User flexibility regulation potential selection method, system and device based on AHP-entropy weight method and medium

Abstract

The invention relates to the technical field of power demands, in particular to a user flexibility regulation potential selection method, system and device based on an AHP-entropy weight method and a medium. Identifying and analyzing indexes influencing the flexibility potential of the user through a hierarchy method, and determining a subjective weight; quantifying an objective variation degree of the index data by an entropy weight method, and calculating an objective weight; and the subjective and objective weights are integrated to calculate the user electric energy regulation capability and the participation willingness value. And constructing an evaluation model by using a fuzzy evaluation method, and comprehensively scoring the user flexibility adjustment capability. Sorting the users according to the scoring result, clearing the sequence and distributing the electric quantity, and optimizing the distribution process to meet the power grid demand and the individual demand of the users; and finally obtaining a list of users participating in orderly power utilization and a distribution electric quantity value.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand, and in particular to a method, system, device and medium for evaluating user flexibility regulation potential based on AHP-entropy weight method. Background Art

[0002] Demand response is a power demand management strategy used to adjust power consumption behavior. When the published demand capacity response fails to meet the standard, orderly power consumption measures are required to force power outages or reduce loads. The power market usually publishes the required capacity requirements and invites users to sign up voluntarily. However, when the final capacity is cleared, the required capacity is not fully met due to the fact that some users may not actually participate. Power operators need to force capacity clearing for users who voluntarily sign up for demand response but ultimately fail to fulfill their commitments. However, in selecting users who need to be forced to implement orderly power consumption measures, we still face a series of challenges: 1) The strategy should be customized and implemented according to the user's electric energy regulation potential to maximize its effectiveness. 2) The willingness of different users to participate must be fully considered to ensure the humanity and rationality of the strategy. 3) Our goal is to maximize user satisfaction and implementation efficiency under the orderly power consumption strategy and contribute to the sustainable development of society and economy.

[0003] First, in the existing orderly power consumption implementation, there may indeed be a problem of inadequate consideration of the multi-dimensional factors that affect the flexibility of user regulation, especially the consideration of the factor of user willingness. The efficiency of orderly power consumption measures and user satisfaction are not high, and the problem of power consumption and the impact on users may not be fully considered. This problem highlights the shortcomings of overall considerations, and a more effective method is needed to effectively improve the formulation of orderly power consumption plans and optimize the orderly power consumption mechanism. Summary of the invention

[0004] Aiming at the problem of insufficient and incomplete consideration in the prior art in terms of efficiency improvement and user satisfaction under orderly power consumption measures, the present invention proposes a method, system, equipment and medium for selecting user flexibility regulation potential based on AHP-entropy weight method; the hierarchical method is used to identify and analyze the indicators affecting the user's flexibility potential, and the subjective weight is determined; the entropy weight method is used to quantify the objective variation degree of the indicator data and calculate the objective weight; the user's power regulation ability and willingness to participate are calculated by combining the subjective and objective weights. The evaluation model is constructed using the fuzzy judgment method to comprehensively score the user's flexibility regulation ability. Users are sorted according to the scoring results, and the electricity is cleared and allocated in sequence, and the allocation process is optimized to meet the grid demand and the user's personalized needs; finally, a list of users participating in orderly power consumption and the allocated electricity value are obtained.

[0005] The specific implementation contents of the present invention are as follows:

[0006] A method for evaluating user flexibility regulation potential based on AHP-entropy weight method specifically includes the following steps:

[0007] Step S1: constructing an orderly power user power regulation potential index system and a user willingness index system based on the acquired power index data;

[0008] Step S2: Analyze the power regulation potential index of orderly power users by using the hierarchical analysis method to determine the weight of the subjective willingness index;

[0009] Step S3: using the entropy weight method to quantify the objective variation degree of the user willingness index and calculate the weight of the objective adjustment index;

[0010] Step S4: Calculate the user's power regulation capability and participation willingness value by combining the subjective willingness index weight and the objective regulation index weight;

[0011] Step S5: constructing an evaluation model using a fuzzy judgment method to obtain a comprehensive score of the user's flexibility adjustment ability;

[0012] Step S6: Clear and allocate electricity in order according to the comprehensive scoring results of the users' flexibility adjustment capabilities, and optimize the allocation process to obtain a list of users participating in orderly electricity consumption and the allocated electricity value.

[0013] In order to better implement the present invention, further, the step S2 specifically includes the following steps:

[0014] Step S21: constructing a hierarchical model according to the set hierarchical levels;

[0015] Step S22: Introducing subjective judgment into the constructed hierarchical model to construct a judgment matrix;

[0016] Step S23: According to the judgment matrix, the power regulation potential indicators of orderly power users are ranked by relative importance and the weight coefficients are calculated;

[0017] Step S24: Perform a consistency check based on the weight coefficients. If the consistency is met, the subjective intention weight is obtained; otherwise, the judgment matrix is ​​adjusted to recalculate the weight coefficients.

[0018] In order to better implement the present invention, further, the step S3 specifically includes the following steps:

[0019] Step S31: determining the original data matrix according to the objective information of the acquired indicator data;

[0020] Step S32: standardize the index data and calculate the index proportion;

[0021] Step S33: Calculate information entropy according to the index proportion;

[0022] Step S34: Calculate the difference coefficient according to the information entropy;

[0023] Step S35: Calculate the objective adjustment index weight according to the difference coefficient.

