CN117713235A - Micro-grid day-ahead scheduling planning method, device, equipment and medium - Google Patents
Micro-grid day-ahead scheduling planning method, device, equipment and medium Download PDFInfo
- Publication number
- CN117713235A CN117713235A CN202311648460.0A CN202311648460A CN117713235A CN 117713235 A CN117713235 A CN 117713235A CN 202311648460 A CN202311648460 A CN 202311648460A CN 117713235 A CN117713235 A CN 117713235A
- Authority
- CN
- China
- Prior art keywords
- grid
- micro
- power
- scheduling
- objective function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 239000002245 particle Substances 0.000 claims abstract description 63
- 238000005457 optimization Methods 0.000 claims abstract description 21
- 230000006870 function Effects 0.000 claims description 47
- 238000004146 energy storage Methods 0.000 claims description 32
- 238000004590 computer program Methods 0.000 claims description 19
- 238000003860 storage Methods 0.000 claims description 10
- 238000010248 power generation Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 4
- 238000007726 management method Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a micro-grid day-ahead scheduling planning method, a device, equipment and a medium, wherein the method comprises the steps of determining scheduling parameters, and constructing an objective function and constraint conditions corresponding to the objective function based on the scheduling parameters; constructing a micro-grid day-ahead scheduling model according to the objective function and the corresponding constraint conditions; and carrying out iterative solution on the daily scheduling model of the micro-grid by adopting a particle swarm optimization algorithm to obtain a daily scheduling plan. The invention constructs a micro-grid dispatching model by using a micro-grid energy management objective function with minimum electric quantity exchanged with a large grid. Finally, solving a scheduling model through a particle swarm optimization algorithm to obtain an optimal solution of a day-ahead scheduling plan, wherein the power of various energy devices in the micro-grid is scheduled, so that the load demand of the micro-grid is met, and meanwhile, the exchange electric quantity with a large power grid is reduced as much as possible, and the grid-connected power fluctuation is reduced. In addition, the model is solved through the PSO algorithm, so that an optimal solution can be obtained, and the prediction accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a micro-grid day-ahead dispatching planning method, device, equipment and medium.
Background
The micro-grid is a small power generation and distribution system composed of a distributed power supply, energy storage, load and the like, has the characteristics of flexibility, localization and modularization, and can operate in a grid-connected or isolated network state. By integrating the distributed power supplies and reasonably configuring the output of each unit, the micro-grid greatly improves the stability of the grid operation and the energy utilization rate. Meanwhile, by effectively utilizing renewable energy sources, the dependence on traditional energy sources is greatly reduced by the micro-grid, the environmental problem caused by the traditional energy sources is relieved, and the aims of energy conservation and emission reduction are fulfilled. Distributed power generation can utilize dispersed renewable energy sources to generate power, and has higher energy utilization rate and better flexibility. The energy storage device can inhibit instantaneous power jump of the micro-grid, improve the electric energy quality level of the micro-grid, fully exert peak clipping and valley filling functions, and have important significance for ensuring stable operation of the micro-grid.
With the continuous development of renewable energy power generation technology and the strong support of national new energy policies, micro-grid technology consisting of units such as distributed power supply, energy storage, load and the like is rapidly developed. The renewable energy sources are influenced by factors such as weather, environment and the like, the output power of the renewable energy sources has the characteristics of randomness and clearance, and the renewable energy sources cannot be well consumed by the micro-grid due to fluctuation of grid-connected power of the large-power grid.
Disclosure of Invention
In view of the above, the invention aims to overcome the defects of the prior art and provide a method, a device, equipment and a medium for day-ahead scheduling planning of a micro-grid, so as to solve the problem of larger grid-connected power fluctuation of the micro-grid and a large grid in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme: a micro-grid day-ahead scheduling planning method comprises the following steps:
determining a scheduling parameter, and constructing an objective function and a constraint condition corresponding to the objective function based on the scheduling parameter;
constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions;
and adopting a particle swarm optimization algorithm to carry out iterative solution on the daily scheduling model of the micro-grid to obtain a daily scheduling plan.
