A method and device for optimizing user power consumption control

Abstract

The present invention relates to a user electricity consumption optimization control method and device, comprising obtaining historical electricity consumption data of users, and classifying users according to the historical electricity consumption data of users; calculating controllable coefficients of various types of users, and performing electricity consumption optimization control on a type of users with the largest controllable coefficients; the present invention provides a user electricity consumption optimization control method, which can classify users according to their historical electricity consumption data, effectively identify users with regular electricity consumption, and perform electricity consumption optimization control on such users, thereby reducing users' electricity expenditures while satisfying user satisfaction, and at the same time alleviating the problems of insufficient power supply during peak periods of electricity consumption and too low utilization of power equipment during trough periods of electricity consumption, thereby achieving safe and stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand response, and in particular to a method and device for optimizing power consumption control of a user. Background Art

[0002] With the continuous deepening of power market reform, breaking the monopoly and introducing a reasonable competition mechanism in the power market will help integrate demand-side resources, optimize grid resource allocation, and improve the operating efficiency of the power market.

[0003] With the continuous increase in residential electricity consumption and the increasing popularity of smart household electrical appliances, electricity users cannot respond to changes in electricity prices or grid incentive policies and change their original electricity consumption behaviors, resulting in insufficient power supply during peak periods and low utilization of power equipment during low periods, affecting the safe and stable operation of the power grid. At the same time, users are increasingly considering their satisfaction with electricity consumption. How to reduce users' electricity expenditures while satisfying their satisfaction is an important research direction for the future electricity market. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a user electricity consumption optimization control method, which can classify users according to their historical electricity consumption data, effectively identify users with regular electricity consumption, and provide electricity consumption optimization suggestions for such users, thereby reducing users' electricity expenditures while satisfying user satisfaction, and at the same time promoting the consumption of renewable energy.

[0005] The purpose of the present invention is achieved by adopting the following technical solutions:

[0006] The present invention provides a user power consumption optimization control method, the improvement of which is that it includes:

[0007] Classify users according to their historical electricity consumption data;

[0008] Calculate the controllability coefficients of various types of users;

[0009] Carry out power consumption optimization control on the users with the largest controllable coefficient.

[0010] Preferably, the classifying of users according to their historical electricity usage data includes:

[0011] Determine the user's electricity consumption status within a historical period based on the user's historical electricity consumption data;

[0012] Users are classified according to their electricity consumption status in historical time periods.

[0013] Furthermore, the user's historical electricity consumption data includes the user's electricity consumption in a historical period;

[0014] Determining the power consumption state of the user in the historical period according to the historical power consumption data of the user includes:

[0015] Determine the power consumption status l of the user in the i-th period of the t-th historical day by the following formula ti :

[0016]

[0017]

[0018]

[0019] In the formula, is the electricity consumption of the user in the i-th period on the t-th historical day, l ti is the power consumption status of the user in the i-th period of the t-th historical day, is the user's electricity consumption on the tth historical day.

[0020] Furthermore, the power consumption of the user in the i-th period of the t-th historical day is determined as follows:

[0021]

[0022] In the formula, is the electricity consumption in the i-th period of the t-th historical day, i∈[1,4], n is 24, y j is the electricity consumption in the jth hour of the tth historical day, j∈[1,24].

[0023] Furthermore, the user classification according to the power consumption status of the user in the historical period includes:

[0024] Based on the electricity consumption status of users in the historical period, the k-means clustering algorithm is used to classify users;

[0025] The initial cluster center of the k-means clustering algorithm is: the cluster center in the clustering result of clustering users using the canopy algorithm based on the power consumption status of the users in the historical period.

[0026] Preferably, the calculating of the controllable coefficients of various types of users includes:

[0027] Determine the controllable coefficient D of the x-th type of user as follows: x :

[0028]

[0029] In the formula, T is the total number of historical days, is the probability that the power consumption state of the x-th type of user changes from state a to state b on the t-th day in the historical period, a, b∈[1,3], and N is 3;

[0030] Said It is determined based on the electricity consumption status of each type of user during the historical period.

[0031] Preferably, the optimizing control of electricity consumption of a category of users with the largest controllable coefficient includes:

[0032] Solve the pre-established power consumption optimization model corresponding to the user with the largest controllable coefficient, and obtain the change in the optimal power consumption of the user with the largest controllable coefficient;

[0033] Determine the optimal power consumption of a class of users with the largest controllable coefficient according to the change in the optimal power consumption of a class of users with the largest controllable coefficient;

[0034] The power consumption of the user group with the largest controllable coefficient is adjusted to the optimal power consumption of the user group with the largest controllable coefficient.

[0035] Furthermore, the objective function in the power consumption optimization model corresponding to the pre-established class of users with the largest controllable coefficient is:

[0036]

[0037] In the formula, ΔL(t) is the change in power consumption of the equipment of the user with the largest controllable coefficient per hour, L(t) is the power consumption of the equipment of the user with the largest controllable coefficient at the tth moment before adjustment, A is the peak-to-valley difference after optimization, and B is the user's power satisfaction.

