Source-load coordination dispatch method for thermal storage electric heating absorbing wind and solar power based on dynamic electricity price

Abstract

A source-load coordination scheduling method for thermal storage electric heating and wind and solar power based on dynamic electricity prices includes: using Latin hypercube and scenario reduction methods according to collected data to obtain typical blocked wind and solar power output scenarios; using a relatively large semi-trapezoidal function and clustering method to divide the obtained blocked wind and solar power output into peak, valley and flat periods, setting the electricity price floating coefficient, and obtaining the dynamic time-of-use electricity price at each time point; defining intelligent control devices, establishing a source-load optimization model that maximizes the absorption of blocked wind and solar power, and minimizes the expected peak-to-valley difference of system load after responding to the time-of-use electricity price, and obtaining the operation plan of electric heating and blocked wind and solar power through day-ahead intraday optimization scheduling, and comparing it with a scheme that does not adopt dynamic time-of-use electricity prices. The results show that the thermal storage electric heating load operating with a dynamic time-of-use electricity price mechanism can change its operation mode following the system peak-shaving demand, effectively absorb blocked wind and solar power, and balance the system load while improving the system operation economy.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy consumption, and in particular to a source-load coordinated scheduling method for thermal storage electric heating consuming wind and solar power based on dynamic electricity prices. Background Art

[0002] my country's rural areas are rich in wind and solar resources, but the areas where new energy power is supplied are often sparsely populated and the load density is relatively low, resulting in serious wind and solar power abandonment. In recent years, clean heating equipment represented by electric heating has been vigorously promoted in rural areas of my country. The thermal storage electric heating load is flexible and adjustable, and can be used as a demand-side response resource to achieve two-way interaction with the power generation side and improve the level of wind and solar power consumption.

[0003] The currently implemented static time-of-use electricity price cannot be dynamically adjusted according to the peak and valley periods of the source load, and it is difficult to effectively guide the electric heating load to adjust its electricity consumption behavior and participate in the dispatch of the power grid. According to the response behavior of the load to the electricity price, dynamic time-of-use electricity price incentives are adopted on the demand side. Users can adjust their electricity consumption according to the wind and solar output, optimize the allocation of resources, and effectively reduce the heating pressure and cost of the power system. Whether the peak, valley and normal period division of the blocked wind and solar power price and the user's electricity demand response model are reasonable is crucial to the absorption of abandoned wind and solar power. Existing studies often use deterministic scenarios to determine the time-of-use electricity price, but because the prediction error of new energy will increase with the increase of time scale, it seriously affects the implementation effect of the time-of-use electricity price. The deterministic scenario model can no longer effectively deal with the formulation of time-of-use electricity prices containing new energy systems. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a source-load coordinated scheduling method for thermal storage electric heating to absorb wind and solar power based on dynamic electricity prices, so as to solve or at least partially solve the problem of formulating dynamic time-of-use electricity prices for blocked wind and solar power and the demand response of thermal storage electric heating load to dynamic electricity prices to improve the wind and solar power absorption level of the system.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A source-load coordination scheduling method for thermal storage electric heating absorbing wind and solar power based on dynamic electricity prices, the method comprises the following steps:

[0007] Step 1: Obtain the historical wind and solar load prediction error, obtain the wind and solar load output predicted on the day before, and obtain the typical output curve of obstructed wind and solar based on Latin hypercube and scenario reduction method;

[0008] Step 2: Based on the large-scale semi-trapezoidal function and clustering method, the obstructed wind and solar power output curve is divided into three periods: peak, valley and flat. The floating coefficient of the dynamic electricity price is set to obtain the electricity price in each period of peak, valley and flat.