Further, the objective function aims at reducing the electric quantity exchanged with the large power grid, and is that
C mg (t)＝P mg (t)-P mgp (t)
C L (t)＝P L (t)-P Lp (t)
C Hess (t)＝P Hess (t)
Wherein lambda is 1 、λ 2 、λ 3 The weight coefficients of the distributed power generation unit, the micro-grid load and the hybrid energy storage system are respectively; c (C) mg (t) is a distributed generation unit power prediction difference; p (P) mg (t) is the power of the distributable power generation unit at the moment t; p (P) mgp (t) predicting power for the t-moment distributable power generation unit; c (C) L (t) is a microgrid load power prediction difference; p (P) L (t) is the microgrid load power at time t; p (P) Lp (t) is a microgrid load power predicted value at the moment t; p (P) Hess (t) is the output power of the hybrid energy storage system at time t; n (N) T Scheduling a period number for a day; c (C) Hess And (t) is the output power of the hybrid energy storage system at the moment t.
Further, the constraint condition includes:
the active power balance constraint of the micro-grid,
P g (t)＝P mg (t)+P Hess (t)-P L (t)
the power constraint of the parallel grid,
(1-α)|P g,min (t)|≤|P g (t)|≤(1+α)|P g,max (t)|
the power constraint of the distributed generation unit,
P mg,min (t)≤P mg (t)≤P mg,max (t)
the hybrid energy storage system is constrained,
wherein P is g (t) is the actual grid-connected power at the moment t; alpha is an allowable tracking error coefficient; p (P) mg，max (t)、P mg，min (t) upper and lower power limits of the distributable power generation unit, respectively; p (P) Hess,max (t)、P Hess,min (t) upper and lower limits of output power of the hybrid energy storage system, respectively; SOC (t) is the remaining battery capacity; SOC (State of Charge) min (t) is the lower limit of the remaining battery capacity; SOC (State of Charge) max And (t) is the upper limit of the remaining capacity of the battery.
Further, the iterative solution is performed on the micro-grid day-ahead scheduling model by adopting a particle swarm optimization algorithm to obtain a day-ahead scheduling plan, including:
initializing a particle swarm, recording and calculating a position and a speed of each particle randomly initialized in a solution space;
calculating the fitness value of each particle according to the current position;
updating the speed and position of the particles according to the current position and speed, the experience of the particles and the group optimal experience;
obtaining a particle objective function value according to the fitness values of all particles;
and judging whether the particle objective function value meets the convergence condition or not based on a preset convergence condition, if so, obtaining an optimal solution of the day-ahead scheduling plan, and if not, continuing iteration until the convergence condition is met.
Further, the velocity and position of the particles are updated in the following way,
x [i][j] ＝x [i][j] +V [i][j]
wherein V is [i][j] Is the speed of the ith particle in the jth dimension; w, c 1 、c 2 Respectively weight factors; r is (r) 1 、r 2 Is a random factor;is the ithThe historically best location of the particle; />Is the historically best location for the entire population; x is x [i][j] Is the position of the ith particle in the jth dimension.
Further, the convergence condition includes:
the maximum number of iterations or fitness value reaches a threshold.
Further, the scheduling parameters include:
energy storage system operating parameters, renewable energy power prediction parameters, and load power prediction parameters.
The embodiment of the application provides a micro-grid day-ahead dispatch planning device, which comprises:
the determining module is used for determining scheduling parameters, and constructing an objective function and a constraint condition corresponding to the objective function based on the scheduling parameters;
the construction module is used for constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions;
and the solving module is used for carrying out iterative solving on the daily front scheduling model of the micro-grid by adopting a particle swarm optimization algorithm to obtain a daily front scheduling plan.
An embodiment of the present application provides a computer device, including: the system comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to enable the processor to execute the steps of the daily schedule planning method of any micro-grid.
The embodiment of the application also provides a computer storage medium, which stores a computer program, wherein the computer program is executed by a processor, so that the processor executes the steps of any one of the micro-grid day-ahead scheduling method.
By adopting the technical scheme, the invention has the following beneficial effects:
the invention provides a method, a device, equipment and a medium for scheduling and planning a micro-grid day-ahead, wherein the micro-grid day-ahead scheduling model provided by the application is used for scheduling the power of various energy devices in a micro-grid in a time range of a future day, so that the load demand of the micro-grid is met, and meanwhile, the exchange electric quantity with a large power grid is reduced as much as possible. The model is a core part of the micro-grid energy management system, and has important significance for realizing the economy and reliability of the micro-grid. The micro-grid day-ahead scheduling model is used for determining the running state and power output of each device of the micro-grid by predicting the information such as the load requirement of the micro-grid and the power output of renewable energy sources, so that the exchange electric quantity with a large grid is reduced as much as possible on the premise of meeting the load requirement of the micro-grid.
According to the method, the optimal solution of the day-ahead scheduling plan is obtained by solving the scheduling model through the particle swarm optimization algorithm, and the load demand of the micro-grid is met by scheduling the power of various energy devices in the micro-grid, meanwhile, the exchange electric quantity with a large power grid is reduced as much as possible, and grid-connected power fluctuation is reduced. In addition, the model is solved through the PSO algorithm, so that an optimal solution can be obtained, and the prediction accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of steps of a micro-grid day-ahead dispatch planning method according to the present invention;
FIG. 2 is a schematic flow chart of a micro-grid day-ahead dispatch planning method of the present invention;
FIG. 3 is a schematic flow chart of solving a micro-grid day-ahead scheduling model by adopting a particle swarm optimization algorithm;
fig. 4 is a schematic structural diagram of a micro-grid day-ahead dispatching planning device according to the present invention;
fig. 5 is a schematic structural diagram of a computer device involved in the future schedule planning method of the micro-grid of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
A specific method, device, equipment and medium for daily schedule planning of a micro-grid are provided in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, the micro-grid day-ahead scheduling method provided in the embodiment of the present application includes:
s101, determining a scheduling parameter, and constructing an objective function and a constraint condition corresponding to the objective function based on the scheduling parameter;
in some embodiments, the scheduling parameters include:
energy storage system operating parameters, renewable energy power prediction parameters, and load power prediction parameters.
In the method, long-time scale predicted values of renewable energy power and load power are obtained, and a micro-grid energy management objective function with minimum electric quantity exchanged with a large grid is designed by combining the state of charge of an energy storage system.
The objective function aims at reducing the electric quantity exchanged with a large power grid, and is that
C mg (t)＝P mg (t)-P mgp (t) (2)
C L (t)＝P L (t)-P Lp (t) (3)
C Hess (t)＝P Hess (t) (4)
Wherein lambda is 1 、λ 2 、λ 3 The weight coefficients of the distributed power generation unit, the micro-grid load and the hybrid energy storage system are respectively; c (C) mg (t) is a distributed generation unit power prediction difference; p (P) mg (t) is the power of the distributable power generation unit at the moment t; p (P) mgp (t) predicting power for the t-moment distributable power generation unit; c (C) L (t) is a microgrid load power prediction difference; p (P) L (t) is the microgrid load power at time t; p (P) Lp (t) is a microgrid load power predicted value at the moment t; p (P) Hess (t) is the output power of the hybrid energy storage system at time t; n (N) T Scheduling a period number for a day; c (C) Hess And (t) is the output power of the hybrid energy storage system at the moment t.
Specifically, the power prediction difference value of the distributed power generation unit represents the difference between the actual power generation amount and the predicted power generation amount of the distributed power generation unit, and the difference affects the energy balance inside the micro-grid, thereby affecting the electric quantity exchange between the micro-grid and the main grid. The microgrid load power forecast difference represents the difference between the actual load and the forecast load of the microgrid, which will affect the energy demand inside the microgrid and thus the power exchange between the microgrid and the main grid. The micro grid hybrid energy storage system output power represents the energy storage system output power that will affect the energy storage and scheduling inside the micro grid, thereby affecting the power exchange between the micro grid and the main grid. Therefore, by minimizing the objective function, the sizes of the three values are reduced as much as possible, so that the optimal scheduling and management of the energy sources in the micro-grid can be realized, the exchange electric quantity with a large power grid is reduced, and the sustainable development and the maximum renewable energy source consumption of the micro-grid system are realized.
The constraint condition includes:
the active power balance constraint of the micro-grid,
P g (t)＝P mg (t)+P Hess (t)-P L (t) (5)
P g and (t) is the actual grid-connected power at the moment t. The actual grid-connected power of the micro-grid refers to the power which needs to be exchanged by the micro-grid from a large power grid, and when the sum of the output power of the renewable energy source and the output power of the hybrid energy storage is smaller than the load transmissionAt power output, the micro-grid needs to draw power from a large grid to meet the load demand.
The power constraint of the parallel grid,
(1-α)|P g,min (t)|≤|P g (t)|≤(1+α)|P g,max (t)| (6)
where α is the allowed tracking error coefficient. Because the error between the grid-connected scheduling plan and the predicted value is influenced by various uncertain factors such as environment, weather and the like, the numerical value of the grid-connected scheduling plan and the predicted value is in a state of real-time fluctuation. In order to reduce the charge and discharge times of the energy storage system, an error band is set for the error value of the grid-connected scheduling plan and the predicted value, and whether the energy storage system operates or not is judged according to the error of the grid-connected scheduling plan and the predicted value. When the error is within the allowable error range, the energy storage system does not work; when the error exceeds the allowable error and the actual grid-connected power is higher than the scheduled grid-connected power, the energy storage system absorbs excessive energy to charge; otherwise, when the actual grid-connected power is lower than the scheduled grid-connected power, the energy storage system discharges to compensate the grid-connected power.
The power constraint of the distributed generation unit,
P mg,min (t)≤P mg (t)≤P mg,max (t) (7)
the hybrid energy storage system is constrained,
wherein P is mg，max (t)、P mg，min (t) upper and lower power limits of the distributable power generation unit, respectively; p (P) Hess,max (t)、P Hess,min (t) upper and lower limits of output power of the hybrid energy storage system, respectively; SOC is the state of charge of the energy storage device, which refers to the available state of the residual charge in the battery, generally expressed in percentage, and SOC (t) is the current SOC value of the energy storage unit; SOC (State of Charge) min (t) is the lower limit of the SOC value of the energy storage unit; SOC (State of Charge) max And (t) is the upper limit of the SOC value of the energy storage unit. P (P) Hess,min (t) and P Hess,max The specific value of (t) is determined by the real-time S of the energy storage deviceAnd OC.
S102, constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions;
it can be understood that the scheduling model objective function is composed of a renewable energy source power prediction difference value, a micro-grid load power prediction difference value and output power of the micro-grid hybrid energy storage system, and aims to reduce electric quantity exchanged with a large power grid, so that the purposes of sustainable development of the micro-grid system and maximum renewable energy source consumption are achieved.
The main task of the micro-grid day-ahead scheduling model is to determine the running state and power output of each device of the micro-grid by predicting the information such as the load requirement of the micro-grid and the power output of renewable energy sources, so that the exchange electric quantity with a large power grid is reduced as much as possible on the premise of meeting the load requirement of the micro-grid.
And S103, carrying out iterative solution on the daily front scheduling model of the micro-grid by adopting a particle swarm optimization algorithm to obtain a daily front scheduling plan.
In some embodiments, the performing iterative solution to the future schedule model of the micro grid by using a particle swarm optimization algorithm to obtain a future schedule plan includes:
initializing a particle swarm, recording and calculating a position and a speed of each particle randomly initialized in a solution space;
calculating the fitness value of each particle according to the current position;
updating the speed and position of the particles according to the current position and speed, the experience of the particles and the group optimal experience;
obtaining a particle objective function value according to the fitness values of all particles;
and judging whether the particle objective function value meets the convergence condition or not based on a preset convergence condition, if so, obtaining an optimal solution of the day-ahead scheduling plan, and if not, continuing iteration until the convergence condition is met.
Specifically, as shown in fig. 3, the basic flow of the PSO algorithm includes the following steps:
(1) And initializing a particle swarm. A number of particles are randomly generated, and each particle is recorded and calculated to randomly initialize a position and velocity in the solution space.
(2) And calculating the fitness value. And calculating the fitness value of each particle according to the current position, and evaluating the quality of the position.
(3) The velocity and position of the particles are updated. Updating the speed and position of the particles to move to a more optimal position according to the current position and speed, the experience of the particles and the group optimal experience, wherein the updating formula is as follows:
x [i][j] ＝x [i][j] +V [i][j] (10)
wherein V is [i][j] Representing the speed of the ith particle in the jth dimension; w, c 1 、c 2 Respectively weight factors; r is (r) 1 、r 2 Is a random factor;indicating the historically best position of the ith particle; />Representing the optimal location historically throughout the population; x is x [i][j] Is the position of the ith particle in the jth dimension.
(4) Updating the group optimal experience. And updating the group optimal experience, namely the global optimal solution, according to the fitness values of all the particles.
(5) And judging the convergence condition. And judging whether to terminate the algorithm according to the set convergence condition, such as the maximum iteration number or the adaptability value reaching a threshold value.
(6) And (5) circularly executing the steps 2-5 until the convergence condition is met, and obtaining the optimal solution of the day-ahead scheduling plan.
The particle swarm optimization algorithm (PSO algorithm) is an optimization algorithm based on swarm intelligence, and an optimal solution is found by simulating the behaviors of swarms such as shoal or shoal. The PSO algorithm has the characteristics of strong global searching capability, high convergence speed and the like, and is widely applied to solving of a micro-grid day-ahead scheduling model.
In each step, each particle can adjust its own position and speed according to its own experience and group information, and continuously update the group optimal solution, thereby realizing the search of the global optimal solution in the complex Gao Weijie space. Compared with other optimization algorithms, the PSO algorithm has the characteristics of easy implementation and strong convergence, and is widely applied in various fields. In the aspect of load nonlinear space prediction, the model is solved through a PSO algorithm, so that an optimal solution can be obtained, and the prediction accuracy is improved.
The working principle of the micro-grid day-ahead scheduling and planning method is as follows: according to the method, the micro-grid energy management objective function with the minimum electric quantity exchanged with the large grid is designed by acquiring the long-time scale predicted values of the renewable energy source power and the load power and combining the state of charge of the energy storage system. And secondly, designing a micro-grid dispatching model by combining an objective function and a constraint equation. And finally, solving the scheduling model through a particle swarm optimization algorithm to obtain an optimal solution of the day-ahead scheduling plan.
As shown in fig. 4, the present application provides a micro-grid day-ahead dispatch planning device, including:
a determining module 201, configured to determine a scheduling parameter, and construct an objective function and a constraint condition corresponding to the objective function based on the scheduling parameter;
a construction module 202, configured to construct a daily scheduling model of the micro-grid according to the objective function and the corresponding constraint conditions;
and the solving module 203 is configured to iteratively solve the future schedule model of the micro-grid by using a particle swarm optimization algorithm to obtain a future schedule plan.
The working principle of the micro-grid day-ahead scheduling planning device provided by the embodiment of the application is that a determining module 201 determines scheduling parameters, and an objective function and constraint conditions corresponding to the objective function are constructed based on the scheduling parameters; the construction module 202 constructs a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions; and the solving module 203 adopts a particle swarm optimization algorithm to carry out iterative solving on the micro-grid day-ahead scheduling model to obtain a day-ahead scheduling plan.
The application provides a computer device comprising: the memory and processor may also include a network interface, the memory storing a computer program, the memory may include volatile memory in a computer readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flash RAM). The computer device stores an operating system, with memory being an example of a computer-readable medium. The computer program, when executed by the processor, causes the processor to perform the method of microgrid day-ahead dispatch planning, the structure shown in fig. 5 is merely a block diagram of a portion of the structure associated with the present application and does not constitute a limitation of the computer device to which the present application is applied, and a specific computer device may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
In one embodiment, the micro-grid day-ahead schedule planning method provided in the present application may be implemented in the form of a computer program, which may be executed on a computer device as shown in fig. 5.
In some embodiments, the computer program, when executed by the processor, causes the processor to perform the steps of: determining a scheduling parameter, and constructing an objective function and a constraint condition corresponding to the objective function based on the scheduling parameter; constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions; and adopting a particle swarm optimization algorithm to carry out iterative solution on the daily scheduling model of the micro-grid to obtain a daily scheduling plan.
The present application also provides a computer storage medium, examples of which include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassette storage or other magnetic storage devices, or any other non-transmission medium, that can be used to store information that can be accessed by a computing device.
In some embodiments, the present invention further provides a computer readable storage medium storing a computer program, where when the computer program is executed by a processor, a scheduling parameter is determined, and an objective function and a constraint condition corresponding to the objective function are constructed based on the scheduling parameter; constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions; and adopting a particle swarm optimization algorithm to carry out iterative solution on the daily scheduling model of the micro-grid to obtain a daily scheduling plan.