[0024] In order to better implement the present invention, further, the step S4 specifically includes the following steps:

[0025] Step S41: constructing an objective function according to the user's willingness to participate in orderly electricity consumption, the amount of electricity allocated by the user to participate in forced clearing, and the user's dissatisfaction value after participating in forced clearing on the day;

[0026] Step S42: constructing a total contribution constraint based on the amount of electricity allocated by the user to participate in the mandatory clearing and the amount of electricity required by the power market on a certain day;

[0027] Step S43: constructing a user's maximum contribution constraint based on the user's allocated power and the cleared power declared by the user on a certain day;

[0028] Step S44: constructing an electricity allocation strategy according to the objective function, the total contribution constraint, and the user's maximum contribution constraint;

[0029] Step S45: constructing a single-layer optimization model of user clearing volume taking into account user satisfaction according to the power allocation strategy;

[0030] Step S46: calling the sequence method and the least squares quadratic programming method to solve the single-layer optimization model of the user's clearing amount and obtain the user's flexibility value score.

[0031] In order to better implement the present invention, further, the step S6 specifically includes the following steps:

[0032] Step S61: Obtain a comprehensive evaluation result according to the comprehensive score of the user's flexibility adjustment ability;

[0033] Step S62: Sort the users according to the comprehensive evaluation results, clear and allocate electricity in sequence, optimize the allocation process, and obtain a list of users participating in orderly electricity consumption and the allocated electricity value.

[0034] In order to better implement the present invention, further, the step S1 specifically includes the following steps:

[0035] Step S11: constructing a flexibility adjustment potential evaluation index system according to factors affecting user adjustment potential; the flexibility adjustment potential evaluation index system includes peak-to-valley difference rate, daily load rate, valley power coefficient, maximum adjustable peak load rate, peak load difference coefficient, peak power consumption proportion, adjustable load proportion, and equipment power outage preparation time;

[0036] Step S12: constructing a user willingness index system based on the acquired user willingness data; the user willingness index system includes the economic benefits of users participating in orderly electricity use, the proportion of electricity expenditure in total cost expenditure, the user economic level coefficient, and the proportion of productive load;

[0037] Step S13: obtaining the user load regulation capability according to the flexibility regulation potential evaluation index system, and obtaining the user participation willingness according to the user willingness index system.

[0038] Based on the above-mentioned user flexibility regulation potential selection method based on the AHP-entropy weight method, in order to better realize the present invention, further, a user flexibility regulation potential selection system based on the AHP-entropy weight method is proposed, which executes the above-mentioned user flexibility regulation potential selection method based on the AHP-entropy weight method; it includes an indicator construction unit, a subjective calculation unit, an objective calculation unit, a comprehensive unit, a scoring unit, and an evaluation unit;

[0039] The index construction unit is used to construct an index system of electric energy regulation potential and a user willingness index system of orderly power users according to the acquired power index data;

[0040] The subjective calculation unit is used to calculate the weight of the subjective willingness index using the hierarchical analysis method;

[0041] The objective calculation unit is used to calculate the objective adjustment index weight using the entropy weight method;

[0042] The comprehensive unit is used to comprehensively consider the subjective willingness index weight and the objective regulation index weight to calculate the user's power regulation capability and participation willingness value;

[0043] The scoring unit is used to construct an evaluation model using a fuzzy judgment method to obtain a comprehensive score of the user's flexibility adjustment ability;

[0044] The selection unit is used to obtain a list of users participating in orderly electricity consumption and allocated electricity values ​​according to the comprehensive scoring results of the users' flexibility adjustment capabilities.

[0045] Based on the above-mentioned method for evaluating the user flexibility regulation potential based on the AHP-entropy weight method, in order to better realize the present invention, further, an electronic device is proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned method for evaluating the user flexibility regulation potential based on the AHP-entropy weight method is implemented.

[0046] Based on the above-mentioned user flexibility regulation potential selection method based on the AHP-entropy weight method, in order to better realize the present invention, further, a computer-readable storage medium is proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned user flexibility regulation potential selection method based on the AHP-entropy weight method is implemented.

[0047] The present invention has the following beneficial effects:

[0048] The present invention combines the subjective will of the orderly electricity consumption user side with the objective regulation potential, and comprehensively considers the two, to provide a method that takes into account both the user's subjective conditions and the user's objective conditions, and provides a ranking selection and the optimal allocation of electricity to the participating users under this ranking selection, thereby solving the shortcomings in the prior art in terms of efficiency improvement and user satisfaction under the orderly electricity consumption measures caused by insufficient and incomplete considerations, and realizing the optimization of the orderly electricity consumption strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention provides a schematic block diagram of the hierarchical analysis method process.

[0050] Figure 2 This is a schematic block diagram of the entropy weight method process provided by the present invention.

[0051] Figure 3 This is a schematic diagram of the fuzzy evaluation method process provided by the present invention.

[0052] Figure 4 A schematic flowchart of the user flexibility evaluation method based on the analytic hierarchy process and the entropy weight method provided by the present invention.

[0053] Figure 5 Nine groups of industrial user load curves in a specific area are provided in an embodiment of the present invention.

[0054] Figure 6 This is a schematic diagram of the structure of the user allocation and clearing electricity optimization model provided by the present invention.

[0055] Figure 7 A distribution diagram of evaluation values ​​of user adjustment potential and participation willingness provided in an embodiment of the present invention.

[0056] Figure 8 A fuzzy scoring and ranking bar chart of user-adjustable values ​​provided in an embodiment of the present invention.

[0057] Fig. 9 A pie chart of optimized participating users and their allocated power provided in an embodiment of the present invention.

[0058] Fig.10A pie chart of participating users and their allocated power before optimization provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Embodiment 1:

[0062] This embodiment proposes a method for evaluating user flexibility control potential based on the AHP-entropy weight method, which specifically includes the following steps:

[0063] Step S1: construct an orderly electricity user power regulation potential index system and a user willingness index system based on the acquired power index data.

[0064] The step S1 specifically includes the following steps:

[0065] Step S11: constructing a flexibility adjustment potential evaluation index system according to factors affecting user adjustment potential; the flexibility adjustment potential evaluation index system includes peak-to-valley difference rate, daily load rate, valley power coefficient, maximum adjustable peak load rate, peak load difference coefficient, peak power consumption proportion, adjustable load proportion, and equipment power outage preparation time;

[0066] Step S12: constructing a user willingness index system based on the acquired user willingness data; the user willingness index system includes the economic benefits of users participating in orderly electricity use, the proportion of electricity expenditure in total cost expenditure, the user economic level coefficient, and the proportion of productive load;

[0067] Step S13: obtaining the user load regulation capability according to the flexibility regulation potential evaluation index system, and obtaining the user participation willingness according to the user willingness index system.

[0068] Step S2: Step S2: Use the hierarchical analysis method to analyze the power regulation potential indicators of orderly power users and determine the weights of subjective willingness indicators.

[0069] The step S2 specifically includes the following steps:

[0070] Step S21: Establish a hierarchical structure model;

[0071] Step S22: constructing a judgment matrix based on the user's subjective judgment;

[0072] Step S23: Calculate the weight coefficient;

[0073] Step S24: consistency check. If the consistency is met, the subjective willingness weight is obtained; otherwise, the judgment matrix is ​​adjusted and returned to step S23.

[0074] Step S3: Use the entropy weight method to quantify the objective variation degree of the user willingness index and calculate the weight of the objective adjustment index.

[0075] The step S3 specifically comprises the following steps:

[0076] Step S31: determining the original data matrix;

[0077] Step S32: standardize the original data and calculate the index weight;

[0078] Step S33: Calculate information entropy according to the indicator weight;

[0079] Step S34: Calculate the difference coefficient according to the information entropy;

[0080] Step S35: Calculate the objective adjustment index weight according to the difference coefficient.

[0081] Step S4: Calculate the user's power regulation capability and participation willingness value by combining the subjective willingness index weight and the objective regulation index weight.

[0082] The step S4 specifically comprises the following steps:

[0083] Step S41: constructing an objective function according to the user's willingness to participate in orderly electricity consumption, the amount of electricity allocated by the user to participate in forced clearing, and the user's dissatisfaction value after participating in the forced clearing of the day;

[0084] Step S42: constructing a total contribution constraint based on the amount of electricity allocated by the user to participate in the mandatory clearing and the amount of electricity required by the power market on a certain day;

[0085] Step S43: constructing a user's maximum contribution constraint based on the user's allocated power and the cleared power declared by the user on a certain day;

[0086] Step S44: constructing an electricity allocation strategy according to the objective function, the total contribution constraint, and the user's maximum contribution constraint;

[0087] Step S45: constructing a single-layer optimization model of user clearing volume taking into account user satisfaction according to the power allocation strategy;

[0088] Step S46: calling the sequence method and the least squares quadratic programming method to solve the single-layer optimization model of the user's clearing amount and obtain the user's flexibility value score.

[0089] Step S5: construct an evaluation model using the fuzzy judgment method to obtain a comprehensive score of the user's flexibility adjustment ability.

[0090] Step S6: According to the comprehensive scoring results of the user's flexibility adjustment ability, the electricity is cleared and allocated in sequence, and the list of users participating in orderly electricity consumption and the allocated electricity value are obtained through the optimization allocation process.

[0091] The step S6 specifically comprises the following steps:

[0092] Step S61: Comprehensive scoring result based on user flexibility adjustment ability;

[0093] Step S62: Sort the users according to the comprehensive evaluation results, clear and allocate electricity in sequence, optimize the allocation process, and obtain a list of users participating in orderly electricity consumption and the allocated electricity value.

[0094] Embodiment 2:

[0095] This embodiment is based on the above embodiment 1. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 , Fig.10 As shown, a specific embodiment is described in detail.

[0096] This embodiment combines the subjective will of the orderly power consumption user side with the objective regulation potential, and provides a method that takes both the subjective and objective conditions of the user into consideration. It also provides a ranking selection and the optimal allocation of power to the participating users under this ranking selection, so as to solve the shortcomings of the prior art in improving efficiency and user satisfaction under the orderly power consumption measures caused by insufficient and incomplete considerations, thereby optimizing the orderly power consumption strategy.

[0097] This embodiment proposes a method for comprehensively evaluating the flexibility adjustment capability of users, which integrates AHP, entropy weight method and fuzzy judgment method. AHP is used to identify and analyze indicators that affect the flexibility potential of users, and determine subjective weights; the entropy weight method is used to quantify the objective variation of indicator data and calculate objective weights. Combining subjective and objective weights, the user's power adjustment capability and willingness to participate are calculated. The evaluation model is constructed using the fuzzy judgment method to comprehensively score the user's flexibility adjustment capability. Users are sorted according to the scoring results, and electricity is cleared and allocated in sequence, optimizing the allocation process to meet the needs of the power grid and the personalized needs of users. Finally, a list of users participating in orderly electricity consumption and the allocated electricity value are obtained.

