CN110943452A - Method for optimizing and scheduling power system - Google Patents

Method for optimizing and scheduling power system Download PDF

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CN110943452A
CN110943452A CN201911280988.0A CN201911280988A CN110943452A CN 110943452 A CN110943452 A CN 110943452A CN 201911280988 A CN201911280988 A CN 201911280988A CN 110943452 A CN110943452 A CN 110943452A
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power system
unit
scheduling
period
output
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CN110943452B (en
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周野
梁剑
余虎
潘力强
李娟�
胡剑宇
蒋云松
苏黎
徐彬焜
潘馨
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan Electric Power Co Ltd
China Energy Engineering Group Hunan Electric Power Design Institute Co Ltd
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan Electric Power Co Ltd
China Energy Engineering Group Hunan Electric Power Design Institute Co Ltd
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    • 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
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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

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Abstract

The invention relates to the technical field of power system automation, and particularly discloses a method for optimizing and scheduling a power system, wherein firstly, an optimized scheduling mathematical model of the power system is established according to actual operating parameters of the power system, and then the established optimized scheduling mathematical model is solved by introducing a mixed ion swarm method so as to obtain an optimized scheduling scheme of the power system; on the other hand, the dimension of a particle search space is effectively reduced by dividing the scheduling period into a plurality of time periods, and the search efficiency of the hybrid particle swarm method is improved. Therefore, the optimized dispatching method of the power system improves the searching efficiency and the searching precision of the complex optimized dispatching model and improves the economical efficiency of the operation of the power grid.

Description

Method for optimizing and scheduling power system
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a method for optimizing and scheduling a power system.
Background
With the continuous development of the technology, the power system becomes the most complex artificial system, the operation mode of the power system is very complex, the traditional day-ahead scheduling generally emphasizes the safety of power grid operation and the convenience of planning and arrangement, and the economical efficiency of system operation is sacrificed to a certain extent. The optimal scheduling of the power system, namely the economic scheduling, is to reduce the fuel cost as much as possible and improve the operating economy of the power system to the maximum extent on the premise of meeting the safe and stable operation of the power system.
The optimization scheduling model belongs to a multi-objective optimization model, a plurality of objectives can be converted into a single objective through a weighting method to be solved by the multi-objective optimization model, and selection of a weight coefficient and a penalty coefficient in the conversion process is generally set artificially and has great randomness. The traditional mathematical solving method comprises a Newton method, an interior point method, a Lagrange relaxation method and the like, and in the face of a modern huge power system, the traditional mathematical solving method has some defects in the aspects of convergence and solving precision. With the development of computer and artificial intelligence technologies, a large number of elegant intelligent optimization algorithms are developed and used for solving the multi-objective optimization problem, and are also widely applied to the solution of the optimized scheduling model. Such as a multi-objective particle swarm optimization algorithm, a multi-objective genetic algorithm, a multi-objective differential evolution algorithm and the like, are widely applied to solving of an optimization scheduling model of a power system, and certain effect is achieved. Compared with the traditional mathematical solving method, the intelligent algorithm has more flexible setting of the target function and higher searching efficiency. However, many current power system optimization scheduling methods based on the group intelligent algorithm have good optimization effects when the system scale is small, are prone to fall into local optimization when facing a system with a large scale, are not ideal in search efficiency and precision, and are difficult to be used for actual production scheduling of a power system.
In view of this, it is an urgent technical problem to be solved by those skilled in the art to research an optimal scheduling method for an electric power system with high search efficiency and high search accuracy.
Disclosure of Invention
The invention aims to provide a method for optimizing and scheduling a power system, which aims to improve the search efficiency and the search precision of the traditional optimizing and scheduling method, reduce the operation cost of the power system and improve the economical efficiency of the operation of a power grid.
