CN118336731A - Method and system for optimizing power grid standby decision based on GWO algorithm - Google Patents
Method and system for optimizing power grid standby decision based on GWO algorithm Download PDFInfo
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Abstract
The invention discloses a power grid standby decision optimization method and system based on GWO algorithm, which relate to the technical field of power system dispatching automation and comprise the following steps: the new energy unit, the direct current transmission system and the capacity of interrupting loads to participate in power balance adjustment of the power grid are considered, and the new energy unit, the direct current transmission system and the interruptible loads are used for power adjustment of the power grid together with the standby of a conventional unit. Aiming at the high-power shortage faults of the power grid, a power grid standby optimization cost model with various resources involved is established, and a gray wolf optimization algorithm is used for solving an objective function. The power grid standby decision optimization method based on GWO algorithm provided by the invention expands the standby range of the power grid from a single power generation side to a plurality of resource standby ranges consisting of a source, a network and a load, can effectively solve the problem of power grid frequency drop or poor economy caused by insufficient power grid standby configuration or excessive power grid standby configuration, and provides good technical support for safe, stable and economic operation of the power grid.
Description
Technical Field
The invention relates to the technical field of power system dispatching automation, in particular to a power grid standby decision optimization method and system based on GWO algorithm.
Background
With large-scale access of new energy, the intermittent, random and fluctuation of the output of the new energy makes the running environment of the power grid increasingly complex, the rotational inertia of a transmitting and receiving end system is reduced, once a direct current locking fault occurs, huge active deficiency is generated, and the available rotary spare capacity of a multi-circuit direct current drop point area is difficult to meet the control requirement under high power deficiency. This presents new challenges for power system dispatch operations and management, where power system standby requirements will be greatly affected.
The reserved certain spare capacity is the basis for ensuring the safe and stable operation of the power system, and when the system is disturbed in a certain range, the existence of the spare capacity can enable the system to be stably transited to a new stable operation state. The rotational reserve of conventional power systems is mainly concentrated on the power generation side, and the most commonly used reserve determination method is to determine the reserve capacity in terms of the maximum unit capacity in the system (N-1 method), a certain percentage of the peak load of the system, or a combination of both. With the continuous increase of the new energy grid-connected proportion, the demand of system rotation reserve is larger and larger, and the deterministic reserve capacity method cannot effectively respond to the change of the operation condition, so that the safe and reliable operation of the power grid is difficult to ensure. Under the condition of ensuring sufficient system standby, the economic benefit is taken into consideration, and the current power system scheduling problem is solved. Therefore, the utilization of the rotational standby adjustment resources is considered to be fully excavated from the source, the net and the load, and the standby pressure on the power generation side is reduced.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the capacity of the photovoltaic power supply, the direct current modulation and the interruptible load to participate in power regulation of the power grid is considered, the capacity is regarded as elastic standby of the power grid, and the standby output cost of a conventional unit is combined to form a multi-resource standby cost model. The cost model considers various standby resources, effectively reduces the power grid accident risk caused by insufficient standby of the power generation side, and simultaneously can effectively reduce the standby construction cost of the power generation side, which is increased for coping with large-scale new energy access.
In order to solve the technical problems, the invention provides the following technical scheme: an optimization method for power grid standby decision based on GWO algorithm, comprising:
Comprehensively considering the regulating capability of a conventional generator set, a photovoltaic power supply, an interruptible load and direct current modulation, and establishing an objective function with the minimum standby output cost as a target;
Processing the objective function according to the conventional unit power generation and standby output cost, the photovoltaic output cost, the direct current modulation output cost and the interruptible load output cost to obtain a power grid standby optimization cost model with various resources involved;
For a power grid standby optimization cost model with participation of various resources, constraint conditions are provided, wherein the constraint conditions comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint;
And (3) establishing an uncertain model and a mixed integer linear programming unit combination model for a power grid standby optimization cost model with participation of various resources, solving the model through GWO algorithm to obtain the total power generation cost, the start-stop state of the unit in each period, the active output power, the reserve interruptible load of positive and negative rotation, the modulation amount of photovoltaic and the direct current output, and performing scheduling optimization on the power system.
