CN113872250A - Multi-energy complementary energy base three-level control method based on thermal power - Google Patents
Multi-energy complementary energy base three-level control method based on thermal power Download PDFInfo
- Publication number
- CN113872250A CN113872250A CN202111114112.6A CN202111114112A CN113872250A CN 113872250 A CN113872250 A CN 113872250A CN 202111114112 A CN202111114112 A CN 202111114112A CN 113872250 A CN113872250 A CN 113872250A
- Authority
- CN
- China
- Prior art keywords
- base
- empire
- energy
- level
- voltage
- 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.)
- Granted
Links
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
- H02J3/38—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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a thermal power-based three-level control method for a multi-energy complementary energy base, wherein the multi-energy complementary energy base adopts a three-level control strategy, a regional controller executes first-level regional control, a base-level controller executes second-level ground-level control, and a power-level controller executes third-level power-level control; the regional controller is used for controlling a plurality of multi-energy complementary energy bases and integrally controlling the multi-energy complementary energy bases serving as basic units; the base level controller is used for controlling various power supplies in the multi-energy complementary energy base, calculating control parameters based on the operating conditions and actual control parameters of all units, and issuing the control parameters to the power supply level for execution to realize accurate control; the power stage controller is used for self-performing quick response adjustment according to the adjustment information from the base-ground stage. The invention can improve the cooperative control capability of the multi-energy complementary energy base and improve the support and contribution of the multi-energy complementary energy base to the power angle, voltage, frequency and dynamic stability of the system.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a thermal power-based multi-energy complementary energy base three-level control method.
Background
The development and consumption of non-fossil energy mainly based on new energy are increased, and the decisive force for improving the occupation ratio of the non-fossil energy is achieved. By preferentially utilizing clean energy resources, fully exerting the regulation performance of water, electricity and coal, properly configuring energy storage facilities and mobilizing the flexible response enthusiasm of a demand side, the advantages of new energy resource enrichment areas are favorably exerted, the large-scale consumption of clean electricity is realized, the energy structure is optimized, the resource environment constraint is broken, the coordinated sustainable development of the energy field and the ecological environment is promoted, and the ecological civilization construction is promoted. The energy pattern of rich coal, poor oil and less gas in China is that new energy (photovoltaic and wind power) is equipped around the existing thermal power generating unit, energy is stored, and the new energy is sent out by means of the original power transmission line, so that a multi-energy complementary energy base is formed, and the energy pattern is the development direction of future thermal power.
The complementation, storage and conversion of various energy sources are realized through the integration of wind, light, water, fire and storage, the problems caused by the intermittence and instability of wind and light resources can be reduced, a stable comprehensive output curve of various energy sources is obtained, and the stability and the reliability of a power system are improved. By means of on-site development and consumption of new energy, flexible adjusting resources of the power grid can be occupied as little as possible or even not, the phenomena of wind and light abandonment are reduced, and dependence on the power grid is reduced to a great extent. Meanwhile, the allocation capacity of the energy storage device can be obviously reduced, and the system cost tends to be reasonable. The control mode of the existing multi-energy complementary energy base is single, the control structure is single, and the cooperative control capability of the multi-energy complementary energy base is not fully exerted.
Disclosure of Invention
The invention aims to provide a thermal power-based multi-energy complementary energy base three-level control method, which improves the cooperative control capability of the multi-energy complementary energy base and the support and contribution of the multi-energy complementary energy base to the power angle, voltage, frequency and dynamic stability of a system.
