CN116646991A - Power generation scheduling method, device, equipment and storage medium of power system - Google Patents
Power generation scheduling method, device, equipment and storage medium of power system Download PDFInfo
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- 238000010248 power generation Methods 0.000 title claims abstract description 235
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Classifications
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
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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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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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
- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention relates to the technical field of power grids, and discloses a power generation scheduling method, device and equipment of a power system and a storage medium. The method comprises the following steps: acquiring power generation configuration information of a power system, and generating a target scheduling function according to the power generation configuration information; acquiring a power generation constraint condition, and optimizing a target scheduling function according to the power generation constraint condition by a genetic optimization back propagation neural network algorithm to acquire power generation parameter values corresponding to all the generators; and controlling each generator to perform power generation scheduling according to the corresponding power generation parameter value of each generator. According to the technical scheme, the genetic optimization back propagation neural network algorithm is adopted to obtain the power generation parameter values corresponding to all the generators based on the power generation constraint conditions, so that the accuracy of power generation scheduling can be improved, the waste of power resources is avoided, and the safe and stable operation of a power system can be ensured.
Description
Technical Field
The present invention relates to the field of power grid technologies, and in particular, to a power generation scheduling method, apparatus, device, and storage medium for a power system.
Background
The stable operation of the power system is the power for the orderly development of society, and is an engine for assisting the stable development of the economy in China. How to realize accurate power generation scheduling of a power system on the premise of meeting power generation requirements and environmental protection requirements is becoming one of the key research directions in the power field.
At present, a pre-trained machine learning model is generally adopted in a power generation scheduling method of an existing power system, and the actual power generation power of each generator in a generator set is determined according to the current power generation environment, such as information of regions, historical power consumption, power generation cost and the like. However, while the generator set is running, many system constraints need to be considered, and the prior art does not introduce system constraints in the power generation scheduling process, which results in lower accuracy of power generation scheduling and is prone to safety problems.
Disclosure of Invention
The invention provides a power generation scheduling method, a device, equipment and a storage medium of a power system, which can improve the accuracy of power generation scheduling, avoid the waste of power resources and ensure the safe and stable operation of the power system.
According to an aspect of the present invention, there is provided a power generation scheduling method of a power system, including:
acquiring power generation configuration information of a power system, and generating a target scheduling function according to the power generation configuration information;
the target scheduling function comprises power generation power parameters corresponding to all the generators;
acquiring a power generation constraint condition, and optimizing the target scheduling function according to the power generation constraint condition by a genetic optimization back propagation neural network algorithm to acquire a power generation parameter value corresponding to each power generator;
and controlling each generator to perform power generation scheduling according to the power generation power parameter value corresponding to each generator.
According to another aspect of the present invention, there is provided a power generation scheduling apparatus of a power system, including:
the target scheduling function generation module is used for acquiring power generation configuration information of the power system and generating a target scheduling function according to the power generation configuration information;
the target scheduling function comprises power generation power parameters corresponding to all the generators;
the power generation parameter value acquisition module is used for acquiring power generation constraint conditions, optimizing the target scheduling function according to the power generation constraint conditions through a genetic optimization back propagation neural network algorithm so as to acquire power generation parameter values corresponding to all the generators;
and the power generation scheduling control module is used for controlling each power generator to perform power generation scheduling according to the power generation power parameter value corresponding to each power generator.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the power generation scheduling method of the power system according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the power generation scheduling method of the power system according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the power generation configuration information of the power system is obtained, and a target scheduling function comprising power generation power parameters corresponding to all the generators is generated according to the power generation configuration information; then, obtaining a power generation constraint condition, and optimizing a target scheduling function according to the power generation constraint condition by genetic optimization back propagation neural network algorithm to obtain a power generation parameter value corresponding to each power generator; and finally, controlling each generator to perform power generation scheduling according to the corresponding power generation parameter values of each generator, and acquiring the corresponding power generation parameter values of each generator based on power generation constraint conditions by adopting a genetic optimization back propagation neural network algorithm, so that the accuracy of power generation scheduling can be improved, the waste of power resources can be avoided, and the safe and stable operation of a power system can be ensured.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a power generation scheduling method of a power system according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a GA-BP algorithm according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a BP neural network according to a first embodiment of the present invention;
fig. 4 is a schematic structural diagram of a power generation scheduling device of a power system according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing a power generation scheduling method of a power system according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," "target," and the like in the description and claims of the present invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Embodiment one:
fig. 1 is a flowchart of a power generation scheduling method of a power system according to an embodiment of the present invention, where the method may be performed by a power generation scheduling device of a power system, where the power generation scheduling device of the power system may be implemented in hardware and/or software, and typically, the power generation scheduling device of the power system may be configured in an electronic device, for example, a computer device or a server. As shown in fig. 1, the method includes:
s110, acquiring power generation configuration information of the power system, and generating a target scheduling function according to the power generation configuration information.
