An air conditioner intelligent flexible regulation method based on a customer side intelligent energy management terminal

Abstract

The application provides an air conditioner intelligent flexible regulation and control method based on a customer side intelligent energy management terminal, which comprises the following steps: the terminal determines a power consumption peak time period, starts a flexible regulation and control strategy for air conditioner equipment, acquires an importance coefficient, a temperature coefficient and a power coefficient, configures weights, obtains a priority order of air conditioner flexible regulation and control, and performs regulation and control management based on the order from high to low. Through the method, the ordered management of air conditioner power consumption load is achieved, the peak power consumption pressure is relieved to the maximum extent on the premise that the basic power consumption demand of users is not affected and the comfort of users is met, peak load shifting is achieved, the stability and reliability of a power grid are improved, and a win-win result is achieved.

Description

Technical Field

[0001] This invention belongs to the field of electricity information collection, and specifically relates to a method for intelligent and flexible control of air conditioning based on a customer-side smart energy management terminal. Background Technology

[0002] With the continuous improvement of economic modernization and the rapid growth of seasonal and time-specific loads, the peak load of the power grid is constantly rising, and the peak-to-valley difference of the power grid is showing a gradual widening trend. The imbalance between power supply and demand in some areas is very serious, which seriously affects the safe and stable operation of the power system. Among them, the electricity consumption of air conditioning systems during the summer peak period is particularly typical, with air conditioning load accounting for a large proportion of the peak load in the power grid, and showing an increasing trend year by year.

[0003] The traditional approach of simply increasing the installed capacity of the power grid to passively meet the peak load demand has created enormous pressure on investment in power generation, transmission, transformation, and distribution construction, while also resulting in low utilization efficiency of power grid facilities.

[0004] Furthermore, in the event of a power grid emergency or peak electricity consumption, the control terminals for distribution, industrial and commercial, and dedicated transformer users in the power system still use methods such as direct power outages or complete load disconnection to achieve load regulation. Although this meets the requirements of demand response management, it seriously affects the normal operation of industrial and commercial users and dedicated transformer users, and even causes unnecessary economic losses to users. It also reduces users' enthusiasm for participating in power grid dispatch and control and cannot meet the needs of the evolving situation. Summary of the Invention

[0005] The purpose of this invention is to address the problem of load regulation at terminals within a power system by proposing a method for intelligent and flexible air conditioning regulation based on a customer-side smart energy management terminal.

[0006] The technical solution of this invention is:

[0007] This invention provides a method for intelligent and flexible control of air conditioning based on a customer-side smart energy management terminal, the method comprising the following steps:

[0008] Step 1: The terminal determines the peak electricity consumption period and activates the flexible control strategy for air conditioning equipment;

[0009] Step 2: Based on electricity demand, set the importance parameters for each air conditioner and normalize them as the importance coefficient for each air conditioner.

[0010] Step 3: Normalize the temperature coefficient of each air conditioner based on the difference between the season, the current set temperature of the air conditioner, and the indoor ambient temperature.

[0011] Step 4: Obtain the average power of the air conditioner within the preset cycle, calculate the difference between the average power and the set maximum allowable power threshold of the air conditioner, and normalize it as the power coefficient of each air conditioner.

[0012] Step 5: Configure the weights of importance coefficient, temperature coefficient and power coefficient to obtain the priority ranking of air conditioning flexible control, and carry out control management from high to low based on the ranking;

[0013] Step 6: During peak electricity consumption periods, repeat steps 2 to 5 to dynamically sort and control the control objects. When the load drops to the preset power, stop the control action and maintain statistical monitoring of various factors. During off-peak electricity consumption periods, restore the original set temperature of each air conditioner and stop flexible control.

[0014] Furthermore, the peak electricity consumption periods identified in step 1 include two types;

[0015] Based on the local electricity price schedule, the time period with the highest electricity price is identified as the peak electricity consumption period.

[0016] Obtain the total power freeze curve and compare it with the local power threshold. The time period that continuously exceeds the threshold is taken as the peak electricity consumption period.

[0017] Furthermore, the total power freeze curve is obtained by having the terminal collect the current total line power consumption based on a preset period, freeze and store it to form a total power freeze curve, with a default freeze period of at least 5 minutes.

[0018] Furthermore, step 2 specifically includes:

[0019] Set the importance parameter for each air conditioner, prio_in. i The range is 0 to 255;

[0020] Normalization is performed using the formula: prioA = prio_in i / max, where i represents the air conditioning equipment number, max is the maximum value of the importance parameter 255, and prio_in i Let prioA be the importance parameter value of the i-th air conditioning device, and prioA be the importance coefficient after normalization.

[0021] Furthermore, in step 3, the terminal periodically obtains the indoor ambient temperature through an external temperature sensor and periodically obtains the current set temperature of the air conditioner by accessing the appliance metering device; the collection period is at least 5 minutes.

[0022] Furthermore, in step 3, the normalized calculation formula for the temperature coefficient prioB is:

[0023] Summer: prioB = △T ac / △Tmin , where △T ac To set the temperature difference between the ambient temperature and the set temperature, ΔT min The difference between the set temperature and the minimum settable temperature; when the ambient temperature is greater than the set temperature, the result is set to 0, indicating that it does not participate in flexible control first.

[0024] Winter: prioB = △T ac / △T max , where △T ac To set the temperature difference between the ambient temperature and the set temperature, ΔT max The result is the difference between the set temperature and the maximum settable temperature. When the ambient temperature is lower than the set temperature, the result is set to 0, indicating that it does not participate in flexible control first.

[0025] Furthermore, in step 4, the average power value is obtained through smoothing calculation, with a sampling period of 4-6 minutes, a smoothing window of at least 3 cycles, and the average power value is at least the average of the samples from the most recent 3 cycles.

[0026] Furthermore, the normalized calculation formula for the power coefficient prioC in step 4 is: prioC = (P i -P) / P i Where i represents the air conditioning unit number, P i The average power of the i-th air conditioning unit is given by , and P is given by the maximum allowable power threshold of the air conditioning unit.

[0027] Furthermore, in step 5, the weights are configured using either an expert survey method or a double-rate comparison method.

[0028] Furthermore, the double-speed ring ratio method specifically includes:

[0029] The three factors—importance coefficient, temperature coefficient, and power coefficient—were randomly ranked.

[0030] The factors are compared in order, and the multiple relationship of importance between the factors is given, i.e., the chain ratio.

[0031] The month-on-month ratios are uniformly converted into the baseline value BASE = (base1, base2, base3);

[0032] Perform normalization to obtain the proportion of the corresponding factor among the three factors. j represents the factor number, so A = (a1, a2, a3).

[0033] The beneficial effects of this invention are:

[0034] This invention provides a smart flexible control method for air conditioning based on a customer-side smart energy management terminal. By leveraging the edge computing capabilities of the customer-side smart energy management terminal and combining it with the operating characteristics of the air conditioning units, the method fully considers the different electricity consumption habits of users and their priority in relying on air conditioning. The air conditioning units are classified and sorted for management. The factors affecting the classification are importance, temperature, and average power. Based on these three factors, the air conditioning is flexibly controlled. The terminal dynamically identifies the air conditioning units that most need control during the current demand response period and performs flexible control in sequence, including increasing the temperature and reducing the air supply volume.

[0035] The method of this invention can manage the air conditioning power load in an orderly manner, and alleviate the peak power pressure to the greatest extent without affecting the user's basic power needs and meeting the user's comfort, thereby achieving peak shaving and valley filling, improving the stability and reliability of the power grid and achieving a win-win situation.

[0036] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0037] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the invention.

[0038] Figure 1 A schematic diagram of the overall process of flexible control according to an embodiment of the present invention is shown.

[0039] Figure 2 A schematic diagram of a temperature determination process according to an embodiment of the present invention is shown. Detailed Implementation

[0040] Preferred embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0041] like Figure 1 , 2 As shown, this invention provides a method for intelligent and flexible control of air conditioning based on a customer-side smart energy management terminal. The method includes the following steps:

[0042] Step 1: The terminal determines the peak electricity consumption period and activates the flexible control strategy for the air conditioning equipment; there are two ways to determine the peak electricity consumption period.

[0043] Based on the local electricity price schedule, the time period with the highest electricity price is identified as the peak electricity consumption period.

[0044] The terminal collects the current total power consumption of the line based on a preset period and freezes and stores it to form a total power freeze curve. The default freeze period is at least 5 minutes. The total power freeze curve is compared with the local power threshold, and the time period that continuously exceeds the threshold is taken as the peak power consumption period.

[0045] Step 2: Based on electricity demand, set the importance parameters for each air conditioner, and normalize them as the importance coefficient for each air conditioner. Specifically:

[0046] Set the importance parameter for each air conditioner, prio_in. i The range is 0 to 255;

[0047] Normalization is performed using the formula: prioA = prio_in i / max, where i represents the air conditioning equipment number, max is the maximum importance parameter of 255, and prip_in i Let prioA be the importance parameter value of the i-th air conditioning device, and prioA be the importance coefficient after normalization.

[0048] Step 3: The terminal periodically obtains the indoor ambient temperature through an external temperature sensor and periodically obtains the current set temperature of the air conditioner by accessing the electrical appliance metering device; the collection period is at least 5 minutes, and the temperature coefficient of each air conditioner is normalized according to the season, the difference between the current set temperature of the air conditioner and the indoor ambient temperature.

[0049] The normalized formula for the temperature coefficient prioB is:

[0050] Summer: prioB = △T ac / △T min , where △T ac To set the temperature difference between the ambient temperature and the set temperature, ΔT min The difference between the set temperature and the minimum settable temperature; when the ambient temperature is greater than the set temperature, the result is set to 0, indicating that it does not participate in flexible control first.

[0051] Winter: prioB = △T ac / △T max , where △T ac To set the temperature difference between the ambient temperature and the set temperature, ΔT max The result is the difference between the set temperature and the maximum settable temperature. When the ambient temperature is lower than the set temperature, the result is set to 0, indicating that it does not participate in flexible control first.

[0052] Step 4: Obtain the average power of the air conditioner within the preset cycle, calculate the difference between the average power and the set maximum allowable power threshold of the air conditioner, and normalize it as the power coefficient of each air conditioner.

[0053] The power average is obtained through smoothing calculation, with a sampling period of 4-6 minutes, a smoothing window of at least 3 periods, and the power average is at least the average of the samples from the most recent 3 periods.

[0054] The normalized formula for the power factor prioC is: prioC = (P i -P) / P i Where i represents the air conditioning unit number, P i The average power of the i-th air conditioning unit is given by , and P is given by the maximum allowable power threshold of the air conditioning unit.

[0055] Step 5: Configure the weights of importance coefficient, temperature coefficient and power coefficient to obtain the priority ranking of air conditioning flexible control, and carry out control management from high to low based on the ranking;

[0056] The weighting method employs either an expert survey or a double-rate comparison method, wherein the double-rate comparison method specifically includes:

[0057] The three factors—importance coefficient, temperature coefficient, and power coefficient—were randomly ranked.

[0058] The factors are compared in order, and the multiple relationship of importance between the factors is given, i.e., the chain ratio.

[0059] The month-on-month ratios are uniformly converted into the baseline value BASE = (base1, base2, base3);

[0060] Perform normalization to obtain the proportion of the corresponding factor among the three factors. j represents the factor number, so A = (a1, a2, a3).

[0061] Step 6: During peak electricity consumption periods, repeat steps 2 to 5 to dynamically sort and control the control objects. When the load drops to the preset power, stop the control action and maintain statistical monitoring of various factors. During off-peak electricity consumption periods, restore the original set temperature of each air conditioner and stop flexible control.

[0062] In step 5, the priority ranking is as follows: based on steps 2-4, the percentage of each factor for each air conditioning unit is PRIO = (prio1, prio2, prio3). Combining the weighting calculation method, it is converted to a percentage system, and the calculation method is: F = PRIO * A T Multiply by 100, and the resulting F is the adjustable priority score for the air conditioner. All air conditioning units are prioritized and categorized according to this score to confirm the flexible control sequence. Scores of 80-100 are considered important priority, 60-80 are secondary priority, and below 60 are general priority.

[0063] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for intelligent and flexible control of air conditioning based on a customer-side smart energy management terminal, characterized in that, The method includes the following steps: Step 1: The terminal determines the peak electricity consumption period and activates the flexible control strategy for air conditioning equipment; Step 2: Based on electricity demand, set the importance parameters for each air conditioner and normalize them as the importance coefficient for each air conditioner. Step 3: Normalize the temperature coefficient of each air conditioner based on the difference between the season, the current set temperature of the air conditioner, and the indoor ambient temperature. Step 4: Obtain the average power of the air conditioner within the preset cycle, calculate the difference between the average power and the set maximum allowable power threshold of the air conditioner, and normalize it as the power coefficient of each air conditioner. Step 5: Configure the weights of importance coefficient, temperature coefficient and power coefficient to obtain the priority ranking of air conditioning flexible control, and carry out control management from high to low based on the ranking; Step 6: During peak electricity consumption periods, repeat steps 2 to 5 to dynamically sort and control the control objects. When the load drops to the preset power, stop the control action and maintain statistical monitoring of various factors. During off-peak electricity consumption periods, restore the original set temperature of each air conditioner and stop flexible control. Step 2 specifically involves: Set the importance parameters for each air conditioner. The range is 0~255; Normalization is performed using the following formula: Where i represents the air conditioning equipment number, The importance parameter has a maximum value of 255. Let i be the importance parameter value of the i-th air conditioning device. Normalized importance coefficient; In step 3, the temperature coefficient The normalization calculation formula is: summer: ,in To set the temperature difference between the ambient temperature and the set temperature, The difference between the set temperature and the minimum settable temperature; when the ambient temperature is greater than the set temperature, the result is set to 0, indicating that it does not participate in flexible control first. winter: ,in To set the temperature difference between the ambient temperature and the set temperature, The difference between the set temperature and the maximum settable temperature; when the ambient temperature is lower than the set temperature, the result is set to 0, indicating that it does not participate in flexible control first. Power coefficient in step 4 The normalization calculation formula is: Where i represents the air conditioning unit number, The average power of the i-th air conditioning unit. This refers to the maximum permissible power threshold of the air conditioning equipment.

2. The intelligent flexible control method for air conditioning based on a customer-side smart energy management terminal according to claim 1, characterized in that, Step 1 identifies two types of peak electricity consumption periods; Based on the local electricity price schedule, the time period with the highest electricity price is identified as the peak electricity consumption period. Obtain the total power freeze curve and compare it with the local power threshold. The time period that continuously exceeds the threshold is taken as the peak electricity consumption period.

3. The method for intelligent flexible control of air conditioning based on a customer-side smart energy management terminal according to claim 2, characterized in that... The total power freeze curve is obtained by the terminal collecting the current total line power consumption based on a preset period and freezing and storing it to form the total power freeze curve. The default freeze period is at least 5 minutes.

4. The method for intelligent flexible control of air conditioning based on a customer-side smart energy management terminal according to claim 1, characterized in that... In step 3, the terminal periodically obtains the indoor ambient temperature through an external temperature sensor and periodically obtains the current set temperature of the air conditioner by accessing the appliance metering device; the minimum collection period is 5 minutes.

5. The intelligent flexible control method for air conditioning based on a customer-side smart energy management terminal according to claim 1, characterized in that... In step 4, the average power value is obtained through smoothing calculation, with a sampling period of 4-6 minutes, a smoothing window of at least 3 periods, and the average power value is at least the average of the samples from the most recent 3 periods.

6. The method for intelligent flexible control of air conditioning based on a customer-side smart energy management terminal according to claim 1, characterized in that... In step 5, the weights are configured using either an expert survey method or a double-rate comparison method.

7. The method for intelligent flexible control of air conditioning based on a customer-side smart energy management terminal according to claim 6, characterized in that... The aforementioned double-speed ring ratio method specifically includes: The three factors—importance coefficient, temperature coefficient, and power coefficient—were randomly ranked. The factors are compared in order, and the multiple relationship of importance between the factors is given, i.e., the chain ratio. The month-on-month ratio was uniformly converted into a benchmark value. ; Perform normalization to obtain the proportion of the corresponding factor among the three factors. , Indicate the factor number, and get .

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