CN115081838A - Dynamic electricity price-based source-charge coordination scheduling method for wind and light absorption of heat accumulating type electric heating - Google Patents
Dynamic electricity price-based source-charge coordination scheduling method for wind and light absorption of heat accumulating type electric heating Download PDFInfo
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Abstract
A source-charge coordination scheduling method for wind and light absorption of heat accumulating type electric heating based on dynamic electricity price comprises the following steps: obtaining a typical hindered wind-solar output scene by adopting a Latin hypercube and a scene subtraction method according to the acquired data; dividing the obtained hindered wind-solar output into peak-valley-level time periods by adopting a large-scale semi-trapezoidal function and a clustering method, and setting a power price floating coefficient to obtain dynamic time-of-use power prices of all time points; the method comprises the steps of defining an intelligent control device, establishing a source charge optimization model for maximally absorbing blocked wind and light, responding to a minimum expected difference between peak and valley of system load after time-of-use electricity price, obtaining an operation plan of electric heating and the blocked wind and light through optimization scheduling in day-ahead days, comparing the operation plan with a scheme which does not adopt dynamic time-of-use electricity price, and indicating that the heat accumulating type electric heating load which adopts a dynamic time-of-use electricity price mechanism to operate can change the operation mode of the heat accumulating type electric heating load along with the peak adjusting requirement of the system, effectively absorbing wind and light blockage, and playing a role in balancing the system load while improving the operation economy of the system.
Description
Technical Field
The invention relates to the technical field of renewable energy consumption, in particular to a dynamic electricity price-based source-charge coordination scheduling method for wind and light consumed by heat accumulating type electric heating.
Background
Wind and light resources in rural areas of China are rich, but new energy power supply areas are often sparse in land, load density is low, and the phenomenon of severe wind and light abandonment occurs. In recent years, clean heating equipment represented by electric heating is vigorously pursued in rural areas of China, and a heat accumulating type electric heating load has the characteristics of flexibility, adjustability and controllability, can be used as a demand side response resource, realizes bidirectional interaction with a power generation side, and improves the absorption level of blocked wind and light.
The static time-of-use electricity price implemented at present cannot be dynamically adjusted according to the peak-valley period of the source charge, and the electricity utilization behavior of the electric heating load cannot be effectively guided to adjust, so that the static time-of-use electricity price can participate in the dispatching of a power grid. According to the response behavior of the load to the electricity price, a dynamic time-of-use electricity price exciting means is adopted on the demand side, so that a user can adjust the electricity utilization mode according to the wind and light output condition, the optimal configuration of resources is realized, and the heating pressure and the cost of the power system are effectively reduced. Whether the peak-valley-average time interval division of the blocked wind-solar electricity price and the user electricity demand response model are reasonable or not is important for eliminating wind and light abandonment. In the existing research, a deterministic scene is often adopted to determine the time-of-use electricity price, but the implementation effect of the time-of-use electricity price is seriously influenced because the prediction error of new energy is increased along with the increase of a time scale, and a deterministic scene model cannot effectively deal with the time-of-use electricity price formulation of a system containing the new energy.
Disclosure of Invention
The invention aims to provide a dynamic electricity price-based source-charge coordinated scheduling method for wind and light consumption of heat accumulating type electric heating, which is used for solving or at least partially solving the problems of establishment of blocked wind and light dynamic time-sharing electricity prices and demand response of heat accumulating type electric heating loads on the dynamic electricity prices so as to improve the wind and light consumption level of a system.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a source-charge coordination scheduling method for wind and light absorption of heat accumulating type electric heating based on dynamic electricity price comprises the following steps:
step1, acquiring historical wind and light load prediction errors, acquiring information such as the day-ahead predicted wind and light load output, and obtaining a blocked wind and light typical output curve according to the Latin hypercube and the scene subtraction method;
step2, dividing the obtained hindered wind-solar output curve into three periods of peak-valley level according to a large-scale semi-trapezoidal function and a clustering method, and setting a floating coefficient of dynamic electricity price to obtain electricity price of each period of peak-valley level;
step3, defining a heat accumulating type electric heating intelligent charging control device, and introducing a demand price elastic coefficient to describe the change of electricity price variation to cause the change of the electricity consumption of the electric heating load;
step4, establishing a day-ahead optimized dispatching model according to the acquired day-ahead wind-light load output data, and acquiring a day-ahead dispatching plan with the largest wind-light consumption and the smallest system load peak-valley difference by using a Yalmip solver;
step5, according to the acquired daily wind-solar load output data, adopting a weighted Euclidean distance method to roll and correct the blocked wind-solar information, and adjusting the output of each resource scheduled before the day;
and Step6, comparing and analyzing the optimization results of the dynamic electricity price mechanism and the power grid peak-valley electricity price mechanism.