[0038] Furthermore, the constraints in the power consumption optimization model corresponding to the pre-established class of users with the largest controllable coefficient are:

[0039] Determine the constraints of transferable and non-interruptible equipment in the category of users with the largest controllable coefficient as follows:

[0040]

[0041] in, is the minimum value of the transferable non-interruptible equipment power, is the maximum value of the transferable non-interruptible equipment power, is the rated power of the transferable non-interruptible equipment, L TL is the total power consumption of the transferable and non-interruptible equipment in one day, S TL (t) is the working state of the transferable non-interruptible device at time t, M is the minimum continuous time when the transferable non-interruptible device is working, c TL.earlyThe earliest time that users are allowed to start working on transferable and non-interruptible devices, c TL.late The latest start time allowed for users to work on transferable and non-interruptible devices;

[0042] Determine the constraints of transferable and interruptible devices in the user class with the largest controllable coefficient as follows:

[0043]

[0044] in, is the minimum value of the power of the transferable interruptible equipment, is the maximum value of the transferable interruptible device power, is the rated power of the transferable interruptible equipment, L IL To transfer the total power consumption of interruptible equipment in one day, S IL (t) is the working state of the power of the transferable and interruptible device at time t, β is the working time that the transferable and interruptible device must continue to work, c IL.early The earliest time that the user is allowed to start working on the transferable and interruptible equipment, c IL.late The latest start time allowed for users to work on transferable and interruptible devices;

[0045] Determine the constraints of the equipment that can be reduced in the user category with the largest controllable coefficient as follows:

[0046]

[0047] in, is the maximum power that can be reduced, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, ε is the heat dissipation coefficient, η is the heat conduction efficiency, A is the thermal conductivity, S a (t) is the working status of the equipment that can be reduced at time t, T s (t) is the set temperature of the air conditioner at time t.

[0048] Based on the same inventive concept, the present invention also provides a user power optimization control device, the improvement of which is that it includes:

[0049] Classification unit: used to classify users according to their historical electricity consumption data;

[0050] Calculation unit: used to calculate the controllable coefficients of various users;

[0051] Control unit: used to optimize the electricity consumption of the user with the largest controllable coefficient.

[0052] Preferably, the classification unit includes:

[0053] The first determination subunit is used to determine the power consumption status of the user in the historical period according to the historical power consumption data of the user;

[0054] The first classification subunit: classifies users according to their electricity consumption status in a historical period.

[0055] Furthermore, the user's historical electricity consumption data includes the user's electricity consumption in a historical period;

[0056] Determining the power consumption state of the user in the historical period according to the historical power consumption data of the user includes:

[0057] Determine the power consumption status l of the user in the i-th period of the t-th historical day by the following formula ti :

[0058]

[0059]

[0060]

[0061] In the formula, is the electricity consumption of the user in the i-th period on the t-th historical day, l ti is the power consumption status of the user in the i-th period of the t-th historical day, is the user's electricity consumption on the tth historical day.

[0062] Furthermore, the power consumption of the user in the i-th period of the t-th historical day is determined as follows:

[0063]

[0064] In the formula, is the electricity consumption in the i-th period of the t-th historical day, i∈[1,4], n is 24, y j is the electricity consumption in the jth hour of the tth historical day, j∈[1,24].

[0065] Furthermore, the first classification subunit is specifically used for:

[0066] Based on the electricity consumption status of users in the historical period, the k-means clustering algorithm is used to classify users;

[0067] The initial cluster center of the k-means clustering algorithm is: the cluster center in the clustering result of clustering users using the canopy algorithm based on the power consumption status of the users in the historical period.

[0068] Preferably, the computing unit is specifically used for:

[0069] Determine the controllable coefficient D of the x-th type of user as follows: x :

[0070]

[0071] In the formula, T is the total number of historical days, is the probability that the power consumption state of the x-th type of user changes from state a to state b on the t-th day in the historical period, a, b∈[1,3], and N is 3;

[0072] Said It is determined based on the electricity consumption status of each type of user during the historical period.

[0073] Preferably, the control unit comprises:

[0074] The second acquisition subunit is used to solve the pre-established power consumption optimization model corresponding to the user with the largest controllable coefficient, and obtain the change in the optimal power consumption of the user with the largest controllable coefficient;

[0075] The second determining subunit is used to determine the optimal power consumption of the user with the largest controllable coefficient according to the change amount of the optimal power consumption of the user with the largest controllable coefficient;

[0076] The second control subunit is used to adjust the power consumption of the user category with the largest controllable coefficient to the optimal power consumption of the user category with the largest controllable coefficient.