The target scheduling function may include a power generation power parameter corresponding to each generator, and the generator may be a virtual generator of the energy storage power station. In this embodiment, an initial power scheduling function may be pre-established, where the unknown quantity is a power generation power parameter corresponding to each generator, and the function coefficient may be a preset initial value; when power dispatching is performed for a specific power system, the function coefficients can be updated according to power generation configuration information of the power system, such as the number of generators, the power generation duration and the like, so as to obtain a target dispatching function corresponding to the power system.
In this embodiment, the power generation configuration information may be extracted from attribute information of the power system; the power generation configuration information may include the number of power generators and configuration information corresponding to each power generator, for example, power generation related information such as brands, maximum and minimum power generation, power generation loss coefficients, and power generation gas emission coefficients. Then, the initial power scheduling function may be updated according to the obtained power generation configuration information, so that each function coefficient in the scheduling function is matched with the current power generation configuration information, thereby obtaining the target scheduling function.
It will be appreciated that in performing power generation scheduling, it is necessary to consider both the power generation cost and the environmental pollution problem, and in this embodiment, the power generation cost function and the pollutant gas emission function may be established as target scheduling functions according to the power generation cost coefficient and the gas emission coefficient of the power generator, respectively. Typically, the power generation cost coefficient and the gas emission coefficient of different power generators are not the same, and by adjusting the power generation power of different power generators, the power generation cost and the pollution gas emission can be reduced as much as possible under the condition that the total power generation power meets the requirement.
S120, acquiring a power generation constraint condition, and optimizing the target scheduling function according to the power generation constraint condition by a genetic optimization back propagation neural network algorithm to acquire power generation parameter values corresponding to all the generators.
The power generation constraint conditions may include a power balance constraint condition, a generator set ramp rate constraint condition, and/or a generator set power generation power range constraint condition, among others. Specifically, the power balance constraint condition is that the total power generation amount of all the generator sets at a certain time is required to balance the predicted power demand power at the certain time and the actual power loss in the transmission line; constraint conditions of the climbing rate of the generator set are that constraint is made on the generation increment and decrement of the generator at different moments so as to ensure long-term stable operation of the generator set; the generator set power generation range constraint is that the power generation of the generator needs to be maintained between a minimum power generation and a maximum power generation.
It can be understood that corresponding power generation constraint conditions can be adaptively set according to different power generation scene requirements so as to realize power generation scheduling control under different conditions.
