CN116111580A - Power optimization scheduling method, device, equipment and storage medium - Google Patents
Power optimization scheduling method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN116111580A CN116111580A CN202211593825.XA CN202211593825A CN116111580A CN 116111580 A CN116111580 A CN 116111580A CN 202211593825 A CN202211593825 A CN 202211593825A CN 116111580 A CN116111580 A CN 116111580A
- Authority
- CN
- China
- Prior art keywords
- load
- power
- measurement
- transferable
- power system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005457 optimization Methods 0.000 title claims abstract description 33
- 238000003860 storage Methods 0.000 title claims abstract description 15
- 229910052799 carbon Inorganic materials 0.000 claims abstract description 105
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 88
- 238000005259 measurement Methods 0.000 claims abstract description 72
- 230000009467 reduction Effects 0.000 claims abstract description 41
- 238000012937 correction Methods 0.000 claims abstract description 26
- 230000004044 response Effects 0.000 claims abstract description 26
- 230000008901 benefit Effects 0.000 claims abstract description 14
- 238000005096 rolling process Methods 0.000 claims abstract description 11
- 230000006870 function Effects 0.000 claims description 23
- 238000010248 power generation Methods 0.000 claims description 15
- 230000007704 transition Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 10
- 238000010586 diagram Methods 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 4
- 230000009194 climbing Effects 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application discloses a power optimization scheduling method, a device, equipment and a storage medium, wherein a load side carbon potential model established based on a carbon emission flow theory is utilized to respectively determine a flexible load volume interval of a power system in a target period according to transferable load constraint conditions and load reduction constraint conditions; optimizing a preset day-ahead objective function according to the flexible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the condition of meeting the preset constraint condition reaches the minimum value, so as to obtain the power output pre-measurement and the flexible load pre-measurement of the power unit of the power system in the target period; and carrying out daily power correction on the power system according to the output pre-measurement and the flexible load pre-measurement of the energy unit by utilizing a preset rolling optimization correction strategy. And the power dispatching is carried out from two stages of day-ahead planning and day-ahead correction, so that the accuracy and the instantaneity of power grid dispatching are ensured, and the effective utilization of energy is realized.
Description
Technical Field
The present disclosure relates to the field of energy scheduling technologies, and in particular, to a power optimization scheduling method, device, equipment, and storage medium.
Background
With the continuous access of new energy sources such as wind power and the like to a power grid, the energy-saving and emission-reducing pressure of the power grid can be effectively relieved due to no carbon emission when the wind power and the like are used as clean energy sources for power generation, but the new energy sources such as wind power and the like have the characteristics of fluctuation and uncertainty, so that the power grid always causes the phenomenon of wind abandoning. With the gradual development of the power market, the low-carbon scheduling of the lifting system becomes a primary goal, so how to reduce the wind-discarding phenomenon of the power grid and the low-carbon scheduling of the lifting system become the problems to be solved.
Disclosure of Invention
The application provides a power optimization scheduling method, device, equipment and storage medium, which are used for solving the technical problem that low-carbon scheduling of a system cannot be improved due to frequent wind abandoning phenomenon caused by current new energy grid connection.
In order to solve the above technical problem, in a first aspect, the present application provides a power optimization scheduling method, including:
the method comprises the steps that a load side carbon potential model established based on a carbon emission flow theory is utilized, a transferable load interval and a reducible load interval of an electric power system in a target period are respectively determined according to a transferable load constraint condition and a reducible load constraint condition, a source side of the electric power system comprises a traditional energy unit and a new energy unit, and the load side of the electric power system comprises a flexible load;
optimizing a preset day-ahead objective function according to the transferable load interval and the reducible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the condition of meeting the preset constraint condition reaches the minimum value, so as to obtain the power unit output pre-measurement, the transferable load pre-measurement and the reducible load pre-measurement of the electric power system in the target period;
and (3) carrying out daily power correction on the power system according to the output pre-measurement, the transferable load pre-measurement and the load-shedding pre-measurement of the energy unit by utilizing a preset rolling optimization correction strategy.
In some implementations, the load side carbon potential model is:
wherein ,Eemi For the node carbon emission, D i In order to be the load capacity of the node,
for node carbon flow density, +.>
Is the branch carbon flow density.
