CN110675275A - Demand side response power load regulation and control method and system of virtual power plant - Google Patents

Demand side response power load regulation and control method and system of virtual power plant Download PDF

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CN110675275A
CN110675275A CN201910837278.7A CN201910837278A CN110675275A CN 110675275 A CN110675275 A CN 110675275A CN 201910837278 A CN201910837278 A CN 201910837278A CN 110675275 A CN110675275 A CN 110675275A
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焦丰顺
邓永生
李铎
郑悦
张�杰
张植华
李志铿
李宝华
张瑞锋
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Shenzhen Power Supply Bureau Co Ltd
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Abstract

The invention provides a method and a system for regulating and controlling demand side response electric load 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 the 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 power output power generated by renewable energy sources according to the time data and the corresponding meteorological data; and 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 electricity price information, the power load data and the power generation output power of the renewable energy source. 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

Demand side response power load regulation and control method and system of virtual power plant
Technical Field
The invention relates to the technical field of power load regulation, 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 big background of electric power market reform, electric power will gradually return commodity attribute, and the price of electricity will fluctuate in real time also, and the fluctuation of price of electricity can influence the size of load promptly, and the fluctuation of load also can influence the size of price of electricity, and both reach the equilibrium in the in-process of mutual influence. The load prediction of the power system is the basic work of the power system for scheduling operation and production planning, is related to the safe and stable operation of the power system, and has immeasurable effect on the actual production life.
Under the premise that the existing topological structure of a power grid is not changed, a control coordination center of a virtual power plant aggregates different types of power supplies such as distributed power supplies, energy storage systems and network-accessible electric vehicles 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 optimization operation of multiple distributed energy sources through an upper-layer software algorithm, so that reasonable optimization configuration and utilization of resources are promoted. The electric power markets which the virtual power plant can participate in include a day-ahead market, a real-time market, a bilateral contract market, an auxiliary service market and the like, the participation in the balance market can help the virtual power plant to stabilize the 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 can enable demand-side resources to be a virtual resource to participate in power grid load scheduling, and the user demand response comprises price-based demand response and incentive-based demand response. The load side resource and the power supply side resource are considered in a combined mode, so that the load side scheduling and the power supply 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 patent number of '201811043955. X' and the name of 'power grid load prediction method, device, computer equipment and storage medium' provides a prediction model established according to historical data of historical similarity days; and predicting the power grid load of the day to be predicted according to the prediction model.
The existing power load prediction research finds that the power utilization habits of users change along with time or weather and other characteristic factors, and the power loads predicted by a single fixed trained power load prediction model along with the time or weather and other characteristic factors become inaccurate gradually.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method and a system for regulating and controlling the power load of the demand side response of a virtual power plant, so as to overcome the defect that the influence of meteorological factors is not considered in the load prediction in the prior art.
In order to solve the technical problem, the invention provides a method for regulating and controlling the response electric load of a demand side 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 the 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 power output power generated by renewable energy sources according to the time data and the corresponding meteorological data;
and 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 electricity price information, the power load data and the power generation output power of the renewable energy source.
The predicting of 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 the 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 the constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value;
and 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 method for constructing 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 processed historical meteorological data and 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 prediction.
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 of the wind power generation output power and historical meteorological data;
processing the historical time data and the historical meteorological data of the wind power generation output power to obtain the processed wind power generation output power data and the 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 a demand side response power load regulation and control system 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 sources, the power generation government subsidy price of the renewable energy sources and the user side electricity price information in real time;
the system comprises a to-be-detected day information acquisition module, a to-be-detected day information acquisition module and a to-be-detected day information acquisition module, wherein the to-be-detected day information acquisition module is used for acquiring time data of a to-be-predicted day and corresponding meteorological data;
the prediction module is used for predicting power load data and power output power generated by the renewable energy source according to the time data and the corresponding meteorological data;
and 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, 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.
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 the 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 the 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 prediction.
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: meteorological factors are considered in the process of predicting the power load of the user and the power output of the renewable energy, the accuracy of prediction is improved, 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, and the overall power balance of the system is improved; and the power consumption requirement is increased at certain low-price stages, the resource distribution of a power distribution system can be improved, and the phenomena of wind abandonment and power abandonment are prevented. If the price incentive strategy is adjusted, the demand response distribution curve changes along with the strategy, and reasonable control of part of loads in the active power distribution network can be realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for regulating and controlling demand-side response electrical load of a virtual power plant according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1 for description, an embodiment of the present invention provides a method for regulating and controlling a demand-side response electrical load of a virtual power plant, where the method includes the following steps:
and S1, acquiring the power generation internet price of the renewable energy sources, the power generation government subsidy price of the renewable energy sources and the user-side electricity price information in real time.
Specifically, a wind power generation internet price, a photovoltaic power generation internet price, a wind power generation government subsidy price, a photovoltaic power generation government subsidy price, and a user-side real-time electricity price are acquired in real time.
And S2, acquiring time data of the day to be predicted and corresponding meteorological data.
