CN115528747A - Power system tight balance multi-time scale disposal method, device, equipment and medium - Google Patents

Power system tight balance multi-time scale disposal method, device, equipment and medium Download PDF

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Publication number
CN115528747A
CN115528747A CN202211247309.1A CN202211247309A CN115528747A CN 115528747 A CN115528747 A CN 115528747A CN 202211247309 A CN202211247309 A CN 202211247309A CN 115528747 A CN115528747 A CN 115528747A
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time
time period
representing
adjustment
charging station
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Inventor
耿建
杨胜春
常逸凡
谢俊
王礼文
吕建虎
刘建涛
刘俊
王勇
徐鹏
仇晨光
熊浩
闫朝阳
丁超杰
郭晓蕊
潘玲玲
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jiangsu Electric Power Co Ltd
Hohai University HHU
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jiangsu Electric Power Co Ltd
Hohai University HHU
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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
    • H02J3/46—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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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

Abstract

The invention discloses a method, a device, equipment and a medium for handling tight balance multi-time scale of a power system, wherein the method comprises the following steps: respectively establishing a first-level time period system tight balance handling model in a day and a second-level time period system tight balance handling model in the day by taking the minimum total operating cost of the power system as a target function; solving a system tight balance treatment model in a first-stage time period in a day and a system tight balance treatment model in a second-stage time period in the day to respectively obtain a first-stage tight balance treatment result and a second-stage tight balance treatment result; establishing a real-time third-level time system tight balance disposal model by taking the minimum total adjustment cost of the power system as a target function; solving a real-time third-stage time system tight balance handling model to obtain a third-stage power system tight balance handling strategy; from the three time scales, a power system tight balance multi-time scale disposal strategy considering the flexibility of the electric vehicle charging station is established, and the level of tight balance disposal is improved.

Description

Power system tight balance multi-time scale disposal method, device, equipment and medium
Technical Field
The invention belongs to the field of power systems, and particularly relates to a method, a device, equipment and a medium for handling tight balance multi-time scale of a power system.
Background
The proportion of renewable energy sources such as wind power and the like in a power system is continuously improved, the dependence on traditional energy sources is favorably reduced, and the strong fluctuation and uncertainty of the output of the renewable energy sources put higher requirements on the flexibility of the system. With the continuous increase of the peak load of the electric power, the installed capacity of the system for generating electricity is insufficient, and the supply and demand of the electric power are short during the short-time peak load, so that the system is in a tight balance state. At the present stage, the flexibility requirement of the power grid in the eastern region of China mainly depends on the supply of the thermal power generating unit. In a state of tight balance of a power system, if flexibility is provided only by a thermal power generating unit, the flexibility of the system is insufficient, and wind abandon and load shedding are caused, and flexibility resources are required to be increased to reduce the wind abandon and load shedding. As a flexible resource, the electric vehicle charging station can provide flexibility for the system, reduce the adjustment burden of the thermal power generating unit, reduce the wind abandoning and load shedding of the system and ensure the safe and economic operation of the system.
Most of documents on the flexibility of the power system aim at improving the flexibility of the system, and deep research is rarely carried out on the problems of insufficient flexibility of a high-wind power permeable power grid under multiple time scales and tight balance caused by insufficient installed capacity of power generation. In the face of high-permeability wind power and short-time peak load, the flexibility requirement and the load requirement of the system cannot be met simultaneously only by the thermal power generating unit, and the access of flexible resources (such as wind power, an electric vehicle charging station and the like) is required to share the pressure for the thermal power generating unit. However, at present, errors exist in wind power and load prediction, so that the precision of a tightly balanced handling strategy formed after access based on flexible resources is not high.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a medium for handling tight balance multiple time scales of a power system, and aims to solve the problem that in the prior art, errors exist in wind power and load prediction at present, so that the precision of a tight balance handling strategy formed after access based on flexible resources is not high.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect of the present invention, a method for handling tight balance of a power system with multiple time scales is provided, which includes the following steps:
respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in a day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than real-time third-level time;
substituting the wind power and the system load data predicted by the first-stage time period in the day into a pre-established first-stage time period system tight balance treatment model for solving to obtain a first-stage tight balance treatment result;
substituting the first-stage tight balance treatment result, the predicted wind power in the second-stage time period in the day and the system load data into a pre-established system tight balance treatment model in the second-stage time period in the day for solving to obtain a second-stage tight balance treatment result;
substituting the second-stage tight balance processing result, the wind power predicted by the real-time third-stage time and the system load data into a pre-established real-time third-stage time system tight balance processing model for solving to obtain a third-stage power system tight balance processing strategy;
and the intra-day first-level time period system tight balance treatment model, the intra-day second-level time period system tight balance treatment model and the real-time third-level time system tight balance treatment model all meet corresponding constraint conditions.
Further, in the step of substituting the wind power and the system load data predicted by the daily first-level time period into the pre-established daily first-level time period system tight balance treatment model for solving, the daily first-level time period system tight balance treatment model is established in the following manner: establishing a system tight balance handling model of a first-level time period in a day by taking the minimum total operation cost of a power system as an objective function, wherein the objective function of the system tight balance handling model of the first-level time period in the day comprises the total operation cost of a thermal power unit, the total operation cost of an electric vehicle charging station and the punishment cost of wind curtailment and load shedding of the system; the total operation cost of the thermal power generating unit comprises the operation cost and the starting cost of the thermal power generating unit, and the conventional auxiliary service and flexible climbing standby cost.
Further, in the step of substituting the first-stage tight balance handling result, the predicted wind power in the second-stage time period in the day, and the system load data into a pre-established second-stage time period system tight balance handling model for solving, the establishment mode of the second-stage time period system tight balance handling model in the day is as follows: establishing a system tight balance handling model of a second-level time period in a day by taking the minimum total operation cost of the power system as a target function, wherein the target function of the system tight balance handling model of the second-level time period in the day comprises the total operation cost of a thermal power unit, the total operation cost of an electric vehicle charging station and the punishment cost of the wind curtailment and load shedding of the system; the total operation cost of the thermal power generating unit comprises the operation cost of the thermal power generating unit, conventional auxiliary service and flexible climbing standby cost.
Further, in the step of substituting the second-stage tight balance processing result, the wind power predicted by the real-time third-level time and the system load data into the pre-established real-time third-level time system tight balance processing model for solving, the third-level time system tight balance processing model is established in the following manner: and establishing a real-time third-level time system tight balance disposal model by taking the minimum total adjustment cost of the power system as a target function, wherein the target function of the real-time third-level time system tight balance disposal model comprises the total operation adjustment cost of the thermal power generating unit, the total operation adjustment cost of the electric vehicle charging station and the punishment cost of the system wind curtailment and load shedding.