In some implementations, the transferable load constraint includes:
transferable load upper and lower limit constraints:
transferable load total constraint:
wherein ,
is the maximum allowable transition value; />
Is the minimum allowable transition value, ">
The total amount of power that can be transferred to the load.
In some implementations, the load-shedding constraints include:
load upper and lower limit constraints can be cut down:
maximum cut-down times constraint condition:
wherein ,
maximum reduction coefficient in the ith period for load reduction, +.>
For the minimum reduction coefficient in the ith period of load reduction, +.>
Is the maximum reduction number.
In some implementations, the pre-set day-ahead objective function is:
wherein ,cg 、c w Respectively are provided withThe power generation cost of the traditional energy unit and the power generation cost of the new energy unit are set; p (P) g,t 、P w,t Respectively carrying out the output pre-measurement of the traditional energy unit and the output pre-measurement of the new energy unit at the moment t; c em i, i and t are load emission reduction benefits at the moment t; ΔE i,t Total reduced carbon emissions before and after load response per unit time; c cut 、c shift The transferable load cost coefficient and the load cost coefficient which can be cut down of each response unit power are respectively; d (D) cut,i,t 、D shift,i,t A transferable load pre-measurement and a load-shedding pre-measurement at time t.
In some implementations, the preset constraints include a legacy energy unit constraint, a new energy unit constraint, a line power flow constraint, and a node power balance constraint.
In some implementations, using a preset rolling optimization correction strategy, performing intra-day power correction on an electrical power system according to an energy unit output pre-measure, a transferable load pre-measure, and a reducible load pre-measure, comprising:
acquiring the actual output force of an energy unit of the power system in the k period;
determining daily power fluctuation of the power system according to the actual output force of the energy unit, the output power pre-measurement of the energy unit, the transferable load pre-measurement and the load-shedding pre-measurement;
and predicting the power output and the flexible load prediction quantity of the power system in the future period of the day according to the power fluctuation in the day.
In a second aspect, the present application further provides a power optimization scheduling device, including:
the determining module is used for respectively determining a transferable load volume interval and a reducible load volume interval of the power system in a target period according to a transferable load constraint condition and a reducible load constraint condition by utilizing a load side carbon potential model established based on a carbon emission flow theory, wherein a source side of the power system comprises a traditional energy unit and a new energy unit, and a load side of the power system comprises a flexible load;
the optimizing module is used for optimizing a preset objective function according to the transferable load interval and the reducible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the condition of meeting the preset constraint condition reaches the minimum value, so as to obtain the power unit output pre-measurement, the transferable load pre-measurement and the reducible load pre-measurement of the electric power system in the target period;
the correction module is used for carrying out daily power correction on the power system according to the power output pre-measurement, the transferable load pre-measurement and the load reduction pre-measurement of the energy unit by utilizing a preset rolling optimization correction strategy.
In a third aspect, the present application also provides a computer device comprising a processor and a memory for storing a computer program which, when executed by the processor, implements the power optimized scheduling method as in the first aspect.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the power optimized scheduling method as in the first aspect.
Compared with the prior art, the application has the following beneficial effects:
determining a transferable load interval and a reducible load interval of the power system in a target period respectively according to a transferable load constraint condition and a reducible load constraint condition by utilizing a load side carbon potential model established based on a carbon emission flow theory, wherein a source side of the power system comprises a traditional energy unit and a new energy unit, and the load side of the power system comprises a flexible load so as to integrate energy output characteristics and consider source load uncertainty; optimizing a preset day-ahead objective function according to the transferable load interval and the reducible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the condition of meeting the preset constraint reaches the minimum value, so as to obtain the power unit output pre-measurement, the transferable load pre-measurement and the reducible load pre-measurement of the power system in the target period, and realize the day-ahead output plan of the low-carbon emission of the system on the basis of ensuring economy; and finally, carrying out daily power correction on the power system according to the output pre-measurement, the transferable load pre-measurement and the load shedding pre-measurement of the energy unit by utilizing a preset rolling optimization correction strategy so as to correct daily power fluctuation of the power system, thereby carrying out power dispatching from two stages of daily planning and daily correction, ensuring the accuracy and instantaneity of power grid dispatching, realizing effective utilization of energy, effectively reducing the wind abandoning phenomenon of the power grid and improving the low-carbon dispatching of the system.