The invention provides a thermal power-based three-level control method for a multi-energy complementary energy base, wherein the multi-energy complementary energy base adopts a three-level control strategy, a regional controller executes first-level regional control, a base-level controller executes second-level ground-level control, and a power-level controller executes third-level power-level control; the regional controller is used for controlling a plurality of multi-energy complementary energy bases and integrally controlling the multi-energy complementary energy bases serving as basic units; the base level controller is used for controlling various power supplies in the multi-energy complementary energy base, calculating control parameters based on the operating conditions and actual control parameters of all units, and issuing the control parameters to the power supply level for execution to realize accurate control; the power supply level controller is used for performing quick response adjustment according to adjustment information from a base level;
the three-level control strategy comprises the following specific steps:
step 1, setting a regional control station, bringing all the multi-energy complementary energy bases in a region into a control system, and monitoring the voltage Ui of each node of the regional system and the frequency Fi of the regional system in a whole process through a regional controller;
step 2, the regional controller judges the system node voltage and the system frequency on the basis of monitoring the voltage and the frequency of a regional system in the whole process, if the voltage or the frequency is within an allowable range, the step 1 is continued, and if the voltage or the frequency exceeds the allowable range, the step 3 is executed;
step 3, the region level controller calculates the deviation of the current voltage or frequency, determines the amount of voltage or frequency to be adjusted, sets an adjustment dead zone and avoids the region level controller from issuing an adjustment instruction all the time;
step 4, the regional controller takes the optimal economic cost as a target function, takes the response sensitivity of the voltage or frequency of the system, the voltage of the base to the bus of the external transmission line, the new energy consumption of the base and the output condition of the base as constraint conditions, and comprehensively sorts the bases in the region through an empire and country competition algorithm;
step 5, the area level controller sends an adjusting instruction to the base level controller for the base which is ranked forward in the area;
step 6, after receiving the instruction sent by the regional controller, the base level controller comprehensively sorts the power supplies in the base by taking the optimal economic cost as a target function, taking the response sensitivity of each power supply in the base to voltage or frequency, the bus voltage of the power supply to an external transmission line and the power supply output condition as constraint conditions and adopting an empire competition algorithm;
step 7, the base level controller transmits a voltage or frequency adjusting instruction to the power level controller for the power supply which is ranked at the front in the base;
and 8, after the power supply level controller receives the regulating instruction of the base-ground level controller, the power supply level controller quickly responds.
Further, the empire competition algorithm in step 4 comprises the following steps:
setting model input parameters, base-to-system in areaVoltage SivdV/dt or frequency SifResponse sensitivity of dF/dt, including upper and lower limits U of system voltagemin\UmaxUpper and lower limits of system frequency fmin\fmaxThe upper and lower limits of the bus voltage of the external transmission line of the base stationdmin\UdmaxBase new energy consumption upper and lower limits rhomin\ρmaxUpper and lower limits of output P of baseimin\Pimax；
According to the number N of the multi-energy complementary basesWAnd voltage S of base-to-base system of multi-energy complementary baseivOr frequency SifSensitivity to external transmission line bus voltage UdBase new energy consumption rho, base output PiCoding to form [1 × (4T +1)]Dimension vector, initializing to form population of N countries; for each country, initializing the value of each dimension of each country into a random number meeting a constraint condition; calculating fitness function values of all the countries generated by initialization, namely total cost F; selecting a plurality of countries as empires according to the calculated fitness function values, and forming a plurality of empire groups; setting the index of the current iteration times as g-0;
step 4.1, moving all the breeding grounds to the positions of the empire countries in the group according to the empire competition algorithm;
step 4.2, adopting a constraint processing algorithm to all countries; checking constraints to make them within a feasible domain;
step 4.3, checking, for each empire group, whether there are one or more colonists having a fitness function value smaller than empire; if yes, performing step 4.4; otherwise, jumping to the step 4.5;
step 4.4, exchanging positions of a colonial place and an empire state with the minimum fitness function value in the group;
step 4.5, calculating the potential values of all empire nations;
step 4.6, selecting a colonial place with the maximum fitness function value from the empire country group with the maximum fitness function value, namely the weakest colonial place in the weakest empire country, and distributing the colonial place to the empire country with the maximum encroachment probability;
step 4.7, checking whether an empire state loses all colonial areas; if yes, performing the step 4.8, otherwise, jumping to the step 4.9;
step 4.8, eliminating empires that have lost all colonial areas;
step 4.9, checking whether only one empire group is left in the population and all countries in the group have the same fitness function value; if yes, jumping to the step 4.11, otherwise, performing the step 4.10;
step 4.10, checking whether the algorithm reaches the maximum iteration times; if yes, performing step 4.11; otherwise, setting g to g +1 and returning to the step 4.1;
and 4.11, outputting the national information with the lowest fitness function value in the population as an optimal decision variable, and taking the fitness function value as an optimal objective function value.
