CN114188970A - Unit sequence recovery optimization method considering light storage system as black start power supply - Google Patents
Unit sequence recovery optimization method considering light storage system as black start power supply Download PDFInfo
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Abstract
The invention discloses a unit sequence recovery optimization method considering a light storage system as a black start power supply. With the proportion increase of photovoltaic and energy storage power stations in a power grid, the photovoltaic power station of the energy storage auxiliary group is taken as a black start power supply, and the photovoltaic power station serves as a power support and simultaneously stores energy to stabilize the intermittence of the output of the photovoltaic power station. The maximum total recovery power in the system recovery process is taken as a target, constraint conditions such as energy storage state switching, network load flow, transient frequency, recovery paths and the like are considered at the same time, a unit recovery sequence optimization model is established, and the optimal unit recovery sequence is solved through an artificial bee colony algorithm. The method makes full use of the output characteristics of the energy storage and the photovoltaic power station, and simultaneously realizes the optimization of the recovery strategy of the power failure unit by making a decision on the starting time of the unit, so that the power supply capacity of the energy storage system can be fully utilized after a major power failure occurs, the recovery efficiency is improved to the maximum extent, and the method has certain theoretical value and engineering value.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a unit sequence recovery optimization method considering a light storage system as a black start power supply.
Background
The research on the black start strategy after the power system is completely stopped or has a large-area power failure is an important subject for ensuring the safe operation of the whole interconnected power grid. The primary condition of the black start is a stable and reliable black start power supply, the traditional black start power supply mainly adopts hydroelectric power generation, but the black start power supply is easily limited by regional resources, so that a part of regions lack the stable and reliable black start power supply. The photovoltaic and energy storage proportion is continuously improved nowadays, the photovoltaic and energy storage are researched to be used as a black start power supply, the number of the black start power supplies of a power grid can be expanded, a thermal power generating unit nearby is helped to start as soon as possible, and the recovery efficiency of the power grid is improved. On one hand, the photovoltaic power station has good self-starting performance and has a precondition as a black-start power supply. On the other hand, the intermittency and uncertainty of the photovoltaic power plant output are serious threats to the vulnerable power grid during the starting process. However, the defect of unstable output of the photovoltaic power station can be overcome by matching with energy storage in a certain proportion, so that the photovoltaic power station becomes an ideal choice for a black-start power supply.
At present, photovoltaic and energy storage are used as black start power sources to participate in optimization of a recovery sequence of a unit, and research is less. There is a paper that proposes to establish a grid reconstruction model with the goal of maximizing the power generation amount of a recovery system and minimizing the load loss amount in a microgrid by considering a high-proportion renewable energy as a black start power supply. There is also a paper that proposes to consider a distributed photovoltaic power station as a black start power supply and a photovoltaic generator as an auxiliary group power supply in a subsequent process to complete a subsequent recovery process. In the prior art, the black-start power supply is formed by combining output of distributed photovoltaic power stations, but the influence of the capacity of the optical storage system on the subsequent recovery efficiency is not considered.
Disclosure of Invention
The invention aims to provide a unit sequence recovery optimization method considering an optical storage system as a black-start power supply.
The technical solution for realizing the purpose of the invention is as follows: a method for optimizing sequential recovery of a unit in consideration of a light storage system as a black start power supply, the method comprising the steps of:
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
A crew sequence restoration optimization system that considers a light storage system as a black start power supply, the system comprising:
the model building module is used for building a unit recovery sequence optimization model of the light storage system as a black start power supply;
and the solving module is used for solving the optimization model based on an artificial bee colony algorithm and determining the recovery time and the recovery path of the unit under each time step.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
Compared with the prior art, the invention has the following remarkable advantages: 1) the method makes full use of the output characteristics of the energy storage and the photovoltaic power station, and simultaneously makes a decision on the starting time of the unit, so that the optimization of the recovery strategy of the power failure unit is realized, the power supply capacity of the energy storage system can be fully utilized after a major power failure occurs, and the recovery efficiency is improved to the maximum extent; 2) for the problem of energy storage capacity limitation, the switching of the energy storage state is provided, the influence of the energy storage capacity limitation on the subsequent recovery efficiency is eliminated, and the recovery efficiency of the power grid is improved.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
Fig. 1 is a flowchart of the unit sequence recovery optimization method of the present invention considering the optical storage system as a black start power supply.
Fig. 2 is a power grid topology diagram of IEEE39 in the embodiment of the present invention.
FIG. 3 is a graph of photovoltaic output curves for an embodiment of the present invention.
FIG. 4 is a graph of recovery power over time in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The invention provides a unit recovery sequence optimization strategy considering a centralized photovoltaic power station and energy storage as a black start power supply, namely, the photovoltaic power station with strong self-starting capability and a certain proportion of energy storage as the black start power supply are matched to provide starting power for a non-black start unit under the background of heavy power failure. Firstly, optimizing the recovery sequence of the generator set according to the maximum recovery output of the non-black start generator set within a certain time as an objective function; then, considering the frequency regulation characteristics of the stored energy and the recovered unit from the output characteristics of the photovoltaic power station, and constructing a recovery path optimization model based on the topological parameters, the electrical parameters and the path recovery time of the power network; in the solving process, in order to reduce the influence of insufficient output of the photovoltaic power station on the recovery efficiency, the switching of the energy storage state in the recovery process is considered, so that a unit recovery sequence optimization strategy taking the light storage system as a black start power supply is formed.
In one embodiment, in conjunction with fig. 1, there is provided a crew sequence restoration optimization method considering a light storage system as a black start power supply, the method comprising the steps of:
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
Further, in one embodiment, the establishing of the unit recovery sequence optimization model using the optical storage system as the black-start power supply in step 1 includes:
step 1-1, the main purpose of black start is to restore the power grid to a normal power supply level as soon as possible, so a non-black start unit energy maximum is adopted as a target establishment function:
in the formula, Etotal-representing the total energy produced by the system over a specified time;
nNBS-represents the total number of thermal power generating units;
t-represents the total recovery time;
PNBSi-representing the energy emitted at time t of the ith thermal power plant;
step 1-2, modeling the rated output characteristic of the thermal power generating unit to determine the output of the thermal power generating unit at each moment, wherein the actual output climbing characteristic of the thermal power generating unit is complex and difficult to apply to theoretical research, a simplified climbing curve of the thermal power generating unit is obtained by generally adopting a linear fitting mode, and the output power P of a generatorGi(t) is:
in the formula, TAiRepresenting the charging moment of the thermal power generating unit i, namely the moment of plant load recovery;
TBirepresenting the preheating time required by starting the thermal power generating unit i to be connected to the grid;
TCirepresenting the time required by the thermal power generating unit i to convey power outwards from the grid connection to the maximum stable output;
ki represents the maximum climbing speed of the thermal power generating unit i;
PMi-representing the rated active power of the thermal power generating unit i;
PGi(t) -representing the generator power output;
and 1-3, considering the output characteristics of the energy storage and photovoltaic power station and the safety constraint which needs to be met, and determining the constraint condition which needs to be considered in the power grid recovery process.
The constraint conditions in steps 1-3 comprise:
(1) and node starting constraint:
in the system recovery process, the starting power of the thermal power generating unit which is not started in black comprises two parts: one part of the output power comes from a black start power supply, namely a photovoltaic power station and the stored energy, and the other part of the output power comes from the recovered thermal power generating unit; considering that the output of the stored energy is limited by its rated power, the starting power constraint of a conventional unit is therefore expressed as:
|PDk-PPVk-PGk|≤PESN
in the formula, PDk-representing the active power required by the grid at time step k;
PPVk-representing the active power required by the photovoltaic plant at the kth time step;
PGk-representing to recover active power generated by the thermal power generating unit;
PESN-representing the maximum active output of the stored energy;
(2) energy storage capacity constraint:
the energy storage system comprises a battery system, an energy storage converter and an energy management system, wherein the battery system formed by connecting a plurality of battery monomers in series and parallel is merged into a power grid through the energy storage converter, and the energy management system monitors and controls the output power and the charging and discharging states of the system; in the recovery process, in order to ensure the normal function of energy storage, the state of charge of the energy storage needs to be ensured to be kept in a safe range:
in the formula, omega represents the rated capacity of the energy storage system;
eta-represents the efficiency of the energy storage converter;
SOCt-1-representing the energy storage state of charge of the previous time step;
SOCt-representing the energy storage state of charge for the current time step;
SOCmin-representing an energy storage minimum state of charge;
SOCmax-representing an energy storage maximum state of charge;
Δ t — time representing two time step intervals;
in order to ensure the frequency modulation capability and flexible charging and discharging of energy storage, the minimum allowable charge level is set to be 20%, and the maximum allowable charge level is 80%.