And S3, predicting power load data and power output power generated by the renewable energy source according to the time data and the corresponding meteorological data.
In a specific embodiment, the predicting the power load data and the renewable energy power generation output power 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 the 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 the constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value; and 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.
In one embodiment, the constructing the power 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 abnormal values in the corresponding historical meteorological data, including deleting the historical time data with abnormal values and the corresponding historical meteorological data, so as to obtain the processed historical time data and the processed historical meteorological data; and training power load prediction by using the processed historical time data, the processed historical meteorological data and historical power load data corresponding to the historical time data, so as to obtain a power load prediction model.
In order to improve the prediction accuracy of the power load prediction model, the power load prediction model adopts one of a space load prediction method, a power elasticity coefficient method, a per-person electricity consumption method, a unit consumption method for output value electricity, a load density method, a unit consumption method, a trend extrapolation method, a time series method, a regression analysis method, an autoregressive sliding average method, a neural network method, a support vector machine, a fuzzy set theory, a chaos theory, a wavelet analysis, a gray system theory, machine learning, a genetic algorithm or an expert system method, or adopts two or more methods of the methods to perform prediction respectively, and then performs weighted average on prediction results.
Taking the radial basis function neural network and extreme learning machine method combined prediction as an example: firstly, a model network is trained by using mass power load historical data acquired by a data acquisition module, secondly, the load influence factor influence weight characteristic value is input into the trained model, and then the future power load is predicted according to the model. And calculating characteristic factors such as historical load of the same week and the same week, daily average temperature, daily temperature difference, holiday type, month number, days and the like on the actual work.
The training network model of the proposed method is represented as:
in the formula, alphai＝[αi1，αi2，...，αin]Connecting weight values between the kernel function and the output layer, wherein n represents the number of the connecting weight values;
Figure BDA0002192584370000062
is the output of the ith kernel function, N is the number of kernel functions, fN(x) Refers to the output of the training model network.
The prediction model is
Figure BDA0002192584370000063
Where y is the predicted value, H is the hidden layer matrix, HTIs 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 predicted performance parameters, i.e. the predicted result error err, are:
in the formula, yiObtaining a predicted value of the power load on the ith day through a prediction model on the ith day; t is tiIs yiAnd K is the predicted days corresponding to the actual value of the power load.
The prediction steps are as follows:
step 1: giving and inputting N training sets, and obtaining M neurons of a radial basis function neural network;
step 2: setting a kernel function center and a center influence width;
and step 3: calculating a hidden layer matrix H and an inverse matrix H of the generalized matrix H+；
And 4, step 4: calculating the weight of an output layer;
and 5: inputting a test set to obtain a prediction result, and calculating the error of a training set;
step 6: and obtaining the optimal prediction result.
In a specific embodiment, the constructing a wind power generation output power prediction model specifically includes: acquiring historical data of wind power generation output power, historical time data of the wind power generation output power and historical meteorological data; processing the historical time data and the historical meteorological data of the wind power generation output power to obtain the processed wind power generation output power data and the 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 distributed renewable energy sources such as wind power generation and photovoltaic power generation can be predicted by algorithms such as an ARIMA model, a neural network, a support vector machine and multivariate linear regression.
Wherein the ARIMA (p, q) model is
Figure BDA0002192584370000071
In the formula, xtFor a value of a power sample utIs a zero-mean white noise sequence, p is an autoregressive order, q is a moving average order,
Figure BDA0002192584370000072
is an Autoregressive (AR) coefficient, θiIs the Moving Average (MA) coefficient.
The prediction method comprises the following steps:
step 1: carrying out stability inspection 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.
And step 3: and estimating the parameters of the ARIMA model by adopting a least square method. In ARIMA, memory
Figure BDA0002192584370000073
Figure BDA0002192584370000074
The residual term is
Δxt＝xt-fi(x,a)
When Δ xtWhen the sum of squares of (a) is minimum, a is the least squares parameter estimate.
And 4, step 4: and (4) checking whether the model is reasonable or not by making a residual sequence autocorrelation function and a partial correlation function.
And 5: the model is determined and the output power of distributed renewable energy sources such as electric wind power generation and photovoltaic power generation is predicted by identifying the model type, determining the order, estimating and checking parameters and the like.
And S4, regulating and controlling the output power 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 electricity price information, the power load data and the power generation output power of the renewable energy source.
Specifically, the power consumption price is macroscopically regulated and controlled according to the national policy, the time-of-use power price is adopted, the power consumption is allowed to be interrupted or reduced at the high-price moment, the power consumption demand is increased at the low-price moment, 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 power load data prediction data.
According to the method for regulating and controlling the response power load of the demand side of the virtual power plant, meteorological factors are considered in the process of predicting the power load of a user and the power output of renewable energy, the accuracy of prediction is improved, in addition, after the user responds to a power 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 power consumption requirement is increased at certain low-price stages, the resource distribution of a power distribution system can be improved, and the phenomena of wind abandonment and power abandonment are prevented. If the price incentive strategy is adjusted, the demand response distribution curve changes along with the strategy, and reasonable control of part of loads in 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 a demand side response electrical load regulation and control system for a virtual power plant, which specifically includes:
the price acquisition module is used for acquiring the power generation internet price of the renewable energy sources, the power generation government subsidy price of the renewable energy sources and the user side electricity price information in real time;
the system comprises a to-be-detected day information acquisition module, a to-be-detected day information acquisition module and a to-be-detected day information acquisition module, wherein the to-be-detected day information acquisition module is used for acquiring time data of a to-be-predicted day and corresponding meteorological data;
the prediction module is used for predicting power load data and power output power generated by the renewable energy source according to the time data and the corresponding meteorological data;
and 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, 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.
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 the 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 the 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 prediction.
The wind power generation output power prediction model and the photovoltaic power generation output power prediction model adopt ARIMA (p, q) models.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (10)

