario in one embodiment;
FIG. 4 is a schematic diagram of an IEEE 39 node system with access to intermittent renewable energy sources in one embodiment;
FIG. 5 is a schematic view of a load prediction curve according to an embodiment;
FIG. 6 is a schematic diagram illustrating the load severity of a faulted line during a shutdown event in which a unit 31 fails in one embodiment;
FIG. 7 is a schematic diagram illustrating a loss of load when the unit 31 fails according to an embodiment;
FIG. 8 is a schematic diagram of backup availability in the event of a failure of a unit 31 in one embodiment;
FIG. 9 is a block diagram showing an exemplary embodiment of an optimal scheduling apparatus for an electric power system;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
With the development of renewable energy power generation technology, by connecting intermittent renewable energy sources to the power system, the compliance pressure borne by the power system can be relieved. Because the intermittent renewable energy power generation output has randomness and is not adjustable, the uncertainty of the power system can be increased after the grid connection, and the stable operation of the power system is influenced. In order to realize real-time balance between the generated energy and the load, the coal-fired unit must arrange the unit for peak regulation at the time of power peak and valley, so that the dispatching cost is increased. The current scheduling method also has the problem that safety and economy cannot be considered at the same time.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The power system optimization scheduling method provided by the application can be applied to the application environment shown in fig. 1. The dispatching center and each generator set can communicate through a network. The dispatching center can obtain the unit data of each generator set, and stores the obtained unit data to obtain the historical unit data corresponding to each generator set. Meanwhile, the dispatching center can also transmit control instructions to each generator set. And each generator set transmits the generator set data to the dispatching center and adjusts the working state of the generator set according to the received control instruction. The working state of the generator set includes, but is not limited to, a start-stop state, a real-time output amount, and the like.
In one embodiment, as shown in fig. 2, a power system optimized scheduling method is provided, which is described by taking the method as an example for being applied to the scheduling center in fig. 1, and includes the following steps:
Specifically, the coal-fired unit data includes, but is not limited to, coal-fired historical output data and coal-fired operation and maintenance costs of the coal-fired unit; the renewable energy unit data includes, but is not limited to, the renewable energy historical output data and the renewable energy operation and maintenance cost of the intermittent renewable energy unit. Intermittent renewable energy sources include, but are not limited to, wind turbines and photovoltaic turbines.
Further, the power generating unit may include a coal burning unit and an intermittent renewable energy unit. The dispatching center can transmit data acquisition instructions to the generator set respectively, and the generator set transmits historical output data and operation and maintenance cost data to the dispatching center through network communication when receiving the corresponding data acquisition instructions; or when the preset period comes, the generator set can transmit the output data and the operation and maintenance cost of the generator set in the preset period to the dispatching center, and the dispatching center performs statistical analysis on the received data to generate the set data corresponding to each generator set. For example, the genset may transmit its output data and operation and maintenance costs to the dispatch center every hour for one hour. The output data in the preset period may be average output data of the generator set in one hour, or a curve of output data in one hour (i.e., in the preset period) changing with time. Similarly, the operation and maintenance cost in the preset period may also be an average operation and maintenance cost, or a curve in which the operation and maintenance cost changes with time within one hour.
Specifically, a deterministic model is established according to the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost by taking the minimum total system operation cost as a target. Wherein the deterministic model comprises an objective function and basic constraints. Further, the basic constraints include, but are not limited to, transmission power constraints and penalty costs for abandoning renewable energy sources.
Specifically, in the conventional power system scheduling, the reserve demand level is generally set to not lower than a certain capacity, but in practical applications, even if the reserve capacity provided can satisfy the total reserve demand of the power system, when the power system is greatly disturbed, the reserved reserve capacity cannot be fully put into use due to the limitation of the line transmission capacity, so that the reserve capacity is insufficient. By generating the standby availability constraint condition, the safety margin of the transmission line can be reasonably configured in the scheduling process.
The Loss of Load ratio (LOLR), i.e. the ratio of the total output power of the power system to the total Load of the system. The load severity is used to characterize the ability of the line to resist overload risks, which reflect the likelihood and extent of damage to the line transmission power overload following an accident in the power system. The line is more susceptible to overload as the line transmission power approaches its capacity limit, i.e. the line load rate is higher. To avoid overloading the line, the line transmission power may be controlled below 90% of its capacity limit. When some lines in the power system are shut down, the load rates of other lines are likely to increase greatly due to power flow diversion. The degree of the load rate increase of the power grid line after the accident occurs can be reflected through the load severity.
Specifically, the basic constraint conditions, the safety constraint conditions and the objective function can be used for generating a nonlinear programming model, and the nonlinear programming model is solved, so that a scheduling result can be obtained. The scheduling result can be used to instruct the power system to perform optimal scheduling, in order to account for the system operation level when the safety constraint is met.
