CN110675275B - Electric load regulation and control method and system for demand side response application of virtual power plant - Google Patents
Electric load regulation and control method and system for demand side response application of virtual power plant Download PDFInfo
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Abstract
The invention provides a method and a system for regulating and controlling electric loads for demand side response application of a virtual power plant, wherein the method comprises the following steps: acquiring the power generation internet price of renewable energy sources, the power generation government subsidy price of renewable energy sources and the user side electricity price information in real time; acquiring time data of a day to be predicted and corresponding meteorological data; predicting power load data and renewable energy power generation output according to the time data and the corresponding meteorological data; and regulating and controlling the power output of the renewable energy according to the power generation internet price of the renewable energy, the power generation government subsidy price of the renewable energy, the user side electricity price information, the power load data and the power generation output power of the renewable energy. The invention provides the accuracy of load and renewable energy output power prediction, and realizes the reasonable control of partial load in the active power distribution network.
Description
Technical Field
The invention relates to the technical field of power load regulation and control, in particular to a method and a system for regulating and controlling power load for demand side response of a virtual power plant.
Background
Under the large background of the reform of the electric power market, electric power gradually returns to commodity attributes, and the electricity price also fluctuates in real time, namely the fluctuation of the electricity price can influence the size of a load, and the fluctuation of the load also can influence the size of the electricity price, so that the electricity price and the electricity price are balanced in the mutual influence process. The load prediction of the power system is the basic work of the power system for scheduling operation and production planning, the load prediction relates to the safe and stable operation of the power system, and the load prediction has immeasurable effect on actual production and life.
On the premise of not changing the existing topological structure of the power grid, the control coordination center aggregates different types of power sources such as distributed power sources, energy storage systems, network-accessible electric vehicles and the like through an advanced coordination control technology, an intelligent metering technology and an information communication technology based on power generation prediction and load prediction, and realizes coordinated and optimized operation of multiple distributed energy sources through an upper software algorithm, so that reasonable and optimized configuration and utilization of resources are promoted. The electric power markets in which the virtual power plant can participate include a daily market, a real-time market, a bilateral contract market, an auxiliary service market and the like, and the participation balance market can help the virtual power plant to stabilize fluctuation of renewable energy sources, reduce the risk of inaccurate output prediction of the renewable energy sources and obtain greater economic benefits.
The demand response may enable the demand side resource to be a virtual resource to participate in grid load scheduling, and the user demand response includes a price-based demand response and an incentive-based demand response. The load side resource and the power side resource are considered in combination, so that the load side scheduling and the power side scheduling are coordinated, and the method is an effective way for solving the problem of uncontrollable new energy power consumption.
The invention patent application with the application number of 201811043955.X and the name of grid load prediction method, device, computer equipment and storage medium provides a prediction model established according to historical data of similar historical days; and predicting the power grid load on the day to be predicted according to the prediction model.
Existing power load prediction researches find that the power consumption habits of users can change along with characteristic factors such as time or weather, and the power load predicted by a single fixed trained power load prediction model can become inaccurate gradually along with the characteristic factors such as time or weather.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method and a system for regulating and controlling electric loads for demand side response application of a virtual power plant, so as to solve the defect that in the prior art, the influence of meteorological factors is not considered in load prediction.
In order to solve the technical problems, the invention provides a method for regulating and controlling electric load for demand side response of a virtual power plant, which comprises the following steps:
acquiring the power generation internet price of renewable energy sources, the power generation government subsidy price of renewable energy sources and the user side electricity price information in real time;
acquiring time data of a day to be predicted and corresponding meteorological data;
predicting power load data and renewable energy power generation output according to the time data and the corresponding meteorological data;
and regulating and controlling the power output of the renewable energy according to the power generation internet price of the renewable energy, the power generation government subsidy price of the renewable energy, the user side electricity price information, the power load data and the power generation output power of the renewable energy.
The method for predicting the power load data and the renewable energy power generation output power according to the time data and the corresponding meteorological data specifically comprises the following steps:
substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed power load prediction model to obtain a predicted value of the power load;
substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value;
substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed photovoltaic power generation output power prediction model to obtain the photovoltaic power generation output power prediction value.
The construction of the power load prediction model specifically comprises the following steps:
acquiring historical power load data of a user side, and time data and historical meteorological data corresponding to the historical power load data;
processing the time data and the historical meteorological data to obtain processed historical time data and historical meteorological data;
and training power load prediction by using the processed historical time data, the historical meteorological data and the historical power load data corresponding to the historical time data, so as to obtain a power load prediction model.
The power load prediction model adopts a radial basis function neural network and an extreme learning machine method to combine and predict.
The method for constructing the wind power generation output power prediction model specifically comprises the following steps:
acquiring historical data of wind power generation output power, historical time data and historical meteorological data of the wind power generation output power;
processing the historical time data and the historical meteorological data of the wind power generation output power to obtain processed wind power generation output power data and corresponding historical time data and historical meteorological data;
and training a wind power generation prediction model by using the processed wind power generation output power data and the historical meteorological data to obtain the wind power generation output power prediction model.
The wind power generation output power prediction model and the photovoltaic power generation output power prediction model adopt ARIMA (p, q) models.
The invention also provides an electric load regulation and control system for demand side response of the virtual power plant, which comprises the following components:
the price acquisition module is used for acquiring the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source and the user side electricity price information in real time;
the day information acquisition module to be detected is used for acquiring time data of a day to be predicted and corresponding meteorological data;
the prediction module is used for predicting power load data and renewable energy source power generation output according to the time data and the corresponding meteorological data;
the regulation and control module is used for regulating and controlling the power load of the user side according to the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source, the user side power price information, the power load data and the power generation output power of the renewable energy source.
Wherein the prediction module comprises:
the power load prediction module is used for substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed power load prediction model to obtain a predicted value of the power load;
the wind power generation output power prediction module is used for substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value;
and the photovoltaic power generation output power prediction module is used for substituting the time data of the day to be predicted and the corresponding meteorological data into the constructed photovoltaic power generation output power prediction model to obtain the photovoltaic power generation output power prediction value.
The power load prediction model adopts a radial basis function neural network and an extreme learning machine method to combine and predict.
The wind power generation output power prediction model and the photovoltaic power generation output power prediction model adopt ARIMA (p, q) models.
The embodiment of the invention has the beneficial effects that: in addition, after the user responds to the electricity price strategy, the electricity consumption is allowed to be interrupted or reduced at certain high price moments, so that the overall power balance of the system is improved; and the electricity consumption requirement is increased in certain low-price stages, so that the distribution of power distribution system resources can be improved, and the phenomena of wind abandoning and electricity abandoning are prevented. If the price incentive strategy is adjusted, the demand response distribution curve is changed along with the strategy, so that reasonable control of partial load of the active power distribution network can be realized.
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 flow chart of a method for regulating and controlling an electric load for demand side response of a virtual power plant according to an embodiment of the invention.
Detailed Description
The following description of embodiments refers to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced.
An embodiment of the present invention provides a method for regulating and controlling an electric load for demand side response of a virtual power plant, which includes the following steps:
s1, acquiring the power generation internet price of renewable energy sources, the power generation government subsidy price of renewable energy sources and the user side electricity price information in real time.
Specifically, the wind power generation internet price, the photovoltaic power generation internet price, the wind power generation government subsidy price, the photovoltaic power generation government subsidy price and the real-time electricity price of a user are obtained in real time.
S2, acquiring time data of a day to be predicted and corresponding meteorological data.
And S3, predicting power load data and renewable energy source power generation output power according to the time data and the corresponding meteorological data.
In one specific embodiment, the predicting the power load data and the renewable energy power generation output according to the time data and the corresponding meteorological data specifically includes:
substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed power load prediction model to obtain a predicted value of the power load; substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value; substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed photovoltaic power generation output power prediction model to obtain the photovoltaic power generation output power prediction value.
In one embodiment, constructing the electrical load prediction model specifically includes: acquiring historical power load data of a user side, and time data and historical meteorological data corresponding to the historical power load data; processing the time data and the historical meteorological data, for example, after obtaining the historical power load data, processing the time of the historical power load data and the abnormal value in the corresponding historical meteorological data, including deleting the historical time data with the abnormal value and the corresponding historical meteorological data, so as to obtain processed historical time data and historical meteorological data; and training power load prediction by using the processed historical time data, the historical meteorological data and the historical power load data corresponding to the historical time data, so as to obtain a power load prediction model.
In order to improve the accuracy of the prediction of the power load prediction model, the power load prediction model adopts one of a space load prediction method, a power elastic coefficient method, a human power consumption method, a power generation unit consumption method, a load density method, a unit consumption method, a trend extrapolation method, a time sequence method, a regression analysis method, an autoregressive moving average method, a neural network method, a support vector machine, a fuzzy set theory, a chaos theory, wavelet analysis, a gray system theory, machine learning, a genetic algorithm or an expert system method, or adopts two or more of the above methods to respectively predict, and then carries out weighted average on the prediction result.
Taking a radial basis function neural network and an extreme learning machine method as an example, the combination prediction is as follows: firstly, training a model network by utilizing massive power load historical data acquired by a data acquisition module, secondly, inputting a load influence factor influence weight characteristic value into a trained model, and then predicting future power loads according to the model. And calculating characteristic factors such as historical load, daily average air temperature, daily temperature difference, holiday type, month number and days and the like of the same week in actual work.
The training network model of the proposed method is expressed as:
wherein alpha is i ＝[α i1 ，α i2 ，...，α in ]For connecting the connection weight between the kernel function and the output layer, n represents the number of the connection weight;for the output of the ith kernel function, N refers to the number of kernel functions, f N (x) Refers to the output of the training model network.
The prediction model is
Wherein y is a predicted value, H is an hidden layer matrix, H T Is the transpose of the H matrix, lambda is the scale factor, T is the time variable, and N is the number of kernel functions.
The prediction performance parameter, namely the prediction result error err is:
wherein y is i The power load predicted value of the ith day is obtained through a predicted model of the ith day; t is t i Is y i And K is the predicted days according to the corresponding actual value of the power load.
The prediction steps are as follows:
step 1: giving N training sets, and M neurons of a radial basis function neural network;
step 2: setting the center of the kernel function and the center influence width;
step 3: calculating hidden layer matrix H and matrix H generalized inverse matrix H + ；
Step 4: calculating the weight of an output layer;
step 5: inputting a test set to obtain a prediction result, and calculating an error of a training set;
step 6: and obtaining an optimal prediction result.
In a specific embodiment, the constructing the wind power generation output power prediction model specifically includes: acquiring historical data of wind power generation output power, historical time data and historical meteorological data of the wind power generation output power; processing the historical time data and the historical meteorological data of the wind power generation output power to obtain processed wind power generation output power data and corresponding historical time data and historical meteorological data; and training a wind power generation prediction model by using the processed wind power generation output power data and the historical meteorological data to obtain the wind power generation output power prediction model.
The output power of the distributed renewable energy sources such as wind power generation, photovoltaic power generation and the like can be predicted by adopting an ARIMA model, a neural network, a support vector machine, a multiple linear regression and other algorithms.
Wherein the ARIMA (p, q) model is
Wherein x is t For the output power sample value, u t Is a zero-mean white noise sequence, p is an autoregressive order, q is a moving average order,is an Autoregressive (AR) coefficient, θ i Is the running average (MA) coefficient.
The prediction method comprises the following steps:
step 1: carrying out stability test on the original data;
step 2: the value of the order (p, q) and the number of differences d of the corresponding ARIMA model are determined.
Step 3: and estimating ARIMA model parameters by adopting a least square method. In ARIMA, record
The residual term is
Δx t ＝x t -f i (x,a)
When Deltax t And a is the least squares parameter estimate when the sum of squares is at a minimum.
Step 4: and checking whether the model is reasonable or not by performing a residual sequence autocorrelation function and a partial correlation function.
Step 5: the model is determined through model type identification, order determination, parameter estimation, inspection and the like, and output power prediction of distributed renewable energy sources such as electric wind power generation, photovoltaic power generation and the like is performed.
S4, regulating and controlling the output power of the renewable energy according to the power generation internet price of the renewable energy, the power generation government subsidy price of the renewable energy, the power price information of the user, the power load data and the power generation output power of the renewable energy.
Specifically, the electricity price is macroscopically regulated and controlled according to the national policy, the time-sharing electricity price is adopted, the electricity consumption is allowed to be interrupted or reduced at the high price time, the electricity demand is increased at the low price time, and the output power of distributed renewable energy sources such as wind power generation, photovoltaic power generation and the like is regulated and controlled by combining the predicted data of the power load data.
According to the electric load regulation and control method for the demand side response of the virtual power plant, provided by the embodiment of the invention, meteorological factors are considered in the process of predicting the electric load of a user and the power output of renewable energy sources, so that the prediction accuracy is improved, in addition, after the user responds to an electric price strategy, the power consumption is allowed to be interrupted or reduced at certain high price moments, and the overall power balance of the system is improved; and the electricity consumption requirement is increased in certain low-price stages, so that the distribution of power distribution system resources can be improved, and the phenomena of wind abandoning and electricity abandoning are prevented. If the price incentive strategy is adjusted, the demand response distribution curve is changed along with the strategy, so that reasonable control of partial load of the active power distribution network can be realized.
Based on the first embodiment of the present invention, the second embodiment of the present invention provides an electric load regulation system for demand side response of a virtual power plant, which specifically includes:
the price acquisition module is used for acquiring the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source and the user side electricity price information in real time;
the day information acquisition module to be detected is used for acquiring time data of a day to be predicted and corresponding meteorological data;
the prediction module is used for predicting power load data and renewable energy source power generation output according to the time data and the corresponding meteorological data;
the regulation and control module is used for regulating and controlling the power load of the user side according to the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source, the user side power price information, the power load data and the power generation output power of the renewable energy source.
Wherein the prediction module comprises:
the power load prediction module is used for substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed power load prediction model to obtain a predicted value of the power load;
the wind power generation output power prediction module is used for substituting the time data of the day to be predicted and the corresponding meteorological data into a constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value;
and the photovoltaic power generation output power prediction module is used for substituting the time data of the day to be predicted and the corresponding meteorological data into the constructed photovoltaic power generation output power prediction model to obtain the photovoltaic power generation output power prediction value.
The power load prediction model adopts a radial basis function neural network and an extreme learning machine method to combine and predict.
The wind power generation output power prediction model and the photovoltaic power generation output power prediction model adopt ARIMA (p, q) models.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.
Claims (5)
1. The electric load regulation and control method for the demand side response of the virtual power plant is characterized by comprising the following steps of:
acquiring the power generation internet price of renewable energy sources, the power generation government subsidy price of renewable energy sources and the user side electricity price information in real time;
acquiring time data and meteorological data of a day to be predicted;
predicting the power load data and the renewable energy source power generation output power according to the time data and the meteorological data, and specifically comprising the following steps: substituting the time data and the meteorological data of the day to be predicted into a constructed power load prediction model to obtain a predicted value of the power load; substituting the time data and the meteorological data of the day to be predicted into a constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value; substituting the time data and the meteorological data of the day to be predicted into a constructed photovoltaic power generation output power prediction model to obtain a photovoltaic power generation output power prediction value;
the method for regulating and controlling the power output of the renewable energy source according to the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source, the user side power price information, the power load data and the power generation output power of the renewable energy source specifically comprises the following steps: the time-sharing electricity price is adopted, the electricity consumption is allowed to be interrupted or reduced at the high price time, the electricity consumption requirement is increased at the low price time, and the wind power generation output power and the photovoltaic power generation output power are regulated and controlled by combining the predicted value of the electric load;
the construction of the power load prediction model specifically comprises the following steps:
acquiring historical power load data of a user side, and time data and historical meteorological data corresponding to the historical power load data; processing the time data and the historical meteorological data to obtain processed historical time data and historical meteorological data; training a power load prediction model by using the processed historical time data, the historical meteorological data and the historical power load data corresponding to the historical time data, so as to obtain the power load prediction model, wherein the power load prediction model is expressed as:
wherein y is a predicted value, H is an hidden layer matrix, H T Is the transpose of the H matrix,is a scale factor, T is a time variable, N is the number of kernel functions, ++>Is->An output of the kernel function;
the prediction performance parameter, namely the prediction result error err is:
in the method, in the process of the invention,is->The predicted value of the natural electric load is obtained through a prediction model of the ith day; />Is->Corresponding actual value of the electrical load, +.>The number of days was predicted.
2. The method according to claim 1, wherein constructing a wind power generation output power prediction model specifically comprises:
acquiring historical data of wind power generation output power, historical time data and historical meteorological data of the wind power generation output power;
processing the historical time data and the historical meteorological data of the wind power generation output power to obtain processed wind power generation output power data and corresponding historical time data and historical meteorological data;
and training a wind power generation output power prediction model by using the processed wind power generation output power data and the historical meteorological data to obtain the wind power generation output power prediction model.
3. The method of claim 2, wherein the wind power generation output power prediction model and the photovoltaic power generation output power prediction model employ ARIMA (p, q) models, p being an autoregressive order, q being a moving average order.
4. An electrical load regulation system for demand side response of a virtual power plant, comprising:
the price acquisition module is used for acquiring the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source and the user side electricity price information in real time;
the day information acquisition module to be detected is used for acquiring time data and meteorological data of the day to be predicted; the prediction module is used for predicting power load data and renewable energy source power generation output according to the time data and the meteorological data;
the regulation and control module is used for regulating and controlling the power load of the user side according to the power generation internet price of the renewable energy source, the power generation government subsidy price of the renewable energy source, the user side power price information, the power load data and the power generation output power of the renewable energy source;
the prediction module includes:
the power load prediction module is used for substituting the time data and the meteorological data of the day to be predicted into a constructed power load prediction model to obtain a predicted value of the power load;
the wind power generation output power prediction module is used for substituting the time data and the meteorological data of the day to be predicted into a constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value;
the photovoltaic power generation output power prediction module is used for substituting the time data and the meteorological data of the day to be predicted into a constructed photovoltaic power generation output power prediction model to obtain a photovoltaic power generation output power prediction value;
the construction of the power load prediction model specifically comprises the following steps:
acquiring historical power load data of a user side, and time data and historical meteorological data corresponding to the historical power load data; processing the time data and the historical meteorological data to obtain processed historical time data and historical meteorological data; training a power load prediction model by using the processed historical time data, the historical meteorological data and the historical power load data corresponding to the historical time data, so as to obtain the power load prediction model, wherein the power load prediction model is expressed as:
wherein y is a predicted value, H is an hidden layer matrix, H T Is the transpose of the H matrix,is a scale factor, T is a time variable, N is the number of kernel functions, ++>Is->An output of the kernel function;
the prediction performance parameter, namely the prediction result error err is:
in the method, in the process of the invention,is->The predicted value of the natural electric load is obtained through a prediction model of the ith day; />Is->Corresponding actual value of the electrical load, +.>The number of days was predicted.
5. The system according to claim 4, wherein:
the wind power generation output power prediction model and the photovoltaic power generation output power prediction model adopt ARIMA (p, q) models, p is an autoregressive order, and q is a moving average order.
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