[0009] Step 3, define the intelligent charging control device for thermal storage electric heating, and introduce the demand price elasticity coefficient to describe the change of electric heating load power consumption caused by electricity price changes;

[0010] Step 4: Establish a day-ahead optimization dispatching model based on the acquired day-ahead wind and solar load output data, and use the Yalmip solver to obtain a day-ahead dispatching plan with the maximum wind and solar power consumption and the minimum system load peak-valley difference;

[0011] Step 5: Based on the acquired daily wind and solar load output data, the weighted Euclidean distance method is used to roll over the blocked wind and solar information and adjust the output of each resource scheduled on the previous day;

[0012] Step 6: Compare and analyze the optimization results of using a dynamic electricity price mechanism and using the grid peak and valley electricity price mechanism.

[0013] The specific steps of Step 1 above are:

[0014] Step 1.1. Obtain historical wind / solar / load prediction errors ξ w,t , pv,t , el,t , the wind / solar / load output forecasted today Its prediction error can be regarded as a random variable that obeys a normal distribution, so its actual output It can be expressed as the sum of the predicted value and the prediction error;

[0015] Step 1.2, the obstructed wind and solar power can be expressed as the difference between the wind and solar power output and the load. The Latin hypercube and scene reduction method are used to process multiple initial scenes to obtain a typical obstructed wind and solar power scene.

[0016] The specific steps of Step 2 above are:

[0017] Step 2.1. Calculate the peak membership of the blocked wind and solar curve at each time point based on the large-scale semi-trapezoidal function

[0018] Step 2.2, use the clustering method to divide each time point in a day into peak T f , Valley T g , flat T p Three time periods;

[0019] Step 2.3, set the single power price e0 of the blocked wind and solar power before the implementation of the dynamic time-of-use electricity price. After the implementation of the time-of-use electricity price, the peak, valley and flat electricity prices of the blocked wind and solar power will fluctuate by a certain proportion on the basis of the original single price. b1, b2 and b3 are the electricity price fluctuation coefficients respectively. That is, the peak, valley and flat electricity prices after the implementation of the dynamic time-of-use electricity price are et,f 、e t,g 、e t,p .

[0020] The specific steps of Step 3 above are:

[0021] Step 3.1. Define the intelligent charging control device for thermal storage electric heating. The user connects the thermal storage electric heating to the charging device and sets the heat load demand. The intelligent charging device automatically arranges the operation status of the thermal storage electric heating equipment according to the dynamic electricity price of the blocked wind and solar power.

[0022] Step 3.2: In order to avoid the over-response of thermal storage electric heating load to electricity price and the phenomenon of peak-valley reversal, the demand price elasticity coefficient ρ is introduced ij After mathematical transformation, the change in electric heating load before and after the electricity price change is obtained. t .

[0023] The specific steps of Step 4 above are:

[0024] Step 4.1. The goal of the model is to optimize the control of the thermal storage electric heating load power by dividing the peak-valley-flat electricity prices of blocked wind and solar power, maximize the consumption of blocked wind and solar power, and minimize the peak-valley difference of system load after responding to the time-of-use electricity price. The objective function of the model is:

[0025]

[0026] Where: is the probability of the sth scene appearing, and its set is N; is the time-of-use electricity price of blocked wind and solar power in scenario s; is the blocked wind and solar power consumption of thermal storage electric heating at time t under scenario s; C E,t is the electricity price of the power grid; is the power purchased from the grid at time t in scenario s; E(L s,t ) is the expected peak-to-valley difference of system load in all scenarios;

[0027] Step 4.2: The constraints of the model include:

[0028] Electric heating power balance constraints, thermal storage electric heating operation constraints, thermal storage device heat storage and release power constraints, thermal storage device capacity constraints, periodic heat storage constraints, user benefit constraints, and obstructed wind and solar output constraints;

[0029] Step 4.3, the variable parameters of the model include:

[0030] Decision variables and target variables, where the decision variables include any one or more combinations of blocked wind and solar power output, dynamic time-of-use electricity prices, peak and valley electricity prices of power grids, and demand price elasticity coefficient matrix;

[0031] The target variables include total system cost, thermal storage electric heating operating power, consumption of blocked wind and solar power, and power purchase from external power grids;

[0032] Step 4.4, the solution method of the model includes:

[0033] According to the obtained variable parameters, MATLAB is used to call the Yalmip toolkit to solve the day-ahead scheduling model.

