A load regulation method and system for a thermal storage electric heating load cluster

Abstract

The application discloses a kind of load regulation method and system of thermal storage electric heating load cluster, method includes obtaining the parameter data of thermal storage electric heating load and power grid price, the parameter data includes the initial indoor temperature of heating building and the initial operating power of heating equipment;According to the parameter data of thermal storage electric heating load and power grid price, obtainable adjustable evaluation index of thermal storage electric heating load cluster is calculated;According to adjustable evaluation index, load regulation is carried out to thermal storage electric heating load cluster.Through from the electric quantity translation amount and real-time power adjustable amount two dimensions, obtainable adjustable evaluation index of thermal storage electric heating load cluster is proposed, and through high index effectively, the load cluster power consumption amount in operating cycle is adjusted and optimized, relieve the peak shaving pressure of traditional unit in power system, effectively play the peak clipping and valley filling effect of load side, promote new energy power generation consumption, and can effectively reduce its overall power consumption in operating cycle.

Description

Technical Field

[0001] This invention relates to a load regulation method and system for a thermal storage electric heating load cluster, belonging to the field of power system technology. Background Technology

[0002] With the continuous growth of renewable energy grid-connected capacity, the issue of renewable energy consumption has become a hot topic of concern for government departments, power grid companies, and power generation companies. The responsibility weight for renewable energy consumption has become an important performance indicator for governments at all levels and responsible entities. With the introduction and implementation of renewable energy consumption guarantee mechanisms, tapping into the potential for renewable energy consumption is an urgent issue to be addressed. In recent years, distributed electricity-heat loads in northern regions have gradually reached a considerable scale and have enormous development potential.

[0003] Jilin Province has a long winter heating season and a large demand for heating. Clean heating, represented by thermal storage electric heating, has gradually taken shape and has great development potential. By the end of 2020, Jilin Province's thermal storage electric heating capacity had reached 690,000 kilowatts, accounting for 6.5% of the province's maximum power supply load, and is expected to grow at a rate of no less than 30% annually. Electricity-heat load clusters, as flexible and adjustable loads with time-shifting characteristics, have significant potential to improve the absorption of new energy and fulfill their absorption weight responsibilities. Further research is needed on the market trading mechanism for new energy and decentralized electricity-heat, fully exploring the reserve, peak-shaving, and frequency regulation potential of electricity-heat loads participating in new energy absorption, promoting the development of the electricity trading market, and fully leveraging the advantages of multi-energy interconnection and complementarity to coordinate the absorption of new energy on a larger scale of energy allocation. However, the loads are located at the end of the power grid and are decentralized, increasing the difficulty for power grid operation and dispatch departments to understand and dispatch the adjustability of thermal storage electric heating load clusters. Currently, there is no method to flexibly assess and adjust the peak-shaving pressure of thermal storage electric heating load clusters. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a load regulation method and system for thermal storage electric heating load clusters. The method defines adjustability evaluation index through two dimensions: power shift amount and real-time power adjustability amount. Based on the index, the adjustability of the thermal storage electric heating load cluster is evaluated and the load is optimized. This can effectively alleviate the peak-shaving pressure of the power system on the thermal storage electric heating load cluster, promote the consumption of new energy power generation, and thus improve the safety level of power grid operation.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a load regulation method for a thermal storage electric heating load cluster, wherein the thermal storage electric heating load cluster comprises multiple thermal storage electric heating loads composed of heating equipment, thermal storage equipment, and heating equipment, and the adjustability evaluation index is defined as the cross-time period power shiftability, the upward power adjustability, and the downward power adjustability; the load regulation method includes:

[0007] Obtain parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment; the grid electricity price includes peak-valley electricity price and flat electricity price.

[0008] Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, the adjustability assessment index of the thermal storage electric heating load cluster is calculated.

[0009] The load of the thermal storage electric heating load cluster is adjusted according to the pre-set adjustability evaluation index, wherein the adjustability evaluation index is the cross-time period power shift, the upward power adjustment, and the downward power adjustment. The cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand. The upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load. The downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load.

[0010] Furthermore, the method for calculating the adjustability assessment index is as follows:

[0011] Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and the pre-constructed thermal dynamic model of the heated building, the operating power of the heating equipment at each moment is calculated.

[0012] Based on the grid electricity price, the operating power of the heating equipment at each time, and the pre-built optimization model for the operation of the heating equipment, the optimal heat storage capacity of the heat storage equipment and the optimal power of the heating equipment at each time are calculated.

[0013] Based on the optimal heat storage capacity of the thermal storage equipment and the optimal power of the heating equipment at each time, the adjustability evaluation index of the thermal storage electric heating load cluster is calculated.