In one embodiment, before the step of obtaining the coal burner group data, the renewable energy source unit data, the historical load data of the power system, and the load rate of each line, the method further includes:
and acquiring topological parameters of the power system.
Specifically, the topology parameter of the power system may be a network structure of the power system, including but not limited to a plurality of nodes and lines connecting the nodes. Further, the topology parameters of the power system may further include types of nodes, for example, the node 31 is a coal-fired unit, and the node 35 is a wind-powered unit.
In one embodiment, the step of establishing the safety constraint condition according to the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate comprises:
generating a backup availability constraint based on the power transmission profile factor according to the following formula:
Sm
l,t<δ
l，l∈L
wherein Sm is
l,tSpare availability for line l during time period t; f
lMaximum transmission power allowed for line l; n is
UCThe number of coal-fired units; n is
DGThe number of intermittent renewable energy source units; n is
loadThe number of load nodes in the power system;
the power transmission distribution factor of the output of the coal-fired unit i to the line l is obtained;
the power transmission distribution factor of the output of the intermittent renewable energy source set j to the line l is obtained;
a power transmission distribution factor of a load pair to a line l at a load node k; p
i,tThe output of the coal-fired unit i in the time period t is obtained; p
j,tThe output of the intermittent renewable energy source unit j in the time period t is obtained; d
k,tLoad node k is the load of time period t; delta
lMinimum constraint for backup availability of line l; l is all line sets;
generating a load loss proportion constraint condition according to the following formula and the coal-fired historical output data and the renewable energy historical output data:
wherein, P
LOLRIs the load loss proportion; gamma is a confidence interval of power supply reliability;
establishing a load severity constraint condition according to the load rate of each line in the power system according to the following formula:
wherein, I
lineLine load severity for the circuit system after shutdown of one or more lines; n is
lThe number of lines in the power system; lm
l(E
k) In scene E for line l
k(iv) load rate severity; κ is the acceptable load loss level of the power system; gamma ray
lIs the load factor of line l; g is a coefficient.
In particular, when the power flow in the power system changes, the remaining transmission capacity may be mobilized. Specifically, the standby availability constraint may be obtained as follows:
Sm
l,t<δ
l，l∈L
from the above, it can be seen that for line l, when line l is operating normally, the standby availability is greater than zero, at which point the standby availability of line l is closer to zero and the line l is closer to a full load state; when the backup availability of line i is less than zero, line i is in an overload state, with the risk that the backup capacity to which it is connected is not available. By adding sparesAvailability constraints such that Sm is passable
l,tThe operating state of line l is obtained and it can be determined whether spare capacity is available based on the operating state of line l.
The loss of load ratio can be determined according to the following formula:
the loss of load ratio constraint may be determined by:
wherein P [ ] is a probability function.
When the power system is normally operated, the load loss proportion P
LOLRThe output active power of the power system can meet the load requirement of the system at the moment. When the power system fails to cause load loss, the load loss proportion is a negative value, and the larger the absolute value of the load loss proportion is, the more the load loss is.
The load severity can be determined by the following formula:
wherein, Lm
l(E
k) In scene E for line l
k(iv) load rate severity; κ is the acceptable load loss level of the power system; gamma ray
lFor the load factor of the line i, i.e. the ratio of the transmission power of the line i to the capacity limit, when the line i is out of operation, gamma
lCan be 1; g is a coefficient, and may be 5In (2).
On the basis, the load severity of the power grid line after one or more lines are shut down can be determined according to the following formula:
wherein, I
lineAfter shutdown for one or more linesLine load severity of the circuitry; n is
lThe number of lines in the power system; κ is the acceptable load loss level of the power system.
In one embodiment, the intermittent renewable energy source set comprises a wind turbine set and a photovoltaic set; the renewable energy historical output data comprises wind power historical output data and photovoltaic historical output data; the operation and maintenance cost of the renewable energy sources comprises wind power operation and maintenance cost and photovoltaic operation and maintenance cost;
processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model, wherein the method comprises the following steps of:
clustering historical wind speed data by adopting a fast search density clustering algorithm to obtain a wind speed typical scene, sampling wind power historical output data of a wind turbine generator corresponding to the wind speed typical scene by adopting a random sampling method, and determining a wind power output predicted value based on a sampling result; the predicted value of the wind power output is uncertainty data;
clustering historical radiation intensity data by adopting a fast search density clustering algorithm to obtain a radiation intensity typical scene, sampling photovoltaic historical output data of a photovoltaic unit corresponding to the radiation intensity typical scene by adopting a random sampling method, and determining a photovoltaic output predicted value based on a sampling result; the photovoltaic output predicted value is uncertainty data;
processing the historical output data of the fire coal, the operation and maintenance cost of the fire coal, the historical output data of the wind power, the historical output data of the photovoltaic, the operation and maintenance cost of the wind power, the operation and maintenance cost of the photovoltaic, a predicted value of the wind power output and a predicted value of the photovoltaic output to obtain a target function;
and establishing basic constraint conditions according to the coal-fired historical output data, the wind power historical output data, the photovoltaic historical output data, the wind power output predicted value and the photovoltaic output predicted value.