Further, the empire competition algorithm in step 6 comprises the following steps:
setting model input parameters including base frequency upper and lower limits fzmin\fzmaxThe upper and lower limits of the bus voltage of the external transmission line of the base stationdmin\UdmaxBus voltage U of power supply to external transmission lineamin/UamaxUpper and lower limits of power output Pwmin\Pwmax；
According to the number N of power supplies in the multi-energy complementary base stationEAnd voltage S of base-to-base system of multi-energy complementary baseWVOr frequency SWFSensitivity of the power supply to the external transmission line bus voltage UdPower supply output PwCoding to form [1 × (4T +1)]Dimension vector, initializing to form population of N countries; for each country, initializing the value of each dimension of each country into a random number meeting a constraint condition; calculating fitness function values of all the countries generated by initialization, namely total cost F; selecting a plurality of countries as empires according to the calculated fitness function values, and forming a plurality of empire groups; setting the index of the current iteration times as g-0;
step 6.1, moving all the breeding grounds to the positions of the empire countries in the group according to the empire competition algorithm;
step 6.2, checking constraint of all countries (empire country and colonial place) by adopting a constraint processing algorithm so as to enable the countries to be in a feasible domain;
step 6.3, checking, for each empire group, whether there are one or more colonists having a fitness function value smaller than empire; if yes, performing step 6.4; otherwise, jumping to step 6.5;
6.4, exchanging the positions of the colonial area and the empire country with the minimum fitness function value in the group;
step 6.5, calculating the potential values of all empire nations;
6.6, selecting a colonial place with the maximum fitness function value from the empire country group with the maximum fitness function value, namely the weakest colonial place in the weakest empire country, and distributing the colonial place to the empire country with the maximum encroachment probability;
step 6.7, checking whether an empire state loses all colonial areas; if yes, performing the step 6.8, otherwise, jumping to the step 6.9;
6.8, eliminating empires which lose all colonial areas;
step 6.9, checking whether only one empire group is left in the population and all countries in the group have the same fitness function value; if yes, jumping to step 6.11, otherwise, performing step 6.10;
step 6.10, checking whether the algorithm reaches the maximum iteration times; if yes, go to step 6.11; otherwise, setting g to g +1 and returning to the step 6.1;
and 6.11, outputting the national information with the lowest fitness function value in the population as an optimal decision variable, and taking the fitness function value as an optimal objective function value.
Further, the step 8 includes:
the control of new energy is optimized in power level control, and a new energy self-synchronizing mode is adopted to control the new energy power supply, so that the autonomy of the new energy power supply is enhanced; in the aspect of frequency modulation, the spare capacity is reserved by means of the inertia of the wind wheel and the characteristic that new energy is quickly connected to the grid to achieve the effect of frequency modulation, and the new energy unit is controlled in a new energy self-synchronization control mode.
By means of the scheme, the cooperative control capability of the multi-energy complementary energy base can be improved through the thermal power-based multi-energy complementary energy base three-level control method, and the support and contribution of the multi-energy complementary energy base to the power angle, voltage, frequency and dynamic stability of the system are improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
FIG. 1 is a three-level control schematic of the present invention;
FIG. 2 is a flow chart of a three-level control method of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1 and 2, in the present embodiment, a thermal power-based multi-energy complementary energy base three-level control method is provided, in which a multi-energy complementary energy base adopts a three-level control strategy, a regional controller performs first-level regional control, a base-level controller performs second-level ground-level control, and a power-level controller performs third-level power-level control; the regional controller is used for controlling a plurality of multi-energy complementary energy bases, overall control is carried out by taking the multi-energy complementary energy bases as basic units, and the regional controller has the capacity of whole-course detection, quick identification and quick calculation of a regional system; the base level controller is used for controlling various power supplies in the multi-energy complementary energy base, calculating control parameters based on the operating conditions and actual control parameters of all units, and issuing the control parameters to the power supply level for execution to realize accurate control; the power supply level controller is used for performing quick response adjustment according to adjustment information from a base level;
the three-level control strategy comprises the following specific steps:
step 1: a province is taken as a region, a region level control station is arranged, and all the multi-energy complementary energy bases in the region are brought into a control system. By means of stability of thermal power, rapid response capability and angle direct control capability of new energy grid-connected control are utilized to realize regional comprehensive coordination control, and voltage U of each node in a regioniFrequency F of the sum-area systemiAnd carrying out whole-process monitoring.