As a preferred scheme of the power grid backup decision optimization method based on GWO algorithm, the invention comprises the following steps: the objective function is represented as a function of,
Cmin＝FSR+FPV+Fd+FIL
Wherein F SR is the power generation and standby output cost of a conventional unit, F PV is the standby output cost of a photovoltaic power supply, F d is the DC modulation output cost, and F IL is the interruptible load output cost.
As a preferred scheme of the power grid backup decision optimization method based on GWO algorithm, the invention comprises the following steps: the photovoltaic power supply standby output cost F wc is expressed as:
Wherein, C PV is the unit loss cost of the abandoned light; for the predicted available capacity of the photovoltaic power source w during period t, For the standby output of the photovoltaic power supply w in the period T, d% is the load shedding ratio of the photovoltaic power supply, N T is the research moment, N w is the number of the photovoltaic power supplies, and T 1 is the standby output time of the photovoltaic power supply;
The interruptible load output cost F IL is expressed as:
FIL＝CILPIL,tT2
Wherein, C IL is the unit cost of the interruptible load, the load aggregate is used for uniformly controlling and setting the price, P IL,t is the amount of the interruptible load reduction in T time period, and T 2 is the output time length of the interruptible load;
The dc modulation output cost F d is expressed as:
Fd＝Cd|Pd0-Pd,t|T3
Wherein, C d is the unit cost of direct current modulation; p d0 is the power fed in when the direct current is not modulated; p d,t is the direct current input power at the moment t; t 3 is the duration of the DC modulation output;
the spare power output cost F SR of the conventional unit is expressed as:
Wherein, C Gi is the standby cost of thermal power, N T is the research period, N G is the number of conventional units, T 4 is the standby power output time of the conventional units, P Gi,t is the output of the conventional unit i in the T period, a i、bi、ci is the electricity quantity quotation curve coefficient of the conventional unit i, u i,t =0/1 is that the conventional unit is in a shutdown and startup state, and SC i is the startup cost.
As a preferred scheme of the power grid backup decision optimization method based on GWO algorithm, the invention comprises the following steps: the power grid standby optimization cost model with the participation of the plurality of resources is expressed as follows:
As a preferred scheme of the power grid backup decision optimization method based on GWO algorithm, the invention comprises the following steps: the photovoltaic power generation constraint comprises a photovoltaic power generation output constraint, a photovoltaic load shedding ratio constraint and a photovoltaic standby constraint;
The conventional unit constraint comprises conventional unit climbing constraint, conventional unit output constraint and conventional unit standby constraint.
As a preferred scheme of the power grid backup decision optimization method based on GWO algorithm, the invention comprises the following steps: the active balance constraint is expressed as:
Pload＝PIL+PL
Wherein, P load is the total load of the system, P L is the conventional load, P Gi,t is the active power output of the conventional unit i in the t period, and P p,t is the photovoltaic output at the t moment;
the active deficit constraint is a reserve constraint under an active deficit incident, expressed as:
d％Pp,t+PIL,t+|Pd0-Pd,t|+ReGi,t≥Pmiss
wherein P miss is the active loss under fault;
the photovoltaic power generation output constraint is expressed as:
Wherein P pv,t is the photovoltaic output of the w-th photovoltaic power supply in the period t;
the photovoltaic load shedding ratio constraint is expressed as:
0≤d％≤d％max
Wherein d% max is the maximum photovoltaic load shedding ratio;
the photovoltaic back-up constraint is expressed as:
Wherein, The available photovoltaic capacity predicted for the w-th photovoltaic power supply t-period,Photovoltaic predicted output for the w-th photovoltaic t period;
The dc modulation constraint, considering only the dc long-term modulation, is expressed as:
Pd,min≤Pd,t≤Pd,max
wherein, minimum modulation power P d,min＝0.9Pd0, maximum modulation power P d,max＝1.1Pd0;
the interruptible load reserve constraint is expressed as:
0≤PIL,t≤PIL,max
wherein P IL,max is the maximum interruptible amount of interruptible load;
the conventional unit climbing constraint is expressed as:
Wherein, For the i-th motor group lower climbing rate limit value,The limit value of the climbing rate of the ith motor unit;
the conventional unit output constraint is expressed as:
Wherein, Is the lower limit of the output force of the ith motor unit,The upper limit of the output force of the ith motor unit;
the conventional unit standby constraint is expressed as:
Wherein, For the initial active output of the ith unit,For the output power of the ith unit, the speed is adjusted upwards, tau is the minimum adjustment time interval allowed by the unit, and Re Gi is the standby provided by the ith unit.