Further, the total operation adjustment cost of the thermal power generating unit is as follows:
Figure BDA0003887202070000031
the total operation adjustment cost of the electric vehicle charging station is expressed as follows:
Figure BDA0003887202070000032
the penalty cost of the system for wind curtailment and load shedding is expressed as follows:
Figure BDA0003887202070000033
wherein:
Figure BDA0003887202070000034
representing the total adjustment cost, N, of the thermal power generating unit in real time at the third level I Representing the total number of the thermal power generating units;
Figure BDA0003887202070000035
representing a thermal power output adjustment cost coefficient; u. of i,t The variable is 0-1, and represents the starting and stopping states of the thermal power generating unit i in the time period t; i Δ p i，t I represents the adjustment quantity of the real-time third-level time relative to the thermal power output in the second-level time period in the day;
Figure BDA0003887202070000036
representing a rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000037
the adjustment quantity of the rotating reserve capacity of the real-time third level time relative to the second level time period in the day is represented;
Figure BDA0003887202070000038
representing a non-rotation standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000039
the adjustment quantity of the real-time third level time relative to the non-rotating reserve capacity of the second level time period in the day is represented;
Figure BDA00038872020700000310
representing a frequency modulation standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA00038872020700000311
the adjustment quantity of the upward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000312
the adjustment quantity of the downward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000313
representing a replacement standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA00038872020700000314
the adjustment quantity of the replacement spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000315
representing an upward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure BDA0003887202070000041
the adjustment quantity of the upward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA0003887202070000045
representing downward flexible climbing standby adjustment of thermal power generating unit i in t periodThe integer cost coefficient;
Figure BDA0003887202070000046
the adjustment quantity of the downward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA0003887202070000042
representing the total operation adjustment cost of the electric vehicle charging station at the real-time third-level time; n is a radical of PL Representing the total number of electric vehicle charging stations;
Figure BDA0003887202070000047
indicating the discharge adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA0003887202070000048
representing the amount of power adjustment of the charging station pl from the charging station to the grid during the time period t;
Figure BDA0003887202070000049
a rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700000410
indicating the amount of rotation reserve capacity adjustment of the charging station pl during the time period t;
Figure BDA00038872020700000411
a non-rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700000412
represents the amount of adjustment of the non-rotating reserve capacity of the charging station pl during the time period t;
Figure BDA00038872020700000413
indicating the frequency modulation standby adjustment cost coefficient of the charging station pl at the time period t;
Figure BDA00038872020700000414
indicates that the charging station is plAn amount of upward frequency modulation reserve capacity adjustment for a time period t;
Figure BDA00038872020700000415
indicating a down-modulation reserve capacity adjustment for charging station pl at time t;
Figure BDA00038872020700000416
a replacement standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700000417
represents the replacement reserve capacity adjustment amount of the charging station pl at the time period t;
Figure BDA00038872020700000418
representing the upward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700000419
representing the upward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA00038872020700000420
representing the downward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700000421
representing the downward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA0003887202070000043
the penalty cost of the system wind abandon and load shedding at the real-time third level time is represented;
Figure BDA0003887202070000044
representing the total time period number of the real-time third level time tight balance treatment; n is a radical of J 、N W Respectively representing the total load and the total wind turbine generator;
Figure BDA00038872020700000422
representing a system wind curtailment penalty cost coefficient;
Figure BDA00038872020700000423
representing wind curtailment generated by wind power w in a time period t;
Figure BDA00038872020700000424
representing a system load shedding punishment cost coefficient; LS (least squares) j，t Representing the shear load generated by load j during time t.
In a second aspect of the present invention, there is provided a power system tight balance multi-time scale handling apparatus, including:
the data acquisition module is used for respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in a day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than real-time third-level time;
the first solving module is used for substituting the wind power and the system load data predicted by the first-level time period in the day into a pre-established system tight balance disposal model of the first-level time period in the day for solving to obtain a first-stage tight balance disposal result;
the second solving module is used for substituting the first-stage tight balance treatment result, the predicted wind power in the second-stage time period in the day and the system load data into a pre-established system tight balance treatment model in the second-stage time period in the day to solve to obtain a second-stage tight balance treatment result;
and the third solving module is used for substituting the second-stage tight balance processing result, the wind power predicted in real-time third-stage time and the system load data into a pre-established real-time third-stage time system tight balance processing model for solving to obtain a third-stage power system tight balance processing strategy.
Further, in the third solving module:
the third-level time system tight balance treatment model is established in the following mode: and establishing a real-time third-level time system tight balance handling model by taking the minimum total adjustment cost of the power system as a target function, wherein the target function of the real-time third-level time system tight balance handling model comprises the total operation adjustment cost of the thermal power unit, the total operation adjustment cost of the electric vehicle charging station and the punishment cost of the system wind curtailment and load shedding.
Further, in the third solving module:
the total operation adjustment cost of the thermal power generating unit is as follows:
Figure BDA0003887202070000051
the total operation adjustment cost of the electric vehicle charging station is expressed as follows:
Figure BDA0003887202070000052
the penalty cost of the system for wind curtailment and load shedding is expressed as follows:
Figure BDA0003887202070000061
wherein:
Figure BDA0003887202070000062
representing the total adjustment cost, N, of the thermal power generating unit in real-time third-level time I Representing the total number of the thermal power generating units;
Figure BDA0003887202070000063
representing a thermal power output adjustment cost coefficient; u. of i,t The variable is 0-1 and represents the starting and stopping states of the thermal power generating unit i in the time period t; i Δ p i，t The | represents the adjustment quantity of the real-time third-level time relative to the thermal power output of the second-level time period in the day;
Figure BDA0003887202070000065
representing a rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000066
the adjustment quantity of the rotating reserve capacity of the real-time third level time relative to the second level time period in the day is represented;
Figure BDA0003887202070000067
representing a non-rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000068
representing an adjustment amount of real-time third level time relative to a non-rotating reserve capacity of a second level time period within a day;
Figure BDA0003887202070000069
representing a frequency modulation standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA00038872020700000610
the adjustment quantity of the upward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000611
the adjustment quantity of the downward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000612
representing a replacement standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA00038872020700000613
the adjustment quantity of the replacement spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000614
representing the spare adjustment cost coefficient of the thermal power generating unit i in the upward flexible climbing at the time period t;
Figure BDA00038872020700000615
the adjustment quantity of the upward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700000616
representing a downward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure BDA00038872020700000617
the adjustment quantity of the downward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA0003887202070000064
representing the total operation adjustment cost of the electric vehicle charging station at the real-time third-level time; n is a radical of PL Representing the total number of electric vehicle charging stations;
Figure BDA00038872020700000618
indicating the discharge adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700000619
represents the amount of power adjustment of the charging station pl from the charging station to the grid during the time period t;
Figure BDA00038872020700000620
a rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA0003887202070000073
indicating the amount of rotation reserve capacity adjustment of the charging station pl during the time period t;
Figure BDA0003887202070000074
a non-rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA0003887202070000075
represents the amount of non-rotational reserve capacity adjustment of charging station pl over time t;
Figure BDA0003887202070000076
indicating the frequency modulation standby adjustment cost coefficient of the charging station pl at the time period t;
Figure BDA0003887202070000077
indicating the amount of upward frequency modulation reserve capacity adjustment of the charging station pl during the time period t;
Figure BDA0003887202070000078
indicating a down-modulation reserve capacity adjustment for charging station pl at time t;
Figure BDA0003887202070000079
a replacement standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700000710
represents the replacement reserve capacity adjustment for charging station pl at time t;
Figure BDA00038872020700000711
representing the upward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700000712
representing the upward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA00038872020700000713
representing the downward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700000714
indicating the downward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA0003887202070000071
the penalty cost of the system wind abandon and load shedding at the real-time third level time is represented;
Figure BDA0003887202070000072
representing the total time period number of the real-time third level time tight balance treatment; n is a radical of J 、N W Respectively representing the total load and the total wind turbine generator;
Figure BDA00038872020700000715
representing a system wind curtailment penalty cost coefficient;
Figure BDA00038872020700000716
representing the abandoned wind generated by the wind power w in the time period t;
Figure BDA00038872020700000717
representing a system load shedding punishment cost coefficient; LS (least squares) j，t Representing the shear load generated by load j during time t.