[0077] Furthermore, the objective function in the power consumption optimization model corresponding to the pre-established class of users with the largest controllable coefficient is:

[0078]

[0079] In the formula, ΔL(t) is the change in power consumption of the equipment of the user with the largest controllable coefficient per hour, L(t) is the power consumption of the equipment of the user with the largest controllable coefficient at the tth moment before adjustment, A is the peak-to-valley difference after optimization, and B is the user's power satisfaction.

[0080] Furthermore, the constraints in the power consumption optimization model corresponding to the pre-established class of users with the largest controllable coefficient are:

[0081] Determine the constraints of transferable and non-interruptible equipment in the category of users with the largest controllable coefficient as follows:

[0082]

[0083] in, is the minimum value of the transferable non-interruptible equipment power, is the maximum value of the transferable non-interruptible equipment power, is the rated power of the transferable non-interruptible equipment, L TL is the total power consumption of the transferable and non-interruptible equipment in one day, S TL (t) is the working state of the transferable non-interruptible device at time t, M is the minimum continuous time when the transferable non-interruptible device is working, c TL.early The earliest time that users are allowed to start working on transferable and non-interruptible devices, c TL.late The latest start time allowed for users to work on transferable and non-interruptible devices;

[0084] Determine the constraints of transferable and interruptible devices in the user class with the largest controllable coefficient as follows:

[0085]

[0086] in, is the minimum value of the power of the transferable interruptible equipment, is the maximum value of the transferable interruptible device power, is the rated power of the transferable interruptible equipment, L IL To transfer the total power consumption of interruptible equipment in one day, S IL (t) is the working state of the power of the transferable and interruptible device at time t, β is the working time that the transferable and interruptible device must continue to work, c IL.early The earliest time that the user is allowed to start working on the transferable and interruptible equipment, c IL.late The latest start time allowed for users to work on transferable and interruptible devices;

[0087] Determine the constraints of the equipment that can be reduced in the user category with the largest controllable coefficient as follows:

[0088]

[0089] in, is the maximum power that can be reduced, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, ε is the heat dissipation coefficient, η is the heat conduction efficiency, A is the thermal conductivity, S a (t) is the working status of the equipment that can be reduced at time t, T s (t) is the set temperature of the air conditioner at time t.

[0090] Compared with the closest prior art, the present invention has the following beneficial effects:

[0091] The present invention relates to a user electricity consumption optimization control method and device, comprising obtaining historical electricity consumption data of users, and classifying users according to the historical electricity consumption data of users; calculating controllable coefficients of various types of users, and performing electricity consumption optimization control on a type of users with the largest controllable coefficients; the present invention provides a user electricity consumption optimization control method, which can classify users according to their historical electricity consumption data, effectively identify users with regular electricity consumption, and perform electricity consumption optimization control on such users, thereby reducing users' electricity expenditures while satisfying user satisfaction, and at the same time alleviating the problems of insufficient power supply during peak periods of electricity consumption and too low utilization of power equipment during trough periods of electricity consumption, thereby achieving safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 This is a flow chart of the user power optimization control method of the present invention;

[0093] Figure 2 It is the flow chart of canopy-k-means algorithm of the present invention;

[0094] Figure 3 is a Markov chain model diagram of the present invention;

[0095] Figure 4 It is the flow chart of the particle swarm algorithm of the present invention;

[0096] Figure 5 This is a diagram of the user electricity optimization control device of the present invention. DETAILED DESCRIPTION

[0097] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0098] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] In order to alleviate the problem of insufficient power supply during peak periods and low utilization of power equipment during off-peak periods, and to achieve safe and stable operation of the power grid, the present invention provides a user power optimization control method, such as Figure 1 As shown, including:

[0100] S11, classifying users according to their historical electricity consumption data;

[0101] S12, calculating the controllability coefficients of various types of users;

[0102] S13. Optimize electricity consumption for the user group with the largest controllable coefficient.

[0103] In order to more clearly illustrate the purpose of the present invention, the method of the present invention is further explained below in conjunction with specific embodiments:

[0104] In an embodiment of the present invention, S11 may include the following steps:

[0105] S111, determining the power consumption status of the user in a historical period according to the user's historical power consumption data;

[0106] S112: Classify users according to their electricity consumption status in historical time periods.

[0107] Preferably, the historical electricity consumption data of the user in S111 includes the historical electricity consumption of the user;

[0108] Preferably, S111 can determine the power consumption state l of the user in the i-th period of the t-th historical day according to the following formula (1): ti :

[0109]

[0110] In the formula, is the electricity consumption of the user in the i-th period on the t-th historical day, is the user's electricity consumption on the tth historical day, l ti is the electricity consumption status of the user in the i-th period of the t-th historical day;

[0111] Specifically, S111 can form a curve Y=[y1,y2,...,y n ] is divided into 4 sections The i-th segment value is calculated according to the following formula (2):

[0112]

[0113] The data processed by formula (2) is symbolized using formula (1) to obtain the power consumption state sequence l = [l1, l2, l3, l4] of the four time periods in each historical day;

[0114] In the formula, is the electricity consumption in the i-th period of the t-th historical day, i∈[1,4], y j is the electricity consumption in the jth hour of the tth historical day, j∈[1,n], n is the total number of hours in each historical day, and the value is 24.