Specifically, historical wind speed data and historical radiation intensity data are obtained. And processing the historical wind speed data by adopting a rapid search density clustering algorithm to obtain a wind speed typical scene, and determining the wind power historical output data corresponding to the wind speed typical scene. Sampling the historical wind power output data by a random sampling method to obtain a wind power output predicted value, wherein the wind power output predicted value is uncertainty data. In one example, a wind power output scenario may be as shown in FIG. 3.
Similarly, historical radiation intensity data is processed by adopting a fast search density clustering algorithm to obtain a radiation intensity typical scene, and photovoltaic historical output data corresponding to the radiation intensity typical scene is determined. Sampling the photovoltaic historical output data by a random sampling method to obtain a photovoltaic output predicted value, wherein the photovoltaic output predicted value is uncertainty data.
In one example, the wind or photovoltaic output prediction value may be determined according to the following equation:
wherein,
the method comprises the steps of obtaining a maximum output predicted value of an intermittent renewable energy source unit, namely a wind power output predicted value or a photovoltaic output predicted value; the maximum fluctuation of the output of the lambda intermittent renewable energy source unit is the maximum fluctuation of the output of the wind speed unit or the maximum fluctuation of the output of the photovoltaic unit.
In one embodiment, the deviation of the actual load value from the predicted load value may be determined according to the following equation:
wherein χ is a disturbance variable;
is the predicted load value of node k; u shape
loadIs the set of all nodes in the power system.
By the above equation, the deviation of the actual load value from the predicted load value can be determined, and thus the uncertainty disturbance in the power system can be obtained.
In one embodiment, the step of establishing the objective function according to the coal-fired historical output data, the coal-fired operation and maintenance cost, the wind power historical output data, the photovoltaic historical output data, the wind power operation and maintenance cost, the photovoltaic operation and maintenance cost, the wind power output predicted value and the photovoltaic output predicted value includes:
generating a punishment item according to the following formula and according to the wind power historical output data, the photovoltaic historical output data, the wind power output predicted value and the photovoltaic output predicted value:
wherein, C
punishIs a penalty item; t is the number of scheduling time periods in a preset period; c. C
WTThe wind abandon penalty cost is the unit electric quantity; c. C
PVThe light abandon penalty cost is the unit electric quantity;
the predicted value of the wind power output in the time period t is;
a photovoltaic output predicted value in a time period t is obtained; p
WT,tThe actual wind power output value of the wind power generator set in the time period t is obtained; p
PV,tThe actual photovoltaic output value of the photovoltaic unit in the time period t is obtained;
generating a secondary operation cost function of the coal-fired unit according to the following formula and the coal-fired historical output data and the coal-fired operation and maintenance cost:
C
UC,i,t＝a
gP
i,t 2+b
gP
i,t+c
gi∈U
UC
wherein, C
UC,i,tThe operation cost of the coal-fired unit i in the time period t is calculated; a is
gA first cost coefficient that is a coal consumption function; b
gA second cost coefficient that is a function of coal consumption; c. C
gA third cost coefficient that is a coal consumption function; p
i,tThe output of the coal-fired unit i in the time period t is obtained; u shape
UCThe method comprises the steps of (1) collecting nodes of all coal burner groups;
generating an operation and maintenance cost function of the intermittent renewable energy source unit according to the following formula and according to the historical wind power output data, the historical photovoltaic output data, the wind power operation and maintenance cost and the photovoltaic operation and maintenance cost:
C
DG,j,t＝P
j,t×c
DC,jj∈U
DG
wherein, C
DG,j,tThe operation and maintenance cost of the intermittent renewable energy source set j in the time period t is obtained; p
j,tThe output of the intermittent renewable energy source unit j in the time period t is obtained; c. C
DC,jThe unit output operation and maintenance cost of the intermittent renewable energy source unit j is obtained; u shape
DGThe method comprises the steps of (1) collecting all intermittent renewable energy source unit nodes;
obtaining a target function according to the following formula and according to the punishment item, the secondary operation cost function and the operation and maintenance cost function:
wherein n is
UCThe number of coal-fired units; n is
DGThe number of intermittent renewable energy source units; c
punish,j,tAnd (4) light abandoning penalty cost for wind abandon of the intermittent renewable energy source unit j in the time period t.