[0034] The specific steps of Step 5 above are:

[0035] Step 5.1. Obtain the wind and solar load output data within the day, use the weighted Euclidean distance method to calculate the similarity between each scene on the day before and the corresponding period of the ultra-short-term forecast scene within the day, extract the scene with high similarity, and replace the data of the scene on the day before with the ultra-short-term forecast data of this period to obtain the new blocked wind and solar output scene for intraday rolling scheduling;

[0036] Step 5.2: Based on the revised obstructed wind and solar power output curve, re-adopt the large-scale semi-trapezoidal function and clustering method to divide the peak, valley and normal periods, repeat Step 4, and revise the day-ahead dispatch plan.

[0037] The above Step 6 also includes:

[0038] The blocked wind and solar power consumption rate, power purchase amount, total system cost, and system load peak-valley difference under dynamic electricity prices are compared with the results of the grid peak-valley electricity price mechanism to verify the effectiveness of the proposed method.

[0039] The present invention provides a source-load coordination scheduling method for thermal storage electric heating to absorb wind and solar power based on dynamic electricity prices. On the power supply side, considering the uncertainties brought to the system operation by the prediction errors of new energy and loads, the scenario analysis method and fuzzy membership function method are used to divide the output curve of blocked wind and solar power into peak, valley and flat dynamic time-of-use electricity prices; on the load side, the flexible and controllable characteristics of the thermal storage electric heating load are used as a demand-side response resource, and an optimization scheduling model with the minimum electric heating operation cost and the minimum expected peak-valley difference of the system load is established. The thermal storage electric heating load operating with a dynamic time-of-use electricity price mechanism can change its operating mode according to the system peak-shaving demand, effectively absorb the blocked wind and solar power, and play a role in balancing the system load while improving the economic efficiency of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0041] Figure 1 It is a flow chart of the source-load coordination dispatching method of thermal storage electric heating absorbing wind and solar power based on dynamic electricity price;

[0042] Figure 2This is a schematic diagram of the blocked wind and solar power output scenario;

[0043] Figure 3 This is a schematic diagram comparing the obstructed wind and solar power consumption under the dynamic electricity price mechanism and the grid peak and valley electricity price mechanism;

[0044] Figure 4 This is a schematic diagram comparing the total load of the system using a dynamic electricity price mechanism and a peak-valley electricity price mechanism of the power grid;

[0045] Figure 5 This is a schematic diagram comparing the blocked wind and solar power consumption during the day when a dynamic electricity price mechanism is adopted and when a peak-valley electricity price mechanism is adopted;

[0046] Figure 6 Schematic diagram comparing the total system load during the day when a dynamic electricity price mechanism is adopted and when a peak-valley electricity price mechanism is adopted. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following content will systematically and completely describe the specific technical solutions of the present invention in conjunction with the accompanying drawings provided according to the present invention.

[0048] Figure 1 A flow chart of a source-load coordinated dispatching method for wind and solar power consumption by thermal storage electric heating based on dynamic electricity prices provided by the present invention is provided, and the method comprises:

[0049] Step 1. Obtain basic data and calculate the typical obstructed wind and solar output curve;

[0050] Step 1.1. Obtain historical wind / solar / load prediction errors ξ w,t , pv,t , el,t , get the day-ahead forecast of wind / solar / load output The actual output values ​​of wind / solar / load are

[0051]

[0052] Step 1.2, using the Latin hypercube sampling method to obtain a large number of scene sets, and then using the scene reduction method to reduce a large number of obstructed wind and solar scene sets to obtain a typical obstructed wind and solar curve;

[0053]