As a preferred scheme of the power grid backup decision optimization method based on GWO algorithm, the invention comprises the following steps: the method comprises the steps that a GWO algorithm is used for solving a model, wherein the model comprises input of a generator set, load, photovoltaic power supply, direct current and other relevant parameters;
Defining independent variables in the unit combination optimization model objective function: the starting and stopping state, active output, rotary reserve capacity and direct current feed-in quantity of the unit in each period;
Establishing a net load and generator uncertainty model;
Calculating the interruptible load and the call quantity of the photovoltaic power supply in each scene of each time period, and obtaining expected values of the interruptible load and the call quantity of the photovoltaic power supply in the corresponding time period by probability weighting to obtain dependent variables in the objective function;
listing and writing an objective function and corresponding constraint conditions;
Linearizing an objective function and a constraint, and establishing a mixed integer linear programming unit combination model;
Initializing the population scale, the maximum iteration number, the dimension, the parameters of the upper and lower boundary ranges and the position of each gray wolf. Solving the model through GWO algorithm, calculating the fitness value of the objective function and updating the coefficient vector and the position of the gray wolf;
Judging whether the maximum iteration times or target values are reached, counting results, and outputting the total power generation cost, the starting and stopping states of the units in each period, the active power output, the reserved interruptible load for positive and negative rotation, the adjustment amount of the photovoltaic power supply and the direct current output.
Another object of the present invention is to provide an optimizing system for power grid backup decision based on GWO algorithm, which can realize minimization of backup output cost by constructing an adjusting system integrating multiple resources, and solve the problems of high cost and non-optimized resource scheduling in the traditional power grid backup decision.
In order to solve the technical problems, the invention provides the following technical scheme: an optimization system for power grid backup decisions based on GWO algorithm, comprising: the system comprises an objective function construction module, a cost model optimization module, a constraint condition setting module and a scheduling optimization module; the objective function construction module is used for comprehensively considering the regulating capacity of a conventional generator set, a photovoltaic power supply, an interruptible load and direct current modulation, and establishing an objective function with the minimum standby output cost as a target; the cost model optimization module is used for processing the objective function according to the conventional unit power generation and standby output cost, the photovoltaic output cost, the direct current modulation output cost and the interruptible load output cost to obtain a power grid standby optimization cost model with various resources involved; the constraint condition setting module is used for providing constraint conditions for a power grid standby optimization cost model with participation of various resources, wherein the constraint conditions comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint; the dispatching optimization module is used for optimizing a power grid standby cost model with participation of various resources, solving the model through an uncertain model and a mixed integer linear programming unit combination model and a GWO algorithm to obtain the total power generation cost, the starting and stopping states of units in each period, the active power output, the reserved interruptible load for positive and negative rotation, the photovoltaic dispatching amount and the direct current output, and therefore dispatching optimization is carried out on the power system.
A computer device comprising a memory storing a computer program and a processor which when executed implements the steps of the method of optimizing a grid backup decision based on GWO algorithm as described above.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the optimization method of grid backup decision based on GWO algorithm as described above.
The invention has the beneficial effects that: the power grid reserve decision optimization method based on GWO algorithm provided by the invention considers the capacity of a new energy unit, a direct current transmission system and interruptible loads to participate in power balance adjustment of a power grid, participates in power adjustment of the power grid together with the reserve of a conventional unit, treats the power grid as elastic reserve of the power grid, establishes a power grid reserve optimization cost model with participation of various resources aiming at high-power shortage faults of the power grid, expands the reserve range of the power grid from a single power generation side to various resource reserve ranges consisting of a source, the power grid and a load, can effectively solve the problem of power grid frequency drop or poor economy caused by insufficient or excessive configuration of the power grid reserve, and provides good technical support for safe, stable and economic operation of the power grid. The cost model considers various standby resources, effectively reduces the power grid accident risk caused by insufficient standby of the power generation side, and simultaneously can effectively reduce the standby construction cost of the power generation side, which is increased for coping with large-scale new energy access.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of an optimization method for power grid backup decision based on GWO algorithm according to one embodiment of the present invention.
Fig. 2 is a flow chart of solving a model through GWO algorithm in an optimization method for power grid backup decision based on GWO algorithm according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of the output situation of backup resources of an optimization method for power grid backup decision based on GWO algorithm according to one embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a method for optimizing a backup decision of a power grid based on GWO algorithm, including:
Step 1, comprehensively considering the regulating capability of a conventional generator set, a photovoltaic power supply, an interruptible load and direct current modulation, and establishing an objective function with the minimum standby output cost as a target;
Step 2, processing an objective function according to the power generation and standby output cost, the photovoltaic output cost, the direct current modulation output cost and the interruptible load output cost of a conventional unit to obtain a power grid standby optimization cost model with participation of various resources;
Step 3, providing constraint conditions for a power grid standby optimization cost model with participation of various resources, wherein the constraint conditions comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint;
and 4, establishing an uncertain model and a mixed integer linear programming unit combination model for a power grid standby optimization cost model with participation of various resources, and solving the model through GWO algorithm to obtain the total power generation cost, the start-stop state of each period of unit, the active power output, the reserved interruptible load for positive and negative rotation standby, the photovoltaic modulation amount and the direct current output, thereby carrying out scheduling optimization on the power system.
Comprehensively considering the regulating capability of a conventional generator set, photovoltaic, interruptible load and direct current modulation, and taking the minimum standby output cost as a target, establishing an objective function, wherein the objective function is shown in the following formula:
Cmin＝FSR+FPV+Fd+FIL
Wherein F SR is the power generation and standby output cost of a conventional unit, F PV is the standby output cost of a photovoltaic power supply, F d is the DC modulation output cost, and F IL is the interruptible load output cost.
Wherein, C PV is the unit loss cost of the abandoned light; for the predicted available capacity of the photovoltaic power source w during period t, For the standby output of the photovoltaic power supply w in the period T, d% is the load shedding ratio of the photovoltaic power supply, N T is the research time, N w is the number of the photovoltaic power supplies, and T 1 is the standby output time of the photovoltaic power supply.
FIL＝CILPIL,tT2
Wherein, C IL is the unit cost of the interruptible load (the interruptible load is uniformly controlled by a load aggregator and the price is formulated); p IL,t is the amount of interruptible load reduction in the t period; t 2 is the interruptible load output duration.
Fd＝Cd|Pd0-Pd,t|T3
Wherein, C d is the unit cost of direct current modulation; p d0 is the power fed in when the direct current is not modulated; p d,t is the direct current input power at the moment t; t 3 is the DC modulation output duration.
Wherein, C Gi is the standby cost of the thermal power; n T is the period of the study; n G is the number of conventional units; t 4 is the standby power-out time of the conventional unit; p Gi,t is the output of the conventional unit i in the t period; a i,bi,ci is the electricity quantity quotation curve coefficient of the conventional unit i; u i,t =0/1 is that the conventional unit is in a shutdown and startup state; SC i is the start-up cost.
In summary, there are:
The constraint conditions mainly comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint.
Active equilibrium constraint:
Pload＝PIL+PL
Wherein, P load is the total load of the system, P L is the conventional load, P Gi,t is the active power output of the conventional unit i in the t period, and P p,t is the photovoltaic output at the t moment;
Standby constraint under active deficit accident:
d％Pp,t+PIL,t+|Pd0-Pd,t|+ReGi,t≥Pmiss
Wherein P miss is the active loss under fault.
Photovoltaic power generation output constraint:
Wherein P pv,t is the photovoltaic output of the w-th photovoltaic power supply in the period t;
photovoltaic load shedding ratio constraint:
0≤d％≤d％max
Wherein d% max is the maximum photovoltaic load shedding ratio;
Photovoltaic standby constraint:
Wherein, The available photovoltaic capacity predicted for the w-th photovoltaic power supply t-period,Photovoltaic predicted output for the w-th photovoltaic t period;
Direct current modulation constraints, to simplify the model, consider only direct current long-term modulation:
Pd,min≤Pd,t≤Pd,max
Wherein, minimum modulation power P d,min＝0.9Pd0, maximum modulation power P d,max＝1.1Pd0.
Interruptible load reserve constraints:
0≤PIL,t≤PIL,max
wherein P IL,max is the maximum interruptible amount of interruptible load.
Conventional unit climbing constraint:
Wherein, A limit value of the climbing rate of the ith motor group; the limit value of the climbing rate of the ith motor group.
Conventional unit output constraint:
Wherein, The lower limit of the output force of the ith motor unit; The upper limit of the output force of the ith motor unit.
Conventional unit standby constraint:
Wherein, Initial active output for the ith unit; The output power of the ith unit is upwardly adjusted to the speed; τ is the minimum adjustment time interval allowed by the unit; re Gi is the reserve provided by the ith unit.
The multi-resource standby cost model solving mainly comprises the following steps: and inputting a generator set, load, photovoltaic, direct current and other relevant parameters.
Defining independent variables in the unit combination optimization model objective function: the starting and stopping state of the machine set, the active output, the reserved capacity for rotation and the direct current feed-in quantity in each period.
And building a net load and generator uncertainty model. The system payload predicted value P Df,t is expressed as:
PDf,t＝PLf,t-Ppf,t
P Lf,t is the actual value of the system load at the t period, and P pf,t is the predicted force at the photovoltaic t moment.
Since the load prediction error and the photovoltaic output prediction error follow two independent normal distributions, the e D,t is known to follow the normal distribution with the mean value of 0 and the standard deviation of sigma D,t from the property of the normal distribution.
E L,t is the t-period system load prediction error, and e p,t is the t-period photovoltaic output prediction error.
The power generation equipment has only two states, namely normal operation and fault shutdown. It is assumed that its state variables are random variables subject to binomial distribution.
Calculating the interruptible load and the photovoltaic call quantity under each scene of each time period, and obtaining expected values of the two corresponding time periods by probability weighting to obtain dependent variables in the objective function. And quantifying uncertainty factors in the system by using a scene analysis method. A set of scenarios is built based on the N-1 criteria, taking into account the generator uncertainty model. Only consider the situation that all generators do not fail and that a single generator fails, and include n+1 scenarios altogether. According to the payload uncertainty model, the prediction error is a continuous random variable, and the uncertainty of the generator is a set of binary random variables. Approximately discretizing the net load prediction error to obtain all scenes of the net load prediction error, and combining with the n+1 scenes of the uncertainty of the generator respectively; then, the objective function and the corresponding constraint conditions are listed and written; linearizing an objective function and a constraint, and establishing a mixed integer linear programming unit combination model; solving the model through GWO algorithm; and outputting the total power generation cost, the start-stop state of the unit in each period, the active power output, the reserved interruptible load for positive and negative rotation, the photovoltaic modulation amount and the direct current output according to the statistical result. The specific flow chart is shown in fig. 2.
The established power grid standby cost model considers various standby resources, effectively reduces the risk of power grid accidents caused by insufficient standby of the power generation side, and simultaneously can effectively reduce the standby construction cost of the power generation side, which is increased for coping with large-scale new energy access.
Fig. 3 shows the output of different standby resources when a large-scale power shortage accident occurs in the power grid. When a small-scale active power shortage accident occurs in the system, the conventional generator set releases the spare capacity, increases the active power output, adjusts the frequency of the system and does not act on other spare resources. When the system is subjected to large-scale active loss and the conventional reserve is insufficient to meet the power regulation requirement of the system, the elastic reserve resource output participates in the system regulation, so that the safe and stable operation of the power grid is ensured.
In GWO algorithm, the individual wolves are divided into 4 social grades, namely alpha, beta, delta, omega and alpha wolves are the strongest in management capacity and are responsible for the decision of wolves; beta wolves are inferior to wolves, and alpha wolves are assisted in making decisions; delta wolf is subject to beta wolf, which dominates the rest of omega wolf.
The GWO algorithm mainly comprises 4 stages of surrounding, hunting, attacking and searching.
Surrounding the prey:
D＝|CXp(t)-X(t)|
X(t+1)＝Xp(t)-AD
Wherein: t is the current iteration number; d is the distance between the prey and the gray wolf; x p (t) is the current position of the prey; x (t) is the current position of the gray wolf; x (t+1) is the updated position of the wolf; a and C are coefficient vectors. The calculation formula is as follows:
A＝2ar1-a
C＝2r2
Wherein: a is a convergence factor linearly decreasing from 2 to 0 with the number of iterations t; n is the maximum iteration number; r 1 and r 2 are random vectors with each component between [0,1 ].
Group hunting:
During hunting, the alpha wolf is typically used to identify the location of the game, and the beta wolf and delta wolf are led to direct the entire gray wolf population around the game. Assuming that alpha wolves, beta wolves and delta wolves can better understand the possible positions of the prey, the positions of the first 3 grades of the gray wolves obtained in the previous iteration are reserved, and the gray wolf population judges the positions of the prey according to the positions of the alpha wolves, the beta wolves and the delta wolves in the next iteration, and meanwhile updates the positions of the alpha wolves, the beta wolves and the delta wolves, so that the position of the prey gradually approaches to the prey. The gray wolf performs a location update according to the following formula.
Dα＝|C1Xα(t)-X(t)|,X1＝Xα(t)-A1Dα
Dβ＝|C2Xβ(t)-X(t)|,X2＝Xβ(t)-A2Dβ
Dδ＝|C3Xδ(t)-X(t)|,X3＝Xδ(t)-A3Dδ
Wherein: d α,Dβ,Dδ is the distance between alpha, beta and delta wolves and the current gray wolves respectively; x α,Xβ,Xδ is the position of alpha, beta, delta wolf respectively; x 1,X2,X3 is the position corresponding to the disturbance of alpha, beta and delta wolf.
Attack prey:
The attack behaviour means that an optimal solution is obtained, this is achieved by adjusting the parameter a, which varies within [ -a, a ] as a decreases from 2 to 0. When |a| <1, the wolf group approaches the prey; when |a| >1, the wolf group is far from the prey.
Searching for prey:
The sirius population usually looks for the prey alone while the prey is caught up according to the positions of alpha, beta and delta wolves. When |A| >1, the wolf group is far away from the prey; when |a| <1, the wolf's group attacks the prey until the condition for algorithm termination is met.
The specific steps of the solving are as follows:
Step 4-1, inputting a generator set, a load, a photovoltaic power supply, direct current and other relevant parameters;
step 4-2, defining independent variables in the unit combination optimization model objective function: the starting and stopping state, active output, rotary reserve capacity and direct current feed-in quantity of the unit in each period;
step 4-3, establishing a net load and generator uncertainty model;
step 4-4, calculating the interruptible load and the call quantity of the photovoltaic power supply in each scene of each period, and obtaining expected values of the interruptible load and the call quantity of the photovoltaic power supply in the corresponding period by probability weighting to obtain dependent variables in the objective function;
Step 4-5, writing an objective function and corresponding constraint conditions;
Step 4-6, linearizing the objective function and the constraint, and establishing a mixed integer linear programming unit combination model;
and 4-7, initializing the population scale, the maximum iteration times, the dimension, the parameters of the upper and lower boundary ranges and the position of each gray wolf. Solving the model through GWO algorithm, calculating the fitness value of the objective function and updating the coefficient vector and the position of the gray wolf;
and 4-8, judging whether the maximum iteration times or target values are reached, counting results, and outputting the total power generation cost, the starting and stopping states of the units in each period, the active power, the reserved interruptible load for positive and negative rotation, the adjustment amount of the photovoltaic power supply and the direct current power.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Example 2
Referring to fig. 2, for one embodiment of the present invention, there is provided an optimization system for power grid backup decision based on GWO algorithm, including:
The system comprises an objective function construction module, a cost model optimization module, a constraint condition setting module and a scheduling optimization module.
The objective function construction module is used for comprehensively considering the regulating capability of a conventional generator set, a photovoltaic power supply, an interruptible load and direct current modulation, and establishing an objective function with minimum standby output cost as a target.
The cost model optimization module is used for processing the objective function according to the conventional unit power generation and standby output cost, the photovoltaic output cost, the direct current modulation output cost and the interruptible load output cost to obtain a power grid standby optimization cost model with participation of various resources.
The constraint condition setting module is used for providing constraint conditions for a power grid standby optimization cost model with participation of various resources, wherein the constraint conditions comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint.
The dispatching optimization module is used for optimizing a power grid standby cost model with participation of various resources, solving the model through an uncertain model and a mixed integer linear programming unit combination model and a GWO algorithm to obtain the total power generation cost, the starting and stopping states of units in each period, the active power output, the reserved interruptible load for positive and negative rotation, the photovoltaic dispatching amount and the direct current output, and therefore dispatching optimization is carried out on the power system.
Example 3
One embodiment of the present invention, which is different from the first two embodiments, is:
The functions, if implemented in the form of software functional units 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 invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb 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.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Claims (10)
1. The utility model provides an optimization method of power grid standby decision based on GWO algorithm, which is characterized by comprising the following steps:
Comprehensively considering the regulating capability of a conventional generator set, a photovoltaic power supply, an interruptible load and direct current modulation, and establishing an objective function with the minimum standby output cost as a target;
Processing the objective function according to the conventional unit power generation and standby output cost, the photovoltaic output cost, the direct current modulation output cost and the interruptible load output cost to obtain a power grid standby optimization cost model with various resources involved;
For a power grid standby optimization cost model with participation of various resources, constraint conditions are provided, wherein the constraint conditions comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint;
And (3) establishing an uncertain model and a mixed integer linear programming unit combination model for a power grid standby optimization cost model with participation of various resources, solving the model through GWO algorithm to obtain the total power generation cost, the start-stop state of the unit in each period, the active output power, the reserve interruptible load of positive and negative rotation, the modulation amount of photovoltaic and the direct current output, and performing scheduling optimization on the power system.
2. The optimization method for power grid backup decision based on GWO algorithm as set forth in claim 1, wherein: the objective function is represented as a function of,
Cmin＝FSR+FPV+Fd+EIL
Wherein F SR is the power generation and standby output cost of a conventional unit, F PV is the standby output cost of a photovoltaic power supply, F d is the DC modulation output cost, and F IL is the interruptible load output cost.
3. The optimization method for power grid backup decision based on GWO algorithm as claimed in claim 2, wherein: the photovoltaic power supply standby output cost F wc is expressed as:
Wherein, C PV is the unit loss cost of the abandoned light; for the predicted available capacity of the photovoltaic power source w during period t, For the standby output of the photovoltaic power supply w in the period T, d% is the load shedding ratio of the photovoltaic power supply, N T is the research moment, N w is the number of the photovoltaic power supplies, and T 1 is the standby output time of the photovoltaic power supply;
The interruptible load output cost F IL is expressed as:
FIL＝CILPIL,tT2
wherein, C IL is the unit cost of the interruptible load, the load aggregate is used for uniformly controlling and setting the price, P IL,t is the amount of the interruptible load reduction in T time period, and T 2 is the output time length of the interruptible load;
The dc modulation output cost F d is expressed as:
Fd＝Cd|Pd0-Pd,t|T3
Wherein, C d is the unit cost of direct current modulation; p d0 is the power fed in when the direct current is not modulated; p d,t is the direct current input power at the moment t; t 3 is the duration of the DC modulation output;
the spare power output cost F SR of the conventional unit is expressed as:
Wherein, C Gi is the standby cost of thermal power, N T is the research period, N G is the number of conventional units, T 4 is the standby power output time of the conventional units, P Gi,t is the output of the conventional unit i in the T period, a i、bi、ci is the electricity quantity quotation curve coefficient of the conventional unit i, u i,t =0/1 is that the conventional unit is in a shutdown and startup state, and SC i is the startup cost.
4. A method for optimizing grid backup decisions based on GWO algorithm as claimed in claim 3, wherein: the power grid standby optimization cost model with the participation of the plurality of resources is expressed as follows:
5. the method for optimizing backup decisions for a power grid based on GWO algorithm as claimed in claim 4, wherein: the photovoltaic power generation constraint comprises a photovoltaic power generation output constraint, a photovoltaic load shedding ratio constraint and a photovoltaic standby constraint;
The conventional unit constraint comprises conventional unit climbing constraint, conventional unit output constraint and conventional unit standby constraint.