[0014] Furthermore, the step of calculating the operating power of the heating equipment at each moment based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and the pre-constructed thermal dynamic model of the heated building; specifically including:

[0015] A thermal dynamic model of the heated building is constructed. The initial indoor temperature of the heated building is input into the thermal dynamic model, and the calculation is continuously iterated to obtain the indoor temperature of the heated building at each time point. The calculation formula is as follows:

[0016]

[0017] T min ≤T in,t ≤T max ,

[0018] t = 1, 2, ..., 24

[0019] In the formula, C air T is the specific heat capacity of indoor air. in，t T represents the indoor temperature of the heated building at time t. max The upper limit of indoor temperature set for users; T min The upper limit of indoor temperature set by the user; T out，t Let t be the outdoor temperature of the heated building; k be the heat dissipation coefficient of the heated building structure; P h，t η is the operating power of the heating equipment at time t; h Δt represents the thermal efficiency of the heating equipment; Δt represents the time step.

[0020] Based on the indoor temperature of the heated building at each time point, the operating status of the heating equipment at each time point is calculated using the following formula:

[0021]

[0022] In the formula, b t The operating status of the heating equipment at time t;

[0023] Based on the operating status of the heating equipment at each moment, the operating power of the heating equipment at each moment is calculated using the following formula:

[0024] P h，t =b t P Re ,

[0025] In the formula, P h，t P represents the operating power of the heating equipment at time t; Re This refers to the rated power of the heating equipment.

[0026] Furthermore, based on the grid electricity price, the operating power of the heating equipment at each time point, and the pre-constructed heating equipment operation optimization model, the optimal heat storage capacity of the heat storage equipment and the optimal power of the heating equipment at each time point are calculated; specifically, this includes:

[0027] Based on the operating power of the heating equipment at various times, a dynamic model of the energy storage state of the thermal storage equipment is established, and the calculation formula is as follows:

[0028] q t =q t-1 (1-β)+p e，t η e DPh，t D 0≤q t ≤Q max ,

[0029] In the formula, q t Let be the amount of heat stored in the thermal storage device at time t; β be the self-heating coefficient of the thermal storage device; p e，t η is the power of the heating equipment at time t; e D represents the electrothermal conversion efficiency of the heating equipment; Q represents the single-time step size; max This represents the maximum heat storage capacity of the thermal storage device.

[0030] Based on the grid electricity price, an optimization model for the operation of heating equipment is established, and the calculation formula is as follows:

[0031]

[0032] In the formula, f represents the total electricity cost; C t Let t be the grid electricity price at time t; N be the total number of time periods within the operating cycle;

[0033] Based on the dynamic model of the energy storage state of the thermal storage device and the operation optimization model of the heating device, the optimal heat storage capacity of the thermal storage device and the optimal power of the heating device at each time are obtained.

[0034] Furthermore, based on the optimal heat storage capacity of the thermal storage equipment and the optimal power of the heating equipment at each time point, the adjustability evaluation index of the thermal storage electric heating load cluster is calculated; specifically including:

[0035] Based on the optimal power of the heating equipment at each time point under the grid electricity price, the amount of electricity that can be shifted across time periods is obtained, and the calculation formula is as follows:

[0036] ΔE e =E e -E e0 ,

[0037]

[0038] In the formula, ΔE e E represents the amount of electricity that can be shifted across time periods. e E represents the total electricity consumption of heating equipment under peak-valley electricity pricing; e0 p′ represents the total electricity consumption of heating equipment under flat electricity prices. e，t p′ represents the optimal power output of the heating equipment at each time point under peak-valley electricity pricing. e0，t The optimal power output of the heating equipment at each time point under flat electricity prices;

[0039] Based on the optimal heat storage capacity and optimal power of the heating equipment at each time point, the upward and downward adjustable power values ​​are calculated using the following formulas:

[0040]

[0041]

[0042] In the formula, q′ represents the upward adjustable amount of power of the thermal storage electric heating load at time t. t The optimal heat storage capacity of the thermal storage equipment at each time point under peak and off-peak electricity pricing; This represents the downward adjustable amount of power for the electric heating load at time t.

[0043] Based on the number of thermal storage electric heating load clusters, the adjustability assessment index of the thermal storage electric heating load clusters is obtained by summing the results. The calculation formulas are as follows:

[0044]

[0045] In the formula, M represents the number of thermal storage electric heating load clusters; ΔE ∑e The amount of electricity that can be shifted across time periods for thermal storage electric heating load clusters; The power up-adjustable amount for thermal storage electric heating load clusters; The power of the thermal storage electric heating load cluster can be adjusted downwards.