[0054] Where: s is the sth scene; The scenery is blocked at time t under scene s; The probability of occurrence of wind power scenario k1 is N wThis is a collection of typical wind power scenarios after scenario reduction. They are photovoltaic and load respectively;

[0055] Step 2: Determine the peak, valley and average periods of the blocked wind and solar power and their dynamic electricity prices;

[0056] Step 2.1. Calculate the peak membership of the blocked wind and solar curve at each time point based on the large-scale semi-trapezoidal function

[0057]

[0058] Where: is the value corresponding to each time point of the blocked wind-light curve, m and n are the maximum and minimum values ​​of the blocked wind-light curve respectively;

[0059] Step 2.2, use the clustering method to divide each time point in a day into peak T f , Valley T g , flat T p Three time periods;

[0060] Step 2.3, set the single power price e0 of the blocked wind and solar power before the implementation of the dynamic time-of-use electricity price. After the implementation of the time-of-use electricity price, the peak, valley and flat electricity prices of the blocked wind and solar power will fluctuate by a certain proportion on the basis of the original single price. b1, b2 and b3 are the electricity price fluctuation coefficients respectively. That is, the peak, valley and flat electricity prices after the implementation of the dynamic time-of-use electricity price are e t,f 、e t,g 、e t,p ;

[0061]

[0062] Step 3, calculate the change in electric heating load before and after the dynamic electricity price changes;

[0063] Step 3.1, define the intelligent charging control device for thermal storage electric heating. The user only needs to connect the thermal storage electric heating to the charging device and set the heat load demand. The intelligent charging device will reasonably arrange the operation status of the thermal storage electric heating equipment according to the dynamic electricity price of the blocked wind and solar power.

[0064] Step 3.2: In order to avoid the over-response of thermal storage electric heating load to electricity price and the phenomenon of peak-valley reversal, the demand price elasticity coefficient ρ is introduced ij ;

[0065]

[0066] Where: ijis the demand price elasticity coefficient; △P and △C are the changes in demand and price respectively; ou and u are the states before and after the electricity price adjustment respectively; i and j represent the time, when i=j, it represents the self-elasticity coefficient, otherwise it represents the cross-elasticity coefficient;

[0067] Step 3.3, after mathematical transformation, the change in electric heating load before and after the electricity price change can be obtained △P t ;

[0068]

[0069] Where: A is the demand price elasticity coefficient matrix.

[0070] Step 4: Establish a day-ahead optimization scheduling model and obtain the day-ahead scheduling plan;

[0071] Step 4.1. The goal of the model is to optimize the control of the thermal storage electric heating load power by dividing the peak-valley-flat electricity prices of blocked wind and solar power, maximize the consumption of blocked wind and solar power, and minimize the peak-valley difference of system load after responding to the time-of-use electricity price. The objective function of the model is:

[0072]

[0073] Where: is the probability of the sth scene appearing, and its set is N; is the time-of-use electricity price of blocked wind and solar power in scenario s; is the blocked wind and solar power consumption of thermal storage electric heating at time t under scenario s; C E,t is the electricity price of the power grid; is the power purchased from the grid at time t in scenario s; E(L s,t ) is the expected peak-to-valley difference of system load in all scenarios;

[0074] Step 4.2: The constraints of the model include:

[0075] Electric and thermal power balance constraints:

[0076]

[0077] Where: is the direct heating power of electric heating at time t in scenario s; is the heat release power of the heat storage device at time t in scenario s; is the heat load at time t in scenario s;

[0078] Thermal storage electric heating operation constraints:

[0079] Q eh,t =η·P eh,t

[0080] Where: Q eh,t P is the heating power of the electric heating equipment at time t; eh,t is the electric heating power consumption at time t; η is the electric heat conversion coefficient;

[0081] Thermal storage device constraints:

[0082]