Step 2: the regional level control station judges the system node voltage and the system frequency on the basis of monitoring the voltage and the frequency of a regional system in the whole process, and if the voltage or the frequency is within an allowable range, the regional level control station comprises the following steps: u shapei∈(Umin～Umax)，fi∈(fmin～fmax)The area level controller will continue with step 1 if the voltage or frequency is outside the allowable range:
step 3 will be performed.
And step 3: the region level controller is used for comparing the current voltage Uace＝min{Ui-Umin,Ui-UmaxOr frequency face min fi-fmin,fi-fmaxThe deviation is calculated and the minimum adjustment is selected as the adjustment required for the voltage or frequency. According to the response time T of the three-stage control systemsAnd setting an adjusting dead zone to avoid the region control station from issuing an adjusting instruction all the time.
And 4, step 4: the regional control station takes the economic cost optimization as an objective function and converts the voltage S of each base pair system in the region into the voltage S of each base pair systemvdV/dt or frequency SfResponse sensitivity of dF/dt, bus voltage Ud of external transmission line of base, new energy consumption rho of base and output P of baseiAnd taking the situation as a constraint condition, and comprehensively sequencing the power supplies in the base through an empire competition algorithm.
The empire competition algorithm comprises the following steps:
setting model input parameters, voltage S of each base pair system in the regionivdV/dt or frequency SifResponse sensitivity of dF/dt, including upper and lower limits U of system voltagemin\UmaxUpper and lower limits of system frequency fmin\fmaxThe upper and lower limits of the bus voltage of the external transmission line of the base stationdmin\UdmaxBase new energy consumption upper and lower limits rhomin\ρmaxUpper and lower limits of output P of baseimin\Pimax。
According to the number N of the multi-energy complementary basesWAnd voltage S of base-to-base system of multi-energy complementary baseivOr frequency SifSensitivity to external transmission line bus voltage UdBase new energy consumption rho, base output PiCoding to form [1 × (4T +1)]Dimension vector, initializing to form population of N countries; for each country, initializing the value of each dimension of each country into a random number meeting a constraint condition; calculating fitness function values of all the countries generated by initialization, namely total cost F; selecting a plurality of countries as empires according to the calculated fitness function values, and forming a plurality of empire groups; setting the index of the current iteration times as g-0;
step 4.1, moving all the breeding grounds to the positions of the empire countries in the group according to the empire competition algorithm;
step 4.2, checking constraint of all countries (empire country and colonial place) by adopting a constraint processing algorithm so as to enable the countries to be in a feasible domain;
step 4.3, checking, for each empire group, whether there are one or more colonists having a fitness function value smaller than empire; if yes, performing step 4.4; otherwise, jumping to the step 4.5;
step 4.4, exchanging positions of a colonial place and an empire state with the minimum fitness function value in the group;
step 4.5, calculating the potential values of all empire nations;
step 4.6, selecting a colonial place with the maximum fitness function value from the empire country group with the maximum fitness function value, namely the weakest colonial place in the weakest empire country, and distributing the colonial place to the empire country with the maximum encroachment probability;
step 4.7, checking whether an empire state loses all colonial areas; if yes, performing the step 4.8, otherwise, jumping to the step 4.9;
step 4.8, eliminating empires that have lost all colonial areas;
step 4.9, checking whether only one empire group is left in the population and all countries in the group have the same fitness function value; if yes, jumping to the step 4.11, otherwise, performing the step 4.10;
step 4.10, checking whether the algorithm reaches the maximum iteration times; if yes, performing step 4.11; otherwise, setting g to g +1 and returning to the step 4.1;
and 4.11, outputting the national information with the lowest fitness function value in the population as an optimal decision variable, and taking the fitness function value as an optimal objective function value.
And 5: and transmitting a regulating instruction to the base level controller for the base which is ranked at the front in the region.
Step 6: the multi-energy complementary energy base is used as a control center, a base-ground level control station is arranged, the upper part receives an instruction issued by the regional level control station, and the lower part finely adjusts each power supply in the base to meet the adjusting requirement of the regional level control station. After the base-level controller receives the instruction issued by the area-level controller, the voltage S is controlled by each power supply in the basewvdV/dt or frequency SwfThe response sensitivity of dF/dt is an objective function, and the bus voltage U of the power supply to the external transmission line is adjustedaPower supply output PwAnd taking the situation as a constraint condition, and comprehensively sequencing the power supplies in the base through an empire competition algorithm.