In order to solve the above technical problem, the present invention provides a method for optimizing and scheduling an electric power system, wherein the method comprises the following steps:
s1, establishing an optimal scheduling mathematical model of the power system according to the actual operation parameters of the power system;
s2, dividing a scheduling cycle of the power system into a plurality of time periods, setting constraint conditions, predicting the total load of the power system in the current time period, and determining the scheduling space value of each unit in the current time period according to the predicted value of the total load of the power system in the current time period and the output information of each unit in the previous time period;
s3, calculating an optimized output scheme of each unit in the current time period by using a hybrid particle swarm optimization method according to the determined scheduling space value of each unit in the current time period;
s4, judging whether the output of each unit meets the constraint condition in the step S2 according to the calculation result in the step S3, if so, obtaining the optimal output scheme of each unit in the current time period and entering the step S6, and if not, entering the step S5;
s5, adjusting the scheduling space value of each unit in the step S2 and entering the step S3;
and S6, calculating the optimal output scheme of each unit in all other periods by using the hybrid particle swarm optimization method, thereby obtaining the output curve and the running total cost in the optimal output scheme of each unit in the power system and completing the optimal scheduling scheme of the power system.
Preferably, the power system optimization scheduling mathematical model in step S1 may be formulated as:
Figure BDA0002316738500000021
in the formula (1), T represents the total number of periods of the scheduling cycle, NcRepresents the total number of units in the power system,
Figure BDA0002316738500000022
representing the fuel cost of the ith unit for the t period,
Figure BDA0002316738500000023
indicating the t-th time period of the ith unitThe start-up and shut-down costs,
Figure BDA0002316738500000024
represents the output, u, of the ith unit during the t-th time periodi,tIndicating the start-stop state of the ith unit in the t period, wherein
Figure BDA0002316738500000025
αi、βi、γiRespectively representing the fuel cost coefficients of the ith unit;
Figure BDA0002316738500000026
Ni,trepresents the starting cost of the ith unit in the t period,
Figure BDA0002316738500000027
represents the hot start cost of the ith unit,
Figure BDA0002316738500000028
representing the cold start cost of the ith unit; t isi minIndicating the minimum allowable downtime, T, of the ith uniti coldIndicates the cold start time of the ith unit,
Figure BDA0002316738500000029
indicating the continuous down time of the ith unit in the t period,
Figure BDA00023167385000000210
representing the sum of the minimum allowable down time and the cold start time of the ith unit.
Preferably, the specific implementation manner of step S2 is:
s21, dividing a scheduling cycle of the power system into a plurality of time periods, setting constraint conditions, and determining whether each unit needs to start and stop according to the power generation amount of the power system in the current time period;
s22, determining a scheduling space value of each unit according to the load increment ratio of the power system and the output condition of each unit in the previous period and by combining the constraint conditions of each unit, wherein the load increment ratio of the power system can be expressed by a formula:
Figure BDA0002316738500000031
in the formula (2), λ represents a load increment ratio of the power system, Pt LRepresenting the total load of the power system during the t-th period, when lambda > 0,
Figure BDA0002316738500000032
when the lambda is less than or equal to 0,
Figure BDA0002316738500000033
wherein P isi minRepresents the lower limit of output, P, of the ith uniti maxIndicating the upper limit of output, Δ P, of the ith uniti +Represents the upper limit of grade climbing, Δ P, of the ith uniti -And the lower limit of the climbing of the ith unit is shown.
Preferably, the step S21 of determining whether each unit needs to be started or stopped according to the power generation amount of the power system in the current period specifically includes: when the power generation capacity of the power system meets the requirement
Figure BDA0002316738500000034
In time, the unit needs to be increased; when the power generation capacity of the power system meets the requirement
Figure BDA0002316738500000035
In time, the machine set needs to be shut down,
Figure BDA0002316738500000036
represents the lower output limit of the ith unit in the t period,
Figure BDA0002316738500000037
representing the upper limit of the output of the ith unit in the t period, wherein RtRepresenting the standby value of the power system during the t-th period.
Preferably, the constraints set in step S2 include a system power balance constraint, a positive/negative backup constraint, and a unit output constraint.
Preferably, the system power balance constraint may be formulated as:
Figure BDA0002316738500000038
in the formula (3), Pt lossRepresenting the grid loss of the power system during the t-th period.