[0098] Demand response is a power demand management strategy that encourages users to voluntarily adjust their power consumption behavior according to price signals from the power market or the guidance of system operators. However, sometimes the published demand capacity response fails to meet the standards, and some measures need to be taken to solve the problem. Orderly power consumption is also a power load regulation management measure, but its characteristic is that it is mandatory. When the power system faces a serious imbalance between supply and demand, power operators will take orderly power consumption measures and require some users to cut off power or reduce load according to a predetermined plan. When faced with insufficient power capacity, power market operators need to screen out users who best meet current power demand from registered users in order to ensure orderly power supply and allocation. In the selection process, the flexibility of these users must first be evaluated to ensure that the selected users can flexibly respond to dynamic changes in the power market, thereby optimizing the allocation and utilization of power resources.

[0099] In order to effectively alleviate the severe challenge of imbalance between power supply and demand, load dispatch has become a vital means. Traditionally, load dispatch mainly relies on two strategies: peak shifting and peak avoidance. By accurately measuring the user's ability to shift and avoid peaks, we can more deeply evaluate their potential for power regulation. However, for a comprehensive study of user regulation flexibility, it is also necessary to comprehensively consider multi-dimensional factors such as their load characteristics and response speed. In addition, the user's willingness to participate is also a key factor affecting the size of their flexibility. The following will conduct a comprehensive and detailed evaluation of user flexibility from the two dimensions of the user's power regulation potential and their willingness to participate.

[0100] (1) Electricity regulation potential model of orderly electricity users:

[0101] During the model construction process, this embodiment comprehensively considers the potential for peak avoidance and peak shifting, as well as a series of other key indicators that affect user flexibility. Through this model, this embodiment hopes to accurately evaluate the user's ability to adjust the power load within a specific time period. In order to accurately measure the user's potential to participate in orderly power consumption regulation, this embodiment starts with analyzing the factors that may affect the user's regulation potential, and constructs a complete set of flexibility regulation potential evaluation index system. The system includes nine core indicators: peak-to-valley difference rate, daily load rate, valley power coefficient, maximum adjustable peak load rate, peak load difference coefficient, peak power consumption ratio, adjustable load ratio, and response speed, which are used to accurately evaluate the user's flexibility in the face of fluctuations in power demand.

[0102] ① Peak-to-valley difference rate L1. It reflects the degree of load difference between peak and valley periods. By analyzing the peak-to-valley difference rate, we can understand the differences in users’ electricity consumption behaviors during peak and valley periods and identify potential space for electricity regulation.

[0103]

[0104] Where: P i max is the maximum load value of user i on that day; P i min is the minimum load value of user i on that day.

[0105] ② Daily load rate L2. Refers to the ratio of the average load to the maximum load within 24 hours. It reflects the distribution of the user's power load in a day. The higher the daily load rate, the more evenly the user's power load is distributed, and the potential for power regulation is relatively small. Conversely, a lower daily load rate means that the user's power load may be more concentrated in certain periods of time, and there is a larger space for power regulation.

[0106]

[0107] Where: P i max is the maximum load value of user i on that day; P i av is the average load value of user i on that day.

[0108] ③ Maximum adjustable peak load rate L3. This indicator reflects the maximum load that users can reduce during peak power demand periods. It is one of the most direct and important indicators for evaluating the user's power regulation potential. Analysis of the maximum adjustable peak load requires consideration of factors such as the user's production process, equipment characteristics, and load distribution to determine the amount of load that can be reduced without affecting normal production.

[0109]

[0110] Where: Pi max is the maximum load value of user i on that day; P i ehigh,av is the average load of user i during the non-peak period of the day.

[0111] ④ Valley power coefficient α1. Usually used to describe the ratio of electricity consumption during off-peak hours to electricity consumption throughout the day. The lower the valley power coefficient, the smaller the proportion of electricity consumption during off-peak hours, which may mean that users have more room for adjustment during off-peak hours.

[0112]

[0113] Where: P i sum is the total load value of user i on that day; P i low,sum is the total load value of user i during the off-peak period of the day.

[0114] ⑤ Peak load difference coefficient α2. This difference coefficient can intuitively reflect the difference between the user's electricity consumption behavior during peak hours and the average power consumption behavior during the whole day. The larger the difference coefficient, the higher the user's electricity load during peak hours relative to the average level during the whole day, which may mean that the user's electricity consumption behavior is more concentrated during peak hours, and the user's regulation potential during peak hours is greater.

[0115]

[0116] Where: P i av is the average load value of user i on that day; P i high,av is the average load of user i during the peak hours of the day.

[0117] ⑥ Peak-to-average ratio b1. The peak-to-average ratio directly reflects the user's electricity consumption characteristics during peak hours, that is, the degree of prominence of peak load relative to average load. This means that the user has greater potential to reduce peak load by adjusting electricity consumption behavior.

[0118]

[0119] Where: P i high,sum is the total load value of user i during the peak period of the day; i p,sum The load value of user i during the electricity consumption period of the day.

[0120] ⑦ Peak power consumption ratio b2. Peak power consumption ratio reflects the proportion of peak power consumption in the whole day, and is a key indicator for evaluating the user's power regulation potential. The level of peak power consumption ratio reflects the user's potential to reduce peak power consumption. For users with a high peak power consumption ratio, their potential to reduce peak power consumption by adjusting production plans, optimizing equipment operation, etc. is relatively large.

[0121]

[0122] Where: P i sum is the total load value of user i on that day; P i high,sum is the total load value of user i during the peak electricity consumption period of the day.