Further, step 6 comprises the following steps:
setting model input parameters including base frequency upper and lower limits fzmin\fzmaxThe upper and lower limits of the bus voltage of the external transmission line of the base stationdmin\UdmaxBus voltage U of power supply to external transmission lineamin/UamaxUpper and lower limits of power output Pwmin\Pwmax。
According to the number of power supplies in the multi-energy complementary baseNEAnd voltage S of base-to-base system of multi-energy complementary baseWVOr frequency SWFSensitivity of the power supply to the external transmission line bus voltage UdPower supply output PwCoding to form [1 × (4T +1)]Dimension vector, initializing to form population of N countries; for each country, initializing the value of each dimension of each country into a random number meeting a constraint condition; calculating fitness function values of all the countries generated by initialization, namely total cost F; selecting a plurality of countries as empires according to the calculated fitness function values, and forming a plurality of empire groups; setting the index of the current iteration times as g-0;
step 6.1, moving all the breeding grounds to the positions of the empire countries in the group according to the empire competition algorithm;
step 6.2, checking constraint of all countries (empire country and colonial place) by adopting a constraint processing algorithm so as to enable the countries to be in a feasible domain;
step 6.3, checking, for each empire group, whether there are one or more colonists having a fitness function value smaller than empire; if yes, performing step 6.4; otherwise, jumping to step 6.5;
6.4, exchanging the positions of the colonial area and the empire country with the minimum fitness function value in the group;
step 6.5, calculating the potential values of all empire nations;
6.6, selecting a colonial place with the maximum fitness function value from the empire country group with the maximum fitness function value, namely the weakest colonial place in the weakest empire country, and distributing the colonial place to the empire country with the maximum encroachment probability;
step 6.7, checking whether an empire state loses all colonial areas; if yes, performing the step 6.8, otherwise, jumping to the step 6.9;
6.8, eliminating empires which lose all colonial areas;
step 6.9, checking whether only one empire group is left in the population and all countries in the group have the same fitness function value; if yes, jumping to step 6.11, otherwise, performing step 6.10;
step 6.10, checking whether the algorithm reaches the maximum iteration times; if yes, go to step 6.11; otherwise, setting g to g +1 and returning to the step 6.1;
and 6.11, outputting the national information with the lowest fitness function value in the population as an optimal decision variable, and taking the fitness function value as an optimal objective function value.
And 7: and transmitting a voltage or frequency regulation instruction to the power supply stage for the power supply which is sequenced at the front in the base.
And 8: and after the power supply stage receives the adjusting instruction of the base-ground stage controller, the power supply stage responds quickly.
In further power level control, new energy control is optimized, a new energy self-synchronizing mode is adopted to control the new energy power supply, and the autonomy of the new energy power supply is enhanced. In the aspect of frequency modulation, the spare capacity can be reserved by means of the characteristic that the new energy can be quickly connected to the grid by means of the inertia of the wind wheel, so that the frequency modulation effect is achieved, and the new energy is controlled by a new energy self-synchronization control mode.
By the thermal power-based three-level control method for the multi-energy complementary energy base, the cooperative control capability of the multi-energy complementary energy base can be improved, and the support and contribution of the multi-energy complementary energy base to the power angle, voltage, frequency and dynamic stability of a system are improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A thermal power-based multi-energy complementary energy base three-level control method is characterized in that a multi-energy complementary energy base adopts a three-level control strategy, a regional controller executes first-level regional control, a base-level controller executes second-level ground-level control, and a power-level controller executes third-level power-level control; the regional controller is used for controlling a plurality of multi-energy complementary energy bases and integrally controlling the multi-energy complementary energy bases serving as basic units; the base level controller is used for controlling various power supplies in the multi-energy complementary energy base, calculating control parameters based on the operating conditions and actual control parameters of all units, and issuing the control parameters to the power supply level for execution to realize accurate control; the power supply level controller is used for performing quick response adjustment according to adjustment information from a base level;
the three-level control strategy comprises the following specific steps:
step 1, setting a regional control station, bringing all the multi-energy complementary energy bases in a region into a control system, and monitoring the voltage Ui of each node of the regional system and the frequency Fi of the regional system in a whole process through a regional controller;
step 2, the regional controller judges the system node voltage and the system frequency on the basis of monitoring the voltage and the frequency of a regional system in the whole process, if the voltage or the frequency is within an allowable range, the step 1 is continued, and if the voltage or the frequency exceeds the allowable range, the step 3 is executed;
step 3, the region level controller calculates the deviation of the current voltage or frequency, determines the amount of voltage or frequency to be adjusted, sets an adjustment dead zone and avoids the region level controller from issuing an adjustment instruction all the time;
step 4, the regional controller takes the optimal economic cost as a target function, takes the response sensitivity of the voltage or frequency of the system, the voltage of the base to the bus of the external transmission line, the new energy consumption of the base and the output condition of the base as constraint conditions, and comprehensively sorts the bases in the region through an empire and country competition algorithm;
step 5, the area level controller sends an adjusting instruction to the base level controller for the base which is ranked forward in the area;
step 6, after receiving the instruction sent by the regional controller, the base level controller comprehensively sorts the power supplies in the base by taking the optimal economic cost as a target function, taking the response sensitivity of each power supply in the base to voltage or frequency, the bus voltage of the power supply to an external transmission line and the power supply output condition as constraint conditions and adopting an empire competition algorithm;
step 7, the base level controller transmits a voltage or frequency adjusting instruction to the power level controller for the power supply which is ranked at the front in the base;
and 8, after the power supply level controller receives the regulating instruction of the base-ground level controller, the power supply level controller quickly responds.
