AU2022211871A1 - Method and system for controlling joint power supply of various emergency power generation equipment - Google Patents

Method and system for controlling joint power supply of various emergency power generation equipment Download PDF

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AU2022211871A1
AU2022211871A1 AU2022211871A AU2022211871A AU2022211871A1 AU 2022211871 A1 AU2022211871 A1 AU 2022211871A1 AU 2022211871 A AU2022211871 A AU 2022211871A AU 2022211871 A AU2022211871 A AU 2022211871A AU 2022211871 A1 AU2022211871 A1 AU 2022211871A1
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power generation
equipment
power supply
generation equipment
emergency power
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AU2022211871A
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Minfu A
Jie Lian
Xiaoqian Wu
Wei Zhang
Ming Zhong
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Inner Mongolia Electric Power Research Institute of Inner Mongolia Power Group Co Ltd
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Inner Mongolia Electric Power Research Institute Branch Of Inner Mongolia Electric Power Group Co Lt
Inner Mongolia Electric Power Research Institute of Inner Mongolia Power Group Co Ltd
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Classifications

    • 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
    • H02J3/28—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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—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
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

Abstract

A method, and a system for controlling joint power supply of various emergency power generation equipment. The method includes: receiving a total amount of power supply demand in a current control period; predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period; determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment; optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment. 1/1 Receiving a total amount of power supply demand in a cunent onsol pa101 Predicting a power operation edunlin and a residual liftie mmain of each onewnw cy powgeneration S102 equipment ie urretcontnol period by tined, mehm Weaning models Determining two or mor of the emergency power gwe ion equipment as joint power spply equipment in S103 *t amentm ontrol period Optimiing power supply portion of t joint power S104 apply equipma Figure 1 Powersupply Integrted Powersupply Powersuppe demandreceiving - perforunance -- quiMen - proportion moduilp ito ne deternanadon mdkmd 201 202 203 204 Figure 2