Specifically, an objective function is generated with the objective of minimizing the total cost of operating the power system, and the objective function may be determined according to the following formula:
wherein T is the number of scheduling periods in a preset cycle, which may be, for example, the number of scheduling periods included in a day; c
UC,i,tThe operation cost of the coal-fired unit i in the time period t is calculated; c
DG,j,tThe operation and maintenance cost of the intermittent renewable energy source set j in the time period t is obtained; c
punish,j,tFor a penalty item, wind and light abandoning penalty cost of the intermittent renewable energy source unit j in a time period t can be obtained; n is
UCThe total number of coal-fired units; n is
DGFor total number of intermittent renewable energy units, e.g. wind and photovoltaic unitsThe total number of groups.
Further, the operating cost of the coal-fired unit i during the time period t can be determined according to the following formula:
C
UC,i,t＝a
gP
i,t 2+b
gP
i,t+c
gi∈U
UC
the operation and maintenance cost of the intermittent renewable energy source set j in the time period t can be determined according to the following formula:
C
DG,j,t＝P
j,t×c
DC,jj∈U
DG
wherein, P
j,tAnd the active power of the intermittent renewable energy source set j in the time period t.
The penalty term may be determined by:
by introducing the penalty term into the objective function, the situation that intermittent renewable energy sources such as wind and light are cut off or the power is generated in a small amount can be reduced, and the consumption of the intermittent renewable energy source power generation is improved.
In one embodiment, the basic constraints include power balance and backup constraints, unit output constraints, unit ramp-up and ramp-down constraints, and power transmission constraints;
the power balance and standby constraints are determined according to the following equations:
wherein n is
UCThe number of coal-fired units; n is
DGFor intermittent regenerationThe number of energy units; n is
loadThe number of load nodes in the power system; p
i,tThe output of the coal-fired unit i in the time period t is obtained; p
j,tThe output of the intermittent renewable energy source unit j in the time period t is obtained; d
k,tLoad node k is the load of time period t;
the total quantity of the positive reserve which can be provided by the coal-fired unit i in the time period t;
the negative standby total amount which can be provided by the coal-fired unit i in the time period t;
a positive standby demand;
is a negative standby demand;
determining a unit output constraint condition according to the following formula:
P
i,min<P
i,t<P
i,maxi∈U
UC
wherein, P
i,minThe lower limit value of the active power output by the coal-fired unit i; p
i,maxAn active power upper limit value output for the coal-fired unit i; u shape
UCThe method comprises the steps of (1) collecting nodes of all coal burner groups;
determining the unit climbing and landslide constraint conditions according to the following formula:
wherein, RU
iThe maximum climbing speed of the coal-fired unit i; RD
iThe maximum landslide rate of the coal-fired unit i;
the power transmission constraints are determined according to the following formula:
P
l,min<P
l,t<P
l,maxl∈L
wherein, P
l,minA lower limit value of transmission active power of the line l; p
l,minAn upper limit value of transmission active power of the line l; p
l,tThe active power transmitted for the line l in the time period t; l is the set of all lines.
In particular, the amount of the solvent to be used,
in order to be a positive backup demand,
the standby demand can be determined according to the preset proportion of the current load level
And
in one embodiment, the unit output constraint conditions include wind turbine output constraint conditions and photovoltaic unit output constraint conditions;
determining the output constraint condition of the wind turbine generator according to the following formula:
wherein,
the predicted value of the wind power output in the time period t is; p
WT,tThe actual wind power output value of the wind power generator set in the time period t is obtained;
determining the output constraint condition of the photovoltaic unit according to the following formula:
wherein,
a photovoltaic output predicted value in a time period t is obtained; p
PV,tThe actual photovoltaic output value of the photovoltaic unit in the time period t is obtained.
In one embodiment, after the step of solving the objective function using the basic constraint condition and the safety constraint condition to obtain the scheduling result, the method further includes:
generating a plurality of unit running states by adopting a Monte Carlo method;
solving an objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result corresponding to the running state of each unit;
and determining the robustness of the deterministic model according to each scheduling result.
Specifically, a random number generator is used for generating random numbers in the interval of [0,1], and when the generated random numbers are smaller than the forced outage rate of the unit, the unit is shut down; when the generated random number is larger than or equal to the forced outage rate of the unit, the unit normally operates, the basic constraint condition and the safety constraint condition are repeatedly utilized to solve the objective function, and therefore a plurality of scheduling results can be obtained. The robustness of the nonlinear programming model can be determined by comparing the scheduling results.