2. The thermal-power-based multi-energy complementary energy base three-level control method is characterized in that an empire competition algorithm in the step 4 comprises the following steps:
setting model input parameters, voltage S of each base pair system in the regionivdV/dt or frequency SifResponse sensitivity of dF/dt, including upper and lower limits U of system voltagemin\UmaxUpper and lower limits of system frequency fmin\fmaxThe upper and lower limits of the bus voltage of the external transmission line of the base stationdmin\UdmaxBase new energy consumption upper and lower limits rhomin\ρmaxUpper and lower limits of output P of baseimin\Pimax；
According to the number N of the multi-energy complementary basesWAnd voltage S of base-to-base system of multi-energy complementary baseivOr frequency SifSensitivity to external transmission line bus voltage UdBase new energy consumption rho, base output PiCoding to form [1 × (4T +1)]Dimension vector, initializing to form population of N countries; for each country, initializing the value of each dimension of each country into a random number meeting a constraint condition; calculating fitness function values of all the countries generated by initialization, namely total cost F; selecting a plurality of countries as empires according to the calculated fitness function values, and forming a plurality of empire groups; setting the index of the current iteration times as g-0;
step 4.1, moving all the breeding grounds to the positions of the empire countries in the group according to the empire competition algorithm;
step 4.2, adopting a constraint processing algorithm to all countries; checking constraints to make them within a feasible domain;
step 4.3, checking, for each empire group, whether there are one or more colonists having a fitness function value smaller than empire; if yes, performing step 4.4; otherwise, jumping to the step 4.5;
step 4.4, exchanging positions of a colonial place and an empire state with the minimum fitness function value in the group;
step 4.5, calculating the potential values of all empire nations;
step 4.6, selecting a colonial place with the maximum fitness function value from the empire country group with the maximum fitness function value, namely the weakest colonial place in the weakest empire country, and distributing the colonial place to the empire country with the maximum encroachment probability;
step 4.7, checking whether an empire state loses all colonial areas; if yes, performing the step 4.8, otherwise, jumping to the step 4.9;
step 4.8, eliminating empires that have lost all colonial areas;
step 4.9, checking whether only one empire group is left in the population and all countries in the group have the same fitness function value; if yes, jumping to the step 4.11, otherwise, performing the step 4.10;
step 4.10, checking whether the algorithm reaches the maximum iteration times; if yes, performing step 4.11; otherwise, setting g to g +1 and returning to the step 4.1;
and 4.11, outputting the national information with the lowest fitness function value in the population as an optimal decision variable, and taking the fitness function value as an optimal objective function value.