Wherein the genetic optimization back propagation neural network (Genetic Algorithm-Back propagation Neural Network, GA-BP) algorithm is a regression prediction algorithm combining a genetic algorithm (Genetic Algorithm, GA) and a back propagation neural network (Back propagation Neural Network, BPNN). Specifically, the flow of the GA-BP algorithm may be as shown in FIG. 2. Firstly, determining the total number of weights and thresholds to be optimized according to the topological structure of a BP neural network, and training the network and calculating fitness by using training sample data; then, according to the calculated fitness value corresponding to each individual, selecting the individual with high fitness value to enter the next generation through three operators in a genetic algorithm, namely a selection operator, a mutation operator and a crossover operator, so as to generate a new population; finally, whether the evolution is stopped or not can be judged according to the maximum evolution times of the population, if the maximum evolution times of the population are not reached, the evolution is continued, if the maximum evolution times are reached, the optimal solution of the weight and the threshold of the BP neural network is obtained, and the optimal solution and the fitness are output through the BP neural network optimization. The structure of the BP neural network may include an input layer, an hidden layer, and an output layer as shown in fig. 3.
In this embodiment, the power generation power parameter of each generator in the target scheduling function may be mapped to a weight and a threshold to be optimized of the BP neural network; therefore, under each power generation constraint condition, the BP neural network is continuously optimized by adopting a genetic algorithm, so that the power generation parameter value corresponding to each expected generator, namely the specific power generation, is obtained.
The power generation constraint conditions can be introduced, the acquisition speed of the power generation parameter value corresponding to each expected generator can be increased, and the calculation accuracy of the power generation parameter value corresponding to each generator can be improved.
And S130, controlling each generator to perform power generation scheduling according to the corresponding power generation parameter value of each generator.
Specifically, after the power generation parameter value corresponding to each generator is obtained, each generator can be controlled to output corresponding power generation power at corresponding time, so as to realize power generation scheduling of the power system.
According to the technical scheme, the power generation configuration information of the power system is obtained, and a target scheduling function comprising power generation power parameters corresponding to all the generators is generated according to the power generation configuration information; then, obtaining a power generation constraint condition, and optimizing a target scheduling function according to the power generation constraint condition by genetic optimization back propagation neural network algorithm to obtain a power generation parameter value corresponding to each power generator; and finally, controlling each generator to perform power generation scheduling according to the corresponding power generation parameter values of each generator, and acquiring the corresponding power generation parameter values of each generator based on power generation constraint conditions by adopting a genetic optimization back propagation neural network algorithm, so that the accuracy of power generation scheduling can be improved, the waste of power resources can be avoided, and the safe and stable operation of a power system can be ensured.
In an alternative implementation of the present embodiment, the target scheduling function may include a power generation cost function and a pollutant gas emission function, and generating the target scheduling function according to the power generation configuration information may include:
based on the formulaGenerating a power generation cost function according to the power generation configuration information;
based on the formulaGenerating a pollutant gas emission function according to the power generation configuration information;
wherein F (P) im ) Represents the power generation cost of the ith power generator at the m time, P im Represents the power generated by the ith generator at M time, M and N represent the total power generation time and the number of generators, a, respectively i 、b i 、c i 、d i And e i Respectively representing the power generation cost coefficients of different generators, P i min Representing the minimum power of the ith generator, alpha i 、β i 、γ i 、δ i And eta i Representing the unit emission characteristic coefficient. Wherein, the value range of i is [1, N]Represents the index of the generator, and the value range of m is [1, M]。
In the present embodiment, the objective call function is expected to be such that the power generation cost f 1 And a pollutant gas discharge amount f 2 Minimum, in order to achieve a better balance between generator operating costs and pollutant gas emissions. Specifically, each function coefficient value, for example, each power generation cost coefficient, each unit emission characteristic coefficient, M, N and the minimum power generation value, can be determined according to the power generation configuration information; then, the generated power of each generator at each moment is adjusted and optimized to achieve the target expectation.
In the embodiment, the power generation cost is optimized, so that the power generated by the generator in a certain time period can meet the use of the demand side, and meanwhile, the power required by the generator is not excessively exceeded, so that the resource waste can be avoided; and secondly, the emission amount of pollutants such as carbon dioxide and sulfur dioxide generated by the generator set in the power generation period is optimized by optimizing the emission amount of the pollutants, so that the pollution can be furthest reduced while the electricity consumption on the demand side is ensured.
In another alternative implementation of the present embodiment, obtaining the power generation constraint may include:
based on the formulaAcquiring a power balance constraint condition;
wherein P is Nm Represents the total power generated at m time, P Dm Represents load power consumption at m time, B ij The value range of i and j is [1, N-1 ]]。
In the embodiment, by setting the power balance constraint condition, the total power generation amount of all the generator sets at a certain time can be balanced with the sum of the predicted power demand power at the certain time and the actual power loss in the power transmission line, so that the stable operation of the power system is ensured.
Alternatively, in this embodiment, a combination of coarse adjustment and fine adjustment may be used to make the power generation of the generator set approximately equal to the sum of the load power consumption and the line loss within a certain accuracy. For example, the coarse adjustment accuracy may be set to 1, the fine adjustment accuracy to 0.1, and when it is detected that the power balance constraint condition is not satisfied (for example, the difference in the equality constraint is greater than a preset threshold), the coarse adjustment may be performed a certain number of times until the difference in the equality constraint is made smaller than 1; and then, performing fine adjustment for a plurality of times until the difference value of the equation constraint is smaller than 0.1, and finally determining that the power balance constraint condition is met.
The values of coarse and fine adjustments may be preset, for example, to a fixed value, or may be set to a decreasing value.
Optionally, obtaining the power generation constraint condition may include:
based on the formulaP im -P i(m-1) ≤UR i And P i(m-1) -P im ≤DR i Obtaining a constraint condition of the climbing rate of the generator set; wherein UR i And DR i The upward slope climbing rate and the downward slope climbing rate are respectively represented and are constant. It should be noted that, for different power generation scene requirements, the values of the upward ramp rate and the downward ramp rate can be adaptively adjusted so as to limit the variation range of the power generation at different moments.
In this embodiment, by setting the constraint condition of the climbing rate of the generator set, each increment or decrement of the generated power of each generator is located in a preset range, so that sudden increase or sudden drop of the generated power can be avoided, and the running stability of the generator set can be improved.
Optionally, obtaining the power generation constraint condition may include:
based on formula P i min ≤P im ≤P i max Acquiring a constraint condition of the generating power range of the generator set; wherein P is i min And P i max Representing the minimum generated power and the maximum generated power of the generator, respectively. The minimum generated power and the maximum generated power can be set according to the departure setting information of the generator.
In the embodiment, by setting the constraint condition of the generating power range of the generating set, the machine can be prevented from being burnt out due to overlarge generating power, the running of the machine can be prevented from being influenced due to overlarge generating power, and the running safety and stability of the generating set can be improved.
In another optional implementation manner of this embodiment, after controlling each of the generators to perform power generation scheduling according to the corresponding power generation parameter value of each of the generators, the method may further include:
based on the formulaCalculating the power generation cost F according to the power generation power parameter values corresponding to the generators T 。
In this embodiment, after each generator is controlled to perform power generation scheduling, each power generation power parameter value may be further brought into a preset cost function to calculate power generation cost, so that quantization of power generation cost may be achieved, and scheduling personnel may view scheduling results more intuitively. Wherein, each parameter value of the cost function can be determined according to the power generation configuration information.
Embodiment two:
fig. 4 is a schematic structural diagram of a power generation scheduling device of a power system according to a second embodiment of the present invention. As shown in fig. 4, the apparatus includes: the system comprises a target scheduling function generating module 210, a power generation power parameter value acquiring module 220 and a power generation scheduling control module 230; wherein, the liquid crystal display device comprises a liquid crystal display device,
the target scheduling function generating module 210 is configured to obtain power generation configuration information of the power system, and generate a target scheduling function according to the power generation configuration information;
the target scheduling function comprises power generation power parameters corresponding to all the generators;
the power generation parameter value obtaining module 220 is configured to obtain a power generation constraint condition, and optimize the target scheduling function according to the power generation constraint condition by using a genetic optimization back propagation neural network algorithm, so as to obtain power generation parameter values corresponding to each power generator;
the power generation scheduling control module 230 is configured to control each of the generators to perform power generation scheduling according to the power generation parameter value corresponding to each of the generators.
According to the technical scheme, the power generation configuration information of the power system is obtained, and a target scheduling function comprising power generation power parameters corresponding to all the generators is generated according to the power generation configuration information; then, obtaining a power generation constraint condition, and optimizing a target scheduling function according to the power generation constraint condition by genetic optimization back propagation neural network algorithm to obtain a power generation parameter value corresponding to each power generator; and finally, controlling each generator to perform power generation scheduling according to the corresponding power generation parameter values of each generator, and acquiring the corresponding power generation parameter values of each generator based on power generation constraint conditions by adopting a genetic optimization back propagation neural network algorithm, so that the accuracy of power generation scheduling can be improved, the waste of power resources can be avoided, and the safe and stable operation of a power system can be ensured.
Optionally, the target scheduling function includes a power generation cost function and a pollutant gas emission function;
the objective scheduling function generating module 210 is specifically configured to:
based on the formulaGenerating a power generation cost function according to the power generation configuration information;
based on the formulaGenerating a pollutant gas emission function according to the power generation configuration information;
wherein F (P) im ) Represents the power generation cost of the ith power generator at the m time, P im Represents the power generated by the ith generator at M time, M and N represent the total power generation time and the number of generators, a, respectively i 、b i 、c i 、d i And e i Respectively representing the power generation cost coefficients of different generators, P i min Representing the minimum power of the ith generator, alpha i 、β i 、γ i 、δ i And eta i Representing the unit emission characteristic coefficient.
Optionally, the power generation constraint condition includes a power balance constraint condition, a generator set ramp rate constraint condition, and/or a generator set power generation power range constraint condition.
Optionally, the generated power parameter value obtaining module 220 is specifically configured to:
based on the formulaAcquiring a power balance constraint condition;
wherein P is Nm Represents the total power generated at m time, P Dm Load shedding representing m timeConsumption of power, B ij And the correlation coefficient of the line loss and the power generation amount is represented.
Optionally, the generated power parameter value obtaining module 220 is specifically configured to:
based on formula P im -P i(m-1) ≤UR i And P i(m-1) -P im ≤DR i Obtaining a constraint condition of the climbing rate of the generator set; wherein UR i And DR i The upward slope climbing rate and the downward slope climbing rate are respectively represented and are constant.
Optionally, the generated power parameter value obtaining module 220 is specifically configured to:
based on formula P i min ≤P im ≤P i max Acquiring a constraint condition of the generating power range of the generator set; wherein P is i min And P i max Representing the minimum generated power and the maximum generated power of the generator, respectively.
Optionally, the power generation scheduling device of the power system further includes a power generation cost calculation module, configured to:
based on the formulaCalculating the power generation cost F according to the power generation power parameter values corresponding to the generators T 。
The power generation scheduling device of the power system provided by the embodiment of the invention can execute the power generation scheduling method of the power system provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Embodiment III:
fig. 5 shows a schematic diagram of an electronic device 30 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 30 includes at least one processor 31, and a memory, such as a Read Only Memory (ROM) 32, a Random Access Memory (RAM) 33, etc., communicatively connected to the at least one processor 31, wherein the memory stores a computer program executable by the at least one processor, and the processor 31 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 32 or the computer program loaded from the storage unit 38 into the Random Access Memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 may also be stored. The processor 31, the ROM 32 and the RAM 33 are connected to each other via a bus 34. An input/output (I/O) interface 35 is also connected to bus 34.