[0115] Preferably, before S11, the missing load values ​​in the historical power consumption data can be supplemented by using the forward and backward moving average method using the following formula (3):

[0116]

[0117] Wherein, L i is the missing data point of the load curve, and L i ' is the corrected data, and L i-h and L i+g respectively represent the h data points forward and the g data points backward of L i ; generally, h1 and g1 can take values of 5 - 10.

[0118] Preferably, S112 may include the following steps:

[0119] Classify users by using the k - means clustering algorithm based on the power consumption status of users in the historical period;

[0120] Among them, the initial clustering center of the k - means clustering algorithm is: the clustering center in the clustering result of clustering users by using the canopy algorithm based on the power consumption status of users in the historical period;

[0121] In the optimal embodiment provided by the present invention, users can be classified based on the operation process as Figure 2 shown, and the specific process is as follows:

[0122] Perform a primary clustering on the historical power consumption data by using the canopy algorithm, that is, first vectorize the data set list to obtain a list, specify two distance thresholds: T1 and T2, where T1 > T2, and the values of T1 and T2 can be determined by cross - validation. Then, randomly select a point P from the list as the center and delete P from the list. Calculate the distance d from the remaining points in the table to P. If d < T1, then classify this point into the canopy1 class centered on P. If d < T2, then classify this point into the canopy1 class and delete this point from the list. Repeat the above steps until the list is empty, forming multiple canopy classes. Use the obtained clustering center and the number of clusters as the initial input of the k - means clustering algorithm, calculate the similarity between the remaining samples and the clustering center, classify them into the most similar class, and then recalculate each clustering center. Repeat the above steps until the clustering center no longer changes.

[0123] According to the above clustering result, obtain the symbolized user power consumption curve of each class of users, that is, the power consumption status curve of each class of users.

[0124] In the embodiment of the present invention, calculating the controllable coefficient of each class of users in S12 includes:

[0125] Determine the controllable coefficient D of the x - th class of users according to the following formula x :

[0126]

[0127] In the formula, T is the total number of historical days, is the probability that the power consumption state of the x-th type of user on the t-th day in the historical period is transferred from state a to state b, a, b ∈ [1, N], N is the type of power consumption state, and the value can be 3, that is, the values ​​of a and b in formula (1) of this embodiment can be 1, 2 or 3, which are three power consumption states.

[0128] In S12, It is determined based on the power consumption status of each type of user in the historical period. In this embodiment, the power consumption curve of a type of user in the historical period for one day is taken as an example to describe the probability of determining that the power consumption status of this type of user in one day is transferred from state a to state b as follows:

[0129] When the time axis is divided into 4 segments and the power consumption axis is divided into 3 segments, the power consumption state curve of this type of user in one day can be constructed as follows: Figure 3 The Markov chain shown;

[0130] S121, based on the above power consumption status results, counting the number of state transitions of each power consumption status between adjacent time periods of the user in one day;

[0131] S122, according to the state transition times of each power consumption state, the state transition probability of each power consumption state is calculated as follows:

[0132]

[0133] Among them, p ab is the state transition probability of the power consumption state of this type of user changing from state a to state b in one day; f ab is the number of state transitions from state a to state b for the power consumption state of this type of user in one day; f kb is the number of state transitions from state k to state b for the electricity consumption state of this type of user in one day; a, b, k∈[1,N], N is the type of electricity consumption state, which can be 3.

[0134] For example, to calculate the state transition probability from state 1 to state 3, f 13 、f 23 、f 33 They are the state transition times from state 1 to state 3, from state 2 to state 3, and from state 3 to state 3, respectively.

[0135] Preferably, according to the state transition times of each power consumption state between adjacent time periods of the user of this type obtained in S121 in one day, a transition number matrix F of the following formula can be obtained:

[0136]

[0137] Preferably, according to the state transition probability obtained in S122, the following transition probability matrix P can be obtained:

[0138]

[0139] In an embodiment of the present invention, S13 may include the following steps:

[0140] S131, solving a pre-established power consumption optimization model corresponding to a category of users with the largest controllable coefficient, and obtaining a change in the optimal power consumption of the category of users with the largest controllable coefficient;

[0141] S132, determining the optimal power consumption of the user with the largest controllable coefficient according to the change in the optimal power consumption of the user with the largest controllable coefficient;

[0142] S133: Adjust the power consumption of the user group with the largest controllable coefficient to the optimal power consumption of the user group with the largest controllable coefficient.

