CN113839415B - Optimized scheduling method for multi-type flexible power supply - Google Patents

Optimized scheduling method for multi-type flexible power supply Download PDF

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CN113839415B
CN113839415B CN202110992668.9A CN202110992668A CN113839415B CN 113839415 B CN113839415 B CN 113839415B CN 202110992668 A CN202110992668 A CN 202110992668A CN 113839415 B CN113839415 B CN 113839415B
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unit
power
output
wind
source
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CN113839415A (en
Inventor
周强
张彦琪
吴悦
马志程
吕清泉
王定美
韩旭杉
马彦宏
李津
高鹏飞
张金平
张健美
张珍珍
张睿骁
甄文喜
陈柏旭
赵龙
丁坤
沈琛云
刘丽娟
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Jiuquan Power Supply Co Of State Grid Gansu Electric Power Co
Linxia Power Supply Company State Grid Gansu Electric Power Co
STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
State Grid Gansu Electric Power Co Ltd
Original Assignee
Jiuquan Power Supply Co Of State Grid Gansu Electric Power Co
Linxia Power Supply Company State Grid Gansu Electric Power Co
STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
State Grid Gansu Electric Power 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H02J3/472—For selectively connecting the AC sources in a particular order, e.g. sequential, alternating or subsets of sources
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/50—Hydropower in dwellings
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to an optimized scheduling method of a multi-type flexible power supply, which uses the following source-network coordination scheduling strategy: arranging the minimum output capacity of the thermal power generating unit, the wind power generating unit and the load of the runoff hydroelectric unit and the reservoir capacity hydroelectric unit; judging the charge source condition at the current moment; when the source is smaller than the load, the output is improved according to the sequence of the reservoir capacity hydroelectric generating set, the thermal power generating set and the gas generating set; if the source is still less than the charge, an external purchase is arranged; when the source is larger than the load, the output is reduced according to the sequence of the thermal power unit, the reservoir capacity hydroelectric unit and the gas unit; if the source is still larger than the load, the output is continuously reduced according to the sequence of the wind power, the photoelectricity and the runoff hydroelectric generating set. The invention coordinates the power generation sequence of wind, light, fire and water combustion through a source-network coordination scheduling strategy based on the space-time complementation characteristic of the multi-type power supply, can effectively reduce the waste wind and waste light, and promote the wind and light cluster digestion, so that the wind power photoelectric digestion capability and the operation benefit of the system are more advantageous under the cooperative action of the source network, and the power system operates more efficiently and stably.

Description

Optimized scheduling method for multi-type flexible power supply
Technical Field
The invention relates to the technical field of new energy optimal scheduling, in particular to an optimal scheduling method for a multi-type flexible power supply.
Background
Wind power, photovoltaic power generation and the like can be different from traditional fossil energy in energy generation, and the output has extremely strong randomness. With the great development of wind power generators and photovoltaic power generators, large-scale wind power and concentrated grid connection of photovoltaic electric fields add a plurality of uncertain factors to the economic dispatch of a power system.
Regarding large-scale wind power and photoelectric centralized grid connection, related researches at home and abroad are concentrated on the aspects of wind power and photoelectric centralized grid connection on the voltage and frequency influences of grid connection points on a local power grid and the impact on the power flow of the power grid, and researches on reactive voltage control, active control and prediction forecast of the power grid of the grid connection points are also carried out, so that the researches on source network coordination control technology for taking into account the efficient consumption of a multi-type flexible power supply lifting wind-light-electricity cluster do not relate too much.
Disclosure of Invention
The invention aims to solve the technical problem of providing an optimized scheduling method for a multi-type flexible power supply, so as to improve the consumption of large-scale wind-light electricity and reduce the running cost of a system.
In order to solve the problems, the optimal scheduling method for the multi-type flexible power supply disclosed by the invention uses the following source-network coordination scheduling strategy:
sequentially arranging the minimum output capacity of the thermal power unit, the wind power photoelectric unit and the load of the runoff hydroelectric unit and the reservoir capacity hydroelectric unit;
calculating and judging the charge source condition at the current moment;
when the output of all units is smaller than the current load, the output is improved according to the sequence of the reservoir capacity hydroelectric unit, the thermal power unit and the gas unit; if the current load is still smaller than the current load, arranging external electricity purchasing;
when the output of all units is larger than the current load, the output is reduced according to the sequence of the thermal power unit, the reservoir capacity hydroelectric unit and the gas unit; if the current load is still larger than the current load, the output is continuously reduced according to the sequence of the wind power, the photoelectricity and the runoff hydroelectric generating set.