[0083] Where: Q in,t S is the heat storage power of the heat storage device at time t; eh,max is the maximum heat storage; k loss is the heat loss coefficient of the heat storage device;

[0084] User benefit constraints:

[0085] To encourage users to participate in the response to the blocked wind and solar time-of-use electricity price, the heating cost after the implementation of the time-of-use electricity price should be lower than the expenditure before the implementation;

[0086]

[0087] Obstructed wind and solar power output constraints:

[0088] Without considering transmission line blockage and grid loss, in order to promote the consumption of blocked wind and solar power, the principle of using thermal storage electric heating to increase the grid's wind and solar power consumption is used as a constraint on the output of blocked wind and solar power.

[0089]

[0090] Step 4.3, according to the parameters in the model, divide it into decision variables and target variables;

[0091] The decision variables include any one or more combinations of blocked wind and solar power output, dynamic time-of-use electricity prices, peak and valley electricity prices of the power grid, and demand price elasticity coefficient matrix;

[0092] The target variables include total system cost, operating power of thermal storage electric heating, absorption of blocked wind and solar power, and purchase of electricity from external power grids.

[0093] Step 4.3, according to the obtained variable parameters, use MATLAB to call the Yalmip toolkit to solve the day-ahead scheduling model;

[0094] Step 5. Obtain the wind / solar / load output data for the day and adjust the output of each resource on the day before;

[0095] Step 5.1. Based on the acquired intraday wind / solar / load output data, the weighted Euclidean distance method is used to calculate the similarity between the day-ahead scenes and the corresponding time periods of the intraday ultra-short-term forecast scenes, extract the scenes with high similarity, and replace the day-ahead scene data with the ultra-short-term forecast data of this period to obtain the new blocked wind / solar output scene for intraday rolling scheduling. The formula for the weighted Euclidean distance is:

[0096]

[0097] Where: ε t is the weight coefficient of the correlation at each time point; χ is the maximum distance that meets the requirements;

[0098] Step 5.2, according to the corrected obstructed wind and solar power output curve, re-adopt the large-scale semi-trapezoidal function and clustering method to divide the peak, valley and average periods, repeat Step 4, and correct the day-ahead dispatch plan;

[0099] Step 6, respectively calculate the optimization results of using the dynamic electricity price mechanism and the grid peak and valley electricity price mechanism;

[0100] The blocked wind and solar power consumption rate, power purchase amount, total system cost, and system load peak-valley difference under dynamic electricity prices are compared with the results of the grid peak-valley electricity price mechanism to verify the effectiveness of the proposed method.

[0101] Taking a rural area in Hebei Province as an example, each household in the village is equipped with photovoltaic panels on its roof, totaling 500kW, and a 2MW wind farm is installed. It is generally believed that renewable energy power generation has no cost. Here it is assumed that the single-system electricity price of wind and solar power is e0, which is 0.2 yuan / (kW·h), b1=0.5, b2=-0.5, b3=0, and the peak electricity price period of the power grid is: 08:00 to 20:00, 0.55 yuan / (kW·h); the valley electricity price period is: 20:00 to 08:00, 0.32 yuan / (kW·h).

[0102] Firstly, Latin hypercube and scene reduction method are used to obtain the obstructed scenery scene as follows Figure 2 As shown in Table 1, the peak membership at each time point calculated according to the large semi-trapezoidal function is shown in Table 1, and the peak-valley-flat electricity price division of the blocked wind and solar power at each time point is obtained by clustering method as shown in Table 2.

[0103] Table 1 Peak membership at each time

[0104]

[0105] Table 2 Time-of-use electricity price classification at different time points

[0106]

[0107] In order to compare the effect of thermal storage electric heating load absorbing blocked wind and solar power before and after the adoption of dynamic time-of-use electricity price, the optimization results of the dynamic electricity price mechanism and the grid peak and valley electricity price mechanism are compared. Figure 3 As shown, the total system load is compared with Figure 4 The optimization results are shown in Table 3.