Preferably, the positive and negative standby constraints can be formulated as:
Figure BDA0002316738500000039
Figure BDA0002316738500000041
preferably, the unit output constraint condition may be formulated as:
Figure BDA0002316738500000042
Figure BDA0002316738500000043
Figure BDA0002316738500000044
Figure BDA0002316738500000045
in the formula (6), the formula (7), the formula (8) and the formula (9),
Figure BDA0002316738500000046
representing the continuous operation time of the ith unit in the t-1 th time period,
Figure BDA0002316738500000047
indicating continuous shutdown of ith unit in t-1 periodIn the middle of the furnace, the gas-liquid separation chamber,
Figure BDA0002316738500000048
the shortest starting time of the ith unit is shown,
Figure BDA0002316738500000049
the shortest downtime of the ith unit is represented.
Preferably, the specific operation of step S3 includes: according to the obtained scheduling space values of each unit in the current time period, an objective function for performing optimal scheduling on the power system by the hybrid particle swarm method is established and calculated, and the objective function can be expressed by a formula:
Figure BDA00023167385000000410
in equation (10), ξ represents a cost power conversion coefficient in the power system.
Preferably, the specific operation of adjusting the scheduling space value of each unit in step S5 is: and expanding the scheduling space value of each unit in the step S2 by 1.2 times.
Compared with the prior art, the method has the advantages that firstly, the optimal scheduling mathematical model of the power system is established according to the actual operation parameters of the power system, and then the hybrid ion swarm optimization method is introduced to solve the established optimal scheduling mathematical model so as to obtain the optimal scheduling scheme of the power system, so that on one hand, more accurate scheduling space values of all units can be determined through the set related constraint conditions, the search space of particles is greatly reduced, and the search speed and the search precision of the hybrid particle swarm optimization method are effectively improved; on the other hand, the dimension of a particle search space is effectively reduced by dividing the scheduling period into a plurality of time periods, and the search efficiency of the hybrid particle swarm method is improved. Therefore, the optimized dispatching method of the power system improves the searching efficiency and the searching precision of the complex optimized dispatching model and improves the economical efficiency of the operation of the power grid.
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Figure 1 is a flow chart of a method for optimizing scheduling of an electric power system according to the present invention,
FIG. 2 is a flow chart of the method for determining the scheduling space value of each unit in the current time period according to the present invention,
FIG. 3 is a graph of cost coefficients and start-stop fees of units of different capacities in a simulation experiment of the present invention,
FIG. 4 is a graph of total output prediction of all units in the simulation experiment of the present invention,
FIG. 5 is a graph of the scheduling space values of the units in the power plant A in each period in the simulation experiment of the present invention,
FIG. 6 is a graph of the output curves of all the units of the power plant in the simulation experiment of the present invention,
FIG. 7 is a graph of the output of each unit of all power plants in actual dispatch operation of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention is further described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a method for optimizing scheduling of an electric power system includes the following steps:
s1, establishing an optimal scheduling mathematical model of the power system according to the actual operation parameters of the power system;
s2, dividing a scheduling cycle of the power system into a plurality of time periods, setting constraint conditions, predicting the total load of the power system in the current time period, and determining the scheduling space value of each unit in the current time period according to the predicted value of the total load of the power system in the current time period and the output information of each unit in the previous time period;
s3, calculating an optimized output scheme of each unit in the current time period by using a hybrid particle swarm optimization method according to the determined scheduling space value of each unit in the current time period;
s4, judging whether the output of each unit meets the constraint condition in the step S2 according to the calculation result in the step S3, if so, obtaining the optimal output scheme of each unit in the current time period and entering the step S6, and if not, entering the step S5;
s5, adjusting the scheduling space value of each unit in the step S2 and entering the step S3;
and S6, calculating the optimal output scheme of each unit in all other periods by using the hybrid particle swarm optimization method, thereby obtaining the output curve and the running total cost in the optimal output scheme of each unit in the power system and completing the optimal scheduling scheme of the power system.