Preferably, before the minimum output capacity of the thermal power generating unit is arranged, the method further comprises: and (3) carrying out sectional treatment on the capacity of the started thermal power unit, and dividing the capacity into a minimum output capacity and an adjustable output capacity.
Preferably, the method further comprises:
constructing an objective function of an optimized scheduling model by taking the minimum running cost as a target based on the source-network coordination scheduling strategy;
solving the optimal scheduling model by combining the objective function and preset constraint conditions to obtain parameters of optimal scheduling and using the parameters for scheduling control; the parameters of the optimized dispatching comprise the output condition of each controllable unit, the transmission power of the connecting wire and the economic cost after the optimized dispatching.
Preferably, the formula of the objective function is as follows:
wherein of represents the running cost of the system in the scheduling period, C g，t Representing the cost of the fire motor g in normal operation at the time t, c t,t P r,t Representing the cost of the gas motor r in normal operation at time t, VOLL×LS i,t Indicating the possibility of being from outside for system stabilityThe cost of the electricity for the department purchase,and->And (3) representing the punishment cost of the waste wind and waste light, wherein VOLW and VOLV are coefficients of the punishment of the waste wind and waste light, and the normal running cost of the water and electricity motor in the system is ignored.
Preferably, the preset constraint conditions comprise a fire motor operation cost constraint condition, an electric power system power balance constraint condition, a rotation positive and negative standby capacity constraint condition, a thermal power generating unit operation constraint condition, a hydroelectric generating unit operation constraint condition, a gas motor operation constraint condition, a conventional generating unit switch constraint condition and wind power and photovoltaic power generation output upper and lower limit constraint conditions.
Compared with the prior art, the invention has the following advantages:
according to the invention, based on the space-time complementary characteristics of wind, light, water, fire and gas multi-type power supplies, the power generation sequence of wind power, photovoltaic power generation, thermal power, hydroelectric power and gas power generation is coordinated through a source-network coordination scheduling strategy, large-scale wind power and photovoltaic power generation grid connection can be effectively realized, wind discarding and light discarding are reduced, wind and light electricity cluster consumption is improved, and economic benefits are maximized as optimization targets, so that the system wind power photoelectric consumption capability and operation benefits have advantages under the cooperation of the source network, and the power system operates more efficiently and stably.
Drawings
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Fig. 1 is a schematic diagram of a source-network coordination scheduling policy according to an embodiment of the present invention.
Fig. 2 is a flowchart of an optimized scheduling method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of piecewise linearization of a secondary cost curve of a fire motor according to an embodiment of the invention.
Fig. 4 is a schematic diagram of a principal parameter model of a hydroelectric generator according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a 24bus model of a power system according to an embodiment of the present invention.
FIG. 6 is a graph showing 24-hour grid-connected capacity of a wind turbine generator system according to an embodiment of the invention.
Fig. 7 is a graph showing the 24-hour grid-connected amount of the photovoltaic generator set provided by the embodiment of the invention.
Fig. 8 is a graph showing 24-hour grid connection quantity of a thermal power generating unit provided by the embodiment of the invention.
FIG. 9 shows a 24-hour grid-connected amount of a gas motor unit according to an embodiment of the present invention.
Fig. 10 shows 24-hour grid-connected quantity of the hydroelectric generating set provided by the embodiment of the invention.
Fig. 11 is a diagram of a multi-type flexible power supply joint optimization scheduling result in case 1 according to an embodiment of the present invention.
Fig. 12 is a model provided by the embodiment of the invention, which only considers the wind, light and fire power generation, namely, the situation 2.
Fig. 13 is a diagram of a multi-type flexible power supply joint optimization scheduling result in case 2 according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1, an embodiment of the present invention provides an optimized scheduling method for a multi-type flexible power supply, where the optimized scheduling method uses the following source-network coordination scheduling policy (the source-network coordination scheduling policy refers to setting switching conditions and loading sequences of various types of units according to a certain operation scheduling logic on the premise of ensuring safe and stable operation of a system):
and step S100, sequentially arranging the minimum output capacity of the thermal power unit, the wind power photoelectric unit and the load of the runoff hydroelectric unit and the reservoir capacity hydroelectric unit.