[0143] Preferably, the objective function in the power consumption optimization model corresponding to the type of user with the largest controllable coefficient in S131 is:

[0144]

[0145] Wherein, ΔL(t) is the change in power consumption of the equipment of the user with the largest controllable coefficient per hour, L(t) is the power consumption of the equipment of the user with the largest controllable coefficient at the tth moment before adjustment, A is the peak-to-valley difference after optimization, B is the user's power satisfaction, and 24 is the 24 time periods of a day.

[0146] Preferably, the constraint conditions in the power consumption optimization model corresponding to the type of users with the largest controllable coefficient in S131 are:

[0147] The power equipment of the user with the largest controllable coefficient is divided into transferable non-interruptible equipment, transferable interruptible equipment and curtailable equipment, and the corresponding constraints are determined according to different equipment:

[0148] Determine the constraints of transferable and non-interruptible equipment in the category of users with the largest controllable coefficient as follows:

[0149]

[0150] in, is the minimum value of the transferable non-interruptible equipment power, is the maximum value of the transferable non-interruptible equipment power, is the rated power of the transferable non-interruptible equipment, L TL is the total power consumption of the transferable and non-interruptible equipment in one day, S TL (t) is the working state of the transferable non-interruptible device at time t, M is the minimum continuous time when the transferable non-interruptible device is working, c TL.early The earliest time that users are allowed to start working on transferable and non-interruptible devices, c TL.late The latest start time allowed for users to work on transferable and non-interruptible devices;

[0151] Determine the constraints of transferable and interruptible devices in the user class with the largest controllable coefficient as follows:

[0152]

[0153] in, is the minimum value of the power of the transferable interruptible equipment, is the maximum value of the transferable interruptible device power, is the rated power of the transferable interruptible equipment, L IL To transfer the total power consumption of interruptible equipment in one day, S IL (t) is the working state of the power of the transferable and interruptible device at time t, β is the working time that the transferable and interruptible device must continue to work, c IL.early The earliest time that the user is allowed to start working on the transferable and interruptible equipment, c IL.late The latest start time allowed for users to work on transferable and interruptible devices;

[0154] Determine the constraints of the equipment that can be reduced in the user category with the largest controllable coefficient as follows:

[0155]

[0156] in, is the maximum power that can be reduced, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, ε is the heat dissipation coefficient, η is the heat conduction efficiency, A is the thermal conductivity, S a (t) is the working status of the equipment that can be reduced at time t, T s (t) is the set temperature of the air conditioner at time t;

[0157] Among them, transferable non-interruptible equipment means that once the equipment starts working, it cannot be stopped before the task is completed; transferable interruptible equipment means that the equipment can be started and stopped at will within the time range set by the user; and curtailable equipment means that the power of the equipment can be adjusted.

[0158] Preferably, in S131, a particle swarm algorithm may be used to solve the power consumption optimization model corresponding to the user with the largest controllable coefficient. The particle swarm algorithm process is as follows: Figure 4 As shown:

[0159] Initialization: Set the maximum number of iterations, the number of independent variables of the objective function, the speed interval of the particles, the position interval of the particles, randomly initialize the speed and position of the particles in the speed interval and position interval, set the particle swarm size to, each particle randomly initializes a flight speed, the number of particles is the number of power consumption states of all power loads, the particles are the potential solutions to the multi-objective optimization problem, and are also the on / off states of all power loads at each moment;

[0160] Individual extreme value and global optimal solution: define the above objective function, the individual extreme value is the optimal solution of each particle, find a global optimal solution from the optimal solution of the particle, compare it with the historical global optimal solution, if it is better, take it as the new global optimal solution;

[0161] Update the particle's velocity and position according to the following formula:

[0162] v is (t+1)=v is (t)+c1r 1s (t)(P is (t)-x is (t))+c2r 2s (t)(P gs (t)-x is (t)) (13)

[0163] x is (t+1)=x is (t)+v is (t+1) (14)

[0164] Where v is the velocity of the particle, x is the position of the particle, r is a random number between (0,1), c1 and c2 are learning factors, usually c1 = c2 = 2, P is is the particle extreme value, P gs It is the global optimal solution.

[0165] Termination condition: If the set number of iterations is reached or the deviation between two adjacent optimal solutions is within a certain range.

[0166] Preferably, the change in the optimal power consumption per hour of the user equipment of the largest controllable coefficient obtained by solving S131 in S132 ΔL(t) is calculated by the formula Determine the optimal power consumption of a category of users with the largest controllable coefficient.

[0167] Based on the same inventive concept, the present invention also provides a user power optimization control device, such as Figure 5 As shown, including:

[0168] Classification unit: used to classify users according to their historical electricity consumption data;

[0169] Calculation unit: used to calculate the controllable coefficients of various users;

[0170] Control unit: used to optimize the electricity consumption of the user with the largest controllable coefficient.