[0108] Depend on Figure 2 It can be seen that the rural areas of Hebei Province are rich in wind and solar resources, but the load density is low, and a large number of wind and solar resources are abandoned. Therefore, the thermal storage electric heating load is optimized to meet the heating demand while absorbing the blocked wind and solar resources locally; Figures 3-4 It can be seen that the thermal storage electric heating adopts the peak-valley electricity price mechanism of the power grid, that is, it only operates at night. In order to ensure the heating demand during the day, the electric heating equipment is prone to high electricity consumption at night, resulting in the phenomenon of "peak-valley inversion" of the power grid load. During the period of 04:00-08:00, the amount of abandoned wind and solar power is insufficient, and the system must purchase electricity from the power grid. The electric heating equipment no longer operates during the daytime to release heat for heating, resulting in a large amount of abandoned wind and solar power during the day; the shape of the blocked wind and solar power consumption curve and the predicted blocked wind and solar power output curve of Scheme 2 are similar, indicating that after adopting the optimized operation of the blocked wind and solar power time-of-use electricity price, the thermal storage electric heating load can be used as a demand response resource to adjust the operating status in time according to the time-of-use electricity price, effectively reducing the amount of abandoned wind and solar power.

[0109] Table 3 Optimization results under different schemes

[0110]

[0111] It can be seen from Table 3 that, compared with Scheme 1, the proposed source-load optimization control greatly improves the absorption rate of blocked wind and solar power, from 56.9% to 81.7%. Scheme 2 can make full use of the blocked wind and solar power with low electricity prices for thermal storage heating, reduce the proportion of power purchase from the power grid, and improve the economy of system operation. The peak-to-valley difference of the total system load under the two schemes is not much different, but compared with Scheme 1, the thermal storage electric heating operated with a dynamic time-of-use electricity price mechanism can flexibly respond to the changes of blocked wind and solar power, and has a certain degree of balancing system load. When the blocked wind and solar power are sufficient, it can make full use of the abandoned wind and solar power for heat storage, and the power purchase from the power grid is 0, which reflects the advantages and characteristics of source-load optimization control.

[0112] According to the existing prediction methods, the larger the time scale span, the greater the prediction error, which will lead to a large deviation between the intraday operation and the day-ahead plan. Therefore, it is necessary to correct the peak, valley and average time division of the blocked wind and solar power according to the ultra-short-term prediction data. The weighted Euclidean distance method is used to correct the blocked wind and solar power output curve, and step 2 is repeated to correct the peak, valley and average time division to obtain the time-of-use electricity price for each period: peak period {t1, t2, t3, t4, t 14 ,t 15,t 16}, valley period {t9, t 10 ,t 11 ,t 19 ,t 20 ,t 21 ,t 22}、Normal period{t5,t6,t7,t8,t 12 ,t 13 ,t 17 ,t 18 ,t 23 ,t 24}.

[0113] The intraday optimization results of the dynamic electricity price mechanism and the peak-valley electricity price mechanism of the power grid are compared. Figure 5 As shown, the total system load is compared with Figure 6 The optimization results are shown in Table 4.

[0114] Depend on Figures 5-6 It can be seen that with the rolling correction of the blocked wind and solar output curve within the day, the amount of blocked wind and solar power absorbed by the peak-valley electricity price mechanism scheme of the power grid is reduced. The reason is mainly related to the amount of blocked wind and solar power at night. Even if the amount of abandoned wind and solar power at night is less than that during the day, in order to ensure that the heat load demand during the day is met, the system must purchase electricity for heating at night, so the proportion of purchased electricity is increased. Since the valley electricity price period of the peak-valley electricity price mechanism is used to store heat, when the heat load is constant, the operating cost of the system remains unchanged. Scheme 2 also reduces the amount of abandoned wind and solar power at night after rolling correction. When the heat load is constant, the absorption rate of blocked wind and solar power increases. The results show that the prediction error of blocked wind and solar power has a great impact on the implementation results of time-of-use electricity prices. If the predicted value of blocked wind and solar power in a certain period is lower than the actual value, the electricity price in that period will be lower than the value that should be set. On the contrary, it will be higher than the set electricity price. Under the effect of the supply price elasticity coefficient, the electricity price will affect the operating state of thermal storage electric heating.