(3) And (3) overvoltage restraint:
because the photovoltaic power station and the energy storage have limited reactive power consumption capability, and the no-load line is charged back to generate a large amount of capacitive reactive power in the starting process. Therefore, the present invention considers the addition of reactive compensation devices to ensure stable operation of the restored network, and the overvoltage constraint can be expressed as:
in the formula, nl-number of lines for charging route;
QLl-charging reactive power for line l;
K2-representing a reactive reliability factor;
Qsithe maximum absorbable power of the thermal power generating unit i is obtained;
Qcomp-reactive power provided to the reactive compensation means;
(4) self-excitation constraint of the generator:
when the charging reactive power of the line is less than the rated power multiplied by the short-circuit ratio, the self-excitation phenomenon does not occur, and the method is represented as follows:
in the formula, KCBi-representing a short circuit ratio;
BBithe rated capacity of the thermal power generating unit i is obtained;
(5) cold and hot start restraint:
the start time is less than the warm start time limit or greater than the cold start time limit:
0≤Ti≤TC i H,Ti≥TC i C
in the formula, Ti-representing the start time of the train i;
(6) and (3) power flow constraint:
the safety and stability of a power grid need to be ensured in the unit recovery process:
in the formula, PnIs the active injected power of node n;
Qnis the reactive injected power of node n;
PLnis the active load power of node n;
QLnis the reactive load power of node n;
Vn-is the voltage of node n;
Gnm-is the conductance between nodes n and m;
Bnm-is the susceptance between nodes n and m;
δnm-is VnAnd VmThe phase angle difference between them; n is the number of nodes;
in the formula, n0For the generator assembly of the recovered systemThe number of the devices;
PGi-representing the active output of the unit i;
QGi-representing the reactive power output of the unit i;
Pl-is the active power flowing on branch l;
nL-is the total number of lines in the recovered system;
Un-is the node voltage;
nb-is the total number of nodes in the recovered system;
(7) and (3) transient frequency constraint:
the energy storage system in the model of the invention adjusts the frequency through the virtual droop control, and can replace the complex transfer function of the virtual droop control through the response coefficient obtained by the measurement test under the condition of certain system parameters. With reference to a typical generator frequency response, the transient frequency constraint is expressed as:
in the formula,. DELTA.Pdck-load added for k time steps;
KBESS-frequency modulation coefficients for stored energy;
PGi-is the rated active power of the lighter unit i;
dfi-is the frequency modulation response coefficient of the lighter unit i;
Δfmax-maximum allowed frequency offset.
Further, in one embodiment, the step 2 of solving the optimization model based on the artificial bee colony algorithm to determine the recovery time and the recovery path of the unit at each time step specifically includes:
the unit recovery sequence problem is a multi-constraint optimization problem, can be solved in various ways, and adopts an artificial bee colony algorithm. The basic idea is as follows: firstly, randomly generating a starting sequence of the unit to be recovered, taking the starting sequence as an initial population, then calculating a fitness function of the initial population, and searching the most recovery sequence by adopting an artificial bee colony algorithm. In the process, a recovery path is solved through a Dijkstra algorithm, and after the climbing power of the non-black start unit can support the recovery of the unit in the next time step, the stored energy is switched from a voltage frequency control mode to an active and reactive control mode to complete the subsequent recovery.
The specific process comprises the following steps:
step 2-1, initializing generators, lines and other electrical parameters, and setting the population quantity (wherein the number of hired bees and the number of following bees respectively account for half), the maximum cycle times and the maximum use times of honey sources of the artificial bee colony algorithm; at the initialization moment, setting the cycle times and the maximum use times of the honey sources to be zero;
step 2-2, randomly generating a plurality of unit recovery sequences as initial honey sources, and calculating the shortest path of each time step through a Dijkstra algorithm based on the generated recovery sequences so as to obtain the network topology of each time step of the corresponding recovery sequences;
step 2-3, sequentially checking whether each time step of each honey source meets constraint conditions, judging whether the current time step does not depend on energy storage and new energy and whether a next thermal power generating unit can be started, if so, switching an energy storage output mode, and if not, checking the next time step;
step 2-4, reserving the honey sources which meet the constraint in each time step, carrying out random generation again on other honey sources which do not meet the constraint, and executing the step 2-3 until all the honey sources meet the constraint;
step 2-5, calculating a honey source function value as an adaptive value;
step 2-6, the hiring bee searches new honey sources around according to the obtained honey sources and calculates adaptive values, and based on the adaptive values and the adaptive values in the step 2-5, the honey sources are selected according to a greedy strategy;
step 2-7, calculating the selection probability of each honey source by the follower bees;
step 2-8, randomly generating a threshold value, judging whether the selection probability of each honey source is greater than the threshold value, if so, searching a new honey source around the honey source, and otherwise, reserving the honey source;
step 2-9, repeating the step 2-3 to the step 2-8 until the given iteration number of the bee colony is reached;
and 2-10, outputting the optimal solution, namely the optimal honey source, obtained in the iterative process to obtain the recovery time and the recovery path of the unit at each time step.