[0046] Furthermore, the peak-valley electricity price will be replaced with a deep-peak adjustment electricity price.

[0047] Furthermore, the load regulation of the thermal storage electric heating load cluster based on the adjustability assessment index specifically includes:

[0048] Based on the adjustability assessment indicators, the load consumption during periods of high electricity price will be transferred to periods of low and high electricity price.

[0049] Secondly, the present invention provides a load regulation system for a thermal storage electric heating load cluster, comprising:

[0050] Acquisition Module: Acquires parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment. The grid electricity price includes peak-valley electricity price and flat electricity price.

[0051] Data processing module: Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, calculate the adjustability assessment index of the thermal storage electric heating load cluster;

[0052] Load optimization module: Used to adjust the load of thermal storage electric heating load cluster according to pre-set adjustability evaluation indicators, wherein the adjustability evaluation indicators are cross-time period power shift, upward power adjustment, and downward power adjustment; the cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand; the upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load; the downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load.

[0053] Thirdly, a load regulation device for a thermal storage electric heating load cluster includes a processor and a storage medium;

[0054] The storage medium is used to store instructions;

[0055] The processor is configured to operate according to the instructions to perform the steps of the method according to any of the foregoing.

[0056] Fourthly, a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods described above.

[0057] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0058] This invention discloses a load regulation method for a thermal storage electric heating load cluster. It proposes a adjustability evaluation index for the thermal storage electric heating load cluster from two dimensions: power shift and real-time power adjustability. This high-index method effectively regulates and optimizes the load consumption of the load cluster during the operating cycle, alleviating the peak-shaving pressure on traditional units in the power system, effectively leveraging the peak-shaving and valley-filling function on the load side, promoting the consumption of renewable energy generation, and effectively reducing the overall electricity cost during its operating cycle. Furthermore, it can be used to balance real-time power deviations that may occur during power system operation. This adjustability evaluation index provides auxiliary services such as secondary frequency regulation and backup for the power grid, addressing real-time power fluctuations in renewable energy generation, thereby improving the safety level of power grid operation. Attached Figure Description

[0059] Figure 1 This is a flowchart of the load regulation method for the thermal storage electric heating load cluster of the present invention;

[0060] Figure 2 This is a heat load-time curve of the thermal storage electric heating load cluster in Embodiment 1 of the present invention;

[0061] Figure 3 This is a graph showing the power grid price-time curve during the operating cycle in Embodiment 1 of the present invention;

[0062] Figure 4These are the electricity load-time curves of the thermal storage electric heating load cluster under different grid electricity prices in Embodiments 1 and 2 of the present invention for each time period;

[0063] Figure 5 This is a graph showing the downward and upward adjustable power curves of the thermal storage electric heating load cluster under peak-valley electricity pricing in the example of this invention. Detailed Implementation

[0064] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0065] Existing thermal energy storage (TES) electric heating loads, including heating equipment, thermal storage equipment, and heating equipment, collectively constitute TES load clusters. In existing technologies, these loads are often distributed at the end of the power grid, increasing the difficulty for power grid operators to assess and optimize the load's adjustability. This invention addresses this by providing a method and system for load regulation of TES load clusters to gain a clear understanding of load adjustability. The method primarily involves defining adjustability assessment indicators for TES load clusters, evaluating their adjustability based on these indicators, and finally optimizing the load based on the assessment results. This method can provide auxiliary services such as secondary frequency regulation and backup power to the power grid, addressing real-time power fluctuations in new energy generation and thus improving the safety level of power grid operation.

[0066] Example 1:

[0067] This embodiment provides a load regulation method for a thermal storage electric heating load cluster, the method including:

[0068] Obtain parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment; the grid electricity price includes peak-valley electricity price and flat electricity price.

[0069] Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, the adjustability assessment index of the thermal storage electric heating load cluster is calculated.

[0070] The load of the thermal storage electric heating load cluster is adjusted according to the pre-set adjustability evaluation index, wherein the adjustability evaluation index is the cross-time period power shift, the upward power adjustment, and the downward power adjustment. The cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand. The upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load. The downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load.

[0071] The method mainly includes three stages: parameter data acquisition stage, adjustability assessment index calculation stage, and assessment and adjustment application stage.

[0072] I. Parameter Data Acquisition Stage

[0073] The defined assessment indicators for the adjustability of thermal storage electric heating load clusters specifically include the cross-time period power shift, the upward power adjustability, and the downward power adjustability.

[0074] The meaning of cross-time period power transfer amount is the amount of power load that can be transferred from a period of low power demand to a period of high power demand within the operating cycle.