[0115] It can be seen from Table 4 that after the peak, valley and flat electricity price divisions at each time point change, the operating state of the thermal storage electric heating changes accordingly compared with the day-ahead optimization results, further improving the obstructed wind and solar power absorption rate, reducing the system's operating costs, and effectively reducing the system load peak-valley difference.

[0116] Table 4 Optimization results under two schemes

[0117]

Claims

1. A source-load coordination dispatching method for thermal storage electric heating and wind and solar power based on dynamic electricity prices. It is characterized in that The steps of the method include: Step 1. Obtain the historical wind and solar load prediction error, obtain the wind and solar load output information predicted on the day before, and obtain the blocked wind and solar output curve based on the Latin hypercube and scenario reduction method; Step 2: Based on the large-scale semi-trapezoidal function and clustering method, the obstructed wind and solar power output curve is divided into three periods: peak, valley and flat. The floating coefficient of the dynamic electricity price is set to obtain the electricity price in each period of peak, valley and flat. Step 3, define the intelligent charging control device for thermal storage electric heating, and introduce the demand price elasticity coefficient to describe the change of electric heating load power consumption caused by electricity price changes; Step 4: Establish a day-ahead optimization dispatching model based on the acquired day-ahead wind and solar load output data, and use the Yalmip solver to obtain a day-ahead dispatching plan with the maximum wind and solar power consumption and the minimum system load peak-valley difference; Step 5: Based on the acquired daily wind and solar load output data, the weighted Euclidean distance method is used to roll over the blocked wind and solar information and adjust the output of each resource scheduled on the previous day; Step 6: Compare and analyze the optimization results of using the dynamic electricity price mechanism and the peak-valley electricity price mechanism of the power grid; The specific steps of Step 4 are: Step 4: Establish a day-ahead optimization scheduling model and obtain the day-ahead scheduling plan; Step 4.

1. The goal of the model is to optimize the control of the thermal storage electric heating load power by dividing the peak-valley-flat electricity prices of blocked wind and solar power, maximize the consumption of blocked wind and solar power, and minimize the peak-valley difference of system load after responding to the time-of-use electricity price. The objective function of the model is: Where: For the The probability of the occurrence of a scene is ; for Time-of-use electricity prices for blocked wind and solar power in scenarios; for In the scenario The obstruction of thermal storage electric heating at all times; is the electricity price of the power grid; for In the scenario Purchase electricity from the power grid at all times; is the expected peak-to-valley difference of system load in all scenarios; Step 4.2: The constraints of the model include: Electric and thermal power balance constraints: Where: for In the scenario Direct heating power of electric heating at all times; for In the scenario The heat release power of the heat storage device at all times; for In the scenario Heat load at all times; Thermal storage electric heating operation constraints: Where: for Heating power of electric heating equipment at all times; for Electric heating power consumption at all times; is the electrothermal conversion coefficient; Thermal storage device constraints: Where: for Thermal storage power of thermal storage device at all times; is the maximum heat storage; is the heat loss coefficient of the heat storage device; User benefit constraints: To encourage users to participate in the response to the blocked wind and solar time-of-use electricity price, the heating cost after the implementation of the time-of-use electricity price should be lower than the expenditure before the implementation; Obstructed wind and solar power output constraints: Without considering transmission line blockage and grid loss, in order to promote the consumption of blocked wind and solar power, the principle of using thermal storage electric heating to increase the grid's wind and solar power consumption is used as a constraint on the output of blocked wind and solar power. Step 4.3, according to the parameters in the model, divide it into decision variables and target variables; The decision variables include any one or more combinations of blocked wind and solar power output, dynamic time-of-use electricity prices, peak and valley electricity prices of the power grid, and demand price elasticity coefficient matrix; The target variables include total system cost, thermal storage electric heating operating power, consumption of blocked wind and solar power, and power purchase from external power grids; Step 4.4, according to the obtained variable parameters, use MATLAB to call the Yalmip toolkit to solve the day-ahead optimization scheduling model.