6. The method for optimizing power grid backup decisions based on GWO algorithm as claimed in claim 5, wherein: the active balance constraint is expressed as:
Pload＝PIL+PL
Wherein, P load is the total load of the system, P L is the conventional load, P Gi,t is the active power output of the conventional unit i in the t period, and P p,t is the photovoltaic output at the t moment;
the active deficit constraint is a reserve constraint under an active deficit incident, expressed as:
d％Pp,t+PIL,t+|Pd0-Pd,t|+ReGi,t≥Pmiss
wherein P miss is the active loss under fault;
the photovoltaic power generation output constraint is expressed as:
Wherein P pv,t is the photovoltaic output of the w-th photovoltaic power supply in the period t;
the photovoltaic load shedding ratio constraint is expressed as:
0≤d％≤d％max
Wherein d% max is the maximum photovoltaic load shedding ratio;
the photovoltaic back-up constraint is expressed as:
Wherein, The available photovoltaic capacity predicted for the w-th photovoltaic power supply t-period,Photovoltaic predicted output for the w-th photovoltaic t period;
The dc modulation constraint, considering only the dc long-term modulation, is expressed as:
Pd,min≤Pd,t≤Pd,max
wherein, minimum modulation power P d,min＝0.9Pd0, maximum modulation power P d,max＝1.1Pd0;
the interruptible load reserve constraint is expressed as:
0≤PIL,t≤PIL,max
wherein P IL,max is the maximum interruptible amount of interruptible load;
the conventional unit climbing constraint is expressed as:
Wherein, For the i-th motor group lower climbing rate limit value,The limit value of the climbing rate of the ith motor unit;
the conventional unit output constraint is expressed as:
Wherein, Is the lower limit of the output force of the ith motor unit,The upper limit of the output force of the ith motor unit;
the conventional unit standby constraint is expressed as:
Wherein, For the initial active output of the ith unit,For the output power of the ith unit, the speed is adjusted upwards, tau is the minimum adjustment time interval allowed by the unit, and Re Gi is the standby provided by the ith unit.
7. The method for optimizing power grid backup decisions based on GWO algorithm as claimed in claim 6, wherein: the method comprises the steps that a GWO algorithm is used for solving a model, wherein the model comprises input of a generator set, load, photovoltaic power supply, direct current and other relevant parameters;
Defining independent variables in the unit combination optimization model objective function: the starting and stopping state, active output, rotary reserve capacity and direct current feed-in quantity of the unit in each period;
Establishing a net load and generator uncertainty model;
Calculating the interruptible load and the call quantity of the photovoltaic power supply in each scene of each time period, and obtaining expected values of the interruptible load and the call quantity of the photovoltaic power supply in the corresponding time period by probability weighting to obtain dependent variables in the objective function;
listing and writing an objective function and corresponding constraint conditions;
Linearizing an objective function and a constraint, and establishing a mixed integer linear programming unit combination model;
Initializing the population scale, the maximum iteration number, the dimension, the parameters of the upper and lower boundary ranges and the position of each gray wolf. Solving the model through GWO algorithm, calculating the fitness value of the objective function and updating the coefficient vector and the position of the gray wolf;
Judging whether the maximum iteration times or target values are reached, counting results, and outputting the total power generation cost, the starting and stopping states of the units in each period, the active power output, the reserved interruptible load for positive and negative rotation, the adjustment amount of the photovoltaic power supply and the direct current output.
8. A system employing the method for optimizing grid backup decisions based on GWO algorithm as claimed in any one of claims 1 to 7, comprising: the system comprises an objective function construction module, a cost model optimization module, a constraint condition setting module and a scheduling optimization module;
The objective function construction module is used for comprehensively considering the regulating capacity of a conventional generator set, a photovoltaic power supply, an interruptible load and direct current modulation, and establishing an objective function with the minimum standby output cost as a target;
The cost model optimization module is used for processing the objective function according to the conventional unit power generation and standby output cost, the photovoltaic output cost, the direct current modulation output cost and the interruptible load output cost to obtain a power grid standby optimization cost model with various resources involved;
the constraint condition setting module is used for providing constraint conditions for a power grid standby optimization cost model with participation of various resources, wherein the constraint conditions comprise active balance constraint, active deficiency constraint, photovoltaic power generation constraint, direct current modulation constraint, interruptible load output constraint and conventional unit constraint;
The dispatching optimization module is used for optimizing a power grid standby cost model with participation of various resources, solving the model through an uncertain model and a mixed integer linear programming unit combination model and a GWO algorithm to obtain the total power generation cost, the starting and stopping states of units in each period, the active power output, the standby reserved interruptible load for positive and negative rotation, the photovoltaic dispatching amount and the direct current output, and dispatching and optimizing the power system.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method for optimizing grid backup decisions based on GWO algorithm as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for optimizing grid backup decisions based on GWO algorithm according to any one of claims 1 to 7.
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| Publication Number | Publication Date |
|---|---|
| CN118336731A true CN118336731A (en) | 2024-07-12 |
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