Specifically, the capacity of the thermal power generating unit started is segmented in advance and divided into a minimum output capacity and an adjustable output capacity. Based on the hour-level wind-solar power generation prediction, a unit loading sequence is set: the minimum output capacity load is first arranged. And carrying out multi-state processing on the photovoltaic and wind turbine generator sets, and preferentially arranging the load of the photovoltaic and wind turbine generator sets. And finally, arranging the runoff hydroelectric generating set and the reservoir capacity hydroelectric generating set to carry loads according to hydrologic conditions.
And step S101, calculating and judging the state of charge source at the current moment.
Step S102, when the output of all units is smaller than the current load (source < load), namely the current state needs climbing, the output is improved according to the sequence of the reservoir capacity hydroelectric unit, the thermal power unit and the gas unit; if the current load is still smaller, an external power purchase is arranged.
Step S103, when the output of all units is larger than the current load (source > load), namely the current state needs to fall, the output is reduced according to the sequence of the thermal power unit, the reservoir capacity hydroelectric unit and the gas unit; if the load is still larger than the current load, the output force is continuously reduced according to the sequence of the wind power, the photoelectricity and the runoff hydroelectric generating set, namely, the phenomena of wind discarding, light discarding and water discarding occur.
Referring to fig. 2, on the basis of the source-network coordinated scheduling policy, the optimized scheduling method of the present invention further includes the following steps:
step 200, constructing an objective function of an optimized scheduling model based on a source-network coordination scheduling strategy and with minimum running cost as a target.
Wherein of represents the running cost of the system in the scheduling period, C g,t Representing the cost of the fire motor g in normal operation at the time t, c r,t P r,t Representing the cost of the gas motor r in normal operation at time t, VOLL×LS i,t Representing the cost of possibly purchasing electricity from the outside for system stability,and->And (3) representing the punishment cost of the waste wind and waste light, wherein VOLW and VOLV are coefficients of the punishment of the waste wind and waste light, and the normal running cost of the water and electricity motor in the system is ignored.
Step S201, pre-constructing constraint conditions.
The constructed constraint conditions comprise a running cost constraint condition of a fire motor, a power system power balance constraint condition, a rotation positive and negative standby capacity constraint condition, a thermal power generating unit running constraint condition, a hydroelectric generating unit running constraint condition, a gas motor running constraint condition, a conventional generating unit switching constraint condition and wind power and photovoltaic power generation output upper and lower limit constraint conditions.
And step S202, solving the optimal scheduling model by combining the objective function of the step S200 and the constraint conditions preset in the step S201 to obtain parameters of optimal scheduling.
Step S203, using the parameters acquired in step S202 for scheduling control.
The parameters of the optimized scheduling comprise the output condition of each controllable unit, the transmission power of a connecting wire and the economic cost after the optimized scheduling, and the wind power grid connection, the photovoltaic grid connection, the thermal generator set, the hydroelectric generator set, the output condition of a gas generator set, the transmission power of the connecting wire and the like in each period are regulated and controlled according to the parameters.
The invention establishes an optimized scheduling model for mixed integer linear programming (Mixed Integer Linear Programming, MILP) problem. And solving by adopting a CPLEX solver.
The details of the constraint conditions previously constructed in step S201 are as follows.
(1) Operating cost constraints for a fire engine
Constructing a piecewise linearization function of the running cost of the thermal power generating unit; and constructing the operation cost constraint condition of the fire motor. In order to make the whole model a mixed integer linearization Model (MILP), the thermal power plant cost function is piecewise linearized. As shown in fig. 3. Wherein the minimum power P of the thermal power generating unit min And maximum power P max Divided into n equal parts, each equal part being represented by the formulaThe linearization cost of the fire motor is expressed by the following formula:
(2) Power balance constraint condition of electric power system
Wherein, sigma g，t P g，t The output value of the No. g thermal power machine in the t period is represented; sigma (sigma) r,t P r,t The output value and sigma of the No. r gas motor in the t time period are represented h，t P h，t The output value of the h water turbine unit in the t time period is represented; sigma (sigma) w,t P w,t Sum sigma v,t P v,t Representing the grid-connected quantity of the wind power generation and photovoltaic generator set; d (D) t Is the base load demand of the t-period system.
(3) Rotation positive and negative reserve capacity constraints
Wherein U 'is' G Is the power generation state of the conventional generator set at the time t, wherein when U' G When the value is 0, the normal motor is in a closed state, U' G 1 represents the normal motor in the starting state, G represents the number of the normal motor groups and P' G,max 、P′ G,min The maximum output force and the minimum output force of the conventional unit at the time t are obtained. R is R L,up 、R L,down And the positive and negative rotation needed by the system is shown for standby when wind and light are not connected. R's' WV,up 、R′ WV,down The wind power and the electric power are respectively rotated up and down for standby.