The Step1 comprises the following specific steps:
step1.1, obtaining historical wind/light/load prediction error xi w,t 、ξ pv,t 、ξ el,t Predicting wind/light/load output day ahead
The prediction error can be regarded as a random variable obeying normal distribution, and the actual output is realized
Can be expressed as the sum of the predicted value and the prediction error;
step1.2, the blocked wind and light can be expressed as the difference value of wind and light output and load, and a plurality of initial scenes are processed by adopting Latin hypercube and scene subtraction method to obtain a typical blocked wind and light scene.
The Step2 comprises the following specific steps:
step2.1, calculating the peak membership degree of each time point of the hindered wind-solar curve according to a large-scale semi-trapezoidal function
Step2.2, dividing each time point in a day into peaks T by adopting a clustering method f Valley T g Flat T p Three periods of time;
step2.3, setting single electricity price e of blocked wind and light before dynamic time-of-use electricity price 0 After time-of-use electricity price is implemented, the peak-to-valley flat electricity price of the blocked wind and light floats for a certain proportion on the basis of the original single price, b 1 、b 2 、b 3 Respectively are the floating coefficients of the electricity price, namely the peak, the valley and the flat electricity price after the dynamic time-of-use electricity price are respectively e t,f 、e t,g 、e t,p 。
The Step3 comprises the following specific steps:
step3.1, defining a heat accumulating type electric heating intelligent charging control device, connecting heat accumulating type electric heating to a charging device by a user, setting heat load demand, and automatically arranging the running state of heat accumulating type electric heating equipment by the intelligent charging device according to the dynamic electricity price of the blocked wind and light;
step3.2, in order to avoid the phenomenon that the peak valley is reversed when the heat accumulating type electric heating load excessively responds to the electricity price, the elastic coefficient rho of the required price is introduced ij The variation quantity delta P of the electric heating load electric quantity before and after the electricity price is changed is obtained through mathematical transformation t 。
The Step4 comprises the following specific steps:
the aim of the Step4.1 model is to optimally control the heat accumulating type electric heating load power by dividing the peak-valley flat electricity price of the blocked wind and light, maximally absorb the blocked wind and light, and after responding to the time-of-use electricity price, the load peak-valley difference of the system is expected to be minimum, and the objective function of the model is as follows:
in the formula:
the probability of the occurrence of the s-th scene is defined as a set N;
the time-of-use electricity price of the blocked wind and light under the s scene is obtained;
the wind and light can be absorbed by the heat accumulating type electric heating at the t moment under the s scene; c E,t Is the electricity price of the power grid;
purchasing power to the power grid at the time t under the scene of s; e (L) s,t ) The expectation of the load peak-valley difference of the system under all scenes;
step4.2, the constraints of the model include:
the method comprises the following steps of electric heat power balance constraint, heat accumulation type electric heating operation constraint, heat accumulation and discharge power constraint of a heat accumulation device, capacity constraint of the heat accumulation device, periodic heat accumulation amount constraint, user income constraint and wind and light output constraint;
step4.3, the variable parameters of the model include:
decision variables and target variables, wherein the decision variables comprise any one or any combination of more of the hindered wind-solar output, the dynamic time-of-use electricity price, the power grid peak-valley electricity price and the demand price elastic coefficient matrix;
the target variables comprise the total system cost, the heat accumulating type electric heating operation power, the consumption of blocked wind and light electric quantity and the electric quantity purchased to an external power grid;
step4.4, the solving method of the model comprises the following steps:
and calling a Yalmip toolkit by matlab to solve the day-ahead scheduling model according to the acquired variable parameters.
The Step5 comprises the following specific steps:
step5.1, acquiring daily wind-solar load output data, calculating the similarity of each scene in the day and the corresponding time period of the daily ultra-short term prediction scene by adopting a weighted Euclidean distance method, extracting the scene with high similarity, and replacing the data of the scene in the day with the ultra-short term prediction data of the time period to obtain a new blocked wind-solar load output scene for rolling scheduling in the day;
and Step5.2, dividing the peak-valley level time period by adopting a large-scale semi-trapezoidal function and a clustering method again according to the corrected hindered wind-solar output curve, repeating Step4, and correcting the day-ahead scheduling plan.
