CN114118787A - Dispatching optimization method for urban distributed source network load storage based on LSTM algorithm - Google Patents
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Abstract
The invention discloses a dispatching optimization method of urban distributed source network load storage based on an LSTM algorithm in the field of load regulation, which comprises the following steps of 1: acquiring historical load data of a medium-voltage line through a power system; step 2: cleaning and reserving an available historical load data sequence, and establishing a line load prediction model based on an LSTM algorithm; and step 3: inputting the real-time load of the line at the current moment into a line load prediction model, and predicting in real time to obtain a line load value at the next moment; and 4, step 4: acquiring an adjustable load interval of a charging station corresponding to a current line; and 5: according to the line load value obtained through prediction in the step 3, an optimal load adjusting method of the charging station in the adjustable load interval at the next moment is obtained based on a particle swarm algorithm; step 6: and the charging station executes the load adjustment method in the step 5, and repeats the steps 3-6. The invention can effectively improve the operation efficiency of the distribution network line after the large-scale access of various distributed source networks for load storage, reduce the network loss and stabilize the load fluctuation.
Description
Technical Field
The invention relates to the field of load regulation, in particular to a dispatching optimization method of urban distributed source network load storage based on an LSTM algorithm.
Background
At present, new energy sources have already established a dominant position in future power systems. Under the background, new energy sources such as wind power, photovoltaic and energy storage can be explosively increased, and electric automobiles and related industries can enter a multiplication stage.
And along with the continuous expansion of the scale of various distributed energy sources, energy storage, charging piles and the like which are connected into the power distribution network, the problems of power reverse transmission, large load level fluctuation and energy waste can be caused due to different power utilization characteristics of different types of energy source units. For example, weather factors have great influence on photovoltaic power generation, which causes that the load of a distribution network line with photovoltaic is easy to generate randomness and fluctuation, so that the load of the line is unbalanced. The superposition of charging times for electric vehicles or the charging behavior during peak load periods will burden the power grid. And if the fluctuation of renewable energy sources such as wind, light and the like is balanced by simply using stored energy, the cost is high and the realization is difficult.
Therefore, large-scale access of photovoltaic stations, energy storage and charging piles and the like under the distribution network has great influence on the load level of the distribution network, the difficulty of economic operation and safety management of a power system is greatly increased, further access and consumption of distributed renewable energy sources are restricted, and no effective method for cooperative scheduling of various distributed energy sources, electric vehicle charging and energy storage exists at present.
In summary, the applicant provides an improvement scheme aiming at the problem of less distributed source network load storage coordination optimization scheduling for photovoltaic power stations and electric vehicle charging stations under distribution network lines.
Disclosure of Invention
The invention aims to provide a dispatching optimization method of urban distributed source network load storage based on an LSTM algorithm, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a dispatching optimization method of urban distributed source network load storage based on an LSTM algorithm comprises the following steps:
step 1: acquiring historical load data of a medium-voltage line through a power system;
step 2: cleaning and reserving an available historical load data sequence, and establishing a line load prediction model based on an LSTM algorithm;
and step 3: inputting the real-time load of the line at the current moment into a line load prediction model, and predicting in real time to obtain a line load value at the next moment;
and 4, step 4: acquiring an adjustable load interval of a charging station corresponding to a current line;
and 5: according to the line load value obtained through prediction in the step 3, an optimal load adjusting method of the charging station in the adjustable load interval at the next moment is obtained based on a particle swarm algorithm;
step 6: and (5) the charging station executes the load adjustment method obtained in the step (5), and repeats the steps (3-6).
In some embodiments, in step 5, if the line load rate corresponding to the predicted line load value is greater than 80%, the current charging load of the charging station is adjusted downward; and if the line load rate corresponding to the predicted line load value is less than 20%, the current charging load of the charging station is adjusted up.
In some embodiments, the optimal load adjustment method is expressed as the load to be adjusted of the charging station, which is obtained by the particle swarm algorithm when the variance of the daily load curve of the line is minimum under the condition that the constraint condition is satisfied.
In some embodiments, a time is indicated as 15 minutes and steps 3-6 are repeated every 15 minutes.
In some embodiments, the line load prediction model is a long-short time memory model obtained by training a historical load data sequence, and the long-short time memory model is represented as Forecast ═ f (t, d, y)1). Where t ∈ [0,24 ]]Time of day, in hours; d ∈ {1, 2., 365}, which represents the number of days of a year, in days, y1Representing historical load data including historical electricity demand.
Has the advantages that: the invention develops research work oriented to the city distributed source network load storage coordination optimization scheduling method, can effectively improve the operation efficiency of distribution network lines after large-scale access of various distributed source networks to load storage, reduces network loss, stabilizes load fluctuation, provides safer, more economic, cleaner and sustainable power supply product service for users, and assists in constructing a novel power system taking new energy as a theme.
Drawings
FIG. 1 is a model diagram of the dispatching optimization of the urban distributed source network load storage based on the LSTM algorithm;
FIG. 2 is a basic flow diagram of the present invention;
FIG. 3 is a load curve diagram of a line in the fertilizer market of an embodiment of the present invention on a typical day;
FIG. 4 is a load curve graph of a line at a typical day based on the line predictive load model prediction in an embodiment of the present invention;
fig. 5 is a comparison graph of load curves before and after the line is adjusted by the scheduling optimization method on a typical day according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, a method for optimizing the scheduling of urban distributed source network load storage based on LSTM algorithm includes the following steps:
step 1: acquiring historical load data of a medium-voltage line through a power system;
step 2: cleaning and reserving an available historical load data sequence, and establishing a line load prediction model based on an LSTM algorithm;
and step 3: inputting the real-time load of the line at the current moment into a line load prediction model, and predicting in real time to obtain a line load value at the next moment;
the method specifically comprises the following steps:
step 3.1: data processing: the short-term load prediction of the line only considers the loads 20 time points before the predicted time point, so that the label value of the training data of the LSTM neural network is the load value of the time node, the characteristic value is the load value of 20 time points before each time node, and the load of the next time point is predicted through the load data of the first 20 points;
step 3.2: construction of an LSTM prediction model: summarizing and integrating historical load data sequences to train and generate a long-time and short-time memory model for predicting the power load; the neural network load prediction model of long-time and short-time memory is expressed as a formula: forecast ═ f (t, d, y)1). Where t ∈ [0,24 ]]Time of day, in hours; d ∈ {1, 2., 365} represents the number of days of the year, in days, y1Historical load data including historical power demand;
step 3.3: model training and optimization: setting training time, observing a descending trend of training errors by continuously adjusting parameters and training times, increasing and reducing the number of basic features, and obtaining a relatively excellent long-time memory training model;
step 3.4: and inputting the real-time load of the line at the current moment based on a line load prediction model obtained by training of an LSTM algorithm, and predicting the load of the line of a time sequence segment in the next year.
And 4, step 4: acquiring an adjustable load interval of a charging station corresponding to a current line;
and 5: according to the line load value obtained through prediction in the step 3, solving an optimal load adjustment method of the charging station in the adjustable load interval at the next moment based on a Particle Swarm Optimization (PSO);
the particle swarm algorithm specifically comprises the following steps:
5.1 initializing population and individuals;
5.2 calculating the objective function value of the particle;
5.3 determining a particle optimal solution and a global optimal solution;
5.4 judging whether the excessive convergence of the population reaches local optimum;
5.4 updating the particle speed and position;
5.6 repeating the steps 5.1 to 5.4 until the maximum iteration number is reached, and outputting the optimal solution of the variance of the load curve if the maximum iteration number is reached, namely the load to be adjusted of the charging station, which is obtained by the particle swarm algorithm when the variance of the daily load curve of the line is minimum under the constraint condition.
Step 6: and (5) executing the load adjusting method obtained in the step (5) by the charging station, and adjusting the upper limit of the electric load of the charging station. If the line load rate corresponding to the predicted line load value is larger than 80%, the current charging load of the charging station is adjusted downwards; and if the line load rate corresponding to the pre-measured line load value is less than 20%, the current charging load of the charging station is adjusted up. And repeating the steps 3-6 to achieve the purpose of peak clipping and valley filling.
The examples provided according to the above method are as follows:
taking a certain line in the fertilizer market as an example, a load curve of the line (including photovoltaic output) in a typical day is shown in fig. 3. After the historical load data of the line is cleaned, a line load prediction model based on the LTSM algorithm is obtained according to the reserved data training, and as shown in FIG. 4, the load value of the line at the next moment is predicted in real time by using the LSTM algorithm, so that predicted line load data is obtained. Through verification, the prediction accuracy of the line load reaches 92%.
Then, the optimal charging station adjustment load is obtained through a particle swarm algorithm, so that the variance of a line daily load curve is minimum under the condition that the load limiting condition is met, wherein the population size is 100, and the inertia weight is omegamax＝0.8，ωmin0.3, learning factor v1＝2＝1.373。
The prediction and the adjustment of the charging station load are repeated every 15 minutes to realize the optimal adjustment of the line load, and then results before and after the optimization as shown in fig. 5 can be obtained, which shows that the invention can stabilize the load fluctuation and ensure that the line can still maintain the high-efficiency and stable operation efficiency after being accessed into various distributed source networks for load storage in a large scale.
Although the present description is described in terms of embodiments, not every embodiment includes only a single embodiment, and such description is for clarity only, and those skilled in the art should be able to integrate the description as a whole, and the embodiments can be appropriately combined to form other embodiments as will be understood by those skilled in the art.
Therefore, the above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application; all changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
Claims (5)
1. A dispatching optimization method of urban distributed source network load storage based on an LSTM algorithm is characterized by comprising the following steps:
step 1: acquiring historical load data of a medium-voltage line through a power system;
step 2: cleaning and reserving an available historical load data sequence, and establishing a line load prediction model based on an LSTM algorithm;
and step 3: inputting the real-time load of the line at the current moment into a line load prediction model, and predicting in real time to obtain a line load value at the next moment;
and 4, step 4: acquiring an adjustable load interval of a charging station corresponding to a current line;
and 5: according to the line load value obtained through prediction in the step 3, an optimal load adjusting method of the charging station in the adjustable load interval at the next moment is obtained based on a particle swarm algorithm;
step 6: and (5) the charging station executes the load adjustment method obtained in the step (5), and repeats the steps (3-6).
2. The dispatching optimization method for urban distributed source network load storage based on the LSTM algorithm according to claim 1, wherein in step 5, if the predicted line load value corresponding to the line load rate is greater than 80%, the current charging load of the charging station is adjusted downward; and if the line load rate corresponding to the predicted line load value is less than 20%, the current charging load of the charging station is adjusted up.
3. The dispatching optimization method for the urban distributed source network load storage based on the LSTM algorithm according to claim 1, characterized in that the optimal load adjustment method is expressed as the load to be adjusted by the charging station, which is obtained by the particle swarm algorithm when the variance of the daily load curve of the line is minimum under the constraint condition.
4. The method for optimizing the dispatching of the urban distributed source network load storage based on the LSTM algorithm according to claim 1, wherein a time is 15 minutes, and the steps 3-6 are repeated every 15 minutes.
5. The method as claimed in claim 1, wherein the line load prediction model is a long-short term memory model obtained by training a historical load data sequence, and the long-short term memory model is expressed as Forecast ═ f (t, d, y)1). Where t ∈ [0,24 ]]Time of day, in hours; d ∈ {1, 2., 365}, which represents the number of days of a year, in days, y1Representing historical load data including historical power usage requirements.
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