In a third aspect of the present invention, an electronic device is provided, which includes a processor and a memory, and the processor is configured to execute a computer program stored in the memory to implement the power system tight balance multi-time scale handling method described above.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores at least one instruction, which when executed by a processor, implements the above-mentioned power system tight-balancing multi-timescale handling method.
Compared with the prior art, the invention has the following beneficial effects:
according to the method for handling the tight balance multiple time scales of the power system, by establishing a system tight balance handling model of three time scales of a first-level time period in a day, a second-level time period in the day and a real-time third-level time, aiming at the characteristic that the wind power and load prediction accuracy is improved along with the shortening of the time scales, flexible climbing capacity modeling is provided for the system flexibility and an electric vehicle charging station, a power system tight balance multiple time scale handling strategy considering the flexibility of the electric vehicle charging station is established from the three time scales of the first-level time period in the day, the second-level time period in the day and the real-time third-level time, and the level of tight balance handling is improved. And through embodiment analysis, the proposed model and method are verified to show good performance in improving the level of tight balance treatment, and have effectiveness in practical application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flowchart of a method for handling tight-balanced multiple time scales of a power system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of thermal power output adjustment amounts at different time scales according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the system load shedding amount of each time period under different time scales in the embodiment of the present invention;
FIG. 4 is a schematic diagram of the influence of the electric vehicle scale on the total operating cost of the system and the saving of thermal power climbing in the embodiment of the invention;
FIG. 5 is a schematic diagram illustrating the impact of electric vehicle size on the total system load shedding in an embodiment of the present invention;
FIG. 6 is a schematic diagram of wind power output in different typical scenarios in the embodiment of the present invention;
FIG. 7 is a block diagram of a power system tight-balance multi-time scale handling device according to an embodiment of the present invention;
fig. 8 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Example 1
The embodiment 1 provides a power system tight balance multi-time scale disposal method, which is suitable for tight balance multi-time scale disposal of various multi-node power systems, and aiming at the characteristic that wind power and load prediction accuracy is improved along with shortening of time scales, flexible climbing capacity modeling is provided for system flexibility and electric vehicle charging stations, the power system tight balance multi-time scale disposal method considering the flexibility of the electric vehicle charging stations is established from three time scales of 4h in a day, 1h in the day and 15min in real time, and the level of tight balance disposal is improved.
As shown in fig. 1, a method for handling tight balance of a power system with multiple time scales includes the following steps:
s1, respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in the day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than that of the real-time third-level time.
Specifically, the first-level time period in a day may be a time length of 4 hours in a day, 6 hours in a day, or the like, the second-level time period in a day may be a time length of 1 hour in a day, 2 hours in a day, or the like, and the real-time third-level time may be a time level of 15min, 10min, 20min, or the like.
Preferably, in the scheme, the first-level time period in a day is 4 hours in the day, the second-level time period in the day is 1 hour in the day, and the real-time third-level time is 15min time level. The obtained predicted wind power and system load data comprise wind power and system load data predicted in 4h of the day, wind power and system load data predicted in 1h of the day and wind power and system load data predicted in 15min levels in real time.
And S2, respectively establishing a system tight balance handling model of the first-level time period in the day and a system tight balance handling model of the second-level time period in the day by taking the minimum total operation cost of the power system as an objective function.
Specifically, the first-level time period system tight balance handling model and the second-level time period system tight balance handling model in the day constructed in the scheme are respectively as follows: a 4 h-day system tight balance treatment model and a 1 h-day system tight balance treatment model.
And substituting the wind power predicted by 4h in the day and the system load data into a 4h system tight balance treatment model in the day to solve to obtain a first-stage tight balance treatment result.
And S3, substituting the first-stage tight balance treatment result, the wind power predicted by 1h in the day and the system load data into the 1 h-day system tight balance treatment model for solving to obtain a second-stage tight balance treatment result.
And S4, establishing a real-time third-level time system tight balance treatment model by taking the minimum total adjustment cost of the power system as a target function. Specifically, the real-time third-level time system tight balance handling model constructed in the scheme is a real-time 15min system tight balance handling model.
And substituting the second-stage tight balance processing result, the wind power predicted in the real-time 15min stage and the system load data into the real-time 15min system tight balance processing model to solve to obtain a third-stage power system tight balance processing strategy. And (3) tightly balancing a disposal strategy of the power system in the third stage as a final disposal method.
In the above steps S1 to S4, the 4h system tight balance treatment model, the intra-day 1h system tight balance treatment model, and the real-time 15min system tight balance treatment model all satisfy the corresponding constraint conditions.
Specifically, in step S2 of the present scheme, the intra-day 4h system tight balance treatment model is as follows:
the intra-day 4h tight balance processing method based on the intra-day 4h system tight balance processing model is characterized in that the time scale is 4h, the time resolution is 15min, the wind power and system load data predicted by the intra-day 4h are substituted into the intra-day 4h system tight balance processing model for solving, a first-stage tight balance processing result is obtained, and the thermal power unit startup and shutdown state and the unit output, the electric vehicle charging station charging and discharging state, the charging and discharging power, the standby and flexible climbing plan in each period of 4h in the future are corrected on line according to the first-stage tight balance processing result.
The objective function of the 4h tight balance treatment model in the day comprises the total operation cost of the thermal power generating unit, including the operation and starting cost of the thermal power generating unit, the conventional auxiliary service cost and the flexible climbing standby cost; the total operation cost of the electric vehicle charging station comprises the discharge cost of the electric vehicle charging station, the conventional auxiliary service cost and the flexible climbing standby cost; the system abandons the punishment cost of wind and load shedding.