3. The thermal-power-based multi-energy complementary energy base three-level control method is characterized in that in the step 6, an empire competition algorithm comprises the following steps:
setting model input parameters including base frequency upper and lower limits fzmin\fzmaxThe upper and lower limits of the bus voltage of the external transmission line of the base stationdmin\UdmaxBus voltage U of power supply to external transmission lineamin/UamaxUpper and lower limits of power output Pwmin\Pwmax；
In accordance with multiple energy complementary basesNumber of power supplies NEAnd voltage S of base-to-base system of multi-energy complementary baseWVOr frequency SWFSensitivity of the power supply to the external transmission line bus voltage UdPower supply output PwCoding to form [1 × (4T +1)]Dimension vector, initializing to form population of N countries; for each country, initializing the value of each dimension of each country into a random number meeting a constraint condition; calculating fitness function values of all the countries generated by initialization, namely total cost F; selecting a plurality of countries as empires according to the calculated fitness function values, and forming a plurality of empire groups; setting the index of the current iteration times as g-0;
step 6.1, moving all the breeding grounds to the positions of the empire countries in the group according to the empire competition algorithm;
step 6.2, checking constraint of all countries (empire country and colonial place) by adopting a constraint processing algorithm so as to enable the countries to be in a feasible domain;
step 6.3, checking, for each empire group, whether there are one or more colonists having a fitness function value smaller than empire; if yes, performing step 6.4; otherwise, jumping to step 6.5;
6.4, exchanging the positions of the colonial area and the empire country with the minimum fitness function value in the group;
step 6.5, calculating the potential values of all empire nations;
6.6, selecting a colonial place with the maximum fitness function value from the empire country group with the maximum fitness function value, namely the weakest colonial place in the weakest empire country, and distributing the colonial place to the empire country with the maximum encroachment probability;
step 6.7, checking whether an empire state loses all colonial areas; if yes, performing the step 6.8, otherwise, jumping to the step 6.9;
6.8, eliminating empires which lose all colonial areas;
step 6.9, checking whether only one empire group is left in the population and all countries in the group have the same fitness function value; if yes, jumping to step 6.11, otherwise, performing step 6.10;
step 6.10, checking whether the algorithm reaches the maximum iteration times; if yes, go to step 6.11; otherwise, setting g to g +1 and returning to the step 6.1;
and 6.11, outputting the national information with the lowest fitness function value in the population as an optimal decision variable, and taking the fitness function value as an optimal objective function value.
4. The thermal-power-based multi-energy complementary energy base three-level control method is characterized in that the step 8 comprises the following steps:
the control of new energy is optimized in power level control, and a new energy self-synchronizing mode is adopted to control the new energy power supply, so that the autonomy of the new energy power supply is enhanced; in the aspect of frequency modulation, the spare capacity is reserved by means of the inertia of the wind wheel and the characteristic that new energy is quickly connected to the grid to achieve the effect of frequency modulation, and the new energy unit is controlled in a new energy self-synchronization control mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111114112.6A CN113872250B (en) | 2021-09-23 | 2021-09-23 | Thermal power-based three-level control method for multi-energy complementary energy base |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111114112.6A CN113872250B (en) | 2021-09-23 | 2021-09-23 | Thermal power-based three-level control method for multi-energy complementary energy base |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113872250A true CN113872250A (en) | 2021-12-31 |
| CN113872250B CN113872250B (en) | 2023-10-20 |
Family
ID=78993516
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111114112.6A Active CN113872250B (en) | 2021-09-23 | 2021-09-23 | Thermal power-based three-level control method for multi-energy complementary energy base |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113872250B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160056628A1 (en) * | 2014-08-19 | 2016-02-25 | Origami Limited | Power distribution control system |
| CN107368961A (en) * | 2017-07-12 | 2017-11-21 | 东南大学 | A kind of regional power grid carbon emission management method under the access background suitable for new energy |
| CN108039726A (en) * | 2017-12-12 | 2018-05-15 | 国网山东省电力公司德州供电公司 | A kind of energy LAN distributed collaboration control method based on multi-agent system |
| CN108631343A (en) * | 2018-06-12 | 2018-10-09 | 上海电力学院 | One kind is provided multiple forms of energy to complement each other energy internet Optimization Scheduling |