[0075] The terms "upward" and "downward" adjustable power refer to the amount of power that can be increased or decreased based on the grid's operational needs during real-time operation.

[0076] Among the three adjustability assessment indicators that need to be calculated, the required parameter data include the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and the grid electricity price. The grid electricity price includes the flat rate and the peak-valley rate.

[0077] In this embodiment, each hour of a 24-hour day is taken as a time period, the operation cycle is 24 hours, the total number of time periods is 24, and the data parameters obtained are all real-time values ​​of each moment within a 24-hour day.

[0078] II. Adjustability Assessment Index Calculation Stage

[0079] Based on the initial indoor temperature of the heated building and the grid electricity price obtained in Phase 1, this phase requires calculating the adjustability assessment index of the thermal storage electric heating load cluster defined in this embodiment. The specific steps are as follows:

[0080] Step 1: Calculate the operating power of the heating equipment at each moment based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and the pre-built thermal dynamic model of the heated building.

[0081] Step 2: Based on the grid electricity price, the operating power of the heating equipment at each time, and the pre-built heating equipment operation optimization model, calculate the optimal heat storage capacity of the heat storage equipment and the optimal power of the heating equipment at each time.

[0082] Step 3: Calculate the adjustability evaluation index of the thermal storage electric heating load cluster based on the optimal heat storage capacity of the thermal storage equipment and the optimal power of the heating equipment at each time.

[0083] In step 1, the first step is to construct a thermal dynamic model of the heated building. The differential equation expression of the thermal dynamic model is as follows:

[0084]

[0085] T min ≤T in，t ≤T max ,

[0086] t = 1, 2, ..., 24,

[0087] In the formula, C air T is the specific heat capacity of indoor air. in，t T represents the indoor temperature of the heated building at time t. max The upper limit of indoor temperature set for users; T min The upper limit of indoor temperature set by the user; T out，t Let t be the outdoor temperature of the heated building; k be the heat dissipation coefficient of the heated building structure; P h，t Let η be the operating power of the heating equipment at time t, where t is the initial operating power of the heating equipment at time 0; h The thermal efficiency of heating equipment.

[0088] Solving the above differential equation, we get:

[0089]

[0090] Next, when the initial state, i.e., t-1 is 0, the initial indoor temperature of the heated building is introduced into the thermal dynamic model of the heated building to obtain the indoor temperature of the heated building at t=1. The results are then iteratively calculated to obtain the indoor temperature of the heated building at each time point.

[0091] Then, based on the indoor temperature of the heated building at each time point, the operating status of the heating equipment at each time point is calculated using the following formula:

[0092]

[0093] In the formula, b t The operating status of the heating equipment at time t.

[0094] Finally, based on the operating status of the heating equipment at each moment, the operating power of the heating equipment at each moment is calculated. The calculation formula is as follows:

[0095] P h，t =b t P Re ,

[0096] In the formula, P h，t P represents the operating power of the heating equipment at time t; Re This refers to the rated power of the heating equipment.

[0097] In step 2, a dynamic model of the energy storage state of the thermal storage device needs to be established based on the operating power of the heating equipment at each moment calculated in the previous steps. The expression of the model is as follows:

[0098] q t =q t-1 (1-β)+p e，t η e DP h，t D0≤q t ≤Q max ,

[0099] In the formula, q t Let be the amount of heat stored in the thermal storage device at time t; β be the self-heating coefficient of the thermal storage device; p e，t η is the power of the heating equipment at time t; e D represents the electrothermal conversion efficiency of the heating equipment; Q represents the single-time step size; max This represents the maximum heat storage capacity of the thermal storage device.

[0100] Then, based on the grid electricity price, an optimization model for the operation of the heating equipment is established, and the calculation formula is as follows:

[0101]

[0102] In the formula, f represents the total electricity cost; C t Let t be the grid electricity price at time t; N is the total number of time periods within the operating cycle, with a value of 24.

[0103] Finally, based on the dynamic model of the energy storage state of the thermal storage device and the operation optimization model of the heating device, the optimal energy storage capacity q′ of the thermal storage device at each time point is obtained. t and the optimal power p′ of the heating equipment at each time. e，t The optimal heat storage capacity and optimal power of the heating equipment at each time point are the heat storage capacity and power at which the total electricity cost is minimized under peak-valley electricity price and flat electricity price.