Specifically, the formula is shown as follows:
Figure BDA0003887202070000101
Figure BDA0003887202070000102
MPC i,t (p i,t )＝a i p i,t 2 +b i p i,t +c i (3)
Figure BDA0003887202070000103
Figure BDA0003887202070000104
wherein:
Figure BDA0003887202070000105
represents the total operating cost of the 4h system in the day; c 1 Expressing the total operation cost of the thermal power generating unit, including the operation cost of the thermal power generating unit, the conventional auxiliary service and the flexible climbing standby cost, C 2 Representing the total operation cost of the electric vehicle charging station, including the discharge cost of the electric vehicle charging station, the conventional auxiliary service and the flexible climbing standby cost, C 3 Indicating wind curtailment and load shedding of the systemPenalty cost of the load; n is a radical of T Represents the total time period number of 4h tight balance treatment, N I Representing the total number of the thermal power generating units; MPC i，t The operation cost of the thermal power generating unit i in the t period is represented; p is a radical of i,t Representing the active power of the thermal power generating unit i in a t period; u. of i,t The variable is 0-1 and represents the starting and stopping states of the thermal power generating unit i in the time period t; s i,t Representing the starting cost of the thermal power generating unit i in a t period;
Figure BDA0003887202070000106
representing the cost coefficients of the rotating standby and the non-rotating standby of the thermal power generating unit i in the time period t;
Figure BDA0003887202070000107
representing the rotating standby capacity and the non-rotating standby capacity of the thermal power generating unit i in a time period t;
Figure BDA0003887202070000108
representing the frequency modulation standby and replacement standby cost coefficients of the thermal power generating unit i in a time period t;
Figure BDA0003887202070000109
representing the upward and downward frequency modulation reserve capacity of the thermal power generating unit i in a time period t;
Figure BDA00038872020700001010
representing the replacement spare capacity of the thermal power generating unit i in the t period;
Figure BDA00038872020700001011
representing upward and downward flexible climbing cost coefficients of the thermal power generating unit i in a time period t;
Figure BDA00038872020700001012
representing the upward and downward flexible climbing capacity of the thermal power generating unit i in the t period; a is a i 、b i 、c i Representing a consumption characteristic coefficient of the thermal power generating unit i; n is a radical of PL Representing the total number of the electric vehicle charging stations;
Figure BDA0003887202070000111
representing the discharge cost coefficient of the charging station pl in the time period t;
Figure BDA0003887202070000112
represents the power of the charging station pl from the charging station to the grid during the time period t;
Figure BDA0003887202070000113
a cost coefficient representing the rotating standby and non-rotating standby of the charging station pl in the time period t;
Figure BDA0003887202070000114
Figure BDA0003887202070000115
indicating the spinning reserve, non-spinning reserve capacity of the charging station pl at time t;
Figure BDA0003887202070000116
indicating frequency modulation standby and replacement standby cost coefficients of the charging station pl at a time t;
Figure BDA0003887202070000117
represents the up and down modulation reserve capacity of the charging station pl during the time period t;
Figure BDA0003887202070000118
represents the replacement reserve capacity of the charging station pl during time t;
Figure BDA0003887202070000119
representing the upward and downward flexible climbing cost coefficients of the charging station pl in the time period t;
Figure BDA00038872020700001110
represents the upward and downward flexible climbing capacity of the charging station pl during the time period t; n is a radical of J 、N W Representing the total load and the total wind turbine generator;
Figure BDA00038872020700001111
representing the cost coefficient of wind curtailment and load shedding punishment of the system; LS (least squares) j,t Representing the load cutting generated by the load j in the period t;
Figure BDA00038872020700001112
representing the wind curtailment generated by the wind power w in the time period t.
The intra-day 4h system tight balance treatment constraints include: 1) And (3) operation constraint of the thermal power generating unit: up/down hill climbing capability constraints, maximum/minimum active power constraints; 2) Electric vehicle charging station restraint: the electric vehicle charging station uncertainty constraint and the electric vehicle charging station provide flexible climbing capacity constraint; 3) And (3) operation constraint of the wind turbine generator: maximum and minimum active power, wind abandon constraint and flexible climbing capacity constraint provided by a wind turbine generator set; 4) System flexibility requirement constraints; the system is restrained by upward and downward flexible climbing requirements; 5) A system power balance constraint; 6) And (5) power transmission line flow constraint.
Specifically, in step S2 of the present scheme, the intra-day 1h system tight balance treatment model is as follows:
the in-day 1h tight balance treatment method based on the in-day 1h system tight balance treatment model is characterized in that the time scale is 1h, the time resolution is 15min, the system tight balance treatment plan is made in a rolling mode once every 1h, the in-day 1h system tight balance treatment plan is substituted into the in-day 1h system tight balance treatment model for solving according to the on-off state of the thermal power generating unit corrected by 4h in the day, the wind power predicted by 1h in the day and the system load data, the second stage tight balance treatment result is obtained, and the thermal power generating unit output state, the charging and discharging power, the standby and flexible climbing plan in each period of 1h in the future, the standby and flexible climbing plan are corrected according to the second stage tight balance treatment result.
The objective function of the 1h in-day tight balance treatment model is consistent with the objective function of the 4h in-day system tight balance treatment model, but the starting cost of the thermal power generating unit is not considered. The objective function of the 1h tight balance treatment model in a day comprises the total operation cost of the thermal power generating unit, including the operation cost, the conventional auxiliary service cost and the flexible climbing standby cost of the thermal power generating unit; the total operation cost of the electric vehicle charging station comprises the discharge cost of the electric vehicle charging station, the conventional auxiliary service cost and the flexible climbing standby cost; the system abandons the punishment cost of wind and load shedding.
Figure BDA00038872020700001113
The constraint conditions of the 1h tight balance handling model in the day are the same as those of the 4h tight balance handling model in the day, and comprise thermal power unit constraint, electric vehicle charging station constraint, wind power unit operation constraint, system flexibility demand constraint, system power balance constraint and power transmission line flow constraint.
Specifically, in step S4 of the present solution, the real-time 15min system tight balance treatment model is as follows:
the real-time 15min tight balance treatment method based on the real-time 15min system tight balance treatment model is characterized in that the time scale is 15min, the time resolution is 15min, and a system tight balance treatment plan is made in a rolling mode every 15 min. And substituting the thermal power unit on-off state established according to 4h in the day, the wind power predicted at 15min level and the system load data into a real-time 15min tight balance handling model for solving to obtain a third-stage power system tight balance handling strategy, and performing online correction on the thermal power unit output, the charging and discharging state and the charging and discharging power of the electric vehicle charging station, and the standby and flexible climbing plan in the next time period (15 min in the future) according to the third-stage power system tight balance handling strategy.
The target function of the real-time 15min tight balance treatment model is the minimum total adjustment cost of the 1h tight balance treatment result in a day, and comprises the total operation adjustment cost of a real-time 15min thermal power generating unit, the standby adjustment cost of conventional auxiliary services, the standby adjustment cost of flexible climbing, the total operation adjustment cost of a real-time 15min electric vehicle charging station and the punishment cost of wind abandoning and load shedding of a real-time 15min system.