In the embodiment, firstly, an optimized scheduling mathematical model of the power system is established according to actual operating parameters of the power system, and then a mixed ion swarm method is introduced to solve the established optimized scheduling mathematical model so as to obtain an optimized scheduling scheme of the power system, in the embodiment, on one hand, more accurate scheduling space values of each unit can be determined by setting relevant constraint conditions of the power system, so that the search space of particles is greatly reduced, and the search speed and precision of the mixed ion swarm method are effectively improved; on the other hand, the dimension of a particle search space is effectively reduced by dividing the scheduling period into a plurality of time periods, and the search efficiency of the hybrid particle swarm optimization method is improved, so that the search efficiency and the search precision of the complex optimization scheduling model are improved by the electric power system optimization scheduling method in the embodiment, and the economical efficiency of power grid operation is improved. In this embodiment, the total load of the power system in the current period is predicted by software in the prior art.
As shown in fig. 1, the power system optimization scheduling mathematical model in step S1 can be formulated as:
Figure BDA0002316738500000061
in the formula (1), T represents the total number of periods of the scheduling cycle, NcRepresents the total number of units in the power system,
Figure BDA0002316738500000062
representing the fuel cost of the ith unit for the t period,
Figure BDA0002316738500000063
showing the start-stop cost of the ith unit in the t period,
Figure BDA0002316738500000064
represents the output, u, of the ith unit during the t-th time periodi,tIndicating the start-stop state of the ith unit in the t period, wherein
Figure BDA0002316738500000065
αi、βi、γiRespectively representing the fuel cost coefficients of the ith unit;
Figure BDA0002316738500000066
Ni,trepresents the starting cost of the ith unit in the t period,
Figure BDA0002316738500000067
represents the hot start cost of the ith unit,
Figure BDA0002316738500000068
representing the cold start cost of the ith unit; t isi minIndicating the minimum allowable downtime, T, of the ith uniti coldIndicates the cold start time of the ith unit,
Figure BDA0002316738500000069
indicating the continuous down time of the ith unit in the t period,
Figure BDA00023167385000000610
representing the sum of the minimum allowable down time and the cold start time of the ith unit.
In this embodiment, the power system optimal scheduling mathematical model is established according to the operating parameters of the power system in consideration of the operating cost. When the ith unit is in a startup state in the t-th time period i,t1 is ═ 1; when the ith unit is in a shutdown state in the t periodi,t＝0。
As shown in fig. 2, the specific implementation manner of step S2 is:
s21, dividing a scheduling cycle of the power system into a plurality of time periods, setting constraint conditions, and determining whether each unit needs to be started or stopped according to the power generation amount of the power system in the current time period;
s22, determining a scheduling space value of each unit according to the load increment ratio of the power system and the output condition of each unit in the previous period and by combining the constraint conditions of each unit, wherein the load increment ratio of the power system can be expressed by a formula:
Figure BDA00023167385000000611
in the formula (2), λ represents a load increment ratio of the power system, Pt LRepresenting the total load of the power system during the t-th period, when lambda > 0,
Figure BDA0002316738500000071
when the lambda is less than or equal to 0,
Figure BDA0002316738500000072
wherein P isi minRepresents the lower limit of output, P, of the ith uniti maxIndicating the upper limit of output, Δ P, of the ith uniti +Represents the upper limit of grade climbing, Δ P, of the ith uniti -And the lower limit of the climbing of the ith unit is shown.
In this embodiment, the scheduling cycle is first divided into a plurality of time periods and constraint conditions are set, and then the scheduling space value of each unit is determined according to the load increment ratio of the power system and the output condition of each unit in the previous time period and by combining the constraint conditions of each unit.
As shown in fig. 2, the step S21 of determining whether each unit needs to be started or stopped according to the power generation amount of the power system in the current time period specifically includes: when the power generation capacity of the power system meets the requirement
Figure BDA0002316738500000073
In time, the unit needs to be increased; when the power generation capacity of the power system meets the requirement
Figure BDA0002316738500000074
In time, the machine set needs to be shut down,
Figure BDA0002316738500000075
represents the lower output limit of the ith unit in the t period,
Figure BDA0002316738500000076
representing the upper limit of the output of the ith unit in the t period, wherein RtRepresenting the standby value of the power system during the t-th period.