Step6 above further includes:
and comparing the blocked wind-solar absorption rate, the electricity purchasing quantity, the total system cost and the load peak-valley difference of the system under the dynamic electricity price with the result of adopting a power grid peak-valley electricity price mechanism, and verifying the effectiveness of the method.
The invention provides a dynamic electricity price-based source-charge coordinated scheduling method for wind and light absorption of heat accumulating type electric heating, which is characterized in that on the power supply side, uncertain factors brought to system operation by prediction errors of new energy and load are considered, and a scene analysis method and a fuzzy membership function method are adopted to divide a peak-valley level dynamic time-of-use electricity price for a hindered wind and light output curve; on the load side, the characteristic that the heat accumulating type electric heating load is flexible and controllable is used as a demand side response resource, and an optimized scheduling model with the minimum electric heating operation cost and the minimum expected system load peak-valley difference is established. The heat accumulating type electric heating load which runs by adopting a dynamic time-of-use electricity price mechanism can change the running mode of the load along with the peak regulation requirement of the system, effectively eliminates blocked wind and light, and plays a role in balancing the system load while improving the running economy of the system.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a flow chart of a source-charge coordination scheduling method of heat accumulating type electric heating wind and light absorption based on dynamic electricity price;
FIG. 2 is a schematic diagram of a blocked wind-solar power scenario;
FIG. 3 is a schematic diagram of a comparison between a consumption-hindered wind-solar energy system using a dynamic electricity price mechanism and a power grid peak-valley electricity price mechanism;
FIG. 4 is a schematic diagram showing a comparison of the total load of the system using the dynamic electricity rate mechanism and the power grid peak-valley electricity rate mechanism;
FIG. 5 is a schematic diagram illustrating a comparison of the solar-wind resistance to absorption in the day using a dynamic electricity price mechanism and a power grid peak-to-valley electricity price mechanism;
fig. 6 is a schematic diagram showing comparison of total loads of the day system by using a dynamic electricity price mechanism and a power grid peak-valley electricity price mechanism.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description will make a system and a complete description of specific embodiments of the present invention, with reference to the accompanying drawings provided in accordance with the present invention.
Fig. 1 is a schematic flow chart of a source-charge coordination scheduling method for wind and light consumption of regenerative electric heating based on dynamic electricity price, which includes:
step1, acquiring basic data, and calculating to obtain a typical hindered wind-solar output curve;
step1.1, obtaining historical wind/light/load prediction error xi w,t 、ξ pv,t 、ξ el,t Obtaining the predicted day-ahead wind/light/load output
The actual values of the wind, light and load are obtained
Step1.2, acquiring a large number of scene sets by adopting a Latin hypercube sampling method, and then reducing the large number of hindered wind and light scene sets by using a scene reduction method to obtain a typical hindered wind and light curve;
in the formula: s is the s-th scene;
the scene is a blocked scene at the t moment under the scene s;
for wind power scene k 1 Has a probability of occurrence of
N w For a typical scene set of wind power after scene reduction,
respectively photovoltaic and load;
step2, determining the peak-valley level period of the obstructed wind and the dynamic electricity price thereof;
step2.1, calculating the peak membership degree of each time point of the hindered wind-solar curve according to a large-scale semi-trapezoidal function
In the formula:
the values are corresponding to each time point of the blocked wind-light curve, and m and n are respectively the maximum value and the minimum value of the blocked wind-light curve;
step2.2, dividing each time point in a day into peaks T by adopting a clustering method f Valley T g Flat T p Three periods of time;
step2.3, setting single electricity price e of blocked wind and light before dynamic time-of-use electricity price 0 After time-of-use electricity price is implemented, the peak-to-valley flat electricity price of the blocked wind and light floats for a certain proportion on the basis of the original single price, b 1 、b 2 、b 3 Respectively are the floating coefficients of the electricity price, namely the peak, the valley and the flat electricity price after the dynamic time-of-use electricity price are respectively e t,f 、e t,g 、e t,p ；
Step3, calculating the variation of the electric heating load before and after the dynamic electricity price variation;
step3.1, defining a heat accumulating type electric heating intelligent charging control device, and enabling a user to set heat load demand by connecting heat accumulating type electric heating to a charging device, wherein the intelligent charging device can reasonably arrange the running state of heat accumulating type electric heating equipment according to the dynamic electricity price of blocked wind and light;
step3.2, in order to avoid the phenomenon that the peak valley is reversed when the heat accumulating type electric heating load excessively responds to the electricity price, the elastic coefficient rho of the required price is introduced ij ；
In the formula: rho ij The required price elastic coefficient; Δ P, Δ C are the amount of change in demand and price, respectively; ou and u are states before and after the electricity price adjustment respectively; i. j represents time, when i equals j, the self-elastic coefficient is represented, otherwise, the cross-elastic coefficient is represented;
step3.3, obtaining the variation quantity delta P of the electric heating load electric quantity before and after the change of the electricity price through mathematical transformation t ；
In the formula: a is a demand price elastic coefficient matrix.