Figure BDA0003887202070000121
Figure BDA0003887202070000122
Figure BDA0003887202070000123
Figure BDA0003887202070000124
Wherein: c RT Represents the total adjustment cost of 15min in real time;
Figure BDA0003887202070000125
representing the total operation adjustment cost of the thermal power generating unit of 15min in real time,
Figure BDA0003887202070000126
representing the total number of time segments of the real-time 15min tight balance treatment, N I Representing the total number of the thermal power generating units;
Figure BDA0003887202070000127
representing the total operation adjustment cost of the real-time 15min electric vehicle charging station; n is a radical of PL Representing the total number of the electric vehicle charging stations;
Figure BDA0003887202070000128
the penalty cost of wind abandon and load shedding of a real-time 15min system is represented; n is a radical of J 、N W Respectively representing the total load and the total wind turbine generator; u. of i,t The variable is 0-1 and represents the starting and stopping states of the thermal power generating unit i in the time period t;
Figure BDA0003887202070000131
expressing the thermal power output adjustment cost coefficient;
Figure BDA0003887202070000132
representing a system wind curtailment penalty cost coefficient;
Figure BDA0003887202070000133
representing wind curtailment generated by wind power w in a time period t;
Figure BDA0003887202070000134
representing a system load shedding penalty cost coefficient; LS (least squares) j，t Representing the shear load generated by load j during time t. Delta (-) represents the adjustment quantity of the real-time 15min relative to the thermal power output of 1h in a day, the conventional auxiliary service standby, the flexible climbing standby and the electric vehicle charging station discharging; specifically, in the above formula, | Δ p i，t The | represents the adjustment amount of the thermal power output of 15min in real time relative to 1h in a day;
Figure BDA0003887202070000135
the adjustment quantity of the reserve capacity is expressed in a real-time manner, wherein the reserve capacity is rotated for 1 hour in a day for 15 min;
Figure BDA0003887202070000136
the adjustment quantity of the real-time 15min relative to the non-rotating spare capacity within 1h of a day is represented;
Figure BDA0003887202070000137
the adjustment quantity of the upward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA0003887202070000138
the adjustment quantity of the downward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001310
the adjustment quantity of the replacement spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA0003887202070000139
the adjustment quantity of the upward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001311
the adjustment quantity of the downward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001312
represents the amount of power adjustment of the charging station pl from the charging station to the grid during the time period t;
Figure BDA00038872020700001319
indicating the amount of rotation reserve capacity adjustment of the charging station pl during the time period t;
Figure BDA00038872020700001320
represents the amount of non-rotational reserve capacity adjustment of charging station pl over time t;
Figure BDA00038872020700001313
indicating the amount of upward frequency modulation reserve capacity adjustment of the charging station pl during the time period t;
Figure BDA00038872020700001316
indicating the adjustment amount of the downward frequency modulation reserve capacity of the charging station pl in the time period t;
Figure BDA00038872020700001315
represents the replacement reserve capacity adjustment for charging station pl at time t;
Figure BDA00038872020700001314
representing the upward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA00038872020700001317
represents the downward flexible climbing capacity adjustment amount of the charging station pl in the period t.
Figure BDA00038872020700001318
Expressing the adjustment cost coefficients of the conventional auxiliary service standby and the flexible climbing standby of the thermal power; specifically, in the above formula, the reaction mixture,
Figure BDA0003887202070000141
representing a rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000142
representing a non-rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000143
representing a frequency modulation standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA0003887202070000144
representing a replacement standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA0003887202070000146
representing an upward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure BDA0003887202070000145
and representing the downward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i in the time period t.
Figure BDA0003887202070000147
Indicating the discharge of the electric vehicle charging station, the standby of the conventional auxiliary service, the standby adjustment cost coefficient of the flexible climbing, specifically, in the above formula,
Figure BDA0003887202070000148
indicating the discharge adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700001411
a rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA0003887202070000149
a non-rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700001410
indicating the frequency modulation standby adjustment cost coefficient of the charging station pl at the time period t;
Figure BDA00038872020700001412
a replacement standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700001414
representing the upward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700001413
represents the flexible ramp-down adjustment cost coefficient of the charging station pl during the time period t.
The constraint conditions of the real-time 15min tight balance handling model are consistent with those of the 4h tight balance handling model in the day, and comprise thermal power unit constraint, electric vehicle charging station constraint, wind power unit operation constraint, system flexibility demand constraint, system power balance constraint and power transmission line flow constraint.
It should be noted that, in the process of solving the model, the wind power and the system load data are used as parameters of known quantities in the model and are solved in cooperation with other known quantities to obtain a tightly balanced handling result/strategy.
In order to more clearly illustrate the embodiments of the present application, the present application also provides specific analysis cases.
The simulation and analysis of the embodiment are carried out based on an IEEE 118 node system, the system is provided with 54 thermal power units, 186 branches and 91 load nodes, one wind power unit is connected to a node 43, two electric vehicle charging stations with 135000 charging potentials are located at nodes 58 and 72, and penalty costs of wind curtailment and load shedding are 40$/MWh and 200$/MWh respectively. The total number of electric vehicles in the electric vehicle charging station, the state of energy (SOE) of the charging station, and the capacity of available electric vehicles in the charging station were modeled for each charging station based on the uncertainty of the behavior of the electric vehicle owner, and other parameters of the electric vehicle charging station are shown in table 1.
TABLE 1
Figure BDA0003887202070000151
The following 4 cases are set in the scheme to analyze the tight balance treatment result, which is shown in table 2.
TABLE 2
Figure BDA0003887202070000152
In different cases, the results of the total operation cost, the total air abandonment quantity, the total load shedding, the thermal power climbing and the thermal power climbing saving of the system in tight balance are shown in table 3.
TABLE 3
Figure BDA0003887202070000153
From the perspective analysis of the model with different time scales, it can be seen from table 3, comparing the treatment results of 4h and multiple time scales in the day and tightly balancing. Under the condition that the electric automobile charging station is not connected, compared with 4h in the day (compared with case 1 in case 3), the total operation cost of the system is reduced by 40.72%, the total air abandonment amount is reduced by 99.65%, and the total load shedding is reduced by 61.21%. After the electric vehicle charging station is accessed, compared with 4h in the day (compared with case 2 in case 4), the total operation cost of the system is reduced by 36.66%, the total air abandonment amount is reduced by 100%, and the total load shedding is reduced by 80.07%. Compared with the multi-time scale tight balance treatment and the intra-day 4h, the total operation cost, the total cutting load and the total abandoned wind of the system are reduced to different degrees.
Fig. 2 and fig. 3 are combined to analyze, and fig. 2 shows thermal power output adjustment amount for 4h single time scale tight balance treatment in a day and thermal power output adjustment amount for multi-time scale tight balance treatment, where total output adjustment amounts are 1421.06MW and 465.96MW, respectively, and thermal power output adjustment amount for multi-time scale tight balance treatment is significantly smaller. Fig. 3 shows the load shedding amount of each time interval of 4h single time scale and multi-time scale tight balance treatment in a day, and the load shedding amount of each time interval of the multi-time scale tight balance treatment is less.
In order to study the influence of the scale of the electric vehicle on the handling result of the system tight balance, this section sets 10 scenes of 20%, 40%, 60%, 80%, 100%, 120%, 140%, 160%, 180%, 200% and the like of the original scale on the basis that the number of the original electric vehicles is 270000, and compares the total operating cost of the system, the saving of the thermal power flexible climbing slope and the change of the total load shedding under the 10 scenes, as shown in fig. 4 to 5. 4-5, as the scale of the electric vehicle is continuously increased, the total operating cost and the total load shedding of the system are continuously reduced, and the flexible thermal power saving climbing of the electric vehicle charging station is in an ascending trend.
In order to research the influence of different wind power outputs on the system tight balance treatment result, the scheme sets the following 3 wind power output typical scenes, and the specific output is shown in fig. 6.
Typical scenario 1: the wind power permeability is 10.75%, and the output fluctuation is low;
typical scenario 2: the wind power permeability is 21.51%, and the output fluctuation is strong;
typical scenario 3: the wind power permeability is 40.66%, and the output fluctuation is strong.
Under different wind power output typical scenes, the results of the total operation cost, the total air abandonment quantity, the total load shedding, the thermal power climbing and the thermal power climbing saving of the system in tight balance are shown in a table 4.
TABLE 4
Figure BDA0003887202070000161
As can be seen from table 4, in the case where no electric vehicle charging station is connected, the total operating cost and the total load shedding of the typical scenario 1 are the highest; typical scenario 2 total operating cost and total load shedding are at an intermediate level; the total operating cost and the total load cut of the typical scenario 3 are lowest. After the electric vehicle charging station is accessed, the total operation cost, the total air abandonment quantity, the total load shedding and the thermal power climbing quantity of each scene are all reduced to different degrees. Under the condition of different wind power permeabilities, the system flexibility of the electric vehicle charging station can be improved, and the safe and economic operation level of the system is improved.
The following conclusions were obtained by example analysis:
with the continuous reduction of the time scale, the prediction precision of the wind power and the system load is continuously improved, and compared with the single time scale tight balance disposal, the power system tight balance multi-time scale disposal method provided by the scheme can improve the system tight balance disposal level.
Example 2
As shown in fig. 7, based on the same inventive concept as the above embodiment, this embodiment 2 provides a power system tight-balanced multi-time scale treatment apparatus, including:
the data acquisition module is used for respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in the day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than that of the real-time third-level time.
The first solving module is used for substituting the wind power and the system load data predicted by the first-level time period in the day into a pre-established system tight balance disposal model of the first-level time period in the day for solving to obtain a first-stage tight balance disposal result;
and the second solving module is used for substituting the first-stage tight balance treatment result, the wind power predicted in the second-stage time period in the day and the system load data into a pre-established second-stage time period system tight balance treatment model in the day to carry out solving so as to obtain a second-stage tight balance treatment result.
And the third solving module is used for substituting the second-stage tight balance processing result, the wind power predicted in real-time third-stage time and the system load data into a pre-established real-time third-stage time system tight balance processing model for solving to obtain a third-stage power system tight balance processing strategy.
In the third solving module:
the third level time system tight balance treatment model is established in the following mode: and establishing a real-time third-level time system tight balance handling model by taking the minimum total adjustment cost of the power system as a target function, wherein the target function of the real-time third-level time system tight balance handling model comprises the total operation adjustment cost of the thermal power unit, the total operation adjustment cost of the electric vehicle charging station and the punishment cost of the system wind curtailment and load shedding.
The total operation adjustment cost of the thermal power generating unit is as follows:
Figure BDA0003887202070000171
the total operation adjustment cost of the electric vehicle charging station is expressed as follows:
Figure BDA0003887202070000172
the penalty cost of the system for wind curtailment and load shedding is expressed as follows:
Figure BDA0003887202070000181
wherein:
Figure BDA0003887202070000182
representing the total adjustment cost, N, of the thermal power generating unit in real-time third-level time I Representing the total number of the thermal power generating units;
Figure BDA0003887202070000183
representing a thermal power output adjustment cost coefficient; u. u i,t The variable is 0-1 and represents the starting and stopping states of the thermal power generating unit i in the time period t; | Δ p i，t I represents the adjustment quantity of the real-time third-level time relative to the thermal power output in the second-level time period in the day;
Figure BDA0003887202070000185
representing a rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000186
the adjustment quantity of the rotating reserve capacity of the real-time third level time relative to the second level time period in the day is represented;
Figure BDA0003887202070000187
representing a non-rotation standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure BDA0003887202070000188
representing an adjustment amount of real-time third level time relative to a non-rotating reserve capacity of a second level time period within a day;
Figure BDA0003887202070000189
representing a frequency modulation standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA00038872020700001810
the adjustment quantity of the upward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001811
the adjustment quantity of the downward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001812
representing a replacement standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure BDA00038872020700001813
the adjustment quantity of the replacement spare capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001816
representing an upward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure BDA00038872020700001815
the adjustment quantity of the upward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA00038872020700001814
representing a downward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure BDA00038872020700001817
the adjustment quantity of the downward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure BDA0003887202070000184
representing the total operation adjustment cost of the electric vehicle charging station at the real-time third level time; n is a radical of hydrogen PL Representing the total number of electric vehicle charging stations;
Figure BDA00038872020700001818
indicating the discharge adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700001820
represents the amount of power adjustment of the charging station pl from the charging station to the grid during the time period t;
Figure BDA00038872020700001819
a rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA0003887202070000193
indicating the amount of rotation reserve capacity adjustment of the charging station pl during the time period t;
Figure BDA0003887202070000194
a non-rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA0003887202070000195
represents the amount of adjustment of the non-rotating reserve capacity of the charging station pl during the time period t;
Figure BDA0003887202070000197
representing the frequency modulation standby adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA0003887202070000196
up-modulated reserve capacity modulation representing charging station pl at time tThe whole amount is calculated;
Figure BDA0003887202070000198
indicating the adjustment amount of the downward frequency modulation reserve capacity of the charging station pl in the time period t;
Figure BDA0003887202070000199
a replacement standby adjustment cost coefficient representing the charging station pl at time t;
Figure BDA00038872020700001910
represents the replacement reserve capacity adjustment amount of the charging station pl at the time period t;
Figure BDA00038872020700001913
representing the upward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700001912
representing the upward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA00038872020700001911
representing the downward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure BDA00038872020700001914
representing the downward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure BDA0003887202070000191
the penalty cost of the system wind abandon and load shedding at the real-time third level time is represented;
Figure BDA0003887202070000192
representing the total time period number of the real-time third level time tight balance treatment; n is a radical of J 、N W Respectively representing the total load and the total wind turbine generator;
Figure BDA00038872020700001915
representing a system wind curtailment penalty cost coefficient;
Figure BDA00038872020700001917
representing the abandoned wind generated by the wind power w in the time period t;
Figure BDA00038872020700001916
representing a system load shedding penalty cost coefficient; LS (least squares) j，t Representing the shear load generated by load j during time t.
Example 3
As shown in fig. 8, based on the same inventive concept as the above-mentioned embodiment, this embodiment 3 further provides an electronic device 100 for implementing a power system tight-balance multi-time scale handling method; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 may be used to store a computer program 103, and the processor 102 implements the power system tight balancing multi-timescale handling method steps of embodiment 1 by running or executing the computer program stored in the memory 101 and invoking data stored in the memory 101.
The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data) created according to the use of the electronic apparatus 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one Processor 102 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, and the processor 102 is the control center of the electronic device 100 and connects the various parts of the electronic device 100 with various interfaces and lines.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement a power system tight balance multi-timescale handling method, and the processor 102 may execute the plurality of instructions to implement:
respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in a day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than the real-time third-level time;
substituting the wind power and system load data predicted by the first-stage time period in the day into a pre-established first-stage time period system tight balance treatment model for solving to obtain a first-stage tight balance treatment result;
substituting the first-stage tight balance treatment result, the predicted wind power in the second-stage time period in the day and the system load data into a pre-established system tight balance treatment model in the second-stage time period in the day for solving to obtain a second-stage tight balance treatment result;
substituting the second-stage tight balance processing result, the wind power predicted by the real-time third-stage time and the system load data into a pre-established real-time third-stage time system tight balance processing model for solving to obtain a third-stage power system tight balance processing strategy;
and the system tight balance treatment model of the first level time period in the day, the system tight balance treatment model of the second level time period in the day and the system tight balance treatment model of the real-time third level time all meet corresponding constraint conditions.