| CN109447307A (en) * | 2018-08-24 | 2019-03-08 | 广西大学 | It is a kind of to be colonized the distributed generation resource site selection model optimization methods of Competitive Algorithms based on improving empire |
| CN112202205A (en) * | 2020-12-07 | 2021-01-08 | 国网江西省电力有限公司电力科学研究院 | Multi-energy three-level autonomous cooperative control method and device |
| CN112803413A (en) * | 2021-04-13 | 2021-05-14 | 国网江西省电力有限公司电力科学研究院 | Three-level partition autonomous and complementary cooperative control method and device for comprehensive energy system |
-
2021
- 2021-09-23 CN CN202111114112.6A patent/CN113872250B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160056628A1 (en) * | 2014-08-19 | 2016-02-25 | Origami Limited | Power distribution control system |
| CN107368961A (en) * | 2017-07-12 | 2017-11-21 | 东南大学 | A kind of regional power grid carbon emission management method under the access background suitable for new energy |
| CN108039726A (en) * | 2017-12-12 | 2018-05-15 | 国网山东省电力公司德州供电公司 | A kind of energy LAN distributed collaboration control method based on multi-agent system |
| CN108631343A (en) * | 2018-06-12 | 2018-10-09 | 上海电力学院 | One kind is provided multiple forms of energy to complement each other energy internet Optimization Scheduling |
| CN109447307A (en) * | 2018-08-24 | 2019-03-08 | 广西大学 | It is a kind of to be colonized the distributed generation resource site selection model optimization methods of Competitive Algorithms based on improving empire |
| CN112202205A (en) * | 2020-12-07 | 2021-01-08 | 国网江西省电力有限公司电力科学研究院 | Multi-energy three-level autonomous cooperative control method and device |
| CN112803413A (en) * | 2021-04-13 | 2021-05-14 | 国网江西省电力有限公司电力科学研究院 | Three-level partition autonomous and complementary cooperative control method and device for comprehensive energy system |
Non-Patent Citations (1)
| Title |
|---|
| 徐承刘;殷婷婷;王飞;李红梅;姚红;汪汉林;唐涵;慧宇翔;: "基于帝国竞争算法的主动配电网优化调度", 智慧电力, no. 07 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113872250B (en) | 2023-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113394817B (en) | Multi-energy capacity optimal configuration method of wind, light, water and fire storage system | |
| CN107069823B (en) | Distributed photovoltaic power distribution network voltage control method and device | |
| CN110190628B (en) | Complex line full-parallel AT traction power supply system and method based on comprehensive energy | |
| US6757591B2 (en) | Energy management system and methods for the optimization of distributed generation | |
| CN109149635B (en) | Distributed photovoltaic parallel optimization configuration method and system for power distribution network | |
| CN112821470B (en) | Micro-grid group optimization scheduling strategy based on niche chaotic particle swarm algorithm | |
| CN108764552A (en) | A kind of determination method of the addressing constant volume planning of power distribution network distributed generation resource | |
| CN110429649A (en) | Consider the high permeability renewable energy assemblage classification method of flexibility | |
| KR20090059389A (en) | The system for producing renewable energy | |
| CN104362671A (en) | Multi-objective optimization coordination method for combined supply of large-scale wind power and pumped storage | |
| CN107834608A (en) | A kind of multiple-energy-source mutually helps the optimal coordinated control method and system of system | |
| CN116667325B (en) | Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm | |
| CN111917140B (en) | Two-layer optimization scheduling model of new energy complementary power generation system | |
| CN116805803A (en) | Energy scheduling method of wind-solar energy storage off-grid hydrogen production system based on self-adaptive MPC | |
| CN113452078B (en) | AGC multi-target coordination optimization strategy based on new energy access and water, fire and electricity characteristics | |
| CN106549421A (en) | A kind of water power and photoelectricity multi-objective optimization design of power and control method for coordinating | |
| CN109386429A (en) | A kind of wind-powered electricity generation and photo-thermal power generation complementary system coordinated operation control method and device | |
| CN110165715A (en) | A method of by electric car energy storage type charging station access of virtual power plant | |
| CN114757388A (en) | Regional comprehensive energy system equipment capacity optimization method based on improved NSGA-III | |
| CN113872250A (en) | Multi-energy complementary energy base three-level control method based on thermal power | |
| CN116073445A (en) | Optimal scheduling method and system for wind-light hydrogen storage micro-grid based on semi-physical model | |
| CN117134409A (en) | Micro-grid system considering electro-hydro-thermal complementation and multi-objective optimal configuration method thereof | |
| CN110098623B (en) | Prosumer unit control method based on intelligent load | |
| CN116316884A (en) | Frequency modulation control method suitable for multi-source cooperation of high-proportion new energy power grid | |
| CN116031889A (en) | Multi-agent distributed voltage control method based on robust deep reinforcement learning |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |