CN117293924A - WSO-based power distribution network day-ahead two-stage optimal scheduling method - Google Patents

WSO-based power distribution network day-ahead two-stage optimal scheduling method Download PDF

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CN117293924A
CN117293924A CN202311275885.1A CN202311275885A CN117293924A CN 117293924 A CN117293924 A CN 117293924A CN 202311275885 A CN202311275885 A CN 202311275885A CN 117293924 A CN117293924 A CN 117293924A
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distribution network
wso
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刘瑞帆
解相朋
周霞
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Nanjing University of Posts and Telecommunications
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • H02J2310/58—The condition being electrical
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Abstract

The invention discloses a WSO-based power distribution network day-ahead two-stage optimization scheduling method, which relates to the technical field of power grid scheduling optimization. In the first stage, a scheduling model is established by comprehensively considering the comprehensive cost of power grid operation and the economic benefit of carbon transaction, and a Cplex solver is utilized to solve the solution model, so that the output size and the electricity purchasing quantity of each distributed power supply are obtained. And then, based on the result of the first stage, establishing a reactive power optimization model by taking the minimum network loss and voltage deviation as an objective function, and constructing a plurality of constraints meeting the safe operation of the distribution network. And finally, obtaining a global optimal solution by adopting a white shark optimization algorithm, and formulating an optimal scheduling strategy. The distribution network day-ahead two-stage optimal scheduling strategy based on the WSO can optimize distribution of output power of a distributed power source, effectively reduce the total running cost and active network loss of the distribution network, and also verify the superiority of the WSO in solving the complex scheduling problem such as reactive power optimization.

Description

WSO-based power distribution network day-ahead two-stage optimal scheduling method
Technical Field
The invention relates to the technical field of power grid dispatching optimization, in particular to a WSO-based power distribution network day-ahead two-stage dispatching optimization method.
Background
Along with the development of intelligent power grids and the large-scale access of distributed power sources, the load types in the power distribution network are more and more abundant, and how to ensure the safe and stable operation of the power grids is to be solved. Meanwhile, due to the fact that the structure of the power system is more and more complex, a proper power distribution network optimization scheduling system is constructed, the electric energy quality can be improved, and efficient operation of the power grid is guaranteed. How to consider economy, safety and environmental protection in operation scheduling of a power distribution network is receiving more and more attention.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a WSO-based power distribution network day-ahead two-stage optimal scheduling method, and provides a WSO-based power distribution network day-ahead two-stage optimal scheduling strategy, which is prepared by flexibly allocating distributed power sources and simultaneously considering the safety, economy and environmental protection of system operation.
In order to solve the technical problems, the invention provides a WSO-based power distribution network day-ahead two-stage optimization scheduling method, which comprises the following steps:
analyzing the regulation characteristics of different types of distributed power supplies, and obtaining output data and load curves of wind and light in different time periods in a day by adopting a prediction method combining a convolutional neural network and a long-short memory network based on historical data; comprehensively considering the comprehensive cost of power grid operation and the economic benefit of carbon transaction, constructing and establishing a scheduling model by taking economy and environmental protection as objective functions, and solving a first-stage optimal scheduling model by using a Cplex solver to obtain the output size and the purchase power of each distributed power supply; the minimum network loss and voltage deviation are used as an objective function, a plurality of constraints meeting the safe and stable operation of the distribution network are built, and a reactive power optimization model is built; and (3) obtaining a global optimal solution by using WSO, and formulating a two-stage optimal scheduling strategy.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the historical data comprises the steps of obtaining historical data of different types of distributed power supplies participating in scheduling; the distributed power sources include gas turbines, small photovoltaic power plants, wind generators, and fuel cells.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the historical data also comprises preprocessing data, establishing a prediction model by adopting a CNN-LSTM neural network based on the historical data, extracting features by utilizing the CNN, and clarifying the internal relation and fluctuation factors of different time periods and resource regulation characteristics, so that the performance of the distributed power supply has time sequence property at the same time, a large amount of distributed power supply generates a large amount of data, two different pooling layers of the same convolution layer of two convolution cores are adopted, the extracted data are input by utilizing the characteristics of LSTM, and various distributed power supplies in different time periods before the day are respectively obtained by adopting a method of combining the CNN and the LSTM.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the method for constructing the dispatching model by taking the economical efficiency and the environmental protection as the objective functions comprises the steps of comprehensively considering the comprehensive cost of the operation of the power grid and the economic benefit of carbon transaction, and constructing the dispatching model by taking the economical efficiency and the environmental protection as the objective functions, wherein the constructed objective functions are as follows:
wherein,
wherein T is the total time period of the schedule, N is the number of nodes, and P buy,t Gamma is the electricity purchase quantity at time t buy For electricity price at time t, C gt,j Is the price of natural gas, the unit is yuan/(kW.h), P gt,i,j At tElectric power output by the gas turbine of the node j, P wd,t,j Power of wind driven generator of node j at time t, P pv,t,j Power, α, of a photovoltaic power plant for node j at time t wd Scheduling cost for unit of wind power, beta wd Costs are scheduled for units of photovoltaic.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the construction of the scheduling model includes the steps of,
wherein,for total carbon trade costs, < >>The economic benefit of total carbon transaction of the distributed power supply in the distribution network is achieved; lambda is the reward coefficient; c is the carbon trade price in the market, alpha is the increasing amplitude of the carbon trade price of each step, v is the carbon emission interval length, delta E represents the difference between the total carbon emission quota and the total carbon emission, and a 1 、a 2 B, respectively obtaining carbon quota of unit electricity purchase amount and unit gas turbine power generation amount 1 、b 2 C, respectively obtaining the carbon emission quantity generated by the electricity purchase quantity and the electricity generation quantity of the gas turbine 1 、c 2 The method comprises the steps of establishing an optimized scheduling model considering an adjustable resource output interval by considering system power balance constraint, output constraint of various distributed power supplies and electricity purchasing constraint conditions for the national evidence voluntary emission reduction carbon trade price of a wind driven generator and a photovoltaic power station respectively; and solving the first-stage optimization scheduling model by using a Cplex solver to obtain the output size and the purchase quantity of each distributed power supply.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the construction scheduling model also comprises constraint conditions of considering system power balance constraint, output constraint of various distributed power supplies and electricity purchasing constraint, wherein the constraint conditions are as follows:
power balance constraint:
wherein,is the load demand during the T period.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the constraints may also include the fact that,
upper and lower limit constraints on gas turbine output:
wherein,respectively outputting the minimum value and the maximum value of the electric power of the gas turbine;
gas turbine ramp rate constraints:
ΔP gt,min ≤P gt,t+1 -P gt,t ≤ΔP gt,max
wherein DeltaP gt,max 、ΔP gt,min The upper limit and the lower limit of the climbing speed of the gas turbine are respectively set;
wind-light output constraint:
wherein,maximum output value of wind generator for node j, +.>The maximum output value of the photovoltaic power station of the node j;
buying electricity constraint:
wherein,is the maximum electricity purchase amount.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the reactive power optimization includes the steps of,
according to an objective function and constraint conditions, an active economic optimization model of the power distribution network is built, a yalminip tool box under MATLAB is utilized to call a Cplex solver to solve, the output size and the purchase quantity of each distributed power supply are obtained, reactive power optimization is carried out on each period, so that the voltage of each node is ensured not to be out of limit, meanwhile, the network loss of the power distribution network in the operation stage is enabled to be minimum, the minimum active network loss and the minimum voltage deviation are considered as the objective function, a voltage out-of-limit penalty function is introduced, punishment is carried out on the solution of the voltage out of limit of each node, a plurality of constraints meeting safe and stable operation of the power distribution network are built, and the built objective function is as follows:
wherein,
wherein, gamma 1 、γ 2 Is a weight coefficient, and the sum of the weight coefficient and the weight coefficient is 1, P lost U is the active network loss of the system * As the voltage deviation, F 1 、F 2 The function values when the objective function has only net loss and only voltage deviation are respectively represented, U i For node voltage, U i,expect U is the expected voltage value of the node i,max 、U i,min Respectively the maximum value and the minimum value of the node voltage, eta is a penalty factor, and the value is 100 in the invention.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the method for obtaining the global optimal solution by using WSO comprises initializing a white shark population, setting initialization parameters of each power supply unit model and electric quantity purchased from a large power grid, setting an fitness function according to an objective function, and randomly generating positions and speeds of all white sharks; the fitness function of each white shark is calculated and used as a selection criterion.
As a preferable scheme of the WSO-based power distribution network day-ahead two-stage optimization scheduling method, the invention comprises the following steps: the method for obtaining the global optimal solution by using WSO further comprises the steps that the white sharks start to move to the position of the prey and approach to the selected optimal prey, other white sharks move to the white sharks which catch the optimal prey by using the characteristics of the population, when the fitness value reaches a threshold value, the positions of all the white sharks are updated, the maximum iteration times are set, and when the termination condition is met, the global optimal solution is output.
The invention has the beneficial effects that: according to the WSO-based power distribution network day-ahead two-stage optimization scheduling strategy, the gas turbine in the distribution network, the carbon transaction cost in the interaction process with the main network and the carbon transaction economic benefit brought by wind and light are considered, economy, safety and environmental friendliness are considered in the power distribution network operation scheduling, the distributed power distribution can be optimized, the total operation cost and the active network loss of the power distribution network are effectively reduced, the voltage level of each node is improved, and the superiority of WSO in solving the complex scheduling problems such as reactive power optimization is verified.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a schematic flow chart of a WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Fig. 2 is an IEEE33 node system diagram of a WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Fig. 3 is a flowchart of a WSO solution optimization problem of a WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Fig. 4 is a diagram showing the electricity purchase amount situation and the output combination of various distributed power sources of the WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Fig. 5 is a diagram of power purchase amount conditions and output combinations of various distributed power sources of a WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Fig. 6 is a graph showing a relationship between carbon emission and carbon transaction price of a WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a reactive power optimization stage of a WSO-based power distribution network day-ahead two-stage optimization scheduling method according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill 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.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 to 3, a first embodiment of the present invention provides a WSO-based power distribution network day-ahead two-stage optimization scheduling method, including:
s1: and analyzing the regulation characteristics of different types of distributed power supplies, and obtaining wind-light output data and load curves in different time periods in a day by adopting a prediction method combining a convolutional neural network and a long-short memory network based on historical data.
The historical data comprises the steps of obtaining historical data of different types of distributed power supplies participating in scheduling; the distributed power sources include gas turbines, small photovoltaic power plants, wind generators, and fuel cells.
The historical data also comprises preprocessing data, establishing a prediction model by adopting a CNN-LSTM neural network based on the historical data, extracting features by utilizing the CNN, and clarifying the internal relation and fluctuation factors of different time periods and resource regulation characteristics, so that the performance of the distributed power supply has time sequence property at the same time, a large amount of distributed power supply generates a large amount of data, two different pooling layers of the same convolution layer of two convolution cores are adopted, the extracted data are input by utilizing the characteristics of LSTM, and various distributed power supplies in different time periods before the day are respectively obtained by adopting a method of combining the CNN and the LSTM.
S2: and comprehensively considering the comprehensive cost of power grid operation and the economic benefit of carbon transaction, constructing and establishing a scheduling model by taking economy and environmental protection as objective functions, and solving the first-stage optimal scheduling model by using a Cplex solver to obtain the output size and the purchase quantity of each distributed power supply.
The method for constructing the dispatching model by taking the economical efficiency and the environmental protection as the objective functions comprises the steps of comprehensively considering the comprehensive cost of the operation of the power grid and the economic benefit of carbon transaction, and constructing the dispatching model by taking the economical efficiency and the environmental protection as the objective functions, wherein the constructed objective functions are as follows:
wherein,
wherein T is the total time period of the schedule, N is the number of nodes, and P buy,t Gamma is the electricity purchase quantity at time t buy For electricity price at time t, C gt,j Is the price of natural gas, the unit is yuan/(kW.h), P gt,i,j Electric power output by the gas turbine as node j at time t, P wd,t,j Power of wind driven generator of node j at time t, P pv,t,j Power, α, of a photovoltaic power plant for node j at time t wd Scheduling cost for unit of wind power, beta wd Costs are scheduled for units of photovoltaic.
The construction of the scheduling model includes the steps of,
wherein,for total carbon trade costs, < >>The economic benefit of total carbon transaction of the distributed power supply in the distribution network is achieved; lambda is the reward coefficient; c is the carbon trade price in the market, alpha is the increasing amplitude of the carbon trade price of each step, v is the carbon emission interval length, delta E represents the difference between the total carbon emission quota and the total carbon emission, and a 1 、a 2 B, respectively obtaining carbon quota of unit electricity purchase amount and unit gas turbine power generation amount 1 、b 2 C, respectively obtaining the carbon emission quantity generated by the electricity purchase quantity and the electricity generation quantity of the gas turbine 1 、c 2 The price of the carbon trade is voluntarily reduced by national certification of wind driven generator and photovoltaic power station, and the system power balance constraint is considered,The method comprises the steps of establishing an optimized scheduling model considering an adjustable resource output interval under the output constraint and the electricity purchasing constraint conditions of various distributed power supplies; and solving the first-stage optimization scheduling model by using a Cplex solver to obtain the output size and the purchase quantity of each distributed power supply.
The construction scheduling model also comprises constraint conditions of considering system power balance constraint, output constraint of various distributed power supplies and electricity purchasing constraint, wherein the constraint conditions are as follows:
power balance constraint:
wherein,is the load demand during the T period.
The constraints may also include the fact that,
upper and lower limit constraints on gas turbine output:
wherein,respectively outputting the minimum value and the maximum value of the electric power of the gas turbine;
gas turbine ramp rate constraints:
ΔP gt,min ≤P gt,t+1 -P gt,t ≤ΔP gt,max
wherein DeltaP gt,max 、ΔP gt,min The upper limit and the lower limit of the climbing speed of the gas turbine are respectively set;
wind-light output constraint:
wherein,maximum output value of wind generator for node j, +.>The maximum output value of the photovoltaic power station of the node j;
buying electricity constraint:
wherein,is the maximum electricity purchase amount.
S3: and constructing a plurality of constraints meeting the safe and stable operation of the distribution network by taking the minimum network loss and voltage deviation as an objective function, and establishing a reactive power optimization model.
The reactive power optimization comprises the steps of constructing an active economic optimization model of the power distribution network according to an objective function and constraint conditions, utilizing a yalminip tool box under MATLAB to call a Cplex solver to solve, obtaining the output size and the purchase quantity of each distributed power supply, carrying out reactive power optimization on each time period, ensuring that the voltage of each node is not out of limit, simultaneously enabling the network loss of the power distribution network in the operation stage to be minimum, taking the minimum active network loss and the minimum voltage deviation as the objective function, introducing a voltage out-of-limit penalty function, punishing the solution of the voltage out-of-limit of each node, constructing a plurality of constraints meeting the safe and stable operation of the power distribution network, and establishing a reactive power optimization model, wherein the objective function is as follows:
wherein,
wherein, gamma 1 、γ 2 Is a weight coefficient, and the sum of the weight coefficient and the weight coefficient is 1, P lost U is the active network loss of the system * As the voltage deviation, F 1 、F 2 The function values when the objective function has only net loss and only voltage deviation are respectively represented, U i For node voltage, U i,expect U is the expected voltage value of the node i,max 、U i,min Respectively the maximum value and the minimum value of the node voltage, eta is a penalty factor, and the value is 100 in the invention.
S4: and (3) obtaining a global optimal solution by using WSO, and formulating a two-stage optimal scheduling strategy.
The method for obtaining the global optimal solution by using WSO comprises initializing a white shark population, setting initialization parameters of each power supply unit model and electric quantity purchased from a large power grid, setting an fitness function according to an objective function, and randomly generating positions and speeds of all white sharks; the fitness function of each white shark is calculated and used as a selection criterion.
The method for obtaining the global optimal solution by using WSO further comprises the steps that the white sharks start to move to the position of the prey and approach to the selected optimal prey, other white sharks move to the white sharks which catch the optimal prey by using the characteristics of the population, when the fitness value reaches a threshold value, the positions of all the white sharks are updated, the maximum iteration times are set, and when the termination condition is met, the global optimal solution is output.
Example 2
Referring to fig. 4 to 7, for one embodiment of the present invention, a WSO-based power distribution network day-ahead two-stage optimization scheduling method is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through experiments.
Fig. 4 and 5 are diagrams of power purchase conditions and output combinations of various distributed power supplies. According to active power prediction of wind and light, photovoltaic is larger in power generated by photovoltaic power generation within a period of 10:00-14:00, and no power is generated within periods of 21:00-0:00 and 0:00-4:00. Wind power generation at 8:00-15: the 00 output power is larger, and the electric power is continuously output in the photovoltaic non-output period. And is provided with an appropriate power generation capacity according to the prediction result. The solution results in a force map based on the first stage of the day-ahead scheduling model mentioned above.
As can be seen from the figure, 0:00-07: at time 00, the amount of electricity purchased from the large grid is more than at other times, at 05: the upper limit of the electricity purchasing quantity is 4.025MW at the moment 00. This is because the cost of purchasing electricity from the grid is lower than the operating cost of the gas turbine, and the electricity is preferentially purchased from the grid at this time, and the distributed power source assists in supplying power. The gas turbine is divided into three nodes accessing different locations, at 0:00-04: at time 00, the cost of scheduling the amount of electricity generated by the gas turbine 3 is the lowest and the operating time is the longest due to the effect of the actual geographical factors. At 08:00-23: at time 00, because the electricity purchasing cost is high, the gas turbine unit is mainly used for providing electricity, and wind-light output and electricity purchasing modes are used for assisting in power supply. At this time, since the integrated operation cost of the gas turbine 2 is the lowest, the operation time is the longest. At 08:00-22: during period 00, gas turbines 1 and 3 remain at maximum output at all times. At 12:00-22:00, the gas turbine 2 always maintains the maximum output state, and provides 1MW electric power.
FIG. 6 is a plot of carbon emissions versus carbon trade price. As can be seen from fig. 6, as the carbon trade base price increases, the cost required for carrying out the carbon trade increases and the overall operating cost of the system increases, and the total cost increases, and the carbon emissions are reduced to reduce the cost of the carbon trade. When the carbon trade price increases to about 1 yuan/kg, the system cost reaches about 480 yuan, and the system is adjusted to reduce carbon emissions due to excessive cost.
Fig. 7 is the result of the reactive power optimization stage. After reactive power optimization, the network loss is obviously reduced, and the network loss at each moment is smaller than 0.2MW. At 11: at time 00, when reactive power optimization is not performed, the network loss value reaches 0.97MW. After optimization, the network loss value is 0.13MW, the overall reduction is 86%, and the optimization effect is obvious. Therefore, by adjusting the reactive compensator and the voltage deviation, the active loss of the system can be effectively reduced.
Table 1 shows the voltage magnitudes at the out-of-limit nodes before and after optimization. The voltage of each node is within a reasonable range after optimization as can be seen from the table. Indicating that the reactive power optimization stage presented herein can improve voltage quality.
TABLE 1
Out-of-limit node Pre-optimization voltage amplitude/pu Optimized voltage amplitude/pu Out-of-limit node Pre-optimization voltage amplitude/pu Optimized voltage amplitude/pu
8 0.896 0.966 17 0.846 0.937
9 0.884 0.959 18 0.845 0.936
10 0.874 0.953 28 0.882 0.961
11 0.872 0.952 29 0.867 0.957
12 0.870 0.950 30 0.861 0.955
13 0.858 0.944 31 0.853 0.952
14 0.854 0.942 32 0.851 0.951
15 0.852 0.941 33 0.851 0.951
16 0.849 0.939
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application 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 solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (10)

1. The WSO-based power distribution network day-ahead two-stage optimal scheduling method is characterized by comprising the following steps of: comprising the steps of (a) a step of,
analyzing the regulation characteristics of different types of distributed power supplies, and obtaining output data and load curves of wind and light in different time periods in a day by adopting a prediction method combining a convolutional neural network and a long-short memory network based on historical data;
comprehensively considering the comprehensive cost of power grid operation and the economic benefit of carbon transaction, constructing and establishing a scheduling model by taking economy and environmental protection as objective functions, and solving a first-stage optimal scheduling model by using a Cplex solver to obtain the output size and the purchase power of each distributed power supply;
the minimum network loss and voltage deviation are used as an objective function, a plurality of constraints meeting the safe and stable operation of the distribution network are built, and a reactive power optimization model is built;
and (3) obtaining a global optimal solution by using WSO, and formulating a two-stage optimal scheduling strategy.
2. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 1, wherein: the historical data comprises the steps of obtaining historical data of different types of distributed power supplies participating in scheduling; the distributed power sources include gas turbines, small photovoltaic power plants, wind generators, and fuel cells.
3. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 2, wherein: the historical data also comprises preprocessing data, establishing a prediction model by adopting a CNN-LSTM neural network based on the historical data, extracting features by utilizing the CNN, and clarifying the internal relation and fluctuation factors of different time periods and resource regulation characteristics, so that the performance of the distributed power supply has time sequence property at the same time, a large amount of distributed power supply generates a large amount of data, two different pooling layers of the same convolution layer of two convolution cores are adopted, the extracted data are input by utilizing the characteristics of LSTM, and various distributed power supplies in different time periods before the day are respectively obtained by adopting a method of combining the CNN and the LSTM.
4. A WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 3, wherein: the method for constructing the dispatching model by taking the economical efficiency and the environmental protection as the objective functions comprises the steps of comprehensively considering the comprehensive cost of the operation of the power grid and the economic benefit of carbon transaction, and constructing the dispatching model by taking the economical efficiency and the environmental protection as the objective functions, wherein the constructed objective functions are as follows:
wherein,
wherein T is the total time period of the schedule, N is the number of nodes, and P buy,t Gamma is the electricity purchase quantity at time t buy For electricity price at time t, C gt,j Is the price of natural gas, the unit is yuan/(kW.h), P gt,i,j Electric power output by the gas turbine as node j at time t, P wd,t,j Power of wind driven generator of node j at time t, P pv,t,j Power, α, of a photovoltaic power plant for node j at time t wd Scheduling cost for unit of wind power, beta wd Costs are scheduled for units of photovoltaic.
5. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 4, wherein: the construction of the scheduling model includes the steps of,
wherein,for total carbon trade costs, < >>The economic benefit of total carbon transaction of the distributed power supply in the distribution network is achieved; lambda is the reward coefficient; c is the carbon trade price in the market, alpha is the increasing amplitude of the carbon trade price of each step, v is the carbon emission interval length, delta E represents the difference between the total carbon emission quota and the total carbon emission, and a 1 、a 2 B, respectively obtaining carbon quota of unit electricity purchase amount and unit gas turbine power generation amount 1 、b 2 C, respectively obtaining the carbon emission quantity generated by the electricity purchase quantity and the electricity generation quantity of the gas turbine 1 、c 2 The method comprises the steps of establishing an optimized scheduling model considering an adjustable resource output interval by considering system power balance constraint, output constraint of various distributed power supplies and electricity purchasing constraint conditions for the national evidence voluntary emission reduction carbon trade price of a wind driven generator and a photovoltaic power station respectively; and solving the first-stage optimization scheduling model by using a Cplex solver to obtain the output size and the purchase quantity of each distributed power supply.
6. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 5, wherein: the construction scheduling model also comprises constraint conditions of considering system power balance constraint, output constraint of various distributed power supplies and electricity purchasing constraint, wherein the constraint conditions are as follows:
power balance constraint:
wherein,is the load demand during the T period.
7. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 6, wherein: the constraints may also include the fact that,
upper and lower limit constraints on gas turbine output:
wherein,respectively outputting the minimum value and the maximum value of the electric power of the gas turbine;
gas turbine ramp rate constraints:
ΔP gt,min ≤P gt,t+1 -P gt,t ≤ΔP gt,max
wherein DeltaP gt,max 、ΔP gt,min The upper limit and the lower limit of the climbing speed of the gas turbine are respectively set;
wind-light output constraint:
wherein,maximum output value of wind generator for node j, +.>The maximum output value of the photovoltaic power station of the node j;
buying electricity constraint:
wherein,is the maximum electricity purchase amount.
8. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 7, wherein: the reactive power optimization includes the steps of,
according to an objective function and constraint conditions, an active economic optimization model of the power distribution network is built, a yalminip tool box under MATLAB is utilized to call a Cplex solver to solve, the output size and the purchase quantity of each distributed power supply are obtained, reactive power optimization is carried out on each period, so that the voltage of each node is ensured not to be out of limit, meanwhile, the network loss of the power distribution network in the operation stage is enabled to be minimum, the minimum active network loss and the minimum voltage deviation are considered as the objective function, a voltage out-of-limit penalty function is introduced, punishment is carried out on the solution of the voltage out of limit of each node, a plurality of constraints meeting safe and stable operation of the power distribution network are built, and the built objective function is as follows:
wherein,
wherein, gamma 1 、γ 2 Is a weight coefficient, and the sum of the weight coefficient and the weight coefficient is 1, P lost U is the active network loss of the system * As the voltage deviation, F 1 、F 2 The function values when the objective function has only net loss and only voltage deviation are respectively represented, U i For node voltage, U i,expect U is the expected voltage value of the node i,max 、U i,min Respectively the maximum value and the minimum value of the node voltage, eta is a penalty factor, and the value is 100 in the invention.
9. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 8, wherein: the method for obtaining the global optimal solution by using WSO comprises initializing a white shark population, setting initialization parameters of each power supply unit model and electric quantity purchased from a large power grid, setting an fitness function according to an objective function, and randomly generating positions and speeds of all white sharks; the fitness function of each white shark is calculated and used as a selection criterion.
10. The WSO-based power distribution network pre-day two-stage optimization scheduling method according to claim 9, wherein: the method for obtaining the global optimal solution by using WSO further comprises the steps that the white sharks start to move to the position of the prey and approach to the selected optimal prey, other white sharks move to the white sharks which catch the optimal prey by using the characteristics of the population, when the fitness value reaches a threshold value, the positions of all the white sharks are updated, the maximum iteration times are set, and when the termination condition is met, the global optimal solution is output.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852727A (en) * 2024-03-07 2024-04-09 南京邮电大学 Supply chain optimization method and system based on overlapping alliances and shared carbon quota
CN117977718A (en) * 2024-04-01 2024-05-03 浙电(宁波北仑)智慧能源有限公司 Coordinated scheduling optimization method and system based on source network load storage

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852727A (en) * 2024-03-07 2024-04-09 南京邮电大学 Supply chain optimization method and system based on overlapping alliances and shared carbon quota
CN117852727B (en) * 2024-03-07 2024-05-28 南京邮电大学 Supply chain optimization method and system based on overlapping alliances and shared carbon quota
CN117977718A (en) * 2024-04-01 2024-05-03 浙电(宁波北仑)智慧能源有限公司 Coordinated scheduling optimization method and system based on source network load storage

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