Example 4
The integrated modules/units of the electronic device 100 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and used for instructing relevant hardware, and when the computer program is executed by a processor, the steps of the above-described embodiments of the method may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, and Read-Only Memory (ROM).
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A power system tight balance multi-time scale disposal method is characterized by comprising the following steps:
respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in a day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than real-time third-level time;
substituting the wind power and the system load data predicted by the first-stage time period in the day into a pre-established first-stage time period system tight balance treatment model for solving to obtain a first-stage tight balance treatment result;
substituting the first-stage tight balance treatment result, the predicted wind power in the second-stage time period in the day and the system load data into a pre-established system tight balance treatment model in the second-stage time period in the day for solving to obtain a second-stage tight balance treatment result;
substituting the second-stage tight balance processing result, the wind power predicted by the real-time third-stage time and the system load data into a pre-established real-time third-stage time system tight balance processing model for solving to obtain a third-stage power system tight balance processing strategy;
and the system tight balance treatment model of the first level time period in the day, the system tight balance treatment model of the second level time period in the day and the system tight balance treatment model of the real-time third level time all meet corresponding constraint conditions.
2. The power system tight balance multi-time scale disposal method according to claim 1, wherein in the step of solving by substituting the wind power and system load data predicted by the intra-day first-level time period into a pre-established intra-day first-level time period system tight balance disposal model, the intra-day first-level time period system tight balance disposal model is established in a manner that: establishing a system tight balance disposal model of a first-level time period in a day by taking the minimum total operation cost of a power system as an objective function, wherein the objective function of the system tight balance disposal model of the first-level time period in the day comprises the total operation cost of a thermal power generating unit, the total operation cost of an electric vehicle charging station and the punishment cost of the wind curtailment and load shedding of the system; the total operation cost of the thermal power generating unit comprises the operation cost and the starting cost of the thermal power generating unit, and the conventional auxiliary service and flexible climbing standby cost.
3. The power system tight balance multi-time scale disposal method according to claim 1, wherein in the step of substituting the first-stage tight balance disposal result, the predicted wind power in the second-stage time period in the day, and the system load data into a pre-established second-stage time period system tight balance disposal model for solving, the second-stage time period system tight balance disposal model in the day is established in a manner that: establishing a system tight balance handling model of a second-level time period in a day by taking the minimum total operation cost of the power system as a target function, wherein the target function of the system tight balance handling model of the second-level time period in the day comprises the total operation cost of a thermal power unit, the total operation cost of an electric vehicle charging station and the punishment cost of the wind curtailment and load shedding of the system; the total operation cost of the thermal power generating unit comprises the operation cost of the thermal power generating unit, conventional auxiliary service and flexible climbing standby cost.
4. The power system tight balance multi-time scale disposal method according to claim 1, wherein in the step of solving by substituting the second stage tight balance disposal result, the wind power predicted in real-time third level time, and the system load data into a pre-established real-time third level time system tight balance disposal model, the third level time system tight balance disposal model is established in a manner that: and establishing a real-time third-level time system tight balance handling model by taking the minimum total adjustment cost of the power system as a target function, wherein the target function of the real-time third-level time system tight balance handling model comprises the total operation adjustment cost of the thermal power unit, the total operation adjustment cost of the electric vehicle charging station and the punishment cost of the system wind curtailment and load shedding.
5. The power system tight-balanced multi-timescale handling method of claim 4,
the total operation adjustment cost of the thermal power generating unit is as follows:
Figure FDA0003887202060000021
the total operation adjustment cost of the electric vehicle charging station is expressed as follows:
Figure FDA0003887202060000022
the penalty cost of the system for wind curtailment and load shedding is expressed as follows:
Figure FDA0003887202060000023
wherein:
Figure FDA0003887202060000024
representing the total adjustment cost, N, of the thermal power generating unit in real time at the third level I Representing the total number of the thermal power generating units;
Figure FDA0003887202060000025
representing a thermal power output adjustment cost coefficient; u. of i,t The variable is 0-1 and represents the starting and stopping states of the thermal power generating unit i in the time period t; i Δ p i，t I represents the adjustment quantity of the real-time third-level time relative to the thermal power output in the second-level time period in the day;
Figure FDA0003887202060000026
representing a rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure FDA0003887202060000027
the adjustment quantity of the rotating reserve capacity of the real-time third level time relative to the second level time period in the day is represented;
Figure FDA0003887202060000028
representing a non-rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure FDA0003887202060000029
representing real-time thirdThe adjustment amount of the level time relative to the non-rotating reserve capacity of the second level time period in the day;
Figure FDA0003887202060000031
representing a frequency modulation standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure FDA0003887202060000032
the adjustment quantity of the upward frequency modulation standby capacity of the thermal power generating unit i in the t period is represented;
Figure FDA0003887202060000033
the adjustment quantity of the downward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure FDA0003887202060000034
representing a replacement standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure FDA0003887202060000035
the adjustment quantity of the replacement spare capacity of the thermal power generating unit i in the t period is represented;
Figure FDA0003887202060000036
representing the spare adjustment cost coefficient of the thermal power generating unit i in the upward flexible climbing at the time period t;
Figure FDA0003887202060000037
the adjustment quantity of the upward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure FDA0003887202060000038
representing a downward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure FDA0003887202060000039
the adjustment quantity of the downward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure FDA00038872020600000310
representing the total operation adjustment cost of the electric vehicle charging station at the real-time third-level time; n is a radical of hydrogen PL Representing the total number of electric vehicle charging stations;
Figure FDA00038872020600000311
indicating the discharge adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000312
representing the amount of power adjustment of the charging station pl from the charging station to the grid during the time period t;
Figure FDA00038872020600000313
a rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure FDA00038872020600000314
indicating the amount of rotation reserve capacity adjustment of the charging station pl during the time period t;
Figure FDA00038872020600000315
a non-rotating standby adjustment cost coefficient representing the charging station pl at time t;
Figure FDA00038872020600000316
represents the amount of adjustment of the non-rotating reserve capacity of the charging station pl during the time period t;
Figure FDA00038872020600000317
representing the frequency modulation standby adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000318
indicating the amount of upward frequency modulation reserve capacity adjustment of the charging station pl during the time period t;
Figure FDA00038872020600000319
indicating the adjustment amount of the downward frequency modulation reserve capacity of the charging station pl in the time period t;
Figure FDA00038872020600000320
a replacement standby adjustment cost coefficient representing the charging station pl at time t;
Figure FDA00038872020600000321
represents the replacement reserve capacity adjustment amount of the charging station pl at the time period t;
Figure FDA00038872020600000322
representing the upward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000323
representing the upward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure FDA00038872020600000324
representing the downward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000325
representing the downward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure FDA0003887202060000041
the penalty cost of the system wind abandon and load shedding at the real-time third level time is represented;
Figure FDA0003887202060000042
representing the total time period number of the real-time third level time tight balance treatment; n is a radical of hydrogen J 、N W Respectively representing the total load and the total wind turbine generator;
Figure FDA0003887202060000043
representing a system wind curtailment penalty cost coefficient;
Figure FDA0003887202060000044
representing wind curtailment generated by wind power w in a time period t;
Figure FDA0003887202060000045
representing a system load shedding punishment cost coefficient; LS (least squares) j，t Representing the shear load generated by load j during time t.