Step4, establishing a day-ahead optimization scheduling model, and acquiring a day-ahead scheduling plan;
the aim of the Step4.1 model is to optimally control the heat accumulating type electric heating load power by dividing the peak-valley flat electricity price of the blocked wind and light, maximally absorb the blocked wind and light, and after responding to the time-of-use electricity price, the load peak-valley difference of the system is expected to be minimum, and the objective function of the model is as follows:
in the formula:
the probability of the occurrence of the s-th scene is defined as a set N;
the time-of-use electricity price of the blocked wind and light under the s scene is obtained;
the wind and light can be absorbed by the heat accumulating type electric heating at the t moment under the s scene; c E,t Is the electricity price of the power grid;
purchasing power to the power grid at the time t under the scene of s; e (L) s,t ) Is a stand forExpectation of system load peak-valley difference under a scene;
step4.2, the constraints of the model include:
electric heating power balance constraint:
in the formula:
the direct heating power for electric heating at the moment t under the scene of s;
the heat release power of the heat storage device at the moment t under the scene of s;
the thermal load at the moment t under the scene of s;
and (3) restricting the operation of heat accumulating type electric heating:
Q eh,t ＝η·P eh,t
in the formula: q eh,t Heating power of the electric heating equipment at the moment t; p eh,t The electric heating power consumption at the time t; eta is the electrothermal conversion coefficient;
the heat storage device is restrained:
in the formula: q in,t The heat storage power of the heat storage device at the moment t; s eh,max The maximum heat storage capacity is obtained; k is a radical of loss Is the heat loss coefficient of the thermal storage device;
and (3) user revenue constraint:
in order to stimulate the response of the users to participate in the blocked wind-solar time-of-use electricity price, the heating cost after the time-of-use electricity price is implemented is lower than the expenditure before the implementation;
and (3) restrained wind-solar output constraint:
on the premise of not considering the blockage and the network loss of the power transmission line, in order to promote the absorption of the blocked wind and light, the principle of improving the wind and light absorption of a power grid by the heat accumulating type electric heating operation is used as the constraint of the blocked wind and light output;
step4.3, dividing the model into decision variables and target variables according to the parameters in the model;
the decision variables comprise any one or any combination of more of a blocked wind-solar output, a dynamic time-of-use electricity price, a power grid peak-valley electricity price and a demand price elastic coefficient matrix;
the target variables comprise the total system cost, the heat accumulating type electric heating operation power, the consumption of blocked wind and light electric quantity and the electric quantity purchased to an external power grid.
Step4.3, calling a Yalmip toolkit by matlab to solve the day-ahead scheduling model according to the acquired variable parameters;
step5, acquiring daily wind/light/load output data, and adjusting the output of each resource scheduled before the day;
step5.1, according to the acquired wind/light/load output data in the day, calculating the similarity of each scene in the day and the corresponding time interval of the ultra-short term prediction scene in the day by adopting a weighted Euclidean distance method, extracting the scene with high similarity, replacing the data of the scene in the day with the ultra-short term prediction data in the time interval to obtain a new blocked wind/light output scene for rolling scheduling in the day, wherein the formula of the weighted Euclidean distance is as follows;
in the formula: epsilon t A weight coefficient which is the relativity of each time point; χ is the maximum distance which meets the requirement;
step5.2, dividing the peak-valley level time period by adopting a large-scale semi-trapezoidal function and a clustering method again according to the corrected hindered wind-solar output curve, repeating Step4, and correcting a day-ahead scheduling plan;
step6, respectively calculating the optimization results of the dynamic electricity price mechanism and the power grid peak-valley electricity price mechanism;
and comparing the blocked wind-solar absorption rate, the electricity purchasing quantity, the total system cost and the load peak-valley difference of the system under the dynamic electricity price with the result of adopting a power grid peak-valley electricity price mechanism, and verifying the effectiveness of the method.
Taking a certain rural area in Hebei province as an example, each house roof of the village is provided with a photovoltaic power generation board with 500kW in total, and a 2MW wind power plant, the new energy power generation is generally considered to have no cost, and here, the blocked wind and light single power generation price e is assumed 0 Is 0.2 yuan/(kWh.h), b 1 ＝0.5、b 2 ＝-0.5、b 3 When the power grid peak electricity price is equal to 0, the power grid peak electricity price period is as follows: 08:00 to 20: 00, 0.55 yuan/(kW.h); the valley electricity price period: 20: 00 to 08:00, 0.32 yuan/(kW h).
Firstly, obtaining a blocked wind and light scene by adopting Latin hypercube and scene subtraction as shown in FIG. 2, obtaining peak membership degrees at all times by calculating according to a large-scale semi-trapezoidal function as shown in Table 1, and obtaining peak-valley average electricity price division of blocked wind and light at all time points by utilizing a clustering method as shown in Table 2.
TABLE 1 degree of peak membership at each time
TABLE 2 time-of-day electricity price division of each time point
In order to compare the effect of the wind and light blocked by the heat storage electric heating load before and after the adoption of the dynamic time-of-use electricity price, the optimization results of the adoption of the dynamic electricity price mechanism and the adoption of the power grid peak-valley electricity price mechanism are compared, the wind and light blocked pair is absorbed, for example, as shown in fig. 3, the total load pair of the system, for example, as shown in fig. 4, and the optimization results are shown in table 3.
As can be seen from fig. 2, wind and light resources in rural areas in the province of north of the river are rich, but the load density is low, and a large amount of wind and light abandoning phenomena occur, so that the heat accumulating type electric heating load participates in optimization, and blocked wind and light are absorbed on the spot while the heating requirement is met; as can be seen from fig. 3 to 4, the heat accumulating type electric heating system operates by adopting a power grid peak-valley electricity price mechanism, namely operates only at night, in order to ensure the heating requirement in the daytime, the electric heating equipment is easy to use electricity at night and has high rate, the phenomenon of 'peak-valley inversion' of the load of the power grid occurs, in the period of 04:00-08:00, the electricity quantity of abandoned wind and light is insufficient, the system must purchase electricity to the power grid, and the electric heating equipment does not operate any more in the daytime to release heat for heating, so that a large amount of abandoned wind and abandoned light occur in the daytime; the shape of the blocked wind-light absorption curve and the predicted blocked wind-light output curve of the second scheme are close to each other, and therefore the heat accumulating type electric heating load can serve as a demand response resource to adjust the operation state in time according to the time-of-use electricity price after the blocked wind-light time-of-use electricity price is adopted for optimized operation, and the amount of abandoned wind light is effectively reduced.
TABLE 3 optimization results under different scenarios
As can be seen from table 3, compared with the first scheme, the absorption rate of the blocked wind and light is greatly improved after the provided source charge is optimally controlled, the absorption rate is improved from 56.9% to 81.7%, the second scheme can fully utilize the low-electricity-price blocked wind and light heat storage for heating, the proportion of electricity purchased by the power grid is reduced, and the economical efficiency of system operation is improved; the difference between the total load peak and the total load peak of the system under the two schemes is not large, but compared with the first scheme, the heat accumulating type electric heating system which operates by adopting a dynamic time-of-use electricity price mechanism can flexibly respond to the change of the blocked wind and light, has a certain degree of system load balancing effect, can fully utilize the electric quantity of the abandoned wind and light to accumulate heat when the amount of the blocked wind and light is sufficient, and has the advantage and characteristic of source charge optimal control, wherein the electric quantity purchased to a power grid is 0.