Various components in electronic device 30 are connected to I/O interface 35, including: an input unit 36 such as a keyboard, a mouse, etc.; an output unit 37 such as various types of displays, speakers, and the like; a storage unit 38 such as a magnetic disk, an optical disk, or the like; and a communication unit 39 such as a network card, modem, wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 31 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 31 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 31 performs the various methods and processes described above, such as a power generation scheduling method of a power system.
In some embodiments, the power generation scheduling method of the power system may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 38. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 30 via the ROM 32 and/or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the power generation scheduling method of the power system described above may be performed. Alternatively, in other embodiments, the processor 31 may be configured to perform the power generation scheduling method of the power system in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A power generation scheduling method of a power system, comprising:
acquiring power generation configuration information of a power system, and generating a target scheduling function according to the power generation configuration information;
the target scheduling function comprises power generation power parameters corresponding to all the generators;
acquiring a power generation constraint condition, and optimizing the target scheduling function according to the power generation constraint condition by a genetic optimization back propagation neural network algorithm to acquire a power generation parameter value corresponding to each power generator;
and controlling each generator to perform power generation scheduling according to the power generation power parameter value corresponding to each generator.
2. The method of claim 1, wherein the target scheduling function comprises a power generation cost function and a pollutant gas emission function, and wherein generating the target scheduling function from the power generation configuration information comprises:
based on the formulaGenerating a power generation cost function according to the power generation configuration information;
based on the formulaGenerating a pollutant gas emission function according to the power generation configuration information;
wherein F (P) im ) Represents the power generation cost of the ith power generator at the m time, P im Represents the power generated by the ith generator at M time, M and N represent the total power generation time and the number of generators, a, respectively i 、b i 、c i 、d i And e i Respectively representing the power generation cost coefficients of different generators, P i min Representing the minimum power of the ith generator, alpha i 、β i 、γ i 、δ i And eta i Representing the unit emission characteristic coefficient.
3. The method of claim 1, wherein the power generation constraints include power balance constraints, generator set ramp rate constraints, and/or generator set power generation power range constraints.
4. A method according to claim 3, wherein obtaining power generation constraints comprises:
based on the formulaAcquiring a power balance constraint condition;
wherein P is Nm Represents the total power generated at m time, P Dm Represents load power consumption at m time, B ij And the correlation coefficient of the line loss and the power generation amount is represented.
5. A method according to claim 3, wherein obtaining power generation constraints comprises:
based on formula P im -P i(m-1) ≤UR i And P i(m-1) -P im ≤DR i Obtaining a constraint condition of the climbing rate of the generator set; wherein UR i And DR i The upward slope climbing rate and the downward slope climbing rate are respectively represented and are constant.
6. A method according to claim 3, wherein obtaining power generation constraints comprises:
based on formula P i min ≤P im ≤P i max Acquiring a constraint condition of the generating power range of the generator set; wherein P is i min And P i max Representing the minimum generated power and the maximum generated power of the generator, respectively.
7. The method according to claim 1, further comprising, after controlling each of the generators to perform power generation scheduling according to the generated power parameter value corresponding to each of the generators:
based on the formulaCalculating the power generation cost F according to the power generation power parameter values corresponding to the generators T 。
8. A power generation scheduling apparatus of an electric power system, comprising:
the target scheduling function generation module is used for acquiring power generation configuration information of the power system and generating a target scheduling function according to the power generation configuration information;
the target scheduling function comprises power generation power parameters corresponding to all the generators;
the power generation parameter value acquisition module is used for acquiring power generation constraint conditions, optimizing the target scheduling function according to the power generation constraint conditions through a genetic optimization back propagation neural network algorithm so as to acquire power generation parameter values corresponding to all the generators;
and the power generation scheduling control module is used for controlling each power generator to perform power generation scheduling according to the power generation power parameter value corresponding to each power generator.
9. An electronic device, the electronic device comprising:
at least one processor, and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the power generation scheduling method of the power system of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the power generation scheduling method of the power system of any one of claims 1-7 when executed.
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