[0104] In step 3, the optimal power of the heating equipment at each time point under the grid electricity price is first used to obtain the cross-time period power shift amount. The calculation formula is as follows:

[0105] ΔE e =E e -E e0 ,

[0106]

[0107] In the formula, ΔE e E represents the amount of electricity that can be shifted across time periods. e E represents the total electricity consumption of heating equipment under peak-valley electricity pricing; e0 p′ represents the total electricity consumption of heating equipment under flat electricity prices. e，t p′ represents the optimal power output of the heating equipment at each time point under peak-valley electricity pricing. e0,t The optimal power output of the heating equipment at each time point under the flat electricity price.

[0108] Next, based on the optimal heat storage capacity of the heat storage device and the optimal power of the heating device at each time point, the upward and downward adjustable power values ​​are calculated using the following formulas:

[0109]

[0110] In the formula, q′ represents the upward adjustable amount of power of the thermal storage electric heating load at time t. t The optimal heat storage capacity of the thermal storage equipment at each time point under peak and off-peak electricity pricing; This represents the downward adjustable amount of power for the electric heating load at time t.

[0111] Finally, based on the number of thermal storage electric heating load clusters, the adjustability assessment index of the thermal storage electric heating load clusters is obtained by summing, thus completing the load assessment of the thermal storage electric heating load clusters. The calculation formulas are as follows:

[0112]

[0113]

[0114] In the formula, M represents the number of thermal storage electric heating load clusters; ΔE ∑e The amount of electricity that can be shifted across time periods for thermal storage electric heating load clusters; The power up-adjustable amount for thermal storage electric heating load clusters; The power of the thermal storage electric heating load cluster can be adjusted downwards.

[0115] III. Adjustment and Application Stage

[0116] This stage involves load regulation of the thermal storage electric heating load cluster based on the adjustability assessment index calculated in stage two.

[0117] The adjustment method is as follows: based on the cross-period power shift, the upward adjustment of power, and the downward adjustment of power, the load power consumption during the high-price period is transferred to the low-price period during the operating cycle.

[0118] This embodiment provides a specific example to further illustrate load regulation. Based on the parameter data of thermal storage electric heating loads in Jilin Province, taking a load cluster of 100 thermal storage electric heating loads as an example, a calculation analysis is conducted. The adjustability evaluation index calculated according to the method described in this invention is as follows: Figure 2 and Figure 3 As shown.

[0119] like Figure 2 As shown, in winter, the heat load power of the thermal storage electric heating load cluster is affected by the ambient temperature. The temperature is lower at night, and the heat load demand is relatively higher, while the temperature is higher during the day, and the heat load demand is relatively lower.

[0120] like Figure 3 As shown, peak-valley electricity pricing is as follows: 0:00–8:00 and 21:00–24:00 are off-peak periods, while other times are peak periods. Under peak-valley pricing, the macro-level phenomenon is that electricity prices are high during the day, resulting in low electricity demand; and electricity prices are low at night, resulting in high electricity demand. Under flat pricing, heating equipment only needs to consider operational adjustment loads, and the heating equipment operates in a constant power mode during each period. Therefore, under flat pricing, the load adjustment curve of the thermal storage electric heating load cluster appears as a horizontal straight line.

[0121] like Figure 5 As shown, during the periods of 0:00–8:00 and 21:00–24:00, the downward adjustment range of the power of the thermal storage electric heating load cluster is 37.4MW, and the upward adjustment range is 38.9MW. Based on the limitations of the downward and upward adjustment ranges, the overall power consumption of heating equipment during the period of 8:00–21:00 when heating is not required is adjusted to the off-peak electricity price period, such as… Figure 4 As shown in the load regulation curve under peak-valley pricing, the total electricity load (represented by the area in the figure) during the period from 0:00 to 8:00 under flat pricing is 151 MWh, while under peak-valley pricing, the total electricity load during the period from 0:00 to 8:00 is 299 MWh. This means that peak-valley pricing can guide the thermal storage electric heating load cluster to shift 148 MWh of electricity consumption to the period from 0:00 to 8:00, an increase of 49.5%. By shifting all electricity demand to the off-peak electricity price period at night, the overall electricity cost during its operating cycle is reduced, effectively leveraging the peak-shaving and valley-filling function on the load side, increasing the consumption of new energy sources, and providing auxiliary services such as secondary frequency regulation and backup for the power grid, it can cope with real-time power fluctuations in new energy generation, thereby improving the safety level of power grid operation.

[0122] This invention proposes an adjustability assessment index for thermal storage electric heating load clusters from two dimensions: power shift and real-time power adjustability. Through high index, it effectively adjusts and optimizes the power load of the load clusters during the operating cycle, alleviates the peak-shaving pressure of traditional units in the power system, effectively plays the role of peak shaving and valley filling on the load side, promotes the consumption of new energy power generation, and effectively reduces the overall electricity cost during its operating cycle.

[0123] Example 2:

[0124] The difference between this embodiment and Embodiment 1 is that a deep peak-shaving price is further added to the peak-valley electricity price to calculate the adjustability assessment index, thereby optimizing the load regulation of the thermal storage electric heating load cluster. Further calculations and analyses are performed on the examples from Embodiment 1.

[0125] like Figure 4 As shown in the load regulation curve under the deep peak-shaving electricity price, 2:00-5:00 is the low-price period. Compared with the peak-valley electricity price, the electric heating load cluster can transfer more electricity demand to the 2:00-5:00 period under the deep peak-shaving electricity price. The total electricity load during the 2:00-5:00 period increases by 120MWh, an increase of 49.8%, which accounts for 26.6% of the total daily electricity load. That is, the grid dispatching department can transfer 26.6% of the electric heating load to the deep peak-shaving period through the deep peak-shaving electricity price compensation, thereby further exerting a greater peak-shaving and valley-filling effect and promoting the consumption of new energy.

[0126] Example 3:

[0127] A load regulation system for a thermal storage electric heating load cluster includes:

[0128] Acquisition Module: Acquires parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment. The grid electricity price includes peak-valley electricity price and flat electricity price.

[0129] Data processing module: Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, calculate the adjustability assessment index of the thermal storage electric heating load cluster;

[0130] Load optimization module: Used to adjust the load of thermal storage electric heating load cluster according to pre-set adjustability evaluation indicators, wherein the adjustability evaluation indicators are cross-time period power shift, upward power adjustment, and downward power adjustment; the cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand; the upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load; the downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load.

[0131] The specific adjustment method of the system can be found in Implementation Example 1, and will not be elaborated here.

[0132] Example 4:

[0133] This invention also provides a load regulation device for a thermal storage electric heating load cluster, including a processor and a storage medium;

[0134] The storage medium is used to store instructions;

[0135] The processor is configured to operate according to the instructions to perform the steps of the following method:

[0136] Obtain parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment; the grid electricity price includes peak-valley electricity price and flat electricity price.

[0137] Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, the adjustability assessment index of the thermal storage electric heating load cluster is calculated.

[0138] The load of the thermal storage electric heating load cluster is adjusted according to the pre-set adjustability evaluation index, wherein the adjustability evaluation index is the cross-time period power shift, the upward power adjustment, and the downward power adjustment. The cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand. The upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load. The downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load.

[0139] The specific adjustment method of the device can be found in Example 1, and will not be elaborated here.

[0140] Example 5:

[0141] This invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the following method:

[0142] Obtain parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment; the grid electricity price includes peak-valley electricity price and flat electricity price.

[0143] Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, the adjustability assessment index of the thermal storage electric heating load cluster is calculated.

[0144] The load of the thermal storage electric heating load cluster is adjusted according to the pre-set adjustability evaluation index, wherein the adjustability evaluation index is the cross-time period power shift, the upward power adjustment, and the downward power adjustment. The cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand. The upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load. The downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load.

[0145] The specific adjustment method for the storage medium can be found in Example 1, and will not be elaborated here.

[0146] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, apparatus, and computer-readable storage media. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0147] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, apparatuses, and computer-readable storage media according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A load regulation method for a thermal storage electric heating load cluster, wherein the thermal storage electric heating load cluster comprises multiple thermal storage electric heating loads consisting of heating equipment, thermal storage equipment, and heating equipment, characterized in that, Obtain parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment; the grid electricity price includes peak-valley electricity price and flat electricity price. Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, the adjustability assessment index of the thermal storage electric heating load cluster is calculated. The load of the thermal storage electric heating load cluster is adjusted according to the pre-set adjustability evaluation indicators, which include the cross-time period power shift, the upward power adjustment, and the downward power adjustment. The cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand. The upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load. The downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load. The method for calculating the adjustability assessment index is as follows: Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and the pre-constructed thermal dynamic model of the heated building, the operating power of the heating equipment at each moment is calculated. Based on the grid electricity price, the operating power of the heating equipment at each time, and the pre-built optimization model for the operation of the heating equipment, the optimal heat storage capacity of the heat storage equipment and the optimal power of the heating equipment at each time are calculated. Based on the optimal heat storage capacity and the optimal power of the heating equipment at each time, calculate the adjustability evaluation index of the thermal storage electric heating load cluster. The calculation of the operating power of the heating equipment at each moment is based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and a pre-constructed thermal dynamic model of the heated building; specifically, it includes: A thermal dynamic model of the heated building is constructed. The initial indoor temperature of the heated building is input into the thermal dynamic model, and the calculation is continuously iterated to obtain the indoor temperature of the heated building at each time point. The calculation formula is as follows: ， ， ， In the formula, The specific heat capacity of indoor air; Let t be the indoor temperature of the heated building. The upper limit of indoor temperature set for users; The upper limit of indoor temperature set for the user; Let t be the outdoor temperature of the heated building. The heat dissipation coefficient of a heated building structure; Let t be the operating power of the heating equipment at time t; ∆t represents the thermal efficiency of the heating equipment; ∆t represents the time step. Based on the indoor temperature of the heated building at each time point, the operating status of the heating equipment at each time point is calculated using the following formula: ， In the formula, b t The operating status of the heating equipment at time t; Based on the operating status of the heating equipment at each moment, the operating power of the heating equipment at each moment is calculated using the following formula: ， In the formula, express The operating power of the heating equipment at all times; This refers to the rated power of the heating equipment.

2. The load regulation method for a thermal storage electric heating load cluster according to claim 1, characterized in that, The process involves calculating the optimal heat storage capacity and optimal power of the heat storage equipment at each time point, based on the grid electricity price, the operating power of the heating equipment at each time point, and a pre-built optimization model for the operation of the heating equipment; specifically, this includes: Based on the operating power of the heating equipment at various times, a dynamic model of the energy storage state of the thermal storage equipment is established, and the calculation formula is as follows: , In the formula, for The amount of heat storage equipment that can store heat at all times; The self-heating coefficient of the heat storage device; Let t be the power of the heating device; D represents the electrothermal conversion efficiency of the heating equipment; D represents the single-time step size. This represents the maximum heat storage capacity of the thermal storage device. Based on the grid electricity price, an optimization model for the operation of heating equipment is established, and the calculation formula is as follows: ， In the formula, Total electricity cost; for Real-time grid electricity price; This represents the total number of time periods within the operating cycle. Based on the dynamic model of the energy storage state of the thermal storage device and the operation optimization model of the heating device, the optimal heat storage capacity of the thermal storage device and the optimal power of the heating device at each time are obtained.

3. The load regulation method for a thermal storage electric heating load cluster according to claim 2, characterized in that, Based on the optimal heat storage capacity and optimal power of the heating equipment at each time point, calculate the adjustability evaluation index of the thermal storage electric heating load cluster; specifically including: Based on the optimal power of the heating equipment at each time point under the grid electricity price, the amount of electricity that can be shifted across time periods is obtained, and the calculation formula is as follows: ， ， ， In the formula, in the formula, This refers to the amount of electricity that can be shifted across different time periods; This represents the total electricity consumption of heating equipment under peak-valley electricity pricing. The total electricity consumption of heating equipment under flat electricity prices; The optimal power output of the heating equipment at each time point under peak-valley electricity pricing; The optimal power output of the heating equipment at each time point under flat electricity prices; Based on the optimal heat storage capacity and optimal power of the heating equipment at each time point, the upward and downward adjustable power values ​​are calculated using the following formulas: ， ， In the formula, For heat storage electric heating load in The amount of power that can be adjusted upwards at any given moment; The optimal heat storage capacity of the thermal storage equipment at each time point under peak and off-peak electricity pricing; For electric heating load in The downward adjustable amount of power at any given moment; Based on the number of thermal storage electric heating load clusters, the adjustability assessment index of the thermal storage electric heating load clusters is obtained by summing the results. The calculation formulas are as follows: ， ， ， In the formula, M represents the number of thermal storage electric heating load clusters; The amount of electricity that can be shifted across time periods for thermal storage electric heating load clusters; The power up-adjustable amount for thermal storage electric heating load clusters; The power of the thermal storage electric heating load cluster can be adjusted downwards.

4. A load regulation system for a thermal storage electric heating load cluster, characterized in that, include: Acquisition Module: Acquires parameter data of thermal storage electric heating load and grid electricity price. The parameter data includes the initial indoor temperature of the heated building and the initial operating power of the heating equipment. The grid electricity price includes peak-valley electricity price and flat electricity price. Data processing module: Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, the peak and off-peak electricity prices, and the flat electricity price, calculate the adjustability assessment index of the thermal storage electric heating load cluster; Load optimization module: Used to adjust the load of the thermal storage electric heating load cluster according to the pre-set adjustability evaluation indicators. The adjustability evaluation indicators are the cross-time period power shift, the upward power adjustment, and the downward power adjustment. The cross-time period power shift is used to evaluate the amount of electricity load that can be shifted from a time period with low electricity demand to a time period with high electricity demand. The upward power adjustment is used to evaluate the amount of power that can be increased in the thermal storage electric heating load. The downward power adjustment is used to evaluate the amount of power that can be reduced in the thermal storage electric heating load. The method for calculating the adjustability assessment index is as follows: Based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and the pre-constructed thermal dynamic model of the heated building, the operating power of the heating equipment at each moment is calculated. Based on the grid electricity price, the operating power of the heating equipment at each time, and the pre-built optimization model for the operation of the heating equipment, the optimal heat storage capacity of the heat storage equipment and the optimal power of the heating equipment at each time are calculated. Based on the optimal heat storage capacity and the optimal power of the heating equipment at each time, calculate the adjustability evaluation index of the thermal storage electric heating load cluster. The calculation of the operating power of the heating equipment at each moment is based on the initial indoor temperature of the heated building, the initial operating power of the heating equipment, and a pre-constructed thermal dynamic model of the heated building; specifically, it includes: A thermal dynamic model of the heated building is constructed. The initial indoor temperature of the heated building is input into the thermal dynamic model, and the calculation is continuously iterated to obtain the indoor temperature of the heated building at each time point. The calculation formula is as follows: ， ， ， In the formula, The specific heat capacity of indoor air; Let t be the indoor temperature of the heated building. The upper limit of indoor temperature set for users; The upper limit of indoor temperature set for the user; Let t be the outdoor temperature of the heated building. The heat dissipation coefficient of a heated building structure; Let t be the operating power of the heating equipment at time t; ∆t represents the thermal efficiency of the heating equipment; ∆t represents the time step. Based on the indoor temperature of the heated building at each time point, the operating status of the heating equipment at each time point is calculated using the following formula: ， In the formula, b t The operating status of the heating equipment at time t; Based on the operating status of the heating equipment at each moment, the operating power of the heating equipment at each moment is calculated using the following formula: ， In the formula, express The operating power of the heating equipment at all times; This refers to the rated power of the heating equipment.

5. The load regulation system for a thermal storage electric heating load cluster according to claim 4, characterized in that, Based on the grid electricity price, the operating power of the heating equipment at each time point, and the pre-built optimization model for the operation of the heating equipment, the optimal heat storage capacity and the optimal power of the heating equipment at each time point are calculated; specifically including: Based on the operating power of the heating equipment at various times, a dynamic model of the energy storage state of the thermal storage equipment is established, and the calculation formula is as follows: , In the formula, for The amount of heat storage equipment that can store heat at all times; The self-heating coefficient of the heat storage device; Let t be the power of the heating device; D represents the electrothermal conversion efficiency of the heating equipment; D represents the single-time step size. This represents the maximum heat storage capacity of the thermal storage device. Based on the grid electricity price, an optimization model for the operation of heating equipment is established, and the calculation formula is as follows: ， In the formula, Total electricity cost; for Real-time grid electricity price; This represents the total number of time periods within the operating cycle. Based on the dynamic model of the energy storage state of the thermal storage device and the operation optimization model of the heating device, the optimal heat storage capacity of the thermal storage device and the optimal power of the heating device at each time are obtained. The method calculates the adjustability evaluation index of the thermal storage electric heating load cluster based on the optimal heat storage capacity of the thermal storage equipment and the optimal power of the heating equipment at each time point; specifically including: Based on the optimal power of the heating equipment at each time point under the grid electricity price, the amount of electricity that can be shifted across time periods is obtained, and the calculation formula is as follows: ， ， ， In the formula, in the formula, This refers to the amount of electricity that can be shifted across different time periods; This represents the total electricity consumption of heating equipment under peak-valley electricity pricing. The total electricity consumption of heating equipment under flat electricity prices; The optimal power output of the heating equipment at each time point under peak-valley electricity pricing; The optimal power output of the heating equipment at each time point under flat electricity prices; Based on the optimal heat storage capacity and optimal power of the heating equipment at each time point, the upward and downward adjustable power values ​​are calculated using the following formulas: ， ， In the formula, For heat storage electric heating load in The amount of power that can be adjusted upwards at any given moment; The optimal heat storage capacity of the thermal storage equipment at each time point under peak and off-peak electricity pricing; For electric heating load in The downward adjustable amount of power at any given moment; Based on the number of thermal storage electric heating load clusters, the adjustability assessment index of the thermal storage electric heating load clusters is obtained by summing the results. The calculation formulas are as follows: ， ， ， In the formula, M represents the number of thermal storage electric heating load clusters; The amount of electricity that can be shifted across time periods for thermal storage electric heating load clusters; The power up-adjustable amount for thermal storage electric heating load clusters; The power of the thermal storage electric heating load cluster can be adjusted downwards.

6. A load regulation device for a thermal storage electric heating load cluster, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 3.

Images