Description

1/1
Receiving a total amount of power supply demand in a cunent onsol pa101
Predicting a power operation edunlin and aresidual liftie mmain of each onewnw cy powgeneration S102 equipment ie urretcontnol period bytined, mehm Weaning models
Determining two or mor of the emergency power gwe ion equipment as joint power spplyequipment in S103 *tamentm ontrol period
Optimiing power supply portionof t joint power S104 apply equipma
Figure 1
Powersupply Integrted Powersupply Powersuppe demandreceiving - perforunance -- quiMen - proportion moduilp ito ne deternanadon mdkmd
201 202 203 204
Figure 2
METHOD AND SYSTEM FOR CONTROLLING JOINT POWER SUPPLY OF VARIOUS EMERGENCY POWER GENERATION EQUIPMENT FIELD
[0001] The present disclosure generally relates to the field of emergency power supply technology, particularly to a method, and a system for controlling joint power supply of various emergency power generation equipment.
BACKGROUND
[0002] There are different types of distributed power sources in the emergency network. Such as the difference between the output characteristics of the emergency diesel generator car and the distributed inverter power source, and the difference of the control mode of the distributed inverter power source. The control mode can be divided into voltage source mode and current source mode distributed power supply. Generally, in order to realize the autonomous operation of emergency network, the real-time balance of active power and reactive power is needed to ensure the frequency requirement, voltage quality and system stability. The traditional hierarchical structure has clear control structure for load-bearing operation, but it is difficult to balance power regulation and system stability. For the group control of multi-emergency power supply, it is necessary to put forward an improved optimal control strategy for the stability control of the system.
[0003] In addition to the conventional optimal control of each emergency power supply, how to achieve the overall optimal performance of the emergency power supply has become an urgent technical problem to be solved.
SUMMARY
[0004] Exemplary embodiments of the present disclosure are to provide a method, and a system for controlling joint power supply of various emergency power generation equipment, which achieve the overall optimal performance of the various emergency power generation equipment.
[0005] According to an exemplary embodiment of the present disclosure, a method for controlling joint power supply of various emergency power generation equipment is provided. The method includes: receiving a total amount of power supply demand in a current control period; predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period; determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment; optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment.
[0006] According to another exemplary embodiment of the present disclosure, a system for controlling joint power supply of various emergency power generation equipment is provided. The system includes: a power supply demand receiving module configured to receive a total amount of power supply demand in a current control period; an integrated performance prediction module configured to predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period; a power supply equipment determination module configured to determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment; a power supply proportion optimization module configured to optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment.
[0007] According to another exemplary embodiment of the present disclosure, a computer readable storage medium is provided. The computer-readable storage medium having computer programs stored thereon, wherein the computer programs, when executed by a processor, implement the method for controlling joint power supply of various emergency power generation equipment.
[0008] According to another exemplary embodiment of the present disclosure, a computer equipment is provided. The computer equipment includes: a processor; a memory having computer programs stored thereon, wherein the computer programs, when executed by the processor, implement the method for controlling joint power supply of various emergency power generation equipment.
[0009] The method, and system for controlling joint power supply of various emergency power generation equipment according to exemplary embodiments of the present disclosure determine at least two of the emergency power generation equipment of the emergency power generation equipment as the joint power supply equipment in the current control cycle based on the residual life estimates of the emergency power generation equipment. And the preset optimization algorithm is used to optimize the power supply proportion of the combined power supply equipment to meet the total power supply demand. Thus, it can meet the total demand of power supply in the current control cycle on the basis of giving priority to the remaining life of each emergency power generation equipment, so as to achieve a stable joint power supply of each emergency power generation equipment. The life of a single emergency power generation equipment will not end earlier than other power generation equipment, so that all the emergency power generation equipment can be combined to achieve the overall optimal performance of the emergency power supply.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] To illustrate more clearly the technical scheme in the embodiment of the present disclosure or in the prior art, a brief description of the drawings required to be used in the embodiment or in the prior art description. The attached drawings described below are some embodiments of the present disclosure, and other attached drawings can be obtained for ordinary technical personnel in this field without creative labor.
[0011] Figure 1 shows a flowchart of a method for controlling joint power supply of various emergency power generation equipment according to an exemplary embodiment of the present disclosure;
[0012] Figure 2 shows a structural block diagram of a system for controlling joint power supply of various emergency power generation equipment according to an exemplary embodiment of the present disclosure.
DETAILED DESCRIPTION
[0013] Reference is made is detail to embodiments of the present disclosure. Examples of the embodiments are illustrated in the drawings, where same reference numerals represent same components. The embodiments are illustrated hereinafter with reference to the drawings to explain the present disclosure.
[0014] Figure 1 shows a flowchart of a method for controlling joint power supply of various emergency power generation equipment according to an exemplary embodiment of the present disclosure.
[0015] In step S101, receiving a total amount of power supply demand in a current control period. Specifically, the total amount of power supply demand can be determined according to the current demand of each load. The determination for the total amount of power supply demand is not the focus of this disclosure, and can refer to the existing technology.
[0016] Instep S102, predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period.
[0017] In step S103, determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment.
[0018] In step S104, optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment.
[0019] The method for controlling joint power supply of various emergency power generation equipment according to exemplary embodiments of the present disclosure determine at least two of the emergency power generation equipment of the emergency power generation equipment as the joint power supply equipment in the current control cycle based on the residual life estimates of the emergency power generation equipment. And the preset optimization algorithm is used to optimize the power supply proportion of the combined power supply equipment to meet the total power supply demand. Thus, it can meet the total demand of power supply in the current control cycle on the basis of giving priority to the remaining life of each emergency power generation equipment, so as to achieve a stable joint power supply of each emergency power generation equipment. The life of a single emergency power generation equipment will not end earlier than other power generation equipment, so that all the emergency power generation equipment can be combined to achieve the overall optimal performance of the emergency power supply.
[0020] The method for controlling joint power supply of various emergency power generation equipment includes at least one of the following multi-preferred embodiments.
[0021] The first preferred embodiment, the determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment step includes: calculating an average residual life of the various emergency power generation equipment; determining whether the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two; taking, the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment, as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two.