1. A method for regulating and controlling demand side response power load of a virtual power plant is characterized by comprising the following steps:
acquiring the power generation internet price of renewable energy sources, the power generation government subsidy price of the 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 power output power generated by renewable energy sources according to the time data and the corresponding meteorological data;
and 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 electricity price information, the power load data and the power generation output power of the renewable energy source.
2. The method of claim 1, wherein predicting the electrical load data and the renewable energy generated output power from the time data and the corresponding meteorological data specifically comprises:
substituting the time data of the day to be predicted and the corresponding meteorological data into the 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 the constructed wind power generation output power prediction model to obtain a wind power generation output power prediction value;
and 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.
3. The method of claim 2, wherein constructing the power load prediction model specifically comprises:
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 a power load prediction model by using the processed historical time data, the processed historical meteorological data and the historical power load data corresponding to the historical time data, so as to obtain the power load prediction model.
4. The method of claim 3, wherein the power load prediction model employs a radial basis function neural network in combination with an extreme learning machine approach to prediction.
5. The method according to claim 2, wherein said constructing a wind power generation output power prediction model comprises in particular:
acquiring historical data of wind power generation output power, historical time data of the wind power generation output power and historical meteorological data;
processing the historical time data and the historical meteorological data of the wind power generation output power to obtain the processed wind power generation output power data and the corresponding historical time data and historical meteorological data;
and training a wind power generation output power prediction model by utilizing the processed wind power generation output power data and the historical meteorological data to obtain the wind power generation output power prediction model.
6. The method of claim 5, wherein the wind power generation output power prediction model and the photovoltaic power generation output power prediction model employ an ARIMA (p, q) model, p being an autoregressive order and q being a moving average order.
7. The utility model provides a demand side response power load regulation and control system of virtual power plant which characterized in that includes:
the price acquisition module is used for acquiring the power generation internet price of the renewable energy sources, the power generation government subsidy price of the renewable energy sources and the user side electricity price information in real time;
the system comprises a to-be-detected day information acquisition module, a to-be-detected day information acquisition module and a to-be-detected day information acquisition module, wherein the to-be-detected day information acquisition module is used for acquiring time data of a to-be-predicted day and corresponding meteorological data;
the prediction module is used for predicting power load data and power output power generated by the renewable energy source according to the time data and the corresponding meteorological data;
and 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, 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.
8. The system of claim 7, 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 the 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 the 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.
9. The system of claim 8, wherein:
the power load prediction model adopts a radial basis function neural network and an extreme learning machine method to combine prediction.
10. The system of claim 9, 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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CN111509705A (en) * 2020-04-24 2020-08-07 明阳智慧能源集团股份公司 Power distribution network prediction system
CN111697570A (en) * 2020-05-25 2020-09-22 国网河北省电力有限公司石家庄市藁城区供电分公司 Power grid load prediction method
CN111898821A (en) * 2020-07-28 2020-11-06 国网山东省电力公司禹城市供电公司 Power load prediction method, system and device
CN113283659A (en) * 2021-06-03 2021-08-20 上海分未信息科技有限公司 Power load response task allocation method for virtual power plant
CN113283659B (en) * 2021-06-03 2022-11-22 上海分未信息科技有限公司 Power load response task allocation method for virtual power plant
CN113256016A (en) * 2021-06-04 2021-08-13 广东电网有限责任公司珠海供电局 Load prediction method and system based on virtual power plant operation
CN114465231A (en) * 2022-02-14 2022-05-10 南京邮电大学盐城大数据研究院有限公司 Power access method and system for offshore wind power generation
CN114465231B (en) * 2022-02-14 2023-06-13 南京邮电大学盐城大数据研究院有限公司 Power access method and system for offshore wind power generation
CN115169734A (en) * 2022-08-01 2022-10-11 周口师范学院 Wind power generation prediction system and method based on Internet of things

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