Drawings
Fig. 1 is a flow chart of a power optimization scheduling method according to an embodiment of the present application;
FIG. 2 is a graph of dynamic data of carbon potential of a load side node based on time, according to an embodiment of the present application;
FIG. 3 is a schematic diagram of dynamic carbon potential data of a load side node based on a load node according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a node carbon potential before demand response according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an output prediction of wind power demand response according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a node carbon potential after demand response according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a power optimization scheduling device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1, fig. 1 is a flow chart of a power optimization scheduling method according to an embodiment of the present application. The power optimization scheduling method can be applied to computer equipment, wherein the computer equipment comprises, but is not limited to, smart phones, notebook computers, tablet computers, desktop computers, physical servers, cloud servers and the like. As shown in fig. 1, the power optimization scheduling method of the present embodiment includes steps S101 to S103, which are described in detail as follows:
step S101, a load side carbon potential model established based on a carbon emission flow theory is utilized, a transferable load volume interval and a reducible load volume interval of the electric power system in the target period are respectively determined according to a transferable load constraint condition and a reducible load constraint condition, a source side of the electric power system comprises a traditional energy unit and a new energy unit, and a load side of the electric power system comprises a flexible load.
In this step, the conventional energy units include, but are not limited to, coal-fired units, gas units, and the like, and the new energy units include, but are not limited to, wind power units, photovoltaic units, and the like. According to the carbon emission flow theory of the electric power system, a network carbon flow distribution model comprising a source side and a load side is established, the carbon emission responsibility is reduced from the power generation side to the load side, and the dynamic data of the carbon potential of the load side are solved, and are particularly shown in fig. 2 and 3.
In some embodiments, the electrical power system carbon emission flow theoretical formula mainly includes a carbon emission flow rate R CEF And carbon flow density I BCEl . Wherein the carbon emission flow rate R CEF Representing carbon emission flow energy C per unit time through a network node or branch CEF Unit tCO 2 And (h) the calculation formula is as follows:
density of carbon flow I BCEl Representing the carbon emission value of the power generation side caused by the consumption of the unit electric quantity transmitted by the branch of the power system, wherein the carbon emission value is the carbon flow rate R of any branch BCEF And active power flow Pline l Ratio of (1), unit tCO 2 And (h), the calculation formula is as follows:
wherein ,RBCEF Indicative of carbon flow rate in either leg, pline l Representing the magnitude of the active power flow of a transmission line of the power system;
alternatively, the carbon emission amount of the consumed unit amount at the node I corresponding to the power generation side is referred to as the node carbon flow density I NCEi Also known as node carbon potential, in units of tCO 2 And (h), the calculation formula is as follows:
wherein: pline l For the active power flow of branch l ρ l Branch carbon flow density for branch l, R l For the carbon emission of the node corresponding to the power generation side, N + Is the set of all branches connected to node i with flows flowing into the node.
Further, after deduction according to the carbon emission flow theory, the carbon flow density of the branch is determined by the head node of the branch, and the calculation formula is as follows:
the carbon emission E of each node can be obtained from the carbon emission coefficient of each unit and the related data of the system trend emi ：/>
wherein ,Eemi For the node carbon emission, D i For node load, +.>
For node carbon flow density, +.>
Is the branch carbon flow density.
Based on the above, a load side dynamic electricity consumption metering model is established, and a 24h time scale is taken as an example, and the method is specifically divided into a dynamic carbon potential model and a carbon potential model of all load nodes of the network at unit moment.
In some embodiments, flexible loads may be categorized into transferable loads and load-shedding loads in such a way that they participate in demand response;
for transferableLoad: assuming transferable load L tran The transfer period of (c) may be [0,1,2, ]]With variable X from 0 to 1 tran Representing the transition condition at each time period, X tran Can be expressed as
wherein ,
indicating that a transition is made during the ith period, +.>
Indicating that no transition was made during the i-th period.
The transfer power should be limited by upper and lower power limits, so that the constraint conditions of the upper and lower transferable loads should be satisfied:
wherein ,/>
Is the maximum allowable transition value; />
Is the minimum allowable transition value.
If the transferable loads are transferred to a plurality of single time periods in a more dispersed way, the external equipment can be started and stopped frequently, so the minimum duration of the transferable loads is also limited, and the minimum duration of the transferable loads is set as
The minimum duration constraint should be satisfied: />
The transferable loads should remain unchanged in the total amount of load power before and after transfer, so the transferable load total amount constraint condition should be satisfied:
wherein ,/>
The total amount of power that can be transferred to the load.
For the load that can be reduced, let a certain load that can be reduced be L cut The power distribution vector before the participation in the scheduling is:
with variable X from 0 to 1 cut Representing the clipping condition at each time period, X cut Can be expressed as:
wherein ,/>
Indicating that the clipping is performed during the ith period,/and>
indicating no curtailment during the ith period; therefore, the i-th period power after the clipping is: />
wherein ,θi Load shedding coefficient for the i-th period, range (0, 1); p (P) i cut For the power of the ith period before scheduling, P i cut* Is the power of the i-th period after scheduling. The load reducible upper and lower limit constraints should be satisfied:
setting the minimum duration of transferable load as
The transferable load minimum duration constraint should be satisfied: />
Taking user satisfaction into consideration, constraint is imposed on maximum continuous reduction time, and the maximum duration of transferable load is set as
The transferable load maximum continuous clip time constraint should be satisfied: />
Considering the experience of the user, in the whole scheduling period, the constraint is set on the maximum reduction times
For the maximum reduction number, the constraint condition of the maximum reduction number should be satisfied: />
Is the maximum reduction number.
Further, predicting the wind power output characteristics before the day, drawing a wind power output prediction curve, and extracting the wind power output power of each period; combining the load side carbon potential model and the transferable load to obtain a transferable load carbon potential model; for obtaining the maximum low-carbon emission reduction, selecting transferable load output of the next time period according to wind power output of each time period, and sequencing transferable loads according to the carbon potential from large to small to obtain a transferable load volume interval.
Further, a load-side carbon potential model and a load-reducible carbon potential model are combined to obtain a load-reducible carbon potential model; the reducible carbon potential model for each moment can reduce the load in order from the large carbon potential to the small carbon potential, and a reducible load interval is obtained.
Step S102, optimizing a preset day-ahead objective function according to the transferable load interval and the reducible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the condition of meeting the preset constraint reaches the minimum value, so as to obtain the power unit output pre-measurement, the transferable load pre-measurement and the reducible load pre-measurement of the electric power system in the target period.
In the step, based on economic and low-carbon indexes, a preset daily objective function of a demand response low-carbon optimization scheduling model with minimum sum of power generation cost, demand response cost and low-carbon emission reduction benefit is established as follows:
wherein ,cg 、c w The power generation cost of the traditional energy unit and the power generation cost of the new energy unit are respectively in units of $/MW; p (P) g,t 、P w,t The power output pre-measurement of the traditional energy unit and the power output pre-measurement of the new energy unit at the moment t are respectively shown in MW; c emi,i,t The unit of the load emission reduction benefit at the moment t is $/tCO 2 ；ΔE i,t Total reduced carbon emissions before and after load response per unit time; c cut 、c shift The transferable load cost coefficient and the load cost coefficient which can be reduced of each response unit power are respectively, and the units are $/MW; d (D) cut,i,t 、D shift,i,t The unit is MW for the transferable load forecast amount and the load reducible forecast amount at time t.
In some embodiments, the preset constraints include legacy energy unit constraints, new energy unit constraints, line flow constraints, and node power balance constraints.
Optionally, the constraint conditions of the traditional energy unit are constraint conditions of the thermal power unit, including constraint conditions of an upper limit and a lower limit of a capacity and constraint conditions of an upper limit and a lower limit of a climbing of the thermal power unit, and the constraint conditions of the upper limit and the lower limit of the capacity are as follows: p (P) min,g ≤P g,t ≤P max,g； wherein ,Pmin,g 、P max,g The upper limit and the lower limit of the active output of each thermal power generating unit are adopted. The upper limit and the lower limit of the climbing of the thermal power generating unit are as follows: ramp (Ramp) min ≤P g,t -P g,t-1 ≤Ramp max (t is more than or equal to 2); wherein, ramp min 、Ramp max The upper limit value and the lower limit value of the climbing of the active output of the thermal power generating unit are respectively set.