Further, in one embodiment, the selection probability of each honey source in steps 2-7 is calculated by the following formula:
in the formula, pdTo represent the selection probability, fit, corresponding to the honey source ddThe adaptive value corresponding to the honey source d is shown, and SN represents the number of the honey sources.
Further, in one embodiment, steps 2-9 further comprise: if the iteration process has the condition that a certain honey source does not iterate for many times, a new honey source is randomly generated through the scout bees.
In one embodiment, there is provided a crew sequence restoration optimization system that considers a light storage system as a black start power supply, the system comprising:
the model building module is used for building a unit recovery sequence optimization model of the light storage system as a black start power supply;
and the solving module is used for solving the optimization model based on an artificial bee colony algorithm and determining the recovery time and the recovery path of the unit under each time step.
For specific limitations of the unit sequence recovery optimization system considering the optical storage system as the black start power supply, reference may be made to the above limitations of the unit sequence recovery optimization method considering the optical storage system as the black start power supply, and details are not described herein again. The above-mentioned each module in the unit sequence recovery optimization system considering the light storage system as a black start power supply can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
For specific limitation of each step, reference may be made to the above limitation on the unit sequence recovery optimization method considering the light storage system as a black start power supply, and details are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
For specific limitation of each step, reference may be made to the above limitation on the unit sequence recovery optimization method considering the light storage system as a black start power supply, and details are not described herein again.
As a specific example, in one embodiment, the method for optimizing the sequential recovery of the unit in consideration of the light storage system as the black start power supply is further verified and explained.
The modified new england 10 machine 39 node system is adopted to verify the unit sequence optimization method using the optical storage system as the black start power supply, and the topological structure of the system is shown in fig. 2. The photovoltaic power station and the energy storage are arranged at a node 30 and comprise a centralized photovoltaic power station with rated output of 40MW and an energy storage system with rated output of 10MW and maximum capacity of 20 MWh. A typical photovoltaic output curve is shown in fig. 3, and in consideration of the output condition of the photovoltaic power station, the recovery start time is set to 10 points, and other non-black start generator set parameters are shown in table 1. The recovery time for a single line is set to 4 minutes.
TABLE 1 thermal power generating unit parameters
Tab.1Thermal power generator parameters
After a power failure accident occurs, the photovoltaic power station is taken as a black start power supply to charge the network firstly by virtue of the quick self-starting capability of the photovoltaic power station. After 12 minutes of line charging, the photovoltaic power station starts to provide active support for the thermal power generating unit with the node number 39, the service power of the thermal power generating unit is recovered, and the auxiliary machine is started. And when the node 39 is started, the power transmission line of the thermal power generating unit to be started next is charged, and the unit 39 begins to climb after ten minutes to transmit active power to the power grid. When the node 38 is started in the fourth time step, the total output of the recovered units reaches 187MW, active support of the photovoltaic power station to the power grid is not needed, so that subsequent recovery efficiency is not affected, and the balance node at the moment is switched to the first thermal power unit 39 started by the photovoltaic power station. The final path optimization results and recovery order are shown in table 2. The optimal start-up sequence can be found to be:
30→39→37→31→38→34→35→36→33→32
TABLE 2 optimal Start-Up sequence
Tab.2Optimal restoration sequence and recovery path
To further illustrate the rationality of the proposed restoration strategy, fig. 4 shows the power generation active curve under two different strategies, switching the energy storage state and not switching the energy storage state during the restoration process. As can be seen from the figure, the recovery strategy without switching the energy storage state is limited by the active power output and capacity of the photovoltaic power station and the energy storage, and the unit with the faster climbing power and the unit with the closer distance cannot be started preferentially, so that the total starting time of the unit is delayed, and the subsequent recovery efficiency is affected. And the recovery strategy for switching the energy storage state does not need to worry about the influence of the energy storage capacity on subsequent recovery, and can start the thermal power generating units which are quick in climbing like No. 31, No. 38 and No. 37 and are relatively close to the black starting point as early as possible, so that the power grid recovery efficiency is improved.
The method fully utilizes the output characteristics of the energy storage and photovoltaic power station, and simultaneously realizes the optimization of the recovery strategy of the power failure unit by making a decision on the starting time of the unit, so that the power supply capacity of the energy storage system can be fully utilized after a major power failure occurs, the recovery efficiency is improved to the greatest extent, and the method has certain theoretical value and engineering value.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A unit sequence recovery optimization method considering a light storage system as a black start power supply is characterized by comprising the following steps:
step 1, establishing a unit recovery sequence optimization model with an optical storage system as a black start power supply;
and 2, solving the optimization model based on an artificial bee colony algorithm, and determining the recovery time and the recovery path of the unit under each time step.
2. The method for optimizing the sequence recovery of the unit by taking the optical storage system as the black-start power supply according to claim 1, wherein the step 1 of establishing the unit recovery sequence optimization model by taking the optical storage system as the black-start power supply comprises the following specific steps:
step 1-1, establishing a function by taking the maximum energy of a non-black starting unit as a target:
in the formula, Etotal-representing the total energy produced by the system over a specified time;
nNBS-representing the number of total thermal power generating units;
t-represents the total recovery time;
PNBSi-representing the energy emitted at time t of the ith thermal power plant;
step 1-2, modeling the rated output characteristic of the thermal power generating unit:
in the formula, TAiRepresenting the charging moment of the thermal power generating unit i, namely the moment of plant load recovery;
TBirepresenting the preheating time required by starting the thermal power generating unit i to be connected to the grid;
TCirepresenting the time required by the thermal power generating unit i to convey power outwards from the grid connection to the maximum stable output;
ki represents the maximum climbing speed of the thermal power generating unit i;
PMi-representing the rated active power of the thermal power generating unit i;
PGi(t) -representing the generator power output;
and 1-3, considering the output characteristics of the energy storage and photovoltaic power station and the safety constraint which needs to be met, and determining the constraint condition which needs to be considered in the power grid recovery process.
3. The method for optimizing the sequential recovery of a unit by considering a light storage system as a black-start power supply according to claim 2, wherein the constraint conditions of the steps 1 to 3 comprise:
(1) and node starting constraint:
in the system recovery process, the starting power of the thermal power generating unit which is not started in black comprises two parts: one part of the output power comes from a black start power supply, namely a photovoltaic power station and the stored energy, and the other part of the output power comes from the recovered thermal power generating unit; considering that the output of the stored energy is limited by its rated power, the starting power constraint of a conventional unit is therefore expressed as:
|PDk-PPVk-PGk|≤PESN
in the formula, PDk-representing the active power required by the grid at time step k;
PPVk-representing the active power required by the photovoltaic plant at the kth time step;
PGk-representing to recover active power generated by the thermal power generating unit;
PESN-representing the maximum active output of the stored energy;
(2) energy storage capacity constraint:
the energy storage system comprises a battery system, an energy storage converter and an energy management system, wherein the battery system formed by connecting a plurality of battery monomers in series and parallel is merged into a power grid through the energy storage converter, and the energy management system monitors and controls the output power and the charging and discharging states of the system; in the recovery process, in order to ensure the normal function of energy storage, the state of charge of the energy storage needs to be ensured to be kept in a safe range:
in the formula, omega represents the rated capacity of the energy storage system;
eta-represents the efficiency of the energy storage converter;
SOCt-1-is represented byAn energy storage state of charge for one time step;
SOCt-representing the energy storage state of charge for the current time step;
SOCmin-representing an energy storage minimum state of charge;
SOCmax-representing an energy storage maximum state of charge;
Δ t — time representing two time step intervals;
(3) and (3) overvoltage restraint:
the overvoltage constraint can be expressed as:
in the formula, nl-number of lines for charging route;
QLl-charging reactive power for line l;
K2-representing a reactive reliability factor;
Qsithe maximum absorbable power of the thermal power generating unit i is obtained;
Qcomp-reactive power provided to the reactive compensation means;
(4) self-excitation constraint of the generator:
when the charging reactive power of the line is less than the rated power multiplied by the short-circuit ratio, the self-excitation phenomenon does not occur, and the method is represented as follows:
in the formula, KCBi-representing a short circuit ratio;
BBithe rated capacity of the thermal power generating unit i is obtained;
(5) cold and hot start restraint:
the start time is less than the warm start time limit or greater than the cold start time limit:
in the formula, Ti-representing the start time of the train i;
(6) and (3) power flow constraint:
the safety and stability of a power grid need to be ensured in the unit recovery process:
in the formula, PnIs the active injected power of node n;
Qnis the reactive injected power of node n;
PLnis the active load power of node n;
QLnis the reactive load power of node n;
Vn-is the voltage of node n;
Gnm-is the conductance between nodes n and m;
Bnm-is the susceptance between nodes n and m;
δnm-is VnAnd VmThe phase angle difference between them; n is the number of nodes;
in the formula, n0-the total number of generators in the recovered system;
PGi-representing the active output of the unit i;
QGi-representing the reactive power output of the unit i;
Pl-is the active power flowing on branch l;
nL-is the total number of lines in the recovered system;
Un-is the node voltage;
nb-is the total number of nodes in the recovered system;
(7) and (3) transient frequency constraint:
the transient frequency constraint is expressed as:
in the formula,. DELTA.Pdck-load added for k time steps;
KBESS-frequency modulation coefficients for stored energy;
PGi-is the rated active power of the lighter unit i;
dfi-is the frequency modulation response coefficient of the lighter unit i;
Δfmax-maximum allowed frequency offset.
4. The method according to claim 1, wherein the step 2 of solving the optimization model based on the artificial bee colony algorithm to determine the recovery time and the recovery path of the unit at each time step specifically comprises:
step 2-1, initializing generators, lines and other electrical parameters, and setting the population number, the maximum cycle number and the maximum use number of honey sources of the artificial bee colony algorithm; at the initialization moment, setting the cycle times and the maximum use times of the honey sources to be zero;
step 2-2, randomly generating a plurality of unit recovery sequences as initial honey sources, and calculating the shortest path of each time step through a Dijkstra algorithm based on the generated recovery sequences so as to obtain the network topology of each time step of the corresponding recovery sequences;
step 2-3, sequentially checking whether each time step of each honey source meets constraint conditions, judging whether the current time step does not depend on energy storage and new energy and whether a next thermal power generating unit can be started, if so, switching an energy storage output mode, and if not, checking the next time step;
step 2-4, reserving the honey sources which meet the constraint in each time step, carrying out random generation again on other honey sources which do not meet the constraint, and executing the step 2-3 until all the honey sources meet the constraint;
step 2-5, calculating a honey source function value as an adaptive value;
step 2-6, the hiring bee searches new honey sources around according to the obtained honey sources and calculates adaptive values, and based on the adaptive values and the adaptive values in the step 2-5, the honey sources are selected according to a greedy strategy;
step 2-7, calculating the selection probability of each honey source by the follower bees;
step 2-8, randomly generating a threshold value, judging whether the selection probability of each honey source is greater than the threshold value, if so, searching a new honey source around the honey source, and otherwise, reserving the honey source;
step 2-9, repeating the step 2-3 to the step 2-8 until the given iteration number of the bee colony is reached;
and 2-10, outputting the optimal solution, namely the optimal honey source, obtained in the iterative process to obtain the recovery time and the recovery path of the unit at each time step.
5. The method for optimizing the sequential recovery of the unit by taking the light storage system as the black-start power supply into consideration according to claim 4, wherein the calculation formula of the selection probability of each honey source in the steps 2 to 7 is as follows:
in the formula, pdTo represent the selection probability, fit, corresponding to the honey source ddIndicating correspondence of honey source dThe adaptation value, SN, represents the number of honey sources.
6. The method for optimizing the sequential recovery of a unit considering a light storage system as a black-start power supply according to claim 4, wherein the steps 2 to 9 further comprise: if the iteration process has the condition that a certain honey source does not iterate for many times, a new honey source is randomly generated through the scout bees.
7. A system for optimizing sequential recovery of a unit in consideration of a light storage system as a black start power supply, the system comprising:
the model building module is used for building a unit recovery sequence optimization model of the light storage system as a black start power supply;
and the solving module is used for solving the optimization model based on an artificial bee colony algorithm and determining the recovery time and the recovery path of the unit under each time step.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented when the computer program is executed by the processor.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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