[0123] ⑧ Adjustable load proportion b3. The adjustable load proportion refers to the proportion of load that can be adjusted to the total load, which is an important indicator for evaluating the user's power regulation potential. The adjustable load proportion provides a specific quantitative indicator for evaluating the user's potential to reduce or increase the load by adjusting the power consumption behavior during peak hours or under specific circumstances. The higher this proportion is, the more loads the user can participate in regulation, thus having greater regulation capabilities.

[0124]

[0125] Where: P i r P is the adjustable load value of user i on that day; i sum is the total load value of user i on that day.

[0126] ⑨ Equipment power outage preparation time t. Equipment power outage preparation time has a direct and important relationship with the potential of industrial and commercial users to participate in orderly power consumption. The length of this period will directly affect the user's ability to respond to orderly power consumption and its flexibility in load adjustment during peak power demand periods. Shorter equipment power outage preparation time means that industrial and commercial users can respond to power dispatch instructions more quickly and reduce losses caused by power outages. In addition, the power outage response time of each industrial and commercial user is determined by the power outage preparation time required by the longest equipment within it.

[0127]

[0128] Where: T i prel Provides the user with the power outage preparation time for each device on that day.

[0129] (2) Construction of user willingness indicators.

[0130] When users find that the sum of the compensation for participating in orderly electricity consumption and the electricity bill savings exceeds the value of their production, they will undoubtedly be more willing to participate in orderly electricity consumption.

[0131] ① Net economic benefit Y from users participating in orderly electricity consumption

[0132]

[0133] Where: is the power consumption of user i at the jth time; is the time-of-use electricity price at the jth hour; V is the unit price of monetary compensation for user i participating in orderly electricity consumption on that day; day,i is the production value of user i on that day.

[0134] ② The proportion of electricity expenditure to total cost expenditure b4

[0135] The proportion of a user's electricity expenditure in their overall production costs has a significant impact on whether the user is willing to participate in the orderly use of electricity. When the proportion of electricity expenditure is low, it means that the electricity cost is not the main burden of the user's production. Therefore, combined with their own production costs and benefits, users may be less willing to participate in orderly use of electricity; when the proportion of electricity expenditure is high, the electricity cost becomes an important part of the user's production and operation costs. In this case, even a small change in electricity prices may have a significant impact on the user's operating profit. Therefore, users become more sensitive to changes in electricity prices and pay more attention to changes in electricity price policies. Through the incentives of the electricity price mechanism, such as rising electricity prices during peak hours and falling electricity prices during off-peak hours, users are more motivated to adjust their electricity consumption behavior to respond to the call for orderly use of electricity, thereby optimizing electricity costs and maximizing production and operation profits.

[0136]

[0137] Where: is the total production cost of a typical working day for user i; The electricity expenditure of user i on a typical working day.

[0138] ③ User economic level coefficient K

[0139] For users with higher economic levels, they may be more able to cope with the additional costs brought by peak electricity prices because they have stronger economic affordability. These users may be more inclined to maintain the stability of their electricity consumption behavior, and may attach relatively less importance to electricity costs and incentive compensation for orderly electricity consumption; for users with lower economic levels, electricity costs and incentive compensation occupy a more important position in their decision-making. These users may be more sensitive to fluctuations in electricity prices and changes in electricity costs, and are therefore more likely to adjust their electricity consumption behavior due to increased costs and compensation. The total capital of industrial users is normalized using the log function.

[0140] K = log 10000 A i (12)

[0141] Where: A i represents the total capital of industrial user i.

[0142] ④Productive load ratio b5

[0143] The degree of users' willingness to participate in orderly electricity consumption is indeed closely related to the proportion of productive load in their production load. Productive load, including main production load and auxiliary production load, is the core part of the industrial production process, usually involving key production equipment and processes. These loads are generally not interrupted, because their shutdown may cause the production line to stop, product quality to decline, and may even cause safety accidents. When the proportion of productive load is high, industrial enterprises tend to pay more attention to the continuity and stability of production, and may be more cautious about participating in orderly electricity consumption. Because orderly electricity consumption may require enterprises to adjust their electricity consumption behavior, such as reducing electricity load or changing electricity consumption time, which may have a certain impact on the normal production of enterprises. Therefore, the higher the productive load, the lower the willingness of enterprises to participate in orderly electricity consumption may be.

[0144]

[0145] Where: is the proportion of productive load of industrial user i.

[0146] 2. Empowerment of user flexibility adjustment potential indicators.

[0147] When evaluating the orderly electricity consumption potential of users, it is crucial to assign appropriate weights to each evaluation indicator. At present, weighting methods are mainly divided into two categories: subjective weighting method and objective weighting method. Subjective weighting methods, such as the analytic hierarchy process and the Delphi method, rely on people's subjective judgment to determine the weights of indicators. Since different people have different judgment criteria, the weight results obtained by this method are often subjective and arbitrary. Relatively speaking, objective weighting methods, such as the entropy weight method, the principal component analysis method, and the CRITIC method, assign weights based on the intrinsic connection between sample data. The weight results of this method are more stable and objective.

[0148] In the process of deeply evaluating the user's power regulation potential and their willingness to participate in orderly power consumption, in order to ensure the comprehensiveness and accuracy of the evaluation results, this embodiment adopts a weight allocation strategy that combines subjective and objective factors, and specifically integrates the two methods of entropy weight method and AHP hierarchical analysis method. The entropy weight method is based on the objective information of the data, and determines its weight by quantifying the information entropy of each indicator, reflecting the objective reality of the user's flexibility regulation potential; while the AHP rule introduces the user's subjective judgment by constructing a hierarchical model, and ranks the indicators at all levels in relative importance, thereby integrating the subjective factors of the user's willingness to participate. This method of combining subjective and objective factors not only respects the objectivity of the data, but also takes into account the user's subjective cognition, making the evaluation process more comprehensive and detailed. Through this method, this embodiment realizes a comprehensive evaluation of the user's ability to participate in orderly power consumption, and its evaluation results have high accuracy and credibility. In addition, based on this evaluation result, this embodiment can also provide specific and targeted policy recommendations for power grid operators, aiming to optimize the allocation of power resources, improve the efficiency of power grid operation, and provide strong support for the stable and efficient operation of the power system.

[0149] 3. Sorting selection method and optimized power allocation method.

[0150] ① Ranking selection method based on fuzzy judgment method

[0151] A single indicator is difficult to fully reflect the overall flexibility regulation value of users. The user regulation value evaluation method based on fuzzy comprehensive evaluation systematically integrates the user's regulation potential and participation willingness through the theoretical framework of fuzzy mathematics and fuzzy logic, thereby obtaining a more comprehensive, objective and accurate user value evaluation result. According to the calculated user value evaluation result, a user ranking is given.

[0152] ②Optimal power allocation method based on SLSQP optimization method

[0153] In the existing research and practice on orderly power consumption, the focus is often on arranging the participation order based solely on the user's power regulation potential value. The optimized orderly power consumption management method significantly enhances the emphasis on user participation willingness based on an in-depth assessment of the user's power regulation potential. This paper establishes a single-layer optimization model for user clearing volume considering user satisfaction. The optimization method used is the SLSQP optimization method, which combines the sequential method (Sequential) and the least squares quadratic programming (Least Squares Quadratic Programming) to approximate the optimal solution through iterative optimization. Under the premise of meeting the total demand on the demand side, users are forced to clear in order, considering their willingness values, and optimizing the amount of electricity allocated to users as much as possible to reduce overall dissatisfaction.

[0154] Users are an important part of the electricity market. When conducting demand-side management, user satisfaction should be fully considered. When users encounter mandatory orderly electricity consumption enforcement measures, a quantitative value of dissatisfaction will naturally be triggered. Given the mandatory nature of this enforcement method, if the amount of electricity allocated to users exceeds their expectations or acceptable range, their dissatisfaction will increase linearly or nonlinearly. In order to achieve optimal management of the system as a whole or for specific goals, our goal is to develop an electricity allocation strategy that aims to minimize the overall dissatisfaction of all affected users and ensure the most reasonable satisfaction balance under mandatory enforcement scenarios.

[0155] (1) Objective function

[0156]

[0157] Where: S Y E is the user's willingness to participate in orderly electricity consumption; C The amount of electricity allocated to the user to participate in the forced clearing. N is the dissatisfaction value of the user after participating in the forced cleanup on that day; n is the user number; N is the total number of users.

[0158] (2)Constraints.

[0159] a. Total contribution constraint;

[0160] The total amount of electricity allocated must be equal to the total demand, and the amount of electricity ultimately allocated to each user must meet the electricity market's requirements.

[0161]

[0162] Where: E D The amount of electricity required by the electricity market on a certain day.

[0163] b. Constraints on the maximum contribution of users;

[0164] The allocated amount for each user must be greater than or equal to 0 and cannot exceed its maximum available power.

[0165] 0≤E n ≤E n.max (16)

[0166] Where: E n is the allocated power of user n; E n.max The amount of electricity cleared by the user on a certain day

[0167] Working principle: The innovative orderly power consumption strategy user selection method proposed in this embodiment shows significant progress and the concept of refined management compared to the universality and non-differentiated execution of traditional strategies. Specifically, this method abandons the "one-size-fits-all" extensive management model and adopts a personalized and differentiated power consumption management strategy based on a deep assessment of the user's flexibility potential; the innovative orderly power consumption strategy user power distribution proposed in this embodiment significantly reflects the high attention paid to user wishes, thereby achieving refinement and humanization in the power management process, enhancing user trust and support, and promoting the smooth progress of power management; it not only takes into account the reasonable allocation of power resources, but also pays attention to the actual needs and satisfaction of users.

[0168] This embodiment proposes an innovative orderly power management strategy, which deeply integrates the user's power regulation potential and active participation willingness, and takes it as an indispensable core consideration when making power arrangements. It aims to significantly improve the overall satisfaction of users, while enhancing the rationality, scientificity and implementation effect of the strategy itself.

[0169] This embodiment aims to implement differentiated orderly power outage strategies by accurately identifying and distinguishing the different flexibility control potentials of power demand among users. It also conducts in-depth analysis of users based on their declared power clearing volume, aiming to scientifically guide users to reasonably arrange power use, so as to promote the optimal allocation and efficient use of power resources.

[0170] 1. Analysis of user flexibility control ability assessment results:

[0171] For nine groups of users from different industrial sectors in a specific region, this embodiment takes users from the steel, nonferrous metals, machinery and cement industries as examples, and constructs a set of load regulation potential index system as shown in Table 1 and orderly power consumption willingness index system as shown in Table 2, aiming to comprehensively analyze the load regulation capabilities and participation willingness of these users. Figure 5 As shown in the figure, through in-depth analysis of user load data, significant differences in load characteristics and electricity demand among users in various industries were identified.

[0172] Table 1 Load regulation potential index system

[0173]

[0174]

[0175] Table 2 Orderly electricity consumption willingness indicator system

[0176]

[0177] Based on the detailed data in the user survey form, such as Table 4, the specific values ​​of the load potential and willingness indicators of each user are calculated, and the results are displayed in the form of charts as follows: Figure 7 This process combines the AHP method (analytic hierarchy process) and the entropy weight method, and scientifically determines the weight distribution of each influencing factor as shown in Table 3, ensuring the fairness and accuracy of the evaluation.

[0178] Table 3 Calculation results of subjective and objective weights affecting user flexibility adjustment potential

[0179]

[0180]

[0181] Table 4 Survey of nine groups of industrial users in a specific region

[0182]

[0183] Table 5 Correspondence table of calculated values ​​of load potential index and willingness index

[0184]

[0185] Using the obtained weight values ​​and index calculation values, the specific scores of each industrial user were calculated from the two dimensions of subjective willingness and objective regulation potential. Through the comprehensive application of fuzzy evaluation method, the two parts of the scores were systematically integrated, and finally a comprehensive evaluation result of the user's flexibility regulation ability was formed, and the ranking was carried out according to this result, such as Figure 8 The evaluation results show that user 8 has the most outstanding performance in flexibility control ability, while user 3 is relatively weak.

[0186] This evaluation result fully reveals the complexity of users' flexibility regulation capabilities: even if some users show a strong willingness to participate, their actual power regulation capabilities may not be outstanding; conversely, users with high regulation capabilities may not have a strong willingness to participate. This difference and diversity requires us to fully consider the personalized characteristics of users when formulating power dispatch strategies to achieve optimal allocation and efficient use of resources.

[0187] Analysis of the optimization results of power distribution for users who participate in orderly power consumption;

[0188] Based on the evaluation and ranking of user flexibility control capabilities, user satisfaction was taken into consideration, and the user's clearing volume allocation was optimized. The results are as follows: Fig. 9 As shown. When the electricity market faces an unmet clearing volume of 1000MWh on a certain day, based on the user's adjustable value ranking, user 8, user 5, user 2 and user 9 are determined as participants to make up for the gap, and the electricity is allocated in this order. The specific allocation is: user 8 gets 441.25MWh, user 5 gets 110.00MWh, user 2 gets 4.00MWh, and user 9 gets 444.75MWh. All allocations are made within the user's declared clearing volume.

[0189] In contrast, if user satisfaction is not considered, power allocation is performed directly based on the sorting order, such as Fig.10 As shown in Table 6, the allocation scheme will be different: User 8 will receive 510.00MWh, User 5 will also receive 110.00MWh, User 2 will still receive 4.00MWh, and User 9 will receive 376.00MWh. This allocation scheme also follows the user's declared clearing limit. In order to evaluate the effectiveness of the optimization measures, the overall satisfaction of user participation and its allocation scheme before and after optimization was calculated and compared. The analysis results show that although the optimization aims to improve user satisfaction, the overall satisfaction after optimization actually increased compared with before optimization. This finding is recorded in Table 6, which clearly reveals the unexpected impact of the optimization measures on the overall user satisfaction level. This result suggests that when formulating electricity market clearing strategies, it is necessary to more finely balance user satisfaction with the overall efficiency and fairness of the electricity market.

[0190] Table 6 Calculation table of total dissatisfaction value before and after optimization of user power allocation

[0191] Optimization phase Dissatisfaction value for that day Before optimization 456.00 After optimization 226.58

[0192] The other parts of this embodiment are the same as those of the above-mentioned embodiment 1, and thus will not be described in detail.

[0193] Embodiment 3:

[0194] Based on any one of the above-mentioned embodiments 1-2, this embodiment proposes a user flexibility control potential selection system based on the AHP-entropy weight method, and executes the above-mentioned user flexibility control potential selection method based on the AHP-entropy weight method; it includes an indicator construction unit, a subjective calculation unit, an objective calculation unit, a comprehensive unit, a scoring unit, and an evaluation unit;

[0195] The index construction unit is used to construct an index system of electric energy regulation potential and a user willingness index system of orderly power users according to the acquired power index data;

[0196] The subjective calculation unit is used to calculate the weight of the subjective willingness index using the hierarchical analysis method;

[0197] The objective calculation unit is used to calculate the objective adjustment index weight using the entropy weight method;

[0198] The comprehensive unit is used to comprehensively consider the subjective willingness index weight and the objective regulation index weight to calculate the user's power regulation capability and participation willingness value;

[0199] The scoring unit is used to construct an evaluation model using a fuzzy judgment method to obtain a comprehensive score of the user's flexibility adjustment ability;

[0200] The selection unit is used to obtain a list of users participating in orderly electricity consumption and allocated electricity values ​​according to the comprehensive scoring results of the users' flexibility adjustment capabilities.

[0201] This embodiment also proposes an electronic device, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned user flexibility control potential evaluation method based on the AHP-entropy weight method is implemented.

[0202] This embodiment also proposes a computer-readable storage medium, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned user flexibility control potential evaluation method based on the AHP-entropy weight method is implemented.

[0203] The other parts of this embodiment are the same as any one of the above-mentioned embodiments 1-2, so they will not be repeated here.

[0204] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for evaluating user flexibility control potential based on AHP-entropy weight method, characterized in that: The specific steps include: Step S1: constructing an orderly power user power regulation potential index system and a user willingness index system based on the acquired power index data; Step S2: Analyze the power regulation potential index of orderly power users by using the hierarchical analysis method to determine the weight of the subjective willingness index; Step S3: using the entropy weight method to quantify the objective variation degree of the user willingness index and calculate the weight of the objective adjustment index; Step S4: Calculate the user's power regulation capability and participation willingness value by combining the subjective willingness index weight and the objective regulation index weight; Step S5: constructing an evaluation model using a fuzzy judgment method to obtain a comprehensive score of the user's flexibility adjustment ability; Step S6: According to the comprehensive scoring results of the user's flexibility adjustment ability, a list of users participating in orderly power consumption and the allocated power value are obtained.

2. According to claim 1, a method for evaluating user flexibility control potential based on AHP-entropy weight method is characterized in that: The step S2 specifically includes the following steps: Step S21: constructing a hierarchical model according to the set hierarchical levels; Step S22: Introducing subjective judgment into the constructed hierarchical model to construct a judgment matrix; Step S23: According to the judgment matrix, the power regulation potential indicators of orderly power users are ranked by relative importance and the weight coefficients are calculated; Step S24: Perform a consistency check based on the weight coefficients. If the consistency is met, the subjective intention weight is obtained; otherwise, the judgment matrix is ​​adjusted to recalculate the weight coefficients.

3. The method for evaluating user flexibility control potential based on AHP-entropy weight method according to claim 1 is characterized in that: The step S3 specifically comprises the following steps: Step S31: determining the original data matrix according to the objective information of the acquired indicator data; Step S32: standardize the index data and calculate the index proportion; Step S33: Calculate information entropy according to the index proportion; Step S34: Calculate the difference coefficient according to the information entropy; Step S35: Calculate the objective adjustment index weight according to the difference coefficient.

4. The method for evaluating user flexibility control potential based on AHP-entropy weight method according to claim 1 is characterized in that: The step S4 specifically comprises the following steps: Step S41: constructing an objective function according to the user's willingness to participate in orderly electricity consumption, the amount of electricity allocated by the user to participate in forced clearing, and the user's dissatisfaction value after participating in forced clearing on the day; Step S42: constructing a total contribution constraint based on the amount of electricity allocated by the user to participate in the mandatory clearing and the amount of electricity required by the power market on a certain day; Step S43: constructing a user's maximum contribution constraint based on the user's allocated power and the cleared power declared by the user on a certain day; Step S44: constructing an electricity allocation strategy according to the objective function, the total contribution constraint, and the user's maximum contribution constraint; Step S45: constructing a single-layer optimization model of user clearing volume taking into account user satisfaction according to the power allocation strategy; Step S46: calling the sequence method and the least squares quadratic programming method to solve the single-layer optimization model of the user's clearing volume and obtain a comprehensive score of the user's flexibility adjustment ability.

5. The method for evaluating user flexibility control potential based on AHP-entropy weight method according to claim 4 is characterized in that: The step S6 specifically comprises the following steps: Step S61: Obtain a comprehensive evaluation result according to the comprehensive score of the user's flexibility adjustment ability; Step S62: Sort the users according to the comprehensive evaluation results, clear and allocate electricity in sequence, optimize the allocation process, and obtain a list of users participating in orderly electricity consumption and the allocated electricity value.

6. The method for evaluating user flexibility control potential based on AHP-entropy weight method according to claim 1 is characterized in that: The step S1 specifically includes the following steps: Step S11: constructing a flexibility adjustment potential evaluation index system according to factors affecting user adjustment potential; the flexibility adjustment potential evaluation index system includes peak-to-valley difference rate, daily load rate, valley power coefficient, maximum adjustable peak load rate, peak load difference coefficient, peak power consumption proportion, adjustable load proportion, and equipment power outage preparation time; Step S12: constructing a user willingness index system based on the acquired user willingness data; the user willingness index system includes the economic benefits of users participating in orderly electricity use, the proportion of electricity expenditure in total cost expenditure, the user economic level coefficient, and the proportion of productive load; Step S13: obtaining the user load regulation capability according to the flexibility regulation potential evaluation index system, and obtaining the user participation willingness according to the user willingness index system.

7. A user flexibility control potential selection system based on the AHP-entropy weight method, which executes the user flexibility control potential selection method based on the AHP-entropy weight method as claimed in claim 1; characterized in that: It includes indicator construction unit, subjective calculation unit, objective calculation unit, comprehensive unit, scoring unit and evaluation unit; The index construction unit is used to construct an index system of electric energy regulation potential and a user willingness index system of orderly power users according to the acquired power index data; The subjective calculation unit is used to calculate the weight of the subjective willingness index using the hierarchical analysis method; The objective calculation unit is used to calculate the objective adjustment index weight using the entropy weight method; The comprehensive unit is used to comprehensively consider the subjective willingness index weight and the objective regulation index weight to calculate the user's power regulation capability and participation willingness value; The scoring unit is used to construct an evaluation model using a fuzzy judgment method to obtain a comprehensive score of the user's flexibility adjustment ability; The selection unit is used to obtain a list of users participating in orderly electricity consumption and allocated electricity values ​​according to the comprehensive scoring results of the users' flexibility adjustment capabilities.

8. An electronic device, characterized in that: It comprises a memory and a processor; a computer program is stored in the memory; when the computer program is executed on the processor, the user flexibility regulation potential evaluation method based on the AHP-entropy weight method as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device as described in claim 8, the user flexibility control potential evaluation method based on the AHP-entropy weight method as described in any one of claims 1-6 is implemented.

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