Optionally, the new energy unit constraint condition is a wind turbine unit constraint condition, including a wind turbine unit capacity upper and lower limit constraint: p (P) min,w ≤P w,t ≤P max,w； wherein ,Pmin,g 、P max,g The upper limit and the lower limit of the active output of each thermal power generating unit are adopted.
Optionally, the line power flow constraint condition is: pline min,l ≤Pline l,t ≤Pline max,l The method comprises the steps of carrying out a first treatment on the surface of the Wherein, pline l,t The active power flow of the line l at the moment t; pline max,l 、Pline min,l An upper and lower limit for transmission power between the lines.
Optionally, the node power balancing constraint is:
wherein, pline l,t in Pline for node inflow power at time t l,t out The power is tapped for the node at time t; d (D) exp,t The predicted load at time t.
And step S103, carrying out daily power correction on the power system according to the power output pre-measurement, the transferable load pre-measurement and the load reducible pre-measurement of the energy unit by utilizing a preset rolling optimization correction strategy.
In the step, the actual output force of an energy unit of the power system in the k period is obtained; determining daily power fluctuation of the power system according to the actual output force of the energy unit, the output power pre-measurement of the energy unit, the transferable load pre-measurement and the load-reducible pre-measurement; and predicting the power output and the flexible load prediction quantity of the power system in the future time period in the day according to the daily power fluctuation.
Illustratively, during intra-day scheduling, according to actual output of wind power and load in the current kth period, predicting output in the k+1, k+2, … and k+T periods based on a prediction algorithm, and updating data to obtain output of a unit and flexible load in the next period; at the k+1 time, the above steps are repeatedly performed.
By way of example and not limitation, for further understanding of the invention, an example analysis is performed using an IEEE 14 node as an example, wherein G1 and G3 are coal-fired units, G2 and G4 are gas turbines, G5 is a wind turbine, and all generator set carbon emission intensity vectors satisfy E G ＝[875.0,525.0,820.0,520.0,0]：
Firstly, according to a basic theory of carbon emission flow of an electric power system, a network carbon flow distribution model with a source side comprising a coal-fired unit G1, a gas turbine G2 and a gas turbine G4 and wind turbines G3 and G5 and a load side being flexible load is established, a carbon emission responsibility is calculated from a power generation side to a load side, and a node carbon potential model of the load side at a unit moment without wind participation is solved, as shown in fig. 4;
according to the graph, the node carbon potential of the 3 nodes is the lowest, the node carbon potential of the 13 nodes is the highest, and because the carbon emission intensity of different generator sets is different, electric energy is transmitted to the load side through the power grid, so that the carbon potentials of different load nodes are different, namely the consumption of CO2 discharged by unit electric energy is different. Here, the 9 and 10 nodes are exemplified as flexible loads, which are classified into transferable loads and load reducible loads.
And predicting the output characteristics of wind power before the day, transferring the load to make a demand response when the wind power is accessed, taking the economy and low carbon property of the power grid into consideration based on the load side node carbon potential obtained in the step one to make a demand response capable of reducing the load, and finally optimizing by an objective function, wherein the relation between the wind power and the response load is shown in a graph in FIG. 5, and a node carbon potential model in unit time after the optimization at the same time is shown in a graph in FIG. 6.
Comparing fig. 4 and fig. 6, when wind power is connected and demand response is performed, the carbon potential of the 9 and 10 nodes of the load is obviously reduced, and the carbon potential of other nodes is slowly reduced. And finally, a daily rolling optimization correction strategy based on model predictive control is used for coping with power fluctuation caused by wind power and load predictive errors and ensuring daily power balance.