In summary, the present invention provides a method, an apparatus, a device, and a medium for scheduling a micro-grid day-ahead, where the micro-grid day-ahead scheduling model provided in the present invention refers to scheduling power of various energy devices in a micro-grid in a time range of a future day, so that load requirements of the micro-grid are met, and exchange electric quantity with a large power grid is reduced as much as possible. The model is a core part of the micro-grid energy management system, and has important significance for realizing the economy and reliability of the micro-grid. The micro-grid day-ahead scheduling model is used for determining the running state and power output of each device of the micro-grid by predicting the information such as the load requirement of the micro-grid and the power output of renewable energy sources, so that the exchange electric quantity with a large grid is reduced as much as possible on the premise of meeting the load requirement of the micro-grid.
According to the method, the optimal solution of the day-ahead scheduling plan is obtained by solving the scheduling model through the particle swarm optimization algorithm, and the load demand of the micro-grid is met by scheduling the power of various energy devices in the micro-grid, meanwhile, the exchange electric quantity with a large power grid is reduced as much as possible, and grid-connected power fluctuation is reduced. In addition, the model is solved through the PSO algorithm, so that an optimal solution can be obtained, and the prediction accuracy is improved.
It can be understood that the above-provided method embodiments correspond to the above-described apparatus embodiments, and corresponding specific details may be referred to each other and will not be described herein.
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, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
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.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The daily scheduling planning method for the micro-grid is characterized by comprising the following steps of:
determining a scheduling parameter, and constructing an objective function and a constraint condition corresponding to the objective function based on the scheduling parameter;
constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions;
and adopting a particle swarm optimization algorithm to carry out iterative solution on the daily scheduling model of the micro-grid to obtain a daily scheduling plan.
2. The method of claim 1, wherein the objective function is to reduce the amount of power exchanged with the large grid, the objective function being
C mg (t)＝P mg (t)-P mgp (t)
C L (t)＝P L (t)-P Lp (t)
C Hess (t)＝P Hess (t)
Wherein lambda is 1 、λ 2 、λ 3 The weight coefficients of the distributed power generation unit, the micro-grid load and the hybrid energy storage system are respectively; c (C) mg (t) is a distributed generation unit power prediction difference; p (P) mg (t) is the power of the distributable power generation unit at the moment t; p (P) mgp (t) predicting power for the t-moment distributable power generation unit; c (C) L (t) load power prediction for microgridA difference value; p (P) L (t) is the microgrid load power at time t; p (P) Lp (t) is a microgrid load power predicted value at the moment t; p (P) Hess (t) is the output power of the hybrid energy storage system at time t; n (N) T Scheduling a period number for a day; c (C) Hess And (t) is the output power of the hybrid energy storage system at the moment t.
3. The method of claim 2, wherein the constraint comprises:
the active power balance constraint of the micro-grid,
P g (t)＝P mg (t)+P Hess (t)-P L (t)
the power constraint of the parallel grid,
(1-α)|P g,min (t)|≤|P g (t)|≤(1+α)|P g,max (t)|
the power constraint of the distributed generation unit,
P mg,min (t)≤P mg (t)≤P mg,max (t)
the hybrid energy storage system is constrained,
wherein P is g (t) is the actual grid-connected power at the moment t; alpha is an allowable tracking error coefficient; p (P) mg，max (t)、P mg，min (t) upper and lower power limits of the distributable power generation unit, respectively; p (P) Hess,max (t)、P Hess,min (t) upper and lower limits of output power of the hybrid energy storage system, respectively; SOC (t) is the remaining battery capacity; SOC (State of Charge) min (t) is the lower limit of the remaining battery capacity; SOC (State of Charge) max And (t) is the upper limit of the remaining capacity of the battery.
4. The method of claim 1, wherein iteratively solving the micro-grid day-ahead dispatch model using a particle swarm optimization algorithm to obtain a day-ahead dispatch plan comprises:
initializing a particle swarm, recording and calculating a position and a speed of each particle randomly initialized in a solution space;
calculating the fitness value of each particle according to the current position;
updating the speed and position of the particles according to the current position and speed, the experience of the particles and the group optimal experience;
obtaining a particle objective function value according to the fitness values of all particles;
and judging whether the particle objective function value meets the convergence condition or not based on a preset convergence condition, if so, obtaining an optimal solution of the day-ahead scheduling plan, and if not, continuing iteration until the convergence condition is met.
5. The method of claim 4, wherein the velocity and position of the particles are updated by,
x [i][j] ＝x [i][j] +V [i][j]
wherein V is [i][j] Is the speed of the ith particle in the jth dimension; w, c 1 、c 2 Respectively weight factors; r is (r) 1 、r 2 Is a random factor;is the historically best location for the ith particle; />Is the historically best location for the entire population; x is x [i][j] Is the position of the ith particle in the jth dimension.
6. The method of claim 4, wherein the convergence condition comprises:
the maximum number of iterations or fitness value reaches a threshold.
7. The method of claim 1, wherein the scheduling parameters comprise:
energy storage system operating parameters, renewable energy power prediction parameters, and load power prediction parameters.
8. A micro-grid day-ahead dispatch planning device, comprising:
the determining module is used for determining scheduling parameters, and constructing an objective function and a constraint condition corresponding to the objective function based on the scheduling parameters;
the construction module is used for constructing a micro-grid day-ahead dispatching model according to the objective function and the corresponding constraint conditions;
and the solving module is used for carrying out iterative solving on the daily front scheduling model of the micro-grid by adopting a particle swarm optimization algorithm to obtain a daily front scheduling plan.
9. A computer device, comprising: a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the micro grid day-ahead schedule planning method of any one of claims 1 to 7.
10. A computer storage medium, characterized in that a computer program is stored, which, when being executed by a processor, causes the processor to perform the micro grid day time schedule planning method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311648460.0A CN117713235A (en) | 2023-12-04 | 2023-12-04 | Micro-grid day-ahead scheduling planning method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311648460.0A CN117713235A (en) | 2023-12-04 | 2023-12-04 | Micro-grid day-ahead scheduling planning method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117713235A true CN117713235A (en) | 2024-03-15 |
Family
ID=90154483
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311648460.0A Pending CN117713235A (en) | 2023-12-04 | 2023-12-04 | Micro-grid day-ahead scheduling planning method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117713235A (en) |
-
2023
- 2023-12-04 CN CN202311648460.0A patent/CN117713235A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Qiu et al. | Safe reinforcement learning for real-time automatic control in a smart energy-hub | |
| Li et al. | Storage aided system property enhancing and hybrid robust smoothing for large-scale PV systems | |
| CN109002892A (en) | A kind of implementation method for improving DE-GWO algorithm | |
| Squartini et al. | Optimization algorithms for home energy resource scheduling in presence of data uncertainty | |
| CN113285490B (en) | Power system scheduling method, device, computer equipment and storage medium | |
| CN112163304B (en) | Power transmission network redundancy constraint identification method, storage medium and computing equipment | |
| CN114069678A (en) | Light storage direct current micro-grid energy scheduling method considering energy storage degradation cost | |
| Panda et al. | Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles | |
| Zhang et al. | Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning | |
| CN113794199A (en) | Maximum profit optimization method of wind power energy storage system considering electric power market fluctuation | |
| Takano et al. | Application example of particle swarm optimization on operation scheduling of microgrids | |
| CN116470543A (en) | Operation control method, device, equipment and medium of virtual power plant | |
| CN114386323A (en) | Power distribution network reactive power optimization method containing distributed power supply based on improved butterfly algorithm | |
| CN117559526A (en) | Router-simulated energy regulation and control method based on optical storage and charging integrated charging station | |
| CN116957362A (en) | Multi-target planning method and system for regional comprehensive energy system | |
| CN117713235A (en) | Micro-grid day-ahead scheduling planning method, device, equipment and medium | |
| CN110059840B (en) | Method and system for selecting address of battery energy storage system in receiving-end power grid | |
| CN113410900B (en) | Micro-grid HESS optimization configuration method and system based on self-adaptive difference whale optimization | |
| CN113555887B (en) | Power grid energy control method and device, electronic equipment and storage medium | |
| Chen et al. | Deep reinforcement learning based research on low‐carbon scheduling with distribution network schedulable resources | |
| Wehinger et al. | Assessing the effect of storage devices and a PHEV cluster on German spot prices by using model predictive and profit maximizing agents | |
| Chen et al. | Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model | |
| Liu et al. | Economic optimal allocation of photovoltaic energy storage system based on quantum particle swarm optimization algorithm | |
| CN115513949B (en) | Power distribution system operation control method and device containing micro-grid access and program product | |
| CN116914861B (en) | Wind power station economic optimization scheduling method and system based on model predictive control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |