CN114069642B - A comprehensive peak-shaving method for temperature-controlled loads considering user satisfaction - Google Patents

Abstract

The invention provides a temperature control load comprehensive peak shaving method considering user satisfaction, which comprises the following steps: judging whether the temperature control load can respond to the peak shaving requirement of the power grid according to the attribute of each temperature control load in the power system; if the temperature control load can respond to the peak regulation requirement, respectively calculating the current comprehensive satisfaction degree of the user and the predicted adjustable capacity of the temperature control load, taking the ratio of the total satisfaction degree of the user to the predicted adjustable capacity as a peak regulation potential index of the temperature control load, otherwise, assigning the peak regulation potential index to infinity; and determining the response priority of the temperature control load based on the ascending order of the peak regulation potential indexes, and regulating and controlling the temperature control load according to the order of the response priority until the total capacity of the temperature control load reaches the peak regulation requirement of the power grid. According to the temperature control load peak regulation priority determined by the peak regulation potential index provided by the invention, peak regulation is performed by comprehensively considering the peak regulation potential and the user satisfaction, and the user satisfaction and the temperature control load group energy efficiency level of the social temperature control group are improved.

Description

A comprehensive peak-shaving method for temperature-controlled loads considering user satisfaction

technical field

The invention belongs to the field of power grid load peak regulation, and in particular relates to a temperature-controlled load comprehensive peak regulation method considering user satisfaction.

Background technique

As the installed capacity of renewable energy in the power system will gradually increase, the peak-to-valley difference in the power system will further widen, and the anti-regulation of renewable energy will further increase the demand for peak-shaving capacity in the power system. In recent years, more and more researches and practical projects regard flexible resources on the demand side as an important resource for power grid peak regulation. As a flexible resource widely used on the demand side, temperature-controlled loads account for a considerable proportion of peak-stable power grid loads. According to statistics, the temperature-controlled load takes up one-third of the peak power in summer. Therefore, temperature-controlled load is one of the important resources for peak shaving in summer.

At present, in the main research on temperature-controlled load participation in peak-shaving strategies and methods, usually only the peak-shaving potential of temperature-controlled loads in predicting adjustable capacity is considered. For example, in the application number 2021104758282, the patent name is "Based on In the technical solution disclosed in "Aggregated Load Scheduling Method for Prediction of Peak-Shaving Potential Parameters", historical air temperature and user power consumption data are mined and used to obtain a high-precision aggregated load baseline, and then accurately predict the peak-shaving potential parameters of the aggregated load. Due to the seasonality and periodicity of the temperature-controlled load, the difference between it and other peak loads is that the regulation of the temperature-controlled load is limited by the user's basic demand for the temperature-controlled load, and the user's satisfaction should be ensured as much as possible during the peak-shaving process. satisfaction level. Because the existing methods are difficult to judge the potential level of peak shaving directly based on the metering data of the dispatching side combined with the degree of user satisfaction, which leads to poor peak shaving effect.

Contents of the invention

In order to solve the shortcomings and deficiencies in the prior art, the present invention proposes a temperature-controlled load comprehensive peak-shaving method considering user satisfaction, including:

According to the attributes of each temperature-controlled load in the power system, determine whether the temperature-controlled load can respond to the peak-shaving demand of the power grid;

If the temperature-controlled load can respond to the peak-shaving demand, the current comprehensive satisfaction of users and the predicted adjustable capacity of the temperature-controlled load are calculated separately, and the ratio of the overall user satisfaction to the predicted adjustable capacity is used as the peak-shaving potential index of the temperature-controlled load. Otherwise, assign a value of infinity to the peak-shaving potential index;

The response priority of the temperature-controlled load is determined based on the ascending order of the peak-shaving potential index, and the temperature-controlled load is regulated according to the order of the response priority until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid.

Optionally, the calculation of the current comprehensive satisfaction of the user and the predicted adjustable capacity of the temperature control load includes:

According to the preset fuzzy rules and the temperature membership function and electricity price membership function corresponding to the fuzzy rules, the current comprehensive satisfaction of users is calculated as:

Among them, S is the current comprehensive satisfaction of users, R is the total number of fuzzy rules, is the temperature membership function corresponding to the i-th fuzzy rule, /> is the electricity price membership function corresponding to the i-th fuzzy rule, /> is the i-th fuzzy rule, EP represents the electricity price of the temperature-controlled load, and T represents the set temperature of the temperature-controlled load.

Optionally, the temperature membership function includes a first membership function μ _TCM (T) for temperature perception as comfortable, a second membership function μ _TCL (T) for temperature perception as cool, and a temperature perception as hot The third membership function μ _THT (T);

The first membership function μ _TCM (T) is:

The second membership function μ _TCL (T) is:

The third membership function μ _THT (T) is:

Min represents the lower limit of the comfortable temperature range, Max represents the upper limit of the comfortable temperature range, Up _Min and Up _Max represent the two middle limits within the comfortable temperature range, where Min<Up _Min <Up _Max <Max.

Optionally, the membership degree function of electricity price is a function established for users with different attributes, and each parameter in the membership degree function of electricity price is determined through data driving.

Optionally, the expression of the fuzzy rule is:

Among them, f ⁱ (EP,T) is the expression of the i-th fuzzy rule, Both are the preset parameters of the i-th fuzzy rule.

Optionally, the calculation of the current comprehensive satisfaction of the user and the predicted adjustable capacity of the temperature control load includes:

Obtain the historical daily load of the temperature-controlled load, select the historical daily load of N days with the lowest external temperature and calculate the average value, and obtain the base load L _base as:

Among them, L _j is the selected historical daily load on the jth day, and D is the set of selected historical daily loads;

Calculate the difference between each historical daily load L _j and the base load L _base to obtain the historical forecast adjustable capacity L _TCL,j ;

Sampling the historical forecast adjustable capacity L _TCL,j based on the preset time interval to determine the maximum value in the sampled data

Based on the least square method, the relationship between the external temperature and the historical daily load of the temperature control load is fitted until the fitting result f(T) and the maximum value The square difference of reaches the minimum;

After obtaining the current external temperature and substituting it into the fitting result, it is multiplied by the preset proportional coefficient to obtain the current predicted adjustable capacity of the temperature control load.

Optionally, the response priority of the temperature-controlled load is determined based on the ascending order of the peak-shaving potential index, and the temperature-controlled load is regulated according to the order of the response priority until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid, including :

Step 1: Arrange the response priority of the temperature-controlled load in ascending order of the peak-shaving potential index. The greater the peak-shaving potential index, the higher the response priority of the temperature-controlled load;

Step 2: According to the order of response priority from high to low, send peak-shaving signals to temperature-controlled loads in sequence;

Step 3: When the temperature-controlled load that receives the peak-shaving signal completes the peak-shaving response, determine whether the total capacity of the temperature-controlled load meets the peak-shaving demand of the power grid at this time;

Step 4: If the peak-shaving demand is not met, continue to send peak-shaving signals to the temperature-controlled load in sequence, and repeat step 3 until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid.

The beneficial effects brought by the technical scheme provided by the invention are:

The present invention uses a fuzzy logic method to analyze user satisfaction, and on this basis, provides a more intuitive index for considering the peak-shaving potential of temperature-controlled loads. The temperature-controlled load peak-shaving priority determined according to the peak-shaving potential index proposed by the present invention can meet engineering application requirements and help power grid dispatchers quickly evaluate the energy efficiency of temperature-controlled loads, thereby comprehensively considering the peak-shaving potential and user satisfaction for peak-shaving , improve the user satisfaction of social temperature control group and the energy efficiency level of temperature control load group.

Description of drawings

In order to illustrate the technical solution of the present invention more clearly, the accompanying drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. Ordinary technicians can also obtain other drawings based on these drawings on the premise of not paying creative work.

Fig. 1 is a schematic flow chart of a temperature-controlled load comprehensive peak-shaving method considering user satisfaction proposed by an embodiment of the present invention;

Fig. 2 is a flow chart of temperature control load regulation in the embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and not necessarily Used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein.

It should be understood that in various embodiments of the present invention, the sequence numbers of the processes do not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, rather than by the implementation order of the embodiments of the present invention. The implementation process constitutes no limitation.

It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to Those steps or elements are not explicitly listed, but may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

It should be understood that in the present invention, "plurality" means two or more. "And/or" is just an association relationship describing associated objects, which means that there can be three kinds of relationships, for example, and/or B, which can mean: A exists alone, A and B exist at the same time, and B exists alone. . The character "/" generally indicates that the contextual objects are an "or" relationship. "Includes A, B and C", "Includes A, B, C" means that A, B, and C are all included, "includes A, B, or C" means includes one of A, B, and C, "Containing A, B and/or C" means containing any 1 or any 2 or 3 of A, B and C.

It should be understood that in the present invention, "B corresponding to A", "B corresponding to A", "A corresponding to B" or "B corresponding to A" means that B is associated with A, and according to A It is possible to determine B. Determining B from A does not mean determining B from A alone, B can also be determined from A and/or other information. The matching between A and B means that the similarity between A and B is greater than or equal to a preset threshold.

Depending on the context, "if" as used herein may be interpreted as "at" or "when" or "in response to determining" or "in response to detecting".

The technical solution of the present invention will be described in detail below with specific embodiments. The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

Embodiment one

As shown in Figure 1, this embodiment proposes a temperature-controlled load comprehensive peak-shaving method considering user satisfaction, including:

S1: According to the properties of each temperature-controlled load in the power system, determine whether the temperature-controlled load can respond to the peak-shaving demand of the power grid;

S2: If the temperature-controlled load can respond to the peak-shaving demand, calculate the current comprehensive satisfaction of users and the predicted adjustable capacity of the temperature-controlled load, and use the ratio of the overall user satisfaction to the predicted adjustable capacity as the peak-shaving potential of the temperature-controlled load index, otherwise, assign the value of the peak-shaving potential index to infinity;

S3: Determine the response priority of the temperature-controlled load based on the ascending order of the peak-shaving potential index, and regulate the temperature-controlled load according to the order of the response priority until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid.

In this embodiment, the parameters of the temperature-controlled load in the power system and the external temperature are determined in advance, and then it is judged whether the temperature-controlled load can participate in peak regulation. The load will not participate in the following analysis and peak shaving process.

For the remaining temperature-controlled loads that can participate in peak-shaving, this embodiment constructs a peak-shaving potential index based on the overall satisfaction of users and the predicted adjustable capacity of temperature-controlled loads According to the peak-shaving potential index, the temperature-controlled load can be peak-shaving, which can achieve the highest comprehensive social satisfaction, which is conducive to the development of temperature-controlled loads participating in peak-shaving.

In this embodiment, the calculation method of the overall user satisfaction S is as follows:

Among them, S is the current comprehensive satisfaction of users, R is the total number of fuzzy rules, is the temperature membership function corresponding to the i-th fuzzy rule, /> is the electricity price membership function corresponding to the i-th fuzzy rule, /> is the i-th fuzzy rule, EP represents the electricity price of the temperature-controlled load, and T represents the set temperature of the temperature-controlled load.

In this embodiment, the expression of the fuzzy rule is:

Among them, f ⁱ (EP,T) is the expression of the i-th fuzzy rule, Both are the preset parameters of the i-th fuzzy rule.

The meaning expressed by the fuzzy rules is when the value of the electricity price is EP and the value of the set temperature is T, the user’s satisfaction with the temperature-controlled load. In this embodiment, it can be determined according to the historical operation of the temperature-controlled load. For example, the temperature-controlled load According to the historical electricity price, historical set temperature and corresponding user complaints, the preset parameters in the fuzzy rule expression are determined, so as to build a fuzzy rule base covering the corresponding situation of user satisfaction, electricity price, and set temperature.

In this embodiment, the temperature membership functions include the first membership function μ _TCM (T) for the temperature perception as comfortable, the second membership function μ _TCL (T) for the temperature perception as cool, and the temperature perception as hot The third membership function μ _THT (T), it can be seen that in the calculation formula of the user's overall satisfaction S, for each fuzzy rule, its Both include three kinds.

The first membership function μ _TCM (T) is:

The second membership function μ _TCL (T) is:

The third membership function μ _THT (T) is:

Min represents the lower limit of the comfortable temperature range, Max represents the upper limit of the comfortable temperature range, Up _Min and Up _Max represent the two middle limits within the comfortable temperature range, where Min<Up _Min <Up _Max <Max.

Through the above three membership functions, this embodiment can obtain the membership degrees of the same temperature control load setting stability in the three user perceptions of comfort, coolness, and heat, so as to more accurately quantify the user's satisfaction with the set temperature Spend.

The membership degree function of electricity price is a function established for users with different attributes, and each parameter in the membership degree function of electricity price is determined through data driving. Data-driven is to collect massive amounts of data, organize the data to form information, integrate and refine relevant information, and form an automated decision-making model through training and fitting on the basis of the data. Because users with different attributes have different satisfaction with electricity prices, this embodiment is based on data-driven technology to collect the electricity price adjustment questionnaires of various users with different attributes such as merchants, residents, enterprises, factories, etc., and the information in the adjustment questionnaires Data modeling is carried out after extraction and integration, and the established model is the membership function of electricity price.

In this embodiment, the calculation method of the overall user satisfaction P is as follows:

Obtain the historical daily load of the temperature-controlled load, select the historical daily load of N days with the lowest external temperature and calculate the average value, and obtain the base load L _base as:

Among them, L _j is the selected historical daily load on the jth day, and D is the set of selected historical daily loads;

Calculate the difference between each historical daily load L _j and the base load L _base to obtain the historical forecast adjustable capacity L _TCL,j .

The base load is a load that is not affected by changes in external temperature or is affected but is very small, so it is not within the scope of analysis of the peak-shaving potential of temperature-controlled loads. In this embodiment, the difference between each historical daily load L _j and the base load L _base can be used to obtain the historically predicted adjustable capacity of the user's temperature-controlled load as training data for subsequent fitting.

In the actual power system, the peaking demand of the power system is often in the peak load period. In the high temperature environment in summer, the peak load is often caused by the increase of the temperature-controlled load. When peak regulation is required, the operating power of the temperature-controlled load is at a relatively high level. Therefore, in order to estimate the response capacity that the temperature-controlled load can provide during peak regulation, this embodiment can obtain the relationship between the two through function fitting according to the daily maximum power of the temperature-controlled load and the ambient temperature in different days in the historical data, specifically including :

Sampling the historical forecast adjustable capacity L _TCL,j based on the preset time interval to determine the maximum value in the sampled data Based on the least square method, the relationship between the external temperature and the historical daily load of the temperature control load is fitted until the fitting result f(T) and the maximum value>> The square difference of reaches the minimum, that is, /> When the minimum value is reached, T _j is the external temperature on the jth day. At this time, it is considered that the fitted f(T) can accurately represent the relationship between the external temperature and the temperature-controlled load forecasting adjustable capacity; obtain the current external temperature and substitute it into the fitting After the result, multiply it by the preset proportional coefficient γ to get the current predicted adjustable capacity P of the temperature control load, that is:

P=γf(T).

In this embodiment, the historical forecasted adjustable capacity L _TCL,j is sampled every 15 minutes to obtain several sampling data L _TCL,j,t , where t represents the sampling time, and then determined

Then, substitute the predicted temperature of the weather forecast in the future into P=γf(T) to obtain the predicted adjustable capacity of the temperature-controlled load, and comprehensively evaluate the peak-shaving potential of the temperature-controlled load based on the overall satisfaction of users.

In this embodiment, the response priority of the temperature-controlled load is determined based on the ascending order of the peak-shaving potential index, and the temperature-controlled load is regulated according to the order of the response priority until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid, including :

Step 1: Arrange the response priority of the temperature-controlled load in ascending order of the peak-shaving potential index. The greater the peak-shaving potential index, the higher the response priority of the temperature-controlled load;

Step 2: According to the order of response priority from high to low, send peak-shaving signals to temperature-controlled loads in turn;

Step 3: When the temperature-controlled load that receives the peak-shaving signal completes the peak-shaving response, determine whether the total capacity of the temperature-controlled load meets the peak-shaving demand of the power grid at this time;

Step 4: If the peak-shaving demand is not met, continue to send peak-shaving signals to the temperature-controlled load in sequence, and repeat step 3 until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid.

In this embodiment, the peak-shaving signal is then sent to the temperature-controlled loads in order of priority from high to low, and the temperature-controlled load with the highest If there are multiple control loads, as shown in Figure 2, at the beginning of peak shaving within the same priority, it first obtains the peak shaving demand capacity ΔP calculated by the dispatch center, and sends a dispatch to the i-th temperature control load from i=1. Peak signal, after the i-th temperature-controlled load responds to the peak-shaving signal, judge whether the total capacity of all temperature-controlled loads is greater than or equal to ΔP at this time, and if it is satisfied, then end the peak-shaving, otherwise i=i+1, continue to cycle the above process . If all temperature-controlled loads with the highest priority are peak shaving but still fail to meet the requirement that the total capacity is greater than or equal to ΔP, then repeat the above process to the next-priority temperature-controlled load until the above requirements are met, or all temperature-controlled loads participate Stop after peak shaving response.

So far, this embodiment determines the order of the temperature-controlled loads participating in peak-shaving according to the peak-shaving potential index EERCS, so that the temperature-controlled loads with better peak-shaving effects can participate in priority, and realize the improvement of temperature-controlled loads under the premise of comprehensive user satisfaction. The efficiency and effect of peak shaving.

The serial numbers in the above embodiments are for description only, and do not represent the sequence of the components during assembly or use.

The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention Inside.

Claims (5)

1. A temperature-controlled load comprehensive peak-shaving method considering user satisfaction, characterized in that, the temperature-controlled load comprehensive peak-shaving method comprises: According to the attributes of each temperature-controlled load in the power system, determine whether the temperature-controlled load can respond to the peak-shaving demand of the power grid; If the temperature-controlled load can respond to the peak-shaving demand, the current comprehensive satisfaction of users and the predicted adjustable capacity of the temperature-controlled load are calculated separately, and the ratio of the overall user satisfaction to the predicted adjustable capacity is used as the peak-shaving potential index of the temperature-controlled load. Otherwise, assign a value of infinity to the peak-shaving potential index; Determine the response priority of the temperature-controlled load based on the ascending order of the peak-shaving potential index, and regulate the temperature-controlled load in accordance with the order of the response priority until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid; The separate calculation of the user's current comprehensive satisfaction and the predicted adjustable capacity of the temperature control load includes: According to the preset fuzzy rules and the temperature membership function and electricity price membership function corresponding to the fuzzy rules, the current comprehensive satisfaction of users is calculated as: Among them, S is the current comprehensive satisfaction of users, R is the total number of fuzzy rules, is the temperature membership function corresponding to the i-th fuzzy rule, /> is the electricity price membership function corresponding to the i-th fuzzy rule, /> is the i-th fuzzy rule, EP represents the electricity price of the temperature-controlled load, and T represents the set temperature of the temperature-controlled load; The temperature membership function includes a first membership degree function μ _TCM (T) for temperature perception as comfortable, a second membership degree function μ _TCL (T) for temperature perception as cool, and a third membership degree function for temperature perception as hot function _μTHT (T); The first membership function μ _TCM (T) is: The second membership function μ _TCL (T) is: The third membership function μ _THT (T) is: Min represents the lower limit of the comfortable temperature range, Max represents the upper limit of the comfortable temperature range, Up _Min and Up _Max represent the two middle limits within the comfortable temperature range, where Min<Up _Min <Up _Max <Max. 2. A kind of temperature-controlled load comprehensive peak-shaving method considering user satisfaction according to claim 1, characterized in that, the membership degree function of electricity price is a function established for users with different attributes, and the membership degree function of electricity price The parameters in are determined through data-driven. 3. a kind of temperature-controlled load comprehensive peak-shaving method considering user satisfaction according to claim 1, is characterized in that, the expression of described fuzzy rule is: Among them, f ⁱ (EP,T) is the expression of the i-th fuzzy rule, Both are the preset parameters of the i-th fuzzy rule. 4. A kind of temperature-controlled load comprehensive peak-shaving method considering user satisfaction according to claim 1, characterized in that said calculating the user's current comprehensive satisfaction and the predicted adjustable capacity of temperature-controlled load respectively comprises: Obtain the historical daily load of the temperature-controlled load, select the historical daily load of N days with the lowest external temperature and calculate the average value, and obtain the base load L _base as: Among them, L _j is the selected historical daily load on the jth day, and D is the set of selected historical daily loads; Calculate the difference between each historical daily load L _j and the base load L _base to obtain the historical forecast adjustable capacity L _TCL,j ; Sampling the historical forecast adjustable capacity L _TCL,j based on the preset time interval to determine the maximum value in the sampled data Based on the least square method, the relationship between the external temperature and the historical daily load of the temperature control load is fitted until the fitting result f(T) and the maximum value The square difference of reaches the minimum; After obtaining the current external temperature and substituting it into the fitting result, it is multiplied by the preset proportional coefficient to obtain the current predicted adjustable capacity of the temperature control load. 5. The comprehensive peak-shaving method of temperature-controlled load considering user satisfaction according to claim 1, characterized in that, the response priority of the temperature-controlled load is determined based on the ascending order of the peak-shaving potential index, and according to the response priority The temperature-controlled loads are regulated in sequence until the total capacity of the temperature-controlled loads reaches the peak-shaving demand of the power grid, including: Step 1: Arrange the response priority of the temperature-controlled load in ascending order of the peak-shaving potential index. The greater the peak-shaving potential index, the higher the response priority of the temperature-controlled load; Step 2: According to the order of response priority from high to low, send peak-shaving signals to temperature-controlled loads in sequence; Step 3: When the temperature-controlled load that receives the peak-shaving signal completes the peak-shaving response, determine whether the total capacity of the temperature-controlled load meets the peak-shaving demand of the power grid at this time; Step 4: If the peak-shaving demand is not met, continue to send peak-shaving signals to the temperature-controlled load in sequence, and repeat step 3 until the total capacity of the temperature-controlled load reaches the peak-shaving demand of the power grid.