In order to execute the power optimization scheduling method corresponding to the method embodiment, corresponding functions and technical effects are achieved. Referring to fig. 7, fig. 7 shows a block diagram of a power optimization scheduling device according to an embodiment of the present application. For convenience of explanation, only the portions related to the present embodiment are shown, and the power optimization scheduling apparatus provided in the embodiment of the present application includes:
a determining module 701, configured to determine a transferable load amount interval and a reducible load amount interval of the electric power system in the target period according to a transferable load constraint condition and a reducible load constraint condition by using a load side carbon potential model established based on a carbon emission flow theory, where a source side of the electric power system includes a conventional energy unit and a new energy unit, and a load side of the electric power system includes a flexible load;
the optimizing module 702 is configured to optimize a preset objective function according to the transferable load interval and the reducible load interval until a sum of a demand response cost and a low-carbon emission reduction benefit of the objective function under a preset constraint condition reaches a minimum value, thereby obtaining an energy unit output pre-measurement, a transferable load pre-measurement and a reducible load pre-measurement of the electric power system in the target period;
and the correction module 703 is configured to perform intra-day power correction on the power system according to the power output preset amount, the transferable load preset amount and the load reducible preset amount by using a preset rolling optimization correction strategy.
In some embodiments, the load side carbon potential model is:
wherein ,Eemi Is a node carbon rowPut quantity, D i In order to be the load capacity of the node,
for node carbon flow density, +.>
Is the branch carbon flow density.
In some embodiments, the transferable load constraint comprises:
transferable load upper and lower limit constraints:
transferable load total constraint:
wherein ,
is the maximum allowable transition value; />
Is the minimum allowable transition value, ">
The total amount of power that can be transferred to the load.
In some embodiments, the load-reducible constraint comprises:
load upper and lower limit constraints can be cut down:
maximum cut-down times constraint condition:
wherein ,
maximum reduction coefficient in the ith period for load reduction, +.>
For the minimum reduction coefficient in the ith period of load reduction, +.>
Is the maximum reduction number.
In some embodiments, the preset day before objective function is:
wherein ,cg 、c w The power generation cost of the traditional energy unit and the power generation cost of the new energy unit are respectively; p (P) g,t 、P w,t Respectively carrying out the output pre-measurement of the traditional energy unit and the output pre-measurement of the new energy unit at the moment t; c em i, i and t are load emission reduction benefits at the moment t; ΔE i,t Total reduced carbon emissions before and after load response per unit time; c cut 、c shift The transferable load cost coefficient and the load cost coefficient which can be cut down of each response unit power are respectively; d (D) cut,i,t 、D shift,i,t A transferable load pre-measurement and a load-shedding pre-measurement at time t.
In some embodiments, the preset constraints include legacy energy unit constraints, new energy unit constraints, line flow constraints, and node power balance constraints.
In some embodiments, the correction module 701 is specifically configured to:
acquiring the actual output force of an energy unit of the power system in the k period;
determining daily power fluctuation of the power system according to the actual output force of the energy unit, the output power pre-measurement of the energy unit, the transferable load pre-measurement and the load-reducible pre-measurement;
and predicting the power output and the flexible load prediction quantity of the power system in the future time period in the day according to the daily power fluctuation.
The power optimization scheduling device can implement the power optimization scheduling method of the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the method embodiments described above, and in this embodiment, no further description is given.
Fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 8, the computer device 8 of this embodiment includes: at least one processor 80 (only one is shown in fig. 8), a memory 81 and a computer program 82 stored in the memory 81 and executable on the at least one processor 80, the processor 80 implementing the steps in any of the method embodiments described above when executing the computer program 82.
The computer device 8 may be a smart phone, a tablet computer, a desktop computer, a cloud server, or the like. The computer device may include, but is not limited to, a processor 80, a memory 81. It will be appreciated by those skilled in the art that fig. 8 is merely an example of computer device 8 and is not intended to be limiting of computer device 8, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 80 may be a central processing unit (Central Processing Unit, CPU), the processor 80 may also be other general purpose processors, digital signal processors (Digital SignalProcessor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 81 may in some embodiments be an internal storage unit of the computer device 8, such as a hard disk or a memory of the computer device 8. The memory 81 may in other embodiments also be an external storage device of the computer device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 8. Further, the memory 81 may also include both an internal storage unit and an external storage device of the computer device 8. The memory 81 is used for storing an operating system, application programs, boot loader (BootLoader), data, other programs etc., such as program codes of the computer program etc. The memory 81 may also be used to temporarily store data that has been output or is to be output.