In this embodiment, if the power generation amount in the power system satisfies
Figure BDA0002316738500000077
When the power generation amount in the current time period is insufficient, the number of the units needs to be increased, and at the moment, the unit with lower energy consumption needs to be selected to be started until the power generation amount of the power system meets the formula (4); if the generated energy in the power system satisfies
Figure BDA0002316738500000078
And (4) if the power generation amount in the current time period is excessive, the number of the units needs to be shut down, and at the moment, the units with higher energy consumption should be shut down until the power generation amount of the power system meets the formula (5).
As shown in fig. 1 and fig. 2, the constraints set in step S2 include a system power balance constraint, a positive/negative backup constraint, and a unit output constraint.
As shown in fig. 1 and fig. 2, the system power balance constraint may be formulated as:
Figure BDA0002316738500000081
in the formula (3), Pt lossRepresenting the grid loss of the power system during the t-th period.
As shown in fig. 1 and fig. 2, the positive and negative standby constraints can be formulated as:
Figure BDA0002316738500000082
Figure BDA0002316738500000083
as shown in fig. 1 and fig. 2, the unit output constraint condition can be expressed by a formula:
Figure BDA0002316738500000084
Figure BDA0002316738500000085
Figure BDA0002316738500000086
Figure BDA0002316738500000087
in the formula (6), the formula (7), the formula (8) and the formula (9),
Figure BDA0002316738500000088
representing the continuous operation time of the ith unit in the t-1 th time period,
Figure BDA0002316738500000089
representing the continuous down time of the ith unit in the t-1 th period,
Figure BDA00023167385000000810
the shortest starting time of the ith unit is shown,
Figure BDA00023167385000000811
the shortest downtime of the ith unit is represented.
In this embodiment, the constraint conditions of the power system include a system power balance constraint condition, a positive/negative standby constraint condition, and a unit output constraint condition, and a more accurate scheduling space value of each unit can be determined by the constraint conditions, so that the search speed and accuracy are improved.
As shown in fig. 1 and fig. 2, the specific operation of step S3 includes: according to the obtained scheduling space values of each unit in the current time period, an objective function for performing optimal scheduling on the power system by the hybrid particle swarm method is established and calculated, and the objective function can be expressed by a formula:
Figure BDA00023167385000000812
in equation (10), ξ represents a cost power conversion coefficient in the power system.
In this embodiment, the key point of calculating the optimized scheduling scheme of the electronic system by using the hybrid particle swarm method is to set an objective function and determine a solution space, and the scheduling space value of each unit in the current time period is determined through the step S2, so that in this embodiment, an objective function for calculating the optimized scheduling scheme of the electric power system by using the hybrid particle swarm method is reestablished according to the obtained scheduling space value of each unit in the current time period and is calculated, and an optimized output scheme of each unit can be obtained.
As shown in fig. 1 and fig. 2, the specific operation of adjusting the scheduling space value of each unit in step S5 is: and expanding the scheduling space value of each unit in the step S2 by 1.2 times. In this embodiment, when the hybrid particle swarm optimization method is used to calculate that the output of each unit in the optimized output scheme of each unit in the current time interval does not satisfy the constraint condition, the scheduling space value of each unit is expanded by 1.2 times, and then the optimized output scheme of each unit in the current time interval is recalculated by the hybrid particle swarm optimization method. In other embodiments, the scheduling space value may also be adjusted accordingly according to the actual situation.
In order to better understand the working principle and technical effect of the present invention, the following description is given by using a simulation experiment to describe the optimal scheduling method of the power system in the present invention.
The application environment in the embodiment comprises 13 thermal power plants, and the total installed capacity is 17845MW, wherein the total number of the plants is 38. And obtaining a total output prediction curve (shown in FIG. 3) of the thermal power generating unit according to the load prediction and other power output prediction curves. In order to simplify the calculation, in this embodiment, the units of a power plant with the same capacity and the same parameters are combined into one unit, and the combined units are 19 units, and the rated capacities of the units are respectively:
2 x (362.5,300,600,600,300,600,310,630,300,300,300,300,600,600,600,300,660,600,660), and setting the minimum output of the unit as 50% of rated power, the maximum output of the unit as 110% of rated power, the climbing rate of the unit as 30% of rated power/hour, the standby value of the unit as 10% of the total installed capacity, and neglecting the starting and stopping time.
As shown in fig. 1, firstly, an optimized scheduling mathematical model of the power system is established, a scheduling space value of each power plant in the current time period is calculated according to a total output predicted value of all units (as shown in fig. 4) (fig. 5 is the scheduling space value of the unit in the power plant a in each time period), then, an optimized output scheme of each power plant in the current time period is calculated by using a hybrid particle swarm optimization method (a cost-power conversion coefficient ξ is set to be 0.001), whether the output of each unit in the current time period meets related constraint conditions is judged, if yes, the optimized calculation of the next time period is carried out, a graph of the output of all units (as shown in fig. 6) and the total operating cost (the average coal consumption is 304g/kwh if the total operating cost is 101069 tons of standard coal) are finally obtained, and if the related constraint conditions are.
As can be seen from fig. 6 and 7, the total operating cost of the operating curves of each unit actually scheduled is 101983 tons of standard coal, the average coal consumption is 307g/kwh, and in the actual scheduling operation, the output of the large-branch unit is basically maintained at the relatively stable stage of the load, only a small number of units participate in the adjustment, and although the scheduling operation work is reduced to a certain extent, certain economy is also sacrificed. In the simulation experiment, each unit participates in system adjustment at any time interval, and compared with actual scheduling operation, the simulation experiment has better economy, 914 tons of standard coal can be saved in daily operation cost, and the average coal consumption is reduced by 3 g/kwh. Meanwhile, the scheduling space of each unit in different time periods is different in the simulation experiment, the scheduling space is not between the minimum output and the maximum output of the traditional unit theory any more, and the scheduling space is compressed in a self-adaptive mode according to the change condition of the load, so that the calculated amount is reduced to a certain extent, and the calculation accuracy is improved. From comparison results in all aspects, simulation experiments performed by the optimal scheduling method for the power system have better optimal scheduling effect.
The method for optimizing and scheduling the power system provided by the invention is described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (10)

1. A method for optimizing scheduling in a power system, the method comprising the steps of:
s1, establishing an optimal scheduling mathematical model of the power system according to the actual operation parameters of the power system;
s2, dividing a scheduling cycle of the power system into a plurality of time periods, setting constraint conditions, predicting the total load of the power system in the current time period, and determining the scheduling space value of each unit in the current time period according to the predicted value of the total load of the power system in the current time period and the output information of each unit in the previous time period;
s3, calculating an optimized output scheme of each unit in the current time period by using a hybrid particle swarm optimization method according to the determined scheduling space value of each unit in the current time period;
s4, judging whether the output of each unit meets the constraint condition in the step S2 according to the calculation result in the step S3, if so, obtaining the optimal output scheme of each unit in the current time period and entering the step S6, and if not, entering the step S5;
s5, adjusting the scheduling space value of each unit in the step S2 and entering the step S3;
and S6, calculating the optimal output scheme of each unit in all other periods by using the hybrid particle swarm optimization method, thereby obtaining the output curve and the running total cost in the optimal output scheme of each unit in the power system and completing the optimal scheduling scheme of the power system.
2. The method for optimizing scheduling of an electric power system according to claim 1, wherein the mathematical model for optimizing scheduling of an electric power system in the step S1 is formulated as:
Figure FDA0002316738490000011
in the formula (1), T represents the total number of periods of the scheduling cycle, NcRepresents the total number of units in the power system,
Figure FDA0002316738490000012
representing the fuel cost of the ith unit for the t period,
Figure FDA0002316738490000013
showing the start-stop cost of the ith unit in the t period,
Figure FDA0002316738490000014
represents the output, u, of the ith unit during the t-th time periodi,tIndicating the start-stop state of the ith unit in the t period, wherein
Figure FDA0002316738490000015
αi、βi、γiRespectively representing the fuel cost coefficients of the ith unit;
Figure FDA0002316738490000016
Ni,trepresents the starting cost of the ith unit in the t period,
Figure FDA0002316738490000017
represents the hot start cost of the ith unit,
Figure FDA0002316738490000018
representing the cold start cost of the ith unit; t isi minIndicating the minimum allowable downtime, T, of the ith uniti coldIndicates the cold start time of the ith unit,
Figure FDA0002316738490000019
indicating the continuous down time of the ith unit in the t period,
Figure FDA00023167384900000110
representing the sum of the minimum allowable down time and the cold start time of the ith unit.
3. The method for optimizing and scheduling of an electric power system according to claim 2, wherein the step S2 is implemented in a specific manner as follows:
s21, dividing a scheduling cycle of the power system into a plurality of time periods, setting constraint conditions, and determining whether each unit needs to be started or stopped according to the power generation amount of the power system in the current time period;
s22, determining a scheduling space value of each unit according to the load increment ratio of the power system and the output condition of each unit in the previous period and by combining the constraint conditions of each unit, wherein the load increment ratio of the power system can be expressed by a formula:
Figure FDA0002316738490000021
in the formula (2), λ represents a load increment ratio of the power system, Pt LRepresenting the total load of the power system during the t-th period, when lambda > 0,
Figure FDA0002316738490000022
when the lambda is less than or equal to 0,
Figure FDA0002316738490000023
wherein P isi minRepresenting i-th unitLower limit of output, Pi maxIndicating the upper limit of output, Δ P, of the ith uniti +Represents the upper limit of grade climbing, Δ P, of the ith uniti -And the lower limit of the climbing of the ith unit is shown.
4. The method for optimizing scheduling of an electric power system according to claim 3, wherein the step S21 of determining whether each unit needs to be started or stopped according to the electric power system power generation amount in the current period is specifically: when the power generation capacity of the power system meets the requirement
Figure FDA0002316738490000024
In time, the unit needs to be increased; when the power generation capacity of the power system meets the requirement
Figure FDA0002316738490000025
When the machine is needed to be shut down,
Figure FDA0002316738490000026
represents the lower output limit of the ith unit in the t period,
Figure FDA0002316738490000027
representing the upper limit of the output of the ith unit in the t period, wherein RtRepresenting the standby value of the power system during the t-th period.
5. The method for optimizing scheduling of an electric power system according to claim 4, wherein the constraints set in step S2 include system power balance constraints, positive and negative standby constraints, and unit output constraints.
6. The method for optimized scheduling of a power system of claim 5, wherein the system power balance constraint is formulated as:
Figure FDA0002316738490000031
in the formula (3), Pt lossRepresenting the grid loss of the power system during the t-th period.
7. The method for optimized scheduling of a power system of claim 6, wherein the positive and negative backup constraints are formulated as:
Figure FDA0002316738490000032
Figure FDA0002316738490000033
8. the method of optimized scheduling of an electric power system of claim 7, wherein the unit output constraints are formulated as:
Figure FDA0002316738490000034
Figure FDA0002316738490000035
Figure FDA0002316738490000036
Figure FDA0002316738490000037
in the formula (6), the formula (7), the formula (8) and the formula (9),
Figure FDA0002316738490000038
representing the continuous operation time of the ith unit in the t-1 th time period,
Figure FDA0002316738490000039
indicating the ith unit is atthe continuous down time of the t-1 period,
Figure FDA00023167384900000310
the shortest starting time of the ith unit is shown,
Figure FDA00023167384900000311
the shortest downtime of the ith unit is represented.
9. The method for optimizing scheduling of an electric power system according to claim 8, wherein the specific operation of the step S3 includes: according to the obtained scheduling space values of each unit in the current time period, an objective function for performing optimal scheduling on the power system by the hybrid particle swarm method is established and calculated, and the objective function can be expressed by a formula:
Figure FDA00023167384900000312
in equation (10), ξ represents a cost power conversion coefficient in the power system.
10. The method for optimizing scheduling of an electric power system according to claim 9, wherein the specific operation of adjusting the scheduling space value of each unit in step S5 is: and expanding the scheduling space value of each unit in the step S2 by 1.2 times.
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