[0171] In an embodiment of the present invention, the classification unit comprises:

[0172] The first determination subunit is used to determine the power consumption status of the user in the historical period according to the historical power consumption data of the user;

[0173] The first classification subunit: classifies users according to their electricity consumption status in a historical period.

[0174] Furthermore, the user's historical electricity consumption data includes the user's electricity consumption in a historical period;

[0175] Determining the power consumption state of the user in the historical period according to the historical power consumption data of the user includes:

[0176] Determine the power consumption status l of the user in the i-th period of the t-th historical day by the following formula ti :

[0177]

[0178]

[0179]

[0180] In the formula, is the electricity consumption of the user in the i-th period on the t-th historical day, l ti is the power consumption status of the user in the i-th period of the t-th historical day, is the user's electricity consumption on the tth historical day.

[0181] Specifically, the power consumption of the user in the i-th period of the t-th historical day is determined as follows:

[0182]

[0183] In the formula, is the electricity consumption in the i-th period of the t-th historical day, i∈[1,4], n is 24, y j is the electricity consumption in the jth hour of the tth historical day, j∈[1,24].

[0184] Specifically, the first classification subunit is specifically used for:

[0185] Based on the electricity consumption status of users in the historical period, the k-means clustering algorithm is used to classify users;

[0186] The initial cluster center of the k-means clustering algorithm is: the cluster center in the clustering result of clustering users using the canopy algorithm based on the power consumption status of the users in the historical period.

[0187] In an embodiment of the present invention, the computing unit is specifically used for:

[0188] Determine the controllable coefficient D of the x-th type of user as follows: x :

[0189]

[0190] In the formula, T is the total number of historical days, is the probability that the power consumption state of the x-th type of user changes from state a to state b on the t-th day in the historical period, a, b∈[1,3], and N is 3;

[0191] Said It is determined based on the electricity consumption status of various types of users during the historical period.

[0192] In an embodiment of the present invention, the control unit comprises:

[0193] The second acquisition subunit is used to solve the pre-established power consumption optimization model corresponding to the user with the largest controllable coefficient, and obtain the change in the optimal power consumption of the user with the largest controllable coefficient;

[0194] The second determining subunit is used to determine the optimal power consumption of the user with the largest controllable coefficient according to the change amount of the optimal power consumption of the user with the largest controllable coefficient;

[0195] The second control subunit is used to adjust the power consumption of the user category with the largest controllable coefficient to the optimal power consumption of the user category with the largest controllable coefficient.

[0196] Specifically, the objective function of the power consumption optimization model corresponding to the type of users with the largest controllable coefficient established in advance is:

[0197]

[0198] In the formula, ΔL(t) is the change in power consumption of the equipment of the user with the largest controllable coefficient per hour, L(t) is the power consumption of the equipment of the user with the largest controllable coefficient at the tth moment before adjustment, A is the peak-to-valley difference after optimization, and B is the user's power satisfaction.

[0199] Specifically, the constraints in the power consumption optimization model corresponding to the type of users with the largest controllable coefficient established in advance are:

[0200] Determine the constraints of transferable and non-interruptible equipment in the category of users with the largest controllable coefficient as follows:

[0201]

[0202] in, is the minimum value of the transferable non-interruptible equipment power, is the maximum value of the transferable non-interruptible equipment power, is the rated power of the transferable non-interruptible equipment, L TL is the total power consumption of the transferable and non-interruptible equipment in one day, S TL (t) is the working state of the transferable non-interruptible device at time t, M is the minimum continuous time when the transferable non-interruptible device is working, c TL.early The earliest time that users are allowed to start working on transferable and non-interruptible devices, c TL.late The latest start time allowed for users to work on transferable and non-interruptible devices;

[0203] Determine the constraints of transferable and interruptible devices in the user class with the largest controllable coefficient as follows:

[0204]

[0205] in, is the minimum value of the power of the transferable interruptible equipment, is the maximum value of the transferable interruptible device power, is the rated power of the transferable interruptible equipment, L IL To transfer the total power consumption of interruptible equipment in one day, S IL (t) is the working state of the power of the transferable and interruptible device at time t, β is the working time that the transferable and interruptible device must continue to work, c IL.early The earliest time that the user is allowed to start working on the transferable and interruptible equipment, c IL.late The latest start time allowed for users to work on transferable and interruptible devices;

[0206] Determine the constraints of the equipment that can be reduced in the user category with the largest controllable coefficient as follows:

[0207]

[0208] in, is the maximum power that can be reduced, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, ε is the heat dissipation coefficient, η is the heat conduction efficiency, A is the thermal conductivity, S a (t) is the working status of the equipment that can be reduced at time t, T s (t) is the set temperature of the air conditioner at time t.