According to the existing prediction method, the prediction error is larger when the time scale span is larger, so that the day-in operation and the day-ahead plan are generated to be largerThe deviation, therefore, requires correction of the division of the peak-to-valley level period of the obstructed view light according to the ultra-short term prediction data. Correcting a blocked wind-solar output curve by adopting a weighted Euclidean distance method, repeating the steps to correct the division of two peak-valley normal periods to obtain time-of-use electricity price each period: peak period t 1 ，t 2 ,t 3 ,t 4 ,t 14 ,t 15 ,t 16 }, valley period { t 9 ，t 10 ,t 11 ,t 19 ,t 20 ,t 21 ,t 22 H, flat period t 5 ，t 6 ,t 7 ,t 8 ,t 12 ,t 13 ,t 17 ,t 18 ,t 23 ,t 24 }。
Comparing the in-day optimization results of the dynamic electricity price mechanism and the power grid peak-valley electricity price mechanism, the consumption hindered wind-solar pair is shown in fig. 5, the total system load is shown in fig. 6, and the optimization results are shown in table 4.
It can be known from fig. 5-6 that along with rolling correction of the hindered wind-light output curve in the day, the hindered wind-light quantity reduction is absorbed by adopting a power grid peak-valley electricity price mechanism scheme, the reason is considered, the hindered wind-light quantity reduction is mainly related to the hindered wind-light quantity at night, even if the wind-light quantity abandoned at night is less than that in the day, in order to ensure that the heat load requirement in the day is met, the system needs to purchase electricity at night for heat supply, so that the electricity purchase proportion is increased, and because the peak-valley electricity price mechanism is used for storing heat in the valley electricity price period, when the heat load is constant, the operation cost of the system is kept unchanged. In the second scheme, the amount of abandoned wind and light at night after the rolling correction is reduced, and the absorption rate of the blocked wind and light is increased when the thermal load is constant. The result shows that the prediction error of the blocked wind and light has great influence on the implementation result of the time-of-use electricity price, if the predicted value of the blocked wind and light in a certain period of time is lower than the actual value, the electricity price in the period of time is lower than the value which should be set, and conversely, the electricity price is higher than the set electricity price, and under the action of the elastic coefficient of the supply price, the electricity price influences the operation state of the heat accumulating type electric heating.
It can be seen from table 4 that, after the peak-valley average electricity price division changes at each time point, the operation state of the heat accumulating type electric heating is correspondingly changed compared with the optimization result before the day, the blocked wind and light absorption rate is further improved, the operation cost of the system is reduced, and the load peak-valley difference of the system is effectively reduced.
Table 4 optimization results under two schemes
Claims (7)
1. A heat accumulating type electric heating wind and light absorption source and charge coordination scheduling method based on dynamic electricity price is characterized by comprising the following steps:
step1, acquiring historical wind and light load prediction errors, acquiring day-ahead predicted wind and light load output information, and acquiring a hindered wind and light output curve according to a Latin hypercube and a scene subtraction method;
step2, dividing the obtained hindered wind-solar output curve into three periods of peak-valley level according to a large-scale semi-trapezoidal function and a clustering method, and setting a floating coefficient of dynamic electricity price to obtain electricity price of each period of peak-valley level;
step3, defining a heat accumulating type electric heating intelligent charging control device, and introducing a demand price elastic coefficient to describe the change of electricity price variation to cause the change of electric heating load electricity consumption;
step4, establishing a day-ahead optimized dispatching model according to the acquired day-ahead wind-light load output data, and acquiring a day-ahead dispatching plan with the largest wind-light consumption and the smallest system load peak-valley difference by using a Yalmip solver;
step5, according to the acquired daily wind-solar load output data, adopting a weighted Euclidean distance method to roll and correct the blocked wind-solar information, and adjusting the output of each resource scheduled before the day;
and Step6, comparing and analyzing the optimization results of the dynamic electricity price mechanism and the power grid peak-valley electricity price mechanism.
2. The method for source-charge coordinated dispatching of wind and light for heat accumulating type electric heating based on dynamic electricity price according to claim 1, wherein Step1 comprises the following steps:
step1.1, obtaining historical wind/light/load prediction error xi w,t 、ξ pv,t 、ξ el,t Predicting wind/light/load output day ahead
The prediction error can be regarded as a random variable obeying normal distribution, and the actual output is realized
Can be expressed as the sum of the predicted value and the prediction error;
step1.2, the blocked wind and light can be expressed as the difference value of wind and light output and load, and a plurality of initial scenes are processed by adopting Latin hypercube and scene subtraction method to obtain a typical blocked wind and light scene.
3. The method for source-charge coordinated dispatching of wind and light for heat accumulating type electric heating based on dynamic electricity price according to claim 2, wherein Step2 comprises the following steps:
step2.1, calculating the peak membership degree of each time point of the hindered wind-solar curve according to a large-scale semi-trapezoidal function
Step2.2, dividing each time point in a day into peaks T by adopting a clustering method f Valley T g Flat T p Three periods of time;
step2.3, setting single electricity price e of blocked wind and light before dynamic time-of-use electricity price 0 After time-of-use electricity price is implemented, the peak-to-valley flat electricity price of the blocked wind and light floats for a certain proportion on the basis of the original single price, b 1 、b 2 、b 3 Respectively are the floating coefficients of the electricity price, namely the peak, the valley and the flat electricity price after the dynamic time-of-use electricity price are respectively e t,f 、e t,g 、e t,p 。
4. The method for source-charge coordinated dispatching of wind and light for heat accumulating type electric heating based on dynamic electricity price according to claim 3, wherein Step3 comprises the following steps:
step3.1, defining a heat accumulating type electric heating intelligent charging control device, connecting heat accumulating type electric heating to a charging device by a user, setting heat load demand, and automatically arranging the running state of heat accumulating type electric heating equipment by the intelligent charging device according to the dynamic electricity price of the blocked wind and light;
step3.2, in order to avoid the phenomenon that the peak valley is reversed when the heat accumulating type electric heating load excessively responds to the electricity price, the elastic coefficient rho of the required price is introduced ij The variation quantity delta P of the electric heating load electric quantity before and after the electricity price is changed is obtained through mathematical transformation t 。
5. The method for source-charge coordinated dispatching of wind and light for heat accumulating type electric heating based on dynamic electricity price according to claim 4, wherein Step4 comprises the following steps:
the aim of the Step4.1 model is to optimally control the heat accumulating type electric heating load power by dividing the peak-valley flat electricity price of the blocked wind and light, maximally absorb the blocked wind and light, and after responding to the time-of-use electricity price, the load peak-valley difference of the system is expected to be minimum, and the objective function of the model is as follows:
in the formula:
the probability of the occurrence of the s-th scene is defined as a set N;
the time-of-use electricity price of the blocked wind and light under the s scene is obtained;
the wind and light can be absorbed by the heat accumulating type electric heating at the t moment under the s scene; c E,t Is the electricity price of the power grid;
purchasing power to the power grid at the time t under the scene of s; e (L) s,t ) The expectation of the load peak-valley difference of the system under all scenes;
step4.2, the constraints of the model include:
the method comprises the following steps of electric heat power balance constraint, heat accumulation type electric heating operation constraint, heat accumulation and discharge power constraint of a heat accumulation device, capacity constraint of the heat accumulation device, periodic heat accumulation amount constraint, user income constraint and wind and light output constraint;
step4.3, the variable parameters of the model include:
decision variables and target variables, wherein the decision variables comprise any one or any combination of more of the hindered wind-solar output, the dynamic time-of-use electricity price, the power grid peak-valley electricity price and the demand price elastic coefficient matrix;
the target variables comprise the total system cost, the heat accumulating type electric heating operation power, the consumption of blocked wind and light electric quantity and the electric quantity purchased to an external power grid;
step4.4, the solving method of the model comprises the following steps:
and calling a Yalmip toolkit by matlab to solve the day-ahead scheduling model according to the acquired variable parameters.
6. The method for source-charge coordinated dispatching of wind and light for heat accumulating type electric heating based on dynamic electricity price according to claim 5, wherein Step5 comprises the following steps:
step5.1, acquiring daily wind-solar load output data, calculating the similarity of each scene in the day and the corresponding time period of the daily ultra-short term prediction scene by adopting a weighted Euclidean distance method, extracting the scene with high similarity, and replacing the data of the scene in the day with the ultra-short term prediction data of the time period to obtain a new blocked wind-solar load output scene for rolling scheduling in the day;
and Step5.2, dividing the peak-valley level time period by adopting a large-scale semi-trapezoidal function and a clustering method again according to the corrected hindered wind-solar output curve, repeating Step4, and correcting the day-ahead scheduling plan.
7. The method for source-charge coordinated dispatching of wind and light for regenerative electric heating based on dynamic electricity prices of claim 6, wherein Step6 further comprises:
and comparing the blocked wind-solar absorption rate, the electricity purchasing quantity, the total system cost and the load peak-valley difference of the system under the dynamic electricity price with the result of adopting a power grid peak-valley electricity price mechanism, and verifying the effectiveness of the method.
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