2. The source-load coordination scheduling method for thermal storage electric heating and wind and solar power consumption based on dynamic electricity price according to claim 1, It is characterized in that The specific steps of Step 1 are: Step 1.

1. Obtain historical wind / solar / load forecast errors , , , the wind / solar / load output forecasted today , , , its prediction error can be regarded as a random variable that obeys a normal distribution, then its actual output , , It can be expressed as the sum of the predicted value and the prediction error; Step 1.2, the obstructed wind and solar power can be expressed as the difference between the wind and solar power output and the load. The Latin hypercube and scene reduction method are used to process multiple initial scenes to obtain a typical obstructed wind and solar power scene.

3. The source-load coordination scheduling method for thermal storage electric heating and wind and solar power consumption based on dynamic electricity price according to claim 2, It is characterized in that The specific steps of Step 2 are: Step 2.

1. Calculate the peak membership of the obstructed wind and solar curve at each time point based on the large-scale semi-trapezoidal function ; Step 2.2: Use clustering method to divide each time point in a day into peaks. ,valley ,flat Three time periods; Step 2.3: Set the single electricity price for wind and solar power that is blocked before implementing dynamic time-of-use electricity prices After the implementation of time-of-use electricity prices, the peak-valley and flat electricity prices of blocked wind and solar power will fluctuate by a certain proportion based on the original single price. are the electricity price floating coefficients respectively, that is, the peak, valley and flat electricity prices after the implementation of dynamic time-of-use electricity prices are .

4. The source-load coordination scheduling method for thermal storage electric heating and wind and solar power consumption based on dynamic electricity price according to claim 3, It is characterized in that The specific steps of Step 3 are: Step 3.

1. Define the intelligent charging control device for thermal storage electric heating. The user connects the thermal storage electric heating to the charging device and sets the heat load demand. The intelligent charging device automatically arranges the operation status of the thermal storage electric heating equipment according to the dynamic electricity price of the blocked wind and solar power. Step 3.2: In order to avoid the over-response of thermal storage electric heating load to electricity price and the phenomenon of peak-valley reversal, the demand price elasticity coefficient is introduced , after mathematical transformation, the change in electric heating load before and after the electricity price change is obtained .

5. The source-load coordination scheduling method for thermal storage electric heating and wind and solar power consumption based on dynamic electricity price according to claim 4, It is characterized in that The specific steps of Step 5 are: Step 5.

1. Obtain the wind and solar load output data within the day, use the weighted Euclidean distance method to calculate the similarity between each scene on the day before and the corresponding period of the ultra-short-term forecast scene within the day, extract the scene with high similarity, and replace the data of the scene on the day before with the ultra-short-term forecast data of this period to obtain the new blocked wind and solar output scene for intraday rolling scheduling; Step 5.2: Based on the revised obstructed wind and solar power output curve, re-adopt the large-scale semi-trapezoidal function and clustering method to divide the peak, valley and normal periods, repeat Step 4, and revise the day-ahead dispatch plan.

6. The source-load coordination scheduling method for thermal storage electric heating and wind and solar power consumption based on dynamic electricity price according to claim 5, It is characterized in that The Step 6 also includes: The blocked wind and solar power consumption rate, power purchase amount, total system cost, and system load peak-valley difference under dynamic electricity prices are compared with the results of the grid peak-valley electricity price mechanism to verify the effectiveness of the proposed method.

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