[0209] In summary, the present invention relates to a user electricity consumption optimization control method and device, including obtaining users' historical electricity consumption data, and classifying users according to their historical electricity consumption data; calculating controllable coefficients of various types of users, and performing electricity consumption optimization control on a type of users with the largest controllable coefficients; the present invention provides a user electricity consumption optimization control method, which can effectively identify users with regular electricity consumption, perform electricity consumption optimization control on such users, reduce users' electricity expenditures while satisfying user satisfaction, and at the same time alleviate the problems of insufficient power supply during peak periods of electricity consumption and too low utilization of power equipment during trough periods of electricity consumption, thereby achieving safe and stable operation of the power grid.

[0210] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0211] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0212] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A user power optimization control method, characterized in that: The method comprises: Classify users according to their historical electricity consumption data; Calculate the controllability coefficients of various types of users; Carry out power consumption optimization control for the users with the largest controllable coefficient; The classifying of users according to their historical electricity usage data includes: Determine the user's electricity consumption status within a historical period based on the user's historical electricity consumption data; Classify users according to their electricity consumption status in historical time periods; The calculation of the controllable coefficients of various types of users includes: Determine the controllable coefficient D of the x-th type of user as follows: x : In the formula, T is the total number of historical days, is the probability that the power consumption state of the x-th type of user changes from state a to state b on the t-th day in the historical period, a, b∈[1,N], N is the type of power consumption state; Said Determined based on the electricity consumption status of various types of users in historical time periods; The optimizing control of electricity consumption for a class of users with the largest controllable coefficient includes: Solve the pre-established power consumption optimization model corresponding to the user with the largest controllable coefficient, and obtain the change in the optimal power consumption of the user with the largest controllable coefficient; Determine the optimal power consumption of a class of users with the largest controllable coefficient according to the change in the optimal power consumption of a class of users with the largest controllable coefficient; The power consumption of the user group with the largest controllable coefficient is adjusted to the optimal power consumption of the user group with the largest controllable coefficient.

2. The method according to claim 1, characterized in that The user's historical electricity consumption data includes the user's electricity consumption in a historical period; Determining the power consumption state of the user in the historical period according to the historical power consumption data of the user includes: Determine the power consumption status l of the user in the i-th period of the t-th historical day by the following formula ti : In the formula, is the electricity consumption of the user in the i-th period on the t-th historical day, l ti is the power consumption status of the user in the i-th period of the t-th historical day, is the user's electricity consumption on the tth historical day.

3. The method according to claim 2, characterized in that Determine the power consumption of the user in the i-th period of the t-th historical day as follows: In the formula, is the electricity consumption in the i-th period of the t-th historical day, i∈[1,4], y j is the electricity consumption in the jth hour of the tth historical day, j∈[1,n], and n is the total number of hours in each historical day.

4. The method according to claim 1, characterized in that The user classification according to the power consumption status of the user in the historical period includes: Based on the electricity consumption status of users in the historical period, the k-means clustering algorithm is used to classify users; The initial cluster center of the k-means clustering algorithm is: the cluster center in the clustering result of clustering users using the canopy algorithm based on the power consumption status of the users in the historical period.

5. The method according to claim 1, characterized in that The objective function of the power consumption optimization model corresponding to the pre-established user with the largest controllable coefficient is: In the formula, ΔL(t) is the change in power consumption of the equipment of the user with the largest controllable coefficient per hour, L(t) is the power consumption of the equipment of the user with the largest controllable coefficient at the tth moment before adjustment, A is the peak-to-valley difference after optimization, and B is the user's power satisfaction.

6. The method according to claim 1, characterized in that The constraint conditions in the power consumption optimization model corresponding to the pre-established user with the largest controllable coefficient are: Determine the constraints of transferable and non-interruptible equipment in the category of users with the largest controllable coefficient as follows: in, is the minimum value of the transferable non-interruptible equipment power, is the maximum value of the transferable non-interruptible equipment power, is the rated power of the transferable non-interruptible equipment, L TL is the total power consumption of the transferable and non-interruptible equipment in one day, S TL (t) is the working state of the transferable non-interruptible device at time t, M is the minimum continuous time when the transferable non-interruptible device is working, c TL.early The earliest time that users are allowed to start working on transferable and non-interruptible devices, c TL.late The latest start time allowed for users to work on transferable and non-interruptible devices; Determine the constraints of transferable and interruptible devices in the user class with the largest controllable coefficient as follows: in, is the minimum value of the power of the transferable interruptible equipment, is the maximum value of the transferable interruptible device power, is the rated power of the transferable interruptible equipment, L IL To transfer the total power consumption of interruptible equipment in one day, S IL (t) is the working state of the power of the transferable and interruptible device at time t, β is the working time that the transferable and interruptible device must continue to work, c IL.early The earliest time that the user is allowed to start working on the transferable and interruptible equipment, c IL.late The latest start time allowed for users to work on transferable and interruptible devices; Determine the constraints of the equipment that can be reduced in the user category with the largest controllable coefficient as follows: in, is the maximum power that can be reduced, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, ε is the heat dissipation coefficient, η is the heat conduction efficiency, A is the thermal conductivity, S a (t) is the working status of the equipment that can be reduced at time t, T s (t) is the set temperature of the air conditioner at time t.