In addition, the embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the steps in any of the above-mentioned method embodiments.
The present embodiments provide a computer program product which, when run on a computer device, causes the computer device to perform the steps of the method embodiments described above.
In several embodiments provided herein, it will be understood that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device to perform all or part of the steps of the method described in the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing embodiments have been provided for the purpose of illustrating the objects, technical solutions and advantages of the present application in further detail, and it should be understood that the foregoing embodiments are merely examples of the present application and are not intended to limit the scope of the present application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art, which are within the spirit and principles of the present application, are intended to be included within the scope of the present application.
Claims (10)
1. A power optimized scheduling method, characterized by comprising:
determining a transferable load capacity interval and a load capacity interval of an electric power system in a target period respectively according to a transferable load constraint condition and a load capacity reduction constraint condition by using a load side carbon potential model established based on a carbon emission flow theory, wherein a source side of the electric power system comprises a traditional energy unit and a new energy unit, and the load side of the electric power system comprises a transferable load and a load reduction;
optimizing a preset day-ahead objective function according to the transferable load interval and the reducible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the preset constraint condition reaches the minimum value, so as to obtain the power unit output pre-measurement, transferable load pre-measurement and reducible load pre-measurement of the power system in the target period;
and carrying out daily power correction on the power system according to the power output pre-measurement, the transferable load pre-measurement and the load reducible pre-measurement of the energy unit by utilizing a preset rolling optimization correction strategy.
2. The power optimization scheduling method of claim 1, wherein the load side carbon potential model is:
wherein ,Eemi For the node carbon emission, D i In order to be the load capacity of the node,
for node carbon flow density, +.>
Is the branch carbon flow density.
3. The power optimized scheduling method of claim 1, wherein the transferable load constraint comprises:
transferable load upper and lower limit constraints:
transferable load total constraint:
wherein ,
is the maximum allowable transition value; />
Is the minimum allowable transition value, ">
The total amount of power that can be transferred to the load.
4. The power optimized scheduling method of claim 1, wherein said load reducible constraint comprises:
load upper and lower limit constraints can be cut down:
maximum cut-down times constraint condition:
wherein ,
maximum reduction coefficient in the ith period for load reduction, +.>
For the minimum reduction coefficient in the ith period of load reduction, +.>
Is the maximum reduction number.
5. The power optimized scheduling method of claim 1, wherein the preset day-ahead objective function is:
wherein ,cg 、c w The power generation cost of the traditional energy unit and the power generation cost of the new energy unit are respectively; p (P) g,t 、P w,t Respectively carrying out the output pre-measurement of the traditional energy unit and the output pre-measurement of the new energy unit at the moment t; c emi,i,t Load emission reduction benefit at time t; ΔE i,t Total reduced carbon emissions before and after load response per unit time; c cut 、c shift The transferable load cost coefficient and the load cost coefficient which can be cut down of each response unit power are respectively; d (D) cut,i,t 、D shift,i,t A transferable load pre-measurement and a load-shedding pre-measurement at time t.
6. The power optimization scheduling method of claim 1, wherein the preset constraints include a legacy energy unit constraint, a new energy unit constraint, a line power flow constraint, and a node power balance constraint.
7. The power optimization scheduling method of claim 1, wherein the performing intra-day power correction on the power system according to the power unit output pre-measurement, the transferable load pre-measurement, and the load shedding pre-measurement using a preset rolling optimization correction strategy comprises:
acquiring the actual output force of an energy unit of the power system in the k period;
determining daily power fluctuation of the power system according to the actual output force of the energy unit, the output power pre-measurement of the energy unit, the transferable load pre-measurement and the load-reducible pre-measurement;
and predicting the power output and the flexible load prediction quantity of the power system in the future time period in the day according to the daily power fluctuation.