To facilitate understanding of the aspects of the present application, a specific example will be described below. As shown in fig. 4, fig. 4 shows an IEEE (Institute of Electrical and Electronics Engineers) 39 node power system in which coal-fired units are located at nodes 30 to 34, and nodes 37 to 39; the wind turbine is located at node 35; the photovoltaic unit is located at node 36; the parameters of each generator set are shown in Table 1, wherein MW is megawatt and MW/h is megawatt per hour.
TABLE 1 Generator set parameters
The total load prediction curve of the power system in one day is shown in fig. 5. When the unit 31 fails and stops running, fig. 6 is a schematic view of the load severity of a faulty line when the unit 31 fails and stops running, and as shown in fig. 6, when the unit 31 fails, the line 3 and the line 27 have insufficient safety margin, that is, there is a safety risk. After the safety constraint condition is added, compared with the traditional scheduling method, the line load severity is reduced, and the safety margin is greatly improved. Meanwhile, the line load severity shown in fig. 6 may reflect a line condition with an overload risk, so as to timely reinforce or expand the corresponding line.
Fig. 7 shows the loss of load when a unit 31 fails, and fig. 8 shows the backup availability when a unit 31 fails. As can be seen from comparison between fig. 7 and fig. 8, after safety constraints such as a standby availability constraint, a loss load proportion constraint, a line load severity constraint, and the like are added, when the circuit bears a trusted expected accident, the scheduling policy can be adjusted according to the scheduling result to meet the load demand of the power system, and the safety and the economy of the power system can be considered at the same time. Meanwhile, as shown in fig. 7 and 8, in the period 18 to 21, since the load pressure of the power system itself is large, a large load loss and a backup shortage occur even if the safety constraint is added. For the situation, measures such as load shedding can be adopted, and the safe operation of the power system is ensured.
According to the power system optimization scheduling method, the coal burner group data and the renewable energy unit data are processed to obtain a target function and a basic constraint condition, the safety constraint condition is established based on the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and the load rate of each line, the target function is solved by using the safety constraint condition and the basic constraint condition to obtain a scheduling result, so that the influence of source load uncertainty fluctuation on the operation of a power system can be determined, when a credible expected accident occurs, the power system can perform optimized scheduling according to the scheduling result, the scheduling flexibility is improved, and the power system can adjust a scheduling strategy according to the scheduling result to meet the system load requirement as far as possible; meanwhile, the safety and the economical efficiency of the operation of the power system are also realized.
It should be understood that although the various steps in the flow charts of fig. 1-8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-8 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 9, there is provided an electric power system optimization scheduling apparatus, including:
the data acquisition module is used for acquiring coal burner group data, renewable energy unit data, historical load data of the power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises the renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
the deterministic model establishing module is used for processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
the safety constraint condition establishing module is used for establishing a safety constraint condition according to the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint conditions comprise standby availability constraint conditions, loss load proportion constraint conditions and load severity constraint conditions;
the scheduling result acquisition module is used for solving the objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
For specific limitations of the electric power system optimal scheduling device, reference may be made to the above limitations of the electric power system optimal scheduling method, which is not described herein again. The modules in the power system optimization scheduling device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing coal burner group data, renewable energy unit data, historical load data, load rates of all lines and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a power system optimal scheduling method.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring coal burner group data, renewable energy unit data, historical load data of an electric power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises the renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
establishing a safety constraint condition according to the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint conditions comprise standby availability constraint conditions, loss load proportion constraint conditions and load severity constraint conditions;
solving an objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring coal burner group data, renewable energy unit data, historical load data of an electric power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises the renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
establishing a safety constraint condition according to the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint conditions comprise standby availability constraint conditions, loss load proportion constraint conditions and load severity constraint conditions;
solving an objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. The optimal scheduling method for the power system is characterized by comprising the following steps of:
acquiring coal burner group data, renewable energy unit data, historical load data of an electric power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
establishing a safety constraint condition according to the power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint condition comprises a standby availability constraint condition, a loss load proportion constraint condition and a load severity constraint condition;
solving the objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
2. The optimal scheduling method of claim 1, wherein the step of establishing a safety constraint according to the power transmission distribution factor, the historical output data of the coal, the historical output data of the renewable energy source, and each of the load rates comprises:
generating the backup availability constraint based on the power transmission profile factor according to the following formula:
Sm
l,t<δ
l，l∈L
wherein Sm is
l,tSpare availability for line l during time period t; f
lMaximum transmission power allowed for line l; n is
UCThe number of the coal-fired units; n is
DGThe number of the intermittent renewable energy source units; n is
loadThe number of load nodes in the power system;
the power transmission distribution factor of the output of the coal-fired unit i to the line l is obtained;
the power transmission distribution factor of the output of the intermittent renewable energy source set j to the line l is obtained;
a power transmission distribution factor of a load pair to a line l at a load node k; p
i,tThe output of the coal-fired unit i in the time period t is obtained; p
j,tThe output of the intermittent renewable energy source unit j in the time period t is obtained; d
k,tLoad node k is the load of time period t; delta
lMinimum constraint for backup availability of line l; l is all line sets;
generating the load loss proportion constraint condition according to the following formula and the coal-fired historical output data and the renewable energy historical output data:
wherein, P
LOLRIs the load loss proportion; gamma is a confidence interval of power supply reliability; p2]Is a probability function;
establishing the load severity constraint condition according to the load rate of each line in the power system according to the following formula:
wherein, I
lineLine load severity for the circuitry after outage for one or more lines; n is
lThe number of lines in the power system; lm
l(E
k) In scene E for line l
k(iv) load rate severity; κ is an acceptable level of shed load for the power system; gamma ray
lIs the load factor of line l; g is a coefficient.
3. The optimal scheduling method for the power system according to claim 1 or 2, wherein the intermittent renewable energy source set comprises a wind turbine set and a photovoltaic set; the renewable energy historical output data comprises wind power historical output data and photovoltaic historical output data; the renewable energy operation and maintenance cost comprises wind power operation and maintenance cost and photovoltaic operation and maintenance cost;
processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model, wherein the deterministic model comprises the following steps:
clustering historical wind speed data by adopting a fast search density clustering algorithm to obtain a wind speed typical scene, sampling wind power historical output data of a wind turbine generator corresponding to the wind speed typical scene by adopting a random sampling method, and determining a wind power output predicted value based on the sampling result; the wind power output predicted value is uncertainty data;
clustering historical radiation intensity data by adopting a fast search density clustering algorithm to obtain a radiation intensity typical scene, sampling photovoltaic historical output data of a photovoltaic unit corresponding to the radiation intensity typical scene by adopting a random sampling method, and determining a photovoltaic output predicted value based on the sampling result; the photovoltaic output predicted value is uncertainty data;
processing the historical output data of the fire coal, the operation and maintenance cost of the fire coal, the historical output data of the wind power, the historical output data of the photovoltaic, the operation and maintenance cost of the wind power, the operation and maintenance cost of the photovoltaic, the predicted value of the wind power output and the predicted value of the photovoltaic output to obtain the target function;
and establishing the basic constraint condition according to the coal-fired historical output data, the wind power historical output data, the photovoltaic historical output data, the wind power output predicted value and the photovoltaic output predicted value.
4. The optimal scheduling method for the power system according to claim 3, wherein the step of establishing the objective function according to the historical coal output data, the historical coal output cost, the historical wind output data, the historical photovoltaic output data, the historical wind power operation and maintenance cost, the historical photovoltaic operation and maintenance cost, the predicted wind output value and the predicted photovoltaic output value comprises:
generating a punishment item according to the wind power historical output data, the photovoltaic historical output data, the wind power output predicted value and the photovoltaic output predicted value according to the following formula:
wherein, C
punishIs the penalty item; t is the number of scheduling time periods in a preset period; c. C
WTThe wind abandon penalty cost is the unit electric quantity; c. C
PVThe light abandon penalty cost is the unit electric quantity;
the predicted value of the wind power output within the time period t is obtained;
is the photovoltaic output within a time period tPredicting a value; p
WT,tThe actual wind power output value of the wind turbine generator is the actual wind power output value of the wind turbine generator within the time period t; p
PV,tThe actual photovoltaic output value of the photovoltaic unit in the time period t is obtained;
generating a secondary operation cost function of the coal-fired unit according to the following formula and the coal-fired operation and maintenance cost according to the coal-fired historical output data:
C
UC,i,t＝a
gP
i,t 2+b
gP
i,t+c
gi∈U
UC
wherein, C
UC,i,tThe operation cost of the coal-fired unit i in the time period t is calculated; a is
gA first cost coefficient that is a coal consumption function; b
gA second cost coefficient that is a function of coal consumption; c. C
gA third cost coefficient that is a coal consumption function; p
i,tThe output of the coal-fired unit i in the time period t is obtained; u shape
UCThe method comprises the steps of (1) collecting nodes of all coal burner groups;
generating an operation and maintenance cost function of the intermittent renewable energy source unit according to the following formula and according to the wind power historical output data, the photovoltaic historical output data, the wind power operation and maintenance cost and the photovoltaic operation and maintenance cost:
C
DG,j,t＝P
j,t×c
DC,jj∈U
DG
wherein, C
DG,j,tThe operation and maintenance cost of the intermittent renewable energy source set j in the time period t is obtained; p
j,tThe output of the intermittent renewable energy source unit j in the time period t is obtained; c. C
DC,jThe unit output operation and maintenance cost of the intermittent renewable energy source unit j is obtained; u shape
DGThe method comprises the steps of (1) collecting all intermittent renewable energy source unit nodes;
obtaining the target function according to the penalty term, the secondary operation cost function and the operation and maintenance cost function according to the following formula:
wherein n is
UCThe number of the coal-fired units; n is
DGTo the intermittent renewable energy source unitThe number of (2); c
punish,j,tAnd (4) light abandoning penalty cost for wind abandon of the intermittent renewable energy source unit j in the time period t.
5. The optimal scheduling method for the power system according to claim 3, wherein the basic constraint conditions comprise power balance and standby constraint conditions, unit output constraint conditions, unit climbing and landslide constraint conditions and power transmission constraint conditions;
the power balance and standby constraints are determined according to the following equations:
wherein n is
UCThe number of the coal-fired units; n is
DGThe number of the intermittent renewable energy source units; n is
loadThe number of load nodes in the power system; p
i,tThe output of the coal-fired unit i in the time period t is obtained; p
j,tThe output of the intermittent renewable energy source unit j in the time period t is obtained; d
k,tLoad node k is the load of time period t;
the total quantity of the positive reserve which can be provided by the coal-fired unit i in the time period t;
the negative standby total amount which can be provided by the coal-fired unit i in the time period t;
a positive standby demand;
is a negative standby demand;
determining the unit output constraint condition according to the following formula:
P
i,min<P
i,t<P
i,maxi∈U
UC
wherein, P
i,minThe lower limit value of the active power output by the coal-fired unit i; p
i,maxAn active power upper limit value output for the coal-fired unit i; u shape
UCThe method comprises the steps of (1) collecting nodes of all coal burner groups;
determining the unit climbing and landslide constraint conditions according to the following formula:
wherein, RU
iThe maximum climbing speed of the coal-fired unit i; RD
iThe maximum landslide rate of the coal-fired unit i;
determining the power transmission constraint according to the following formula:
P
l,min<P
l,t<P
l,maxl∈L
wherein, P
l,minA lower limit value of transmission active power of the line l; p
l,minAn upper limit value of transmission active power of the line l; p
l,tThe active power transmitted for the line l in the time period t; l is the set of all lines.
6. The optimal scheduling method of the power system according to claim 5, wherein the generator output constraints comprise wind generator output constraints and photovoltaic generator output constraints;
determining the output constraint condition of the wind turbine generator according to the following formula:
wherein,
the predicted value of the wind power output within the time period t is obtained; p
WT,tThe actual wind power output value of the wind turbine generator is the actual wind power output value of the wind turbine generator within the time period t;
determining the output constraint condition of the photovoltaic unit according to the following formula:
wherein,
the photovoltaic output predicted value in the time period t is obtained; p
PV,tAnd the actual photovoltaic output value of the photovoltaic unit in the time period t is obtained.
7. The optimal scheduling method of the power system according to claim 1 or 2, wherein after the step of solving the objective function by using the basic constraint condition and the safety constraint condition to obtain the scheduling result, the method further comprises:
generating a plurality of unit running states by adopting a Monte Carlo method;
solving the objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result corresponding to the running state of each unit;
determining the robustness of the deterministic model from each of the scheduling results.
8. An apparatus for optimizing scheduling in an electric power system, the apparatus comprising:
the data acquisition module is used for acquiring coal burner group data, renewable energy unit data, historical load data of the power system and load rates of all lines; the coal-fired machine group data comprises coal-fired historical output data and coal-fired operation and maintenance cost of the coal-fired machine group; the renewable energy source unit data comprises renewable energy source historical output data of the intermittent renewable energy source unit and the operation and maintenance cost of the renewable energy source;
the deterministic model establishing module is used for processing the coal-fired historical output data, the coal-fired operation and maintenance cost, the renewable energy historical output data and the renewable energy operation and maintenance cost to obtain a deterministic model; the deterministic model comprises an objective function and basic constraints;
the safety constraint condition establishing module is used for establishing a safety constraint condition according to a power transmission distribution factor, the coal-fired historical output data, the renewable energy historical output data and each load rate; the safety constraint condition comprises a standby availability constraint condition, a loss load proportion constraint condition and a load severity constraint condition;
the scheduling result acquisition module is used for solving the objective function by using the basic constraint condition and the safety constraint condition to obtain a scheduling result; and the scheduling result is used for indicating the power system to perform optimized scheduling.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111786385A (en) * | 2020-07-10 | 2020-10-16 | 广东电网有限责任公司 | Power grid operation and maintenance scheme planning method, system and equipment |
| CN112348268A (en) * | 2020-11-11 | 2021-02-09 | 山东电力研究院 | Method and system for evaluating maximum output of thermal power generating unit in real time based on coal amount data |
| CN112434874A (en) * | 2020-12-02 | 2021-03-02 | 华北电力大学 | Line capacity optimization method and system for renewable energy consumption |
| CN113268527A (en) * | 2021-05-27 | 2021-08-17 | 中清能绿洲科技股份有限公司 | Intelligent photovoltaic energy storage scheduling analysis method and device, server and storage medium |
| CN114462783A (en) * | 2021-12-30 | 2022-05-10 | 昆明能讯科技有限责任公司 | Method and system for calculating voltage level electric power gap in power transmission network subareas |
| CN114552672A (en) * | 2022-04-26 | 2022-05-27 | 阿里巴巴（中国）有限公司 | Data processing method and storage medium for power system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5011230B2 (en) * | 2008-08-04 | 2012-08-29 | 株式会社東芝 | Secondary battery control device and control method |
| CN104809519A (en) * | 2015-04-29 | 2015-07-29 | 国家电网公司 | Power-system economic dispatching method considering power grid topology optimization |
| CN106058917A (en) * | 2016-05-31 | 2016-10-26 | 国网山东省电力公司济南供电公司 | Dynamic economic dispatching method for power system considering photovoltaic power generation |
| CN108898265A (en) * | 2018-05-02 | 2018-11-27 | 广州供电局有限公司 | A kind of integrated energy system integration planing method |
| CN109980660A (en) * | 2019-04-17 | 2019-07-05 | 陈建华 | The Robust Interval wind-powered electricity generation dispatching method of frequency response characteristic is improved based on generator |
-
2019
- 2019-09-19 CN CN201910887599.8A patent/CN110783963B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5011230B2 (en) * | 2008-08-04 | 2012-08-29 | 株式会社東芝 | Secondary battery control device and control method |
| CN104809519A (en) * | 2015-04-29 | 2015-07-29 | 国家电网公司 | Power-system economic dispatching method considering power grid topology optimization |
| CN106058917A (en) * | 2016-05-31 | 2016-10-26 | 国网山东省电力公司济南供电公司 | Dynamic economic dispatching method for power system considering photovoltaic power generation |
| CN108898265A (en) * | 2018-05-02 | 2018-11-27 | 广州供电局有限公司 | A kind of integrated energy system integration planing method |
| CN109980660A (en) * | 2019-04-17 | 2019-07-05 | 陈建华 | The Robust Interval wind-powered electricity generation dispatching method of frequency response characteristic is improved based on generator |
Non-Patent Citations (2)
| Title |
|---|
| HONGJI LIN等: "Real-time Dispatch with Flexible Ramping Products in a Power System with High Penetration of Renewable Energy Generation", 《2019 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT ASIA)》 * |
| 邓卿等: "电—气集成能源系统环境下的电力系统动态输电断面识别", 《电力科学与技术学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111786385A (en) * | 2020-07-10 | 2020-10-16 | 广东电网有限责任公司 | Power grid operation and maintenance scheme planning method, system and equipment |
| CN111786385B (en) * | 2020-07-10 | 2022-03-04 | 广东电网有限责任公司 | Power grid operation and maintenance scheme planning method, system and equipment |
| CN112348268A (en) * | 2020-11-11 | 2021-02-09 | 山东电力研究院 | Method and system for evaluating maximum output of thermal power generating unit in real time based on coal amount data |
| CN112348268B (en) * | 2020-11-11 | 2023-05-02 | 国网山东省电力公司电力科学研究院 | Method and system for evaluating maximum output of thermal power unit in real time based on coal quantity data |
| CN112434874A (en) * | 2020-12-02 | 2021-03-02 | 华北电力大学 | Line capacity optimization method and system for renewable energy consumption |
| CN112434874B (en) * | 2020-12-02 | 2024-05-10 | 华北电力大学 | Line capacity optimization method and system for renewable energy consumption |
| CN113268527A (en) * | 2021-05-27 | 2021-08-17 | 中清能绿洲科技股份有限公司 | Intelligent photovoltaic energy storage scheduling analysis method and device, server and storage medium |
| CN113268527B (en) * | 2021-05-27 | 2022-02-11 | 中清能绿洲科技股份有限公司 | Intelligent photovoltaic energy storage scheduling analysis method and device, server and storage medium |
| CN114462783A (en) * | 2021-12-30 | 2022-05-10 | 昆明能讯科技有限责任公司 | Method and system for calculating voltage level electric power gap in power transmission network subareas |
| CN114552672A (en) * | 2022-04-26 | 2022-05-27 | 阿里巴巴（中国）有限公司 | Data processing method and storage medium for power system |
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