7. A user power optimization control device, characterized in that: The device comprises: Classification unit: used to classify users according to their historical electricity consumption data; Calculation unit: used to calculate the controllable coefficients of various users; Control unit: used to optimize the power consumption of the user with the largest controllable coefficient; The classification unit includes: The first determination subunit is used to determine the power consumption status of the user in the historical period according to the historical power consumption data of the user; The first classification subunit: classifies users according to their electricity consumption status in a historical period; The computing unit is specifically used for: Determine the controllable coefficient D of the x-th type of user as follows: x : In the formula, T is the total number of historical days, is the probability that the power consumption state of the x-th type of user changes from state a to state b on the t-th day in the historical period, a, b∈[1,3], and N is 3; Said Determined based on the electricity consumption status of various types of users in historical time periods; The control unit comprises: The second acquisition subunit is used to solve the pre-established power consumption optimization model corresponding to the user with the largest controllable coefficient, and obtain the change in the optimal power consumption of the user with the largest controllable coefficient; The second determining subunit is used to determine the optimal power consumption of the user with the largest controllable coefficient according to the change amount of the optimal power consumption of the user with the largest controllable coefficient; The second control subunit is used to adjust the power consumption of the user category with the largest controllable coefficient to the optimal power consumption of the user category with the largest controllable coefficient.

8. The device according to claim 7, characterized in that The user's historical electricity consumption data includes the user's electricity consumption in a historical period; Determining the power consumption state of the user in the historical period according to the historical power consumption data of the user includes: Determine the power consumption status l of the user in the i-th period of the t-th historical day by the following formula ti : In the formula, is the electricity consumption of the user in the i-th period on the t-th historical day, l ti is the power consumption status of the user in the i-th period of the t-th historical day, is the user's electricity consumption on the tth historical day.

9. The device according to claim 7, characterized in that Determine the power consumption of the user in the i-th period of the t-th historical day as follows: In the formula, is the electricity consumption in the i-th period of the t-th historical day, i∈[1,4], n is 24, y j is the electricity consumption in the jth hour of the tth historical day, j∈[1,24].

10. The device according to claim 7, characterized in that The first classification subunit is specifically used for: Based on the electricity consumption status of users in the historical period, the k-means clustering algorithm is used to classify users; The initial cluster center of the k-means clustering algorithm is: the cluster center in the clustering result of clustering users using the canopy algorithm based on the power consumption status of the users in the historical period.

11. The device according to claim 7, characterized in that The objective function of the power consumption optimization model corresponding to the pre-established user with the largest controllable coefficient is: In the formula, ΔL(t) is the change in power consumption of the equipment of the user with the largest controllable coefficient per hour, L(t) is the power consumption of the equipment of the user with the largest controllable coefficient at the tth moment before adjustment, A is the peak-to-valley difference after optimization, and B is the user's power satisfaction.

12. The device according to claim 7, characterized in that The constraint conditions in the power consumption optimization model corresponding to the pre-established user with the largest controllable coefficient are: Determine the constraints of transferable and non-interruptible equipment in the category of users with the largest controllable coefficient as follows: in, is the minimum value of the transferable non-interruptible equipment power, is the maximum value of the transferable non-interruptible equipment power, is the rated power of the transferable non-interruptible equipment, L TL is the total power consumption of the transferable and non-interruptible equipment in one day, S TL (t) is the working state of the transferable non-interruptible device at time t, M is the minimum continuous time when the transferable non-interruptible device is working, c TL.early The earliest time that users are allowed to start working on transferable and non-interruptible devices, c TL.late The latest start time allowed for users to work on transferable and non-interruptible devices; Determine the constraints of transferable and interruptible devices in the user class with the largest controllable coefficient as follows: in, is the minimum value of the power of the transferable interruptible equipment, is the maximum value of the transferable interruptible device power, is the rated power of the transferable interruptible equipment, L IL To transfer the total power consumption of interruptible equipment in one day, S IL (t) is the working state of the power of the transferable and interruptible device at time t, β is the working time that the transferable and interruptible device must continue to work, c IL.early The earliest time that the user is allowed to start working on the transferable and interruptible equipment, c IL.late The latest start time allowed for users to work on transferable and interruptible devices; Determine the constraints of the equipment that can be reduced in the user category with the largest controllable coefficient as follows: in, is the maximum power that can be reduced, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, ε is the heat dissipation coefficient, η is the heat conduction efficiency, A is the thermal conductivity, S a (t) is the working status of the equipment that can be reduced at time t, T s (t) is the set temperature of the air conditioner at time t.

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