6. A power system tight-balanced multi-time scale handling device, comprising:
the data acquisition module is used for respectively acquiring wind power and system load data predicted by a first level time period in a day, a second level time period in the day and a real-time third level time; the time length of the first-level time period in a day is greater than that of the second-level time period in the day, and the time length of the second-level time period in the day is greater than real-time third-level time;
the first solving module is used for substituting the wind power and the system load data predicted by the first-level time period in the day into a pre-established system tight balance disposal model of the first-level time period in the day for solving to obtain a first-stage tight balance disposal result;
the second solving module is used for substituting the first-stage tight balance treatment result, the predicted wind power in the second-stage time period in the day and the system load data into a pre-established system tight balance treatment model in the second-stage time period in the day to solve to obtain a second-stage tight balance treatment result;
and the third solving module is used for substituting the second-stage tight balance processing result, the wind power predicted in real-time third-stage time and the system load data into a pre-established real-time third-stage time system tight balance processing model for solving to obtain a third-stage power system tight balance processing strategy.
7. The power system tight balance multi-time scale handling device of claim 6, wherein in the third solving module:
the third-level time system tight balance treatment model is established in the following mode: and establishing a real-time third-level time system tight balance disposal model by taking the minimum total adjustment cost of the power system as a target function, wherein the target function of the real-time third-level time system tight balance disposal model comprises the total operation adjustment cost of the thermal power generating unit, the total operation adjustment cost of the electric vehicle charging station and the punishment cost of the system wind curtailment and load shedding.
8. The power system tight balance multi-time scale handling device of claim 7, wherein in a third solving module:
the total operation adjustment cost of the thermal power generating unit is as follows:
Figure FDA0003887202060000051
the total operation adjustment cost of the electric vehicle charging station is expressed as follows:
Figure FDA0003887202060000052
the penalty cost of the system for wind curtailment and load shedding is expressed as follows:
Figure FDA0003887202060000053
wherein:
Figure FDA0003887202060000054
representing the total adjustment cost, N, of the thermal power generating unit in real-time third-level time I Representing the total number of the thermal power generating units;
Figure FDA0003887202060000055
representing a thermal power output adjustment cost coefficient; u. of i,t The variable is 0-1, and represents the starting and stopping states of the thermal power generating unit i in the time period t; | Δ p i，t I represents the adjustment quantity of the real-time third-level time relative to the thermal power output in the second-level time period in the day;
Figure FDA0003887202060000056
representing a rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure FDA0003887202060000057
the adjustment quantity of the rotating reserve capacity of the real-time third level time relative to the second level time period in the day is represented;
Figure FDA0003887202060000058
representing a non-rotating standby adjustment cost coefficient of the thermal power generating unit i in a t period;
Figure FDA0003887202060000059
the adjustment quantity of the real-time third level time relative to the non-rotating reserve capacity of the second level time period in the day is represented;
Figure FDA00038872020600000510
representing a frequency modulation standby adjustment cost coefficient of the thermal power generating unit i in a time period t;
Figure FDA00038872020600000511
the adjustment quantity of the upward frequency modulation standby capacity of the thermal power generating unit i in the t period is represented;
Figure FDA00038872020600000512
the adjustment quantity of the downward frequency modulation spare capacity of the thermal power generating unit i in the t period is represented;
Figure FDA00038872020600000513
indicating a replacement backup of a thermal power generating unit i during a time period tAdjusting the cost coefficient;
Figure FDA00038872020600000514
the adjustment quantity of the replacement spare capacity of the thermal power generating unit i in the t period is represented;
Figure FDA00038872020600000515
representing an upward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure FDA0003887202060000061
the adjustment quantity of the upward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure FDA0003887202060000062
representing a downward flexible climbing standby adjustment cost coefficient of the thermal power generating unit i at a time t;
Figure FDA0003887202060000063
the adjustment quantity of the downward flexible climbing capacity of the thermal power generating unit i in the t period is represented;
Figure FDA0003887202060000064
representing the total operation adjustment cost of the electric vehicle charging station at the real-time third-level time; n is a radical of PL Representing the total number of electric vehicle charging stations;
Figure FDA0003887202060000065
indicating the discharge adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA0003887202060000066
represents the amount of power adjustment of the charging station pl from the charging station to the grid during the time period t;
Figure FDA0003887202060000067
rotating standby adjustment cost coefficient representing charging station pl at time t；
Figure FDA0003887202060000068
A rotation reserve capacity adjustment amount indicating a time period t of the charging station pl;
Figure FDA0003887202060000069
a non-rotating standby adjustment cost coefficient representing a time period t for charging station pl;
Figure FDA00038872020600000610
represents the amount of adjustment of the non-rotating reserve capacity of the charging station pl during the time period t;
Figure FDA00038872020600000611
representing the frequency modulation standby adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000612
indicating an amount of up-modulation reserve capacity adjustment for charging station pl over a time period t;
Figure FDA00038872020600000613
indicating the adjustment amount of the downward frequency modulation reserve capacity of the charging station pl in the time period t;
Figure FDA00038872020600000614
a replacement backup adjustment cost coefficient representing the charging station pl at time t;
Figure FDA00038872020600000615
represents the replacement reserve capacity adjustment amount of the charging station pl at the time period t;
Figure FDA00038872020600000616
representing the upward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000617
representing the upward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure FDA00038872020600000618
representing the downward flexible climbing adjustment cost coefficient of the charging station pl in the time period t;
Figure FDA00038872020600000619
representing the downward flexible climbing capacity adjustment amount of the charging station pl in the time period t;
Figure FDA00038872020600000620
the penalty cost of the system wind abandon and load shedding at the real-time third level time is represented;
Figure FDA00038872020600000621
representing the total time period number of the real-time third level time tight balance treatment; n is a radical of J 、N W Respectively representing the total load and the total wind turbine generator;
Figure FDA00038872020600000622
representing a system wind curtailment penalty cost coefficient;
Figure FDA00038872020600000623
representing the abandoned wind generated by the wind power w in the time period t;
Figure FDA00038872020600000624
representing a system load shedding penalty cost coefficient; LS (least squares) j，t Representing the shear load generated by load j during time t.
9. An electronic device comprising a processor and a memory, the processor configured to execute a computer program stored in the memory to implement the power system tight balance multi-timescale handling method of any of claims 1 to 7.
10. A computer-readable storage medium storing at least one instruction which, when executed by a processor, implements a power system tight-balancing multi-timescale handling method as recited in any one of claims 1 to 7.
CN202211247309.1A 2022-10-12 2022-10-12 Power system tight balance multi-time scale disposal method, device, equipment and medium Pending CN115528747A (en)

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