(4) Thermal power generating unit operation constraint condition
Comprises climbing running of a thermal power unit and upper and lower limit constraint conditions of output power
Where j represents the unit number, X (t, j) represents the operation state of the j-th unit in the period t (0 represents that the unit has been shut down, and 1 represents that the unit is running). Simultaneously, the power p of the thermal power generating unit T (t, j) is subject to the climbing rate of the unitAnd downhill climbing rate->Is used in the present invention,
(5) Operation constraint of hydroelectric generating set
Including hydroelectric motor operating constraints, hydropower conversion, hydropower daily flow constraints FIG. 4 is a principal model parameter for hydropower, where x is the total water storage in the reservoir (m 3 )；For the maximum operating volume (m 3 )；/>For minimum operating volume of reservoir (m 3 ) The method comprises the steps of carrying out a first treatment on the surface of the q is the displacement (m) 3 S); s is the overflow flow (m) 3 /s). The hydroelectric motor follows the water-electricity conversion capability constraint:
P h ＝k·η·h g ·q
p in the formula h The power (MW) obtained in the process of converting the hydraulic potential energy into electric energy; k is equal to the gravitational constant multiplied by the specific gravity of water, which is 0.00981[ MW/(m) 3 /s)/m]。
In order to ensure the normal and stable operation of the upstream and downstream of the hydropower station, the daily flow restriction constraint of the hydropower station needs to be set,
the upper limit and the lower limit of the output power of the hydroelectric generating set are constrained as follows:
(6) Gas motor operation constraint condition
The gas daily control amount is set as one operation constraint of the gas motor.
The gas power station also needs output constraint and climbing constraint
(7) Switch constraint condition of conventional unit
Wherein Y (t, j) and Z (t, j) are the start-up state and the stop state of the j-th unit in the period t, and for Y (t, j), 0 represents the non-start-up state, and 1 represents the start-up state; for Z (t, j), 0 indicates no shutdown state, 1 indicates shutdown; s represents a start-up and stop time period; s represents the minimum start-up or shut-down time step. Further, the three key state variables X, Y and Z of the unit are logically constrained by the start-up and stop running states of the unit:
(8) Wind power and photovoltaic power generation output upper and lower limit constraint conditions
Wind power and photoelectric power which are connected into a power grid are smaller than or equal to electric quantity actually generated by a wind motor and a photoelectric motor
Simulation experiment analysis:
as shown in fig. 5, the tested multi-type flexible power system network consisted of a modified IEEE RTS-24-bus power system. The system consists of wind power, photovoltaic power, hydroelectric power, thermal power and other flexible power supplies. And selecting a Hexi wind power base and a Dunhuang photovoltaic base in Gansu province as research objects. Wherein, the lines 8, 19 and 20 are provided with 3 wind power stations with the capacities of 250MW, 200MW and 300MW respectively. And 3 photovoltaic electric fields are arranged on lines 13, 15 and 22, and the capacities are 300MW, 300MW and 250MW respectively. Hydropower stations are on lines 16 and 23, and the capacities are 155MW and 300MW. With 1 day as the scheduling period, 1h is 1 scheduling period. The operation parameters of the hydropower station are shown in annex A, and the salt pot gorges and the eight dish gorges in annex A are selected as a unit for running the hydropower station participating in adjustment. Other parameters ηu=ηd=5%, ηe=8%, ωu=ωd=25%.
Taking a typical day data in winter as an example, fig. 6-11 are the coordination and optimization scheduling results of wind, water, gas, fire and multiple types of flexible power supplies, and the model realizes the full consumption of wind power and photoelectricity. In practice, because the objective function is the minimum of the solving cost and the light and wind discarding punishment cost is added, wind power and light electricity are preferentially integrated into the power grid. It can be seen from the figure that other flexible power supplies reduce the output in the period of abundant wind and light resources, and make room for the power system to consume wind power and light power. The hydropower station and the gas turbine unit increase output in the system load peak period (18:00-22:00), and meanwhile, the generated energy is reduced in the load valley period, and the purpose of stabilizing fluctuation and reducing load peak-valley difference when wind power and photoelectricity are connected is achieved by utilizing the flexible adjusting capability of the hydropower station and the gas turbine unit. The addition of water electricity and gas electricity ensures that the load of the thermal power generating unit is as stable as possible under the condition of meeting the system load requirement, and reduces the frequent opening and closing of the fire motor.
A multi-type power coordination optimization model is considered as a case 1, and a model only of wind power photoelectricity and thermal power generation is considered as a case 2. Fig. 12 is a power system model of case 2, and fig. 13 is various power scheduling results in case 2. Two different scene results are compared. In case 1, wind power and photovoltaic power generation can be completely consumed, and in case 2, the waste wind and waste light rate reaches 5.13%. The method and the device indicate that large-scale wind power and photovoltaic power generation cannot be completely consumed by a power grid simply by means of the complementary characteristics between the wind power and the photovoltaic power generation. The standby capacity is increased in a mode of hybrid access of various power supplies, and fluctuation of new energy sources when the new energy sources are accessed into a power grid is smoothed. In terms of cost, since the cost when the hydropower is connected to the power grid is neglected in case 1, and the phenomenon of wind and light discarding occurs in case 2, the economic cost of case 1 is much lower than that of case 2. The total cost of all units in case 1 is reduced by 25.16% relative to the cost of case 2. If this reduced cost is converted into standard combustion coal and released SO for a thermal power plant 2 And CO 2 And the gas is equal, so that the coordinated optimization scheduling method considering the multiple types of power supplies has considerable economic and environmental benefits.
In order to solve the problem of large-scale wind power photoelectric centralized grid-connected consumption, the invention provides an optimal scheduling method for promoting wind power photoelectric consumption by taking into account the cooperation of multiple types of flexible power supplies, and simulation examples show that: the scheduling method can reduce the abandoned amount of wind power and photovoltaic power generation, increase the grid-connected amount of the abandoned amount, and reduce the operation cost of the thermal power generating unit while guaranteeing the system load and the standby requirement. In addition, by utilizing the complementary difference of wind energy and solar energy in time and space, the fluctuation of wind energy and solar energy power generation in the combined area is smoothed, the influence of random fluctuation of wind energy and photovoltaic power generation on a power grid is slowed down, and the method has important significance for improving the safe and stable operation of the power grid.
The technical scheme provided by the invention is described in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.

Claims (1)

1. An optimized scheduling method for a multi-type flexible power supply is characterized by using the following source-network coordination scheduling strategy:
sequentially arranging the minimum output capacity of the thermal power unit, the wind power photoelectric unit and the load of the runoff hydroelectric unit and the reservoir capacity hydroelectric unit;
calculating and judging the charge source condition at the current moment;
when the output of all units is smaller than the current load, the output is improved according to the sequence of the reservoir capacity hydroelectric unit, the thermal power unit and the gas unit; if the current load is still smaller than the current load, arranging external electricity purchasing;
when the output of all units is larger than the current load, the output is reduced according to the sequence of the thermal power unit, the reservoir capacity hydroelectric unit and the gas unit; if the current load is still greater than the current load, continuously reducing the output according to the sequence of the wind power, the photoelectricity and the runoff hydroelectric generating set;
the method further comprises the following steps before the minimum output capacity of the thermal power generating unit is arranged: the method comprises the steps of carrying out sectional treatment on the capacity of a starting thermal power unit, and dividing the capacity into a minimum output capacity and an adjustable output capacity;
constructing an objective function of an optimized scheduling model by taking the minimum running cost as a target based on the source-network coordination scheduling strategy; solving the optimal scheduling model by combining the objective function and preset constraint conditions to obtain parameters of optimal scheduling and using the parameters for scheduling control; the parameters of the optimized dispatching comprise the output condition of each controllable unit, the transmission power of a connecting wire and the economic cost after the optimized dispatching;
the formula of the objective function is as follows:
wherein of represents the running cost of the system in the scheduling period, cg, t represents the cost of the fire motor g when it is running normally at time t, cr, tPr, t represents the cost of the gas motor r when it is running normally at time t, VOLL x LSi, t represents the cost of electricity which may be purchased from outside for system stability,and->The punishment cost of the waste wind and waste light is represented, wherein VOLW and VOLV are coefficients of the punishment of the waste wind and waste light, and the normal running cost of a water motor in the system is ignored;
the preset constraint conditions comprise a fire motor operation cost constraint condition, an electric power system power balance constraint condition, a rotation positive and negative standby capacity constraint condition, a thermal power generating unit operation constraint condition, a hydroelectric generating unit operation constraint condition, a gas motor operation constraint condition, a conventional generating unit switch constraint condition and wind power and photovoltaic power generation output upper and lower limit constraint conditions.
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