[00221 The second preferred embodiment, the various emergency power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel power generation vehicle, hydrogen energy equipment and fuel cell equipment.
[00231 The third preferred embodiment, the trained machine learning models are neural network models. Specifically, based on historical information about the environment of the emergency power generation equipment (such as wind resource data, wind speed, wind direction, weather changes, etc.) and operating conditions (such as rotational speed, torque, pitch angle, yaw angle, etc.), the trained neural network models can also be used to predict the generation estimation and residual life estimation of emergency power generation equipment in the current control cycle.
[0024] The preset optimization algorithm is a genetic algorithm. The structure of genetic algorithm is as follows. The genetic algorithm model is used to randomly generate multiple individuals as the initial population. Each individual is a multi-dimensional vector with the same number of dimensions as the number of emergency generators. For example, when the various types of emergency power generation equipment, including wind power equipment, photovoltaic equipment, energy storage equipment, emergency diesel electric vehicles, hydrogen energy equipment and fuel cell equipment, the dimension of each individual is 6. The individual is represented as a vector (G, G2, G3, G4, G5, G6), Gi, G2, G3, G4, G5, G6 in the vector are corresponding to the power supply proportion of wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel power generation vehicles, hydrogen energy equipment and fuel cell equipment.
[00251 The wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel power generation vehicles, hydrogen energy equipment and fuel cell equipment, the current control cycle of power generation estimates are denoted as F1 , F2, F3, F4, F5, F6, a total amount of power supply demand in a current control period is denoted as Q, and then the fitness of each individual can be calculated by
1/((F1G1+F2G2+F3G3+F4 G4+F5 G5+F G)- Q).
[00261 The genetic algorithm model is utilized to continuously optimize the calculation until, when the genetic algorithm model converges, the individuals with the maximum fitness are used as the power supply proportion of wind power equipment, photovoltaic equipment, energy storage equipment, emergency diesel engine, hydrogen energy equipment and fuel cell equipment.
[0027] The fourth preferred embodiment, after the step of calculating an average residual life of the various emergency power generation equipment, the method for controlling joint power supply of various emergency power generation equipment includes: taking an emergency power generation equipment with the largest residual life estimation as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation is greater than the average residual life is less than two.
[0028] The fifth preferred embodiment, the taking, the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment, as the joint power supply equipment in the current control period step includes: ranking the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment from the largest to smallest power generation estimation of the various emergency power generation equipment in the current control period; selecting the first N emergency power generation equipment basing on the ranking step as a first priority joint power supply equipment in the current control period, N is a preset value.
[0029] The optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm step includes: and optimizing power supply proportion of the first priority joint power supply equipment by using the preset optimization algorithm.
[0030] Figure 2 shows a structural block diagram of a system for controlling joint power supply of various emergency power generation equipment according to an exemplary embodiment of the present disclosure. As shown in figure 2, the system for controlling joint power supply of various emergency power generation equipment according to an exemplary embodiment of the present disclosure includes: a power supply demand receiving module 201, an integrated performance prediction module202, a power supply equipment determination module 203 and a power supply proportion optimization module 204.
[0031] The power supply demand receiving module 201 is configured to receive a total amount of power supply demand in a current control period. The integrated performance prediction module 202 configured to predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period. The power supply equipment determination module 203 configured to determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment. The power supply proportion optimization module 204 configured to optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment.
[0032] Specifically, the power supply equipment determination module 203 includes: an average life calculation unit configured to calculate an average residual life of the various emergency power generation equipment; an equipment number determination unit configured to determine whether the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two;
a power supply equipment determination unit configured to take, the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment, as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two. Specifically, the various emergency power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel power generation vehicle, hydrogen energy equipment and fuel cell equipment. The trained machine learning models are neural network models, and the preset optimization algorithm is a genetic algorithm.
[0033] Specifically, the system further includes: an emergency power generation control unit to take an emergency power generation equipment with the largest residual life estimation as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation is greater than the average residual life is less than two. The power supply equipment determination module is further configured to rank the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment from the largest to smallest power generation estimation of the various emergency power generation equipment in the current control period. And then the power supply equipment determination module is further configured to select the first N emergency power generation equipment basing on the ranking step as a first priority joint power supply equipment in the current control period, N is a preset value. The power supply proportion optimization module is further configured to optimize power supply proportion of the first priority joint power supply equipment by using the preset optimization algorithm.
[0034] The system for controlling joint power supply of various emergency power generation equipment according to exemplary embodiments of the present disclosure determine at least two of the emergency power generation equipment of the emergency power generation equipment as the joint power supply equipment in the current control cycle based on the residual life estimates of the emergency power generation equipment. And the preset optimization algorithm is used to optimize the power supply proportion of the combined power supply equipment to meet the total power supply demand. Thus, it can meet the total demand of power supply in the current control cycle on the basis of giving priority to the remaining life of each emergency power generation equipment, so as to achieve a stable joint power supply of each emergency power generation equipment. The life of a single emergency power generation equipment will not end earlier than other power generation equipment, so that all the emergency power generation equipment can be combined to achieve the overall optimal performance of the emergency power supply.
[0035] According to another exemplary embodiment of the present disclosure, a computer readable storage medium is provided. The computer-readable storage medium having computer programs stored thereon, wherein the computer programs, when executed by a processor, implement the method for controlling joint power supply of various emergency power generation equipment.
[0036] According to another exemplary embodiment of the present disclosure, a computer equipment is provided. The computer equipment includes: a processor; a memory having computer programs stored thereon, wherein the computer programs, when executed by the processor, implement the method for controlling joint power supply of various emergency power generation equipment.
[0037] Although the present disclosure has been illustrated and described with reference to some exemplary embodiments thereof, it should be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the spirit and scope of the present disclosure as defined in the appended claims and equivalents thereof.

Claims (10)

1. A method for controlling joint power supply of various emergency power generation equipment, wherein the method comprises:
receiving a total amount of power supply demand in a current control period;
predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period;
determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment;
optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment.
2. The method according to claim 1, wherein the determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment step comprises:
calculating an average residual life of the various emergency power generation equipment;
determining whether the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two;
taking, the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment, as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two.
3. The method according to claim 2, wherein the various emergency power generation equipment comprises wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel power generation vehicle, hydrogen energy equipment and fuel cell equipment.
4. The method according to claim 3, wherein the trained machine learning models are neural network models, and the preset optimization algorithm is a genetic algorithm;
the taking, the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment, as the joint power supply equipment in the current control period step comprises:
ranking the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment from the largest to smallest power generation estimation of the various emergency power generation equipment in the current control period;
selecting the first N emergency power generation equipment basing on the ranking step as a first priority joint power supply equipment in the current control period, N is a preset value;
the optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm step comprises:
optimizing power supply proportion of the first priority joint power supply equipment by using the preset optimization algorithm.
5. The method according to claim 1, wherein after the step of calculating an average residual life of the various emergency power generation equipment comprises:
taking an emergency power generation equipment with the largest residual life estimation as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation is greater than the average residual life is less than two.
6. A system for controlling joint power supply of various emergency power generation equipment, wherein the system comprises:
a power supply demand receiving module configured to receive a total amount of power supply demand in a current control period;
an integrated performance prediction module configured to predicting a power generation estimation and a residual life estimation of each one emergency power generation equipment in the current control period by trained machine learning models, basing on environment information and operating condition information of the corresponding emergency power generation equipment in a period before the current control period;
a power supply equipment determination module configured to determining two or more of the emergency power generation equipment as joint power supply equipment in the current control period according to the residual life estimates of the various emergency power generation equipment;
a power supply proportion optimization module configured to optimizing power supply proportion of the joint power supply equipment by using a preset optimization algorithm, wherein the optimization algorithm converges in a condition that a difference between a total amount of power generation of all joint power supply equipment and the total amount of power supply demand in the current control period is minimum, the total amount of power generation of all joint power supply equipment is equal to the sum of the power generation estimation of each joint power supply equipment multiplied by the power supply proportion of the corresponding joint power supply equipment.
7. The system according to claim 6, wherein the power supply equipment determination module comprises:
an average life calculation unit configured to calculate an average residual life of the various emergency power generation equipment;
an equipment number determination unit configured to determine whether the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two; a power supply equipment determination unit configured to take, the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment, as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation are greater than the average residual life is greater than or equal to two.
8. The system according to claim 7, wherein the various emergency power generation equipment comprises wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel power generation vehicle, hydrogen energy equipment and fuel cell equipment; the trained machine learning models are neural network models, and the preset optimization algorithm is a genetic algorithm;
the system further comprises: an emergency power generation control unit to take an emergency power generation equipment with the largest residual life estimation as the joint power supply equipment in the current control period, on a condition that the number of the emergency power generation equipment whose residual life estimation is greater than the average residual life is less than two;
the power supply equipment determination module is further configured to rank the emergency power generation equipment whose residual life estimation are greater than the average residual life of the various emergency power generation equipment from the largest to smallest power generation estimation of the various emergency power generation equipment in the current control period; and select the first N emergency power generation equipment basing on the ranking step as a first priority joint power supply equipment in the current control period, N is a preset value;
the power supply proportion optimization module is further configured to optimize power supply proportion of the first priority joint power supply equipment by using the preset optimization algorithm.
9. A computer-readable storage medium having computer programs stored thereon, wherein
the computer programs, when executed by a processor, implement the method for controlling joint power supply of various emergency power generation equipment according to any one of claims 1 to 5.
10. A computer equipment, comprises:
a processor;
a memory having computer programs stored thereon, wherein the computer programs, when executed by the processor, implement the method for controlling joint power supply of various emergency power generation equipment according to any one of claims 1 to 5.
AU2022211871A 2022-07-08 2022-08-04 Method and system for controlling joint power supply of various emergency power generation equipment Abandoned AU2022211871A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210806424.1A CN115173404A (en) 2022-07-08 2022-07-08 Combined power supply control method and system for multiple emergency power generation devices
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