8. A power optimized scheduling device, characterized by comprising:
a determination module for determining a transferable load amount section and a reducible load amount section of the electric power system within a target period according to a transferable load constraint condition and a reducible load constraint condition, respectively, using a load side carbon potential model established based on a carbon emission flow theory, the source side of the power system comprises a traditional energy unit and a new energy unit, and the load side of the power system comprises a transferable load and a load-shedding load;
the optimizing module is used for optimizing a preset objective function according to the transferable load interval and the reducible load interval until the sum of the demand response cost and the low-carbon emission reduction benefit of the objective function under the condition of meeting the preset constraint reaches the minimum value, so as to obtain the power unit output pre-measurement, the transferable load pre-measurement and the reducible load pre-measurement of the power system in the target period;
and the correction module is used for correcting the power system in the day by utilizing a preset rolling optimization correction strategy according to the power output pre-measurement, the transferable load pre-measurement and the load reduction pre-measurement of the energy unit.
9. A computer device comprising a processor and a memory for storing a computer program which when executed by the processor implements the power optimized scheduling method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the power optimized scheduling method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211593825.XA CN116111580A (en) | 2022-12-12 | 2022-12-12 | Power optimization scheduling method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211593825.XA CN116111580A (en) | 2022-12-12 | 2022-12-12 | Power optimization scheduling method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116111580A true CN116111580A (en) | 2023-05-12 |
Family
ID=86255241
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211593825.XA Pending CN116111580A (en) | 2022-12-12 | 2022-12-12 | Power optimization scheduling method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116111580A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117374974A (en) * | 2023-12-06 | 2024-01-09 | 国网浙江省电力有限公司 | Distribution network scheduling method, system, medium and equipment |
-
2022
- 2022-12-12 CN CN202211593825.XA patent/CN116111580A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117374974A (en) * | 2023-12-06 | 2024-01-09 | 国网浙江省电力有限公司 | Distribution network scheduling method, system, medium and equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107679658B (en) | Power transmission network planning method under high-proportion clean energy access | |
| CN107276122B (en) | Peak-shaving resource calling decision method suitable for large-scale renewable energy grid connection | |
| Wang et al. | An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems | |
| CN109272353B (en) | System dynamic probability energy flow analysis method considering comprehensive demand response uncertainty | |
| Zhou et al. | Interval optimization combined with point estimate method for stochastic security-constrained unit commitment | |
| CN111934315A (en) | Source network load storage cooperative optimization operation method considering demand side and terminal equipment | |
| CN116111580A (en) | Power optimization scheduling method, device, equipment and storage medium | |
| Wang et al. | Coordinated dispatch of networked energy storage systems for loading management in active distribution networks | |
| CN115313519A (en) | Power distribution network energy storage optimal configuration method, device, equipment and storage medium | |
| Ma et al. | Energy consumption scheduling in smart grid: A non-cooperative game approach | |
| CN109787217B (en) | Standby clearing method based on wind power multi-state model and opportunity cost correction | |
| CN116826859A (en) | Power supply carbon-electricity collaborative planning method, device, equipment and storage medium | |
| CN114677064B (en) | Cascade reservoir scheduling decision support method coupling optimality and stability | |
| CN116565874A (en) | Multi-time scale optimal scheduling method for power distribution network considering grid connection of optical storage users | |
| CN115659098A (en) | Distributed new energy consumption capacity calculation method, device, equipment and medium | |
| CN115549137A (en) | Distributed power grid regulation and control system and regulation and control method | |
| CN115759775A (en) | NCFI carbon emission responsibility allocation method and device considering fairness | |
| CN115360768A (en) | Power scheduling method and device based on muzero and deep reinforcement learning and storage medium | |
| CN111695235A (en) | Regional decomposition method, device and system for renewable energy consumption responsibility weight | |
| CN113067329B (en) | Renewable energy source adaptability optimization method and terminal of power system | |
| CN110365010A (en) | The index selection method of evaluating reliability of distribution network containing DG based on 2m point estimations | |
| CN113162124B (en) | New energy concentrated grid-connected node output optimization method, device, equipment and medium | |
| CN111193258A (en) | Method and system for optimizing power generation amount of micro-grid | |
| CN114444915B (en) | New energy consumption cost calculation method and device for electric power system in market environment | |
| CN116523193B (en) | Virtual power plant energy storage scheduling method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |