Time-phased gradient electricity price planning method and system associated with user power supply quantity

Abstract

The invention discloses a time-phased gradient electricity price planning method associated with user power supply quantity, and the method comprises the steps: S1, calculating an average user time-phased electricity consumption matrix according to a user daily time-phased electricity consumption matrix, and carrying out the sorting of the average user time-phased electricity consumption matrix, and obtaining a sorted average user time-phased electricity consumption vector; s2, defining an independent variable step electricity price matrix and an independent variable time-phased step electricity quantity vector, and calculating a total electricity price expectation; s3, establishing an equation set of an electricity consumption fluctuation matrix caused by electricity price change according to the average user time-phased electricity consumption matrix, the stepped electricity consumption vector, the stepped electricity price matrix and the electricity price stepped number, solving the equation set, and calculating a total pressure expectation; and S4, establishing an optimization problem and solving the optimization problem to obtain a step electricity price planning scheme. On the basis of guaranteeing basic electricity utilization of residents, fluctuation of the power supply quantity of the residents caused by electricity price changes is considered to the maximum extent, and therefore the user-defined electricity price adjustment requirements of the power grid under different backgrounds are met.

Description

Technical Field

[0001] The present invention relates to a time-division gradient electricity price planning method and system associated with user power supply, and is specifically applicable to the ladder electricity price planning of power grids. Background Art

[0002] The power supply demand of users is concentrated and intermittent in time and space. Most users have high power supply demand concentrated in several time periods every day, and the power supply demand is relatively stable in the remaining time periods. In the power grid power supply system, the power demand of users is mapped to the power supply pressure of the system. During the period when the power supply demand of users is concentrated, the system capacity pressure is relatively large, which may cause accidents such as insufficient power supply or equipment failure. For power supply demand, by adjusting the electricity price appropriately, it can affect the power consumption of users in higher power consumption stages, so that the centralized power supply can meet the power generation level of the system. In addition, under the premise of ensuring the basic power demand of users, adjusting the electricity price in different intervals can also recover part of the power generation cost, thereby further reducing the power supply pressure.

[0003] However, the degree of fluctuation in electricity consumption caused by changes in electricity prices varies. Specifically, when the electricity price in the lower electricity consumption interval increases or decreases, the change in electricity consumption caused will be smaller than the change in the electricity price corresponding to the high electricity consumption interval. Therefore, for the potential waste of electricity in the high electricity consumption interval, raising the electricity price in this interval can effectively suppress this phenomenon. Therefore, how to suppress potential waste of electricity as much as possible while considering the basic electricity demand of users and rationally planning the tiered electricity price has become a key and difficult point. Summary of the invention

[0004] The purpose of the present invention is to overcome the problem of unreasonable electricity price planning in the prior art, and to provide a time-based gradient electricity price planning method and system associated with the power supply of users.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] In a first aspect, the present invention provides a method for planning time-based gradient electricity prices associated with user power supply, comprising the following steps:

[0007] Step 1: Calculate the average user time-based electricity consumption matrix J according to the user's daily time-based electricity consumption matrix D, and sort it to obtain the sorted average user time-based electricity consumption vector J;

[0008] Step 2: Define the independent variable ladder electricity price matrix N and the independent variable time-phase ladder electricity quantity vector Q, and calculate the total electricity price expectation M;

[0009] Step 3: According to the average user time-based electricity consumption matrix J, the step electricity consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H, establish and solve the equation group of the electricity consumption fluctuation matrix O caused by electricity price changes, and calculate the total pressure expectation P according to the power supply vector I of the power grid in different time periods, the electricity consumption fluctuation O caused by electricity price changes, and the average user time-based electricity consumption matrix J;

[0010] Step 4: Establish an optimization problem and solve it to obtain the ladder electricity price planning scheme, namely the ladder electricity consumption vector Q and the ladder electricity price matrix N.

[0011] In step 1, the average user time-division power consumption matrix J is calculated based on the user's daily time-division power consumption matrix D:

[0012]

[0013] Among them, the matrix size of the average user time-divided electricity consumption matrix J is A*C; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth time period; A is the number of users; B is the number of days of power consumption record; C is the number of daily time periods; D is the daily power consumption matrix of users in different time periods; D a,b,c is the element of the ath row, bth column, and cth layer of the user's daily time-divided electricity consumption matrix D, which means the electricity consumption of the ath user in the cth period of day b;

[0014] Sort the average user time-based electricity consumption matrix J to obtain the sorted average user time-based electricity consumption vector

[0015]

[0016] Among them, J is the average user time-based electricity consumption matrix; J(:) is the flattening operation of the matrix J, that is, the operation of converting the multi-dimensional matrix J into a one-dimensional vector, which flattens the average user time-based electricity consumption matrix J with a matrix size of A*C into a vector with a length of A*C; sort(J(:)) is a sorting operation, which sorts the elements in the vector J(:) from small to large.

[0017] In step 2, the independent variable step electricity price matrix N is defined:

[0018]

[0019] Where N is the ladder electricity price matrix, and its matrix size is H*C; N h,c ∈N,N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period, h∈[1,H], c∈[1,C]; and satisfies E≤N 1,c≤N 2,c ≤…≤N H,c ≤F, c=1,2,..,C; E is the lowest standard electricity price; F is the highest standard electricity price; H is the number of electricity price tiers;

[0020] Define the independent variable step power consumption vector Q:

[0021] Q=(Q1,Q2,..,Q H+1 )

[0022] Among them, Q is the step power consumption vector, and its vector length is H+1; Q h ∈Q,Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step, h∈[1,H]; and satisfies Q1≤Q2≤…≤Q H+1 ; Q1 = 0; H is the number of electricity price steps;

[0023] According to the number of users A, the number of daily time periods C, the average user time-based electricity consumption matrix J, the step electricity price matrix N, and the step electricity consumption vector Q, the expected total electricity price M is calculated as:

[0024]

[0025] Among them, M is the total electricity price expectation, A is the number of users; C is the number of daily time periods; H is the number of electricity price tiers; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; Q h+1 is the h+1th element of the step power consumption vector Q, which means the power consumption of the h+1th power consumption step; min{J a,c ,Q h+1} to get J a,c and Qh+1, max{min{J a,c ,Q h+1}-Q h ,0} is to take min{J a,c ,Q h+1}-Q h The larger value between 0 and 0 is calculated.

[0026] In step 3, the equation group of the power consumption fluctuation matrix O caused by the change in electricity price is established according to the average user time-divided power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H:

[0027]

[0028] Among them, f(x) is the piecewise function related to the variable x; J is the average user power consumption matrix in different time periods, is to predict the user's electricity consumption, which is an intermediate variable; J a,c is the element in the ath row and cth column of the matrix, which means the average daily electricity consumption of the ath user in the cth period; H is the number of electricity price steps; Q is the step electricity consumption vector, Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step; Q h+1 is the power consumption of the h+1th power consumption level; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period; A is the number of users; C is the number of daily time periods; is the standard step electricity price matrix, whose vector length is the number of electricity price steps H. is the standard tiered electricity price matrix The element of the nth row and cth column of is the standard electricity price of the nth electricity consumption ladder in the cth period; O is the electricity consumption fluctuation matrix caused by the change of electricity price, and its matrix size is A*C, O a,c is the a-th row and c-th column element of the electricity consumption fluctuation matrix O caused by electricity price changes, which means the average daily electricity consumption fluctuation caused by the a-th user in the c-th time period due to the influence of electricity prices;

[0029] The total pressure expectation P is calculated based on the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user power consumption matrix J in different time periods:

[0030]

[0031] Where P is the total pressure expectation, I is the power supply vector of the power grid in different time periods, and the vector length of the power supply vector I of the power grid in different time periods is C, and its cth element I c The meaning is the upper limit of the power supply of the power grid in the cth period of each day.

[0032] In step 4, an optimization problem is established:

[0033]

[0034] stE≤N 1,c ≤N 2,c ≤..≤N H,c ≤F,c=1,2,..,C

[0035] Q1≤Q2≤..≤Q H+1

[0036] Q1=0

[0037] Among them, α is the expected weight parameter of the total electricity price, β is the expected weight parameter of the total pressure, M is the expected total electricity price, P is the expected total pressure, Q is the step electricity consumption vector, N is the step electricity price matrix, and H is the number of electricity price steps.

[0038] In a second aspect, the present invention provides a time-based gradient electricity price planning system associated with user power supply, including: a data acquisition and analysis module, a total electricity price expectation calculation module, a total pressure expectation calculation module and an optimization solution module;

[0039] The data collection and analysis module is used to calculate the average user time-based electricity consumption matrix J based on the user's daily time-based electricity consumption matrix D, and sort it to obtain the sorted average user time-based electricity consumption vector J;

[0040] The total electricity price expectation calculation module is used to define the independent variable ladder electricity price matrix N and the independent variable time-division ladder electricity quantity vector Q, and calculate the total electricity price expectation M;

[0041] The total pressure expectation calculation module is used to establish and solve the equation group of the power consumption fluctuation matrix O caused by the change of electricity price according to the average user's time-sharing power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H, and calculate the total pressure expectation P according to the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user's time-sharing power consumption matrix J;

[0042] The optimization solution module is used to establish and solve the optimization problem to obtain the ladder electricity price planning scheme, namely the ladder electricity consumption vector Q and the ladder electricity price matrix N.

[0043] In the data collection and analysis module, the average user time-based electricity consumption matrix J is calculated based on the user's daily time-based electricity consumption matrix D:

[0044]

[0045] Among them, the matrix size of the average user time-divided electricity consumption matrix J is A*C; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth time period; A is the number of users; B is the number of days of power consumption record; C is the number of daily time periods; D is the daily power consumption matrix of users in different time periods; D a,b,c is the element of the ath row, bth column, and cth layer of the user's daily time-divided electricity consumption matrix D, which means the electricity consumption of the ath user in the cth period of day b;

[0046] Sort the average user time-based electricity consumption matrix J to obtain the sorted average user time-based electricity consumption vector

[0047]

[0048] Among them, J is the average user time-based electricity consumption matrix; J(:) is the flattening operation of the matrix J, that is, the operation of converting the multi-dimensional matrix J into a one-dimensional vector, which flattens the average user time-based electricity consumption matrix J with a matrix size of A*C into a vector with a length of A*C; sort(J(:)) is a sorting operation, which sorts the elements in the vector J(:) from small to large.

[0049] In the total electricity price expectation calculation module, the independent variable ladder electricity price matrix N is defined:

[0050]

[0051] Where N is the ladder electricity price matrix, and its matrix size is H*C; N h,c ∈N,N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period, h∈[1,H], c∈[1,C]; and satisfies E≤N 1,c ≤N 2,c ≤…≤N H,c ≤F, c=1,2,..,C; E is the lowest standard electricity price; F is the highest standard electricity price; H is the number of electricity price tiers;

[0052] Define the independent variable step power consumption vector Q:

[0053] Q=(Q1,Q2,..,Q H+1 )

[0054] Among them, Q is the step power consumption vector, and its vector length is H+1; Q h ∈Q,Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step, h∈[1,H]; and satisfies Q1≤Q2≤…≤Q H+1 ; Q1 = 0; H is the number of electricity price steps;

[0055] According to the number of users A, the number of daily time periods C, the average user time-based electricity consumption matrix J, the step electricity price matrix N, and the step electricity consumption vector Q, the expected total electricity price M is calculated as:

[0056]

[0057] Among them, M is the total electricity price expectation, A is the number of users; C is the number of daily time periods; H is the number of electricity price tiers; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; J a,cis the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; Q h+1 is the h+1th element of the step power consumption vector Q, which means the power consumption of the h+1th power consumption step; min{J a,c ,Q h+1} to get J a,c and Qh+1, max{min{J a,c ,Q h+1}-Q h ,0} is to take min{J a,c ,Q h+1}-Q h The larger value between 0 and 0 is calculated.

[0058] In the total pressure expectation calculation module, the equation group of the power consumption fluctuation matrix O caused by the change in electricity price is established according to the average user time-sharing power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H:

[0059]

[0060] Among them, f(x) is the piecewise function related to the variable x; J is the average user power consumption matrix in different time periods, is to predict the user's electricity consumption, which is an intermediate variable; J a,c is the element in the ath row and cth column of the matrix, which means the average daily electricity consumption of the ath user in the cth period; H is the number of electricity price steps; Q is the step electricity consumption vector, Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step; Q h+1 is the power consumption of the h+1th power consumption level; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period; A is the number of users; C is the number of daily time periods; is the standard step electricity price matrix, whose vector length is the number of electricity price steps H. is the standard tiered electricity price matrix The element of the nth row and cth column of is the standard electricity price of the nth electricity consumption ladder in the cth period; O is the electricity consumption fluctuation matrix caused by the change of electricity price, and its matrix size is A*C, O a,c is the a-th row and c-th column element of the electricity consumption fluctuation matrix O caused by electricity price changes, which means the average daily electricity consumption fluctuation caused by the a-th user in the c-th period due to the influence of electricity prices;

[0061] The total pressure expectation P is calculated based on the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user power consumption matrix J in different time periods:

[0062]

[0063] Where P is the total pressure expectation, I is the power supply vector of the power grid in different time periods, and the vector length of the power supply vector I of the power grid in different time periods is C, and its cth element I c The meaning is the upper limit of the power supply of the power grid in the cth period of each day.

[0064] In the optimization solution module, an optimization problem is established:

[0065]

[0066] stE≤N 1,c ≤N 2,c ≤..≤N H,c ≤F,c=1,2,..,C

[0067] Q1≤Q2≤..≤Q H+1

[0068] Q1=0

[0069] Among them, α is the expected weight parameter of the total electricity price, β is the expected weight parameter of the total pressure, M is the expected total electricity price, P is the expected total pressure, Q is the step electricity consumption vector, N is the step electricity price matrix, and H is the number of electricity price steps.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The present invention proposes a method for planning time-divided gradient electricity prices for associated user power supply and a system to address the deficiencies of the existing time-divided gradient electricity price planning and adjustment scheme for residential power consumption in the power grid, especially for the characteristics of concentrated and intermittent residential power consumption in my country. The scheme aims to consider suppressing potential power waste as much as possible under the premise of ensuring the basic power demand of users, while fully considering the power price recovery demand and power supply pressure demand under different conditions of the power grid, and maximizing the power price and negative power supply pressure expectations.

[0072] The core of the present invention is: first, based on the historical data of the user's electricity consumption in different time periods, the influence of different ladder electricity prices on the user's electricity consumption, that is, the power supply of the power grid, is analyzed, so as to obtain the fluctuation function of the user's power supply caused by the change in electricity price. At the same time, based on the fluctuation function, the influence of the user's power supply on the pressure of the power grid generation point is analyzed. Taking the above two elements into consideration, an optimization problem is established, and the ladder electricity price and ladder electricity consumption are used as optimization variables. The problem is solved with the goal of maximizing the recovery cost and negative power generation pressure of the customized power grid electricity price, and finally a planning and adjustment plan for the time-divided gradient electricity price under customized demand is obtained. On the basis of ensuring the basic electricity consumption of residents, this scheme takes into account the fluctuations in the residents' power supply caused by changes in electricity prices and the further fluctuations in the power generation pressure of the power grid to the greatest extent, thereby realizing the customized electricity price adjustment needs of the power grid under different backgrounds. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of the method of the present invention.

[0074] Figure 2 It is a structural distribution diagram of the optimization problem constructed by the invention.

[0075] Figure 3 It is a structural diagram of the system of the present invention.

[0076] Figure 4 It is a schematic diagram of Example 3. DETAILED DESCRIPTION

[0077] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0078] Embodiment 1:

[0079] The present invention fully considers the impact of electricity prices on the normal power supply to residents, and returns this impact to the power grid in the form of cost recovery and power supply pressure. The basis of this idea is that considering the people's livelihood background of power supply to residents in my country and the perfect power supply capacity, electricity prices are difficult to re-formulate or significantly adjust. Therefore, we can only consider the power generation pressure of the power grid and the demand for cost recovery under different backgrounds (seasons, coal prices, policies, etc.) (step 4 establishes the objective function parameters of the problem) to make small adjustments to the electricity price on the basis of the standard electricity price to meet actual needs as much as possible. Compared with innovations in scientific research theory, this solution focuses more on analyzing the needs that may arise in practical applications.

[0080] See also Figure 1 to Figure 2 , a time-based gradient electricity price planning method associated with user power supply, comprising the following steps:

[0081] Step 1: Calculate the average user time-based electricity consumption matrix J according to the user's daily time-based electricity consumption matrix D, and sort it to obtain the sorted average user time-based electricity consumption vector J;

[0082] In step 1, the average user time-division power consumption matrix J is calculated based on the user's daily time-division power consumption matrix D:

[0083]

[0084] Among them, the matrix size of the average user time-divided electricity consumption matrix J is A*C=100*24; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; A is the number of users; B=14 is the number of days of power consumption record; C is the number of daily periods; D is the daily power consumption matrix of users in different periods; D a,b,c is the element of the ath row, bth column, and cth layer of the user's daily time-divided electricity consumption matrix D, which means the electricity consumption of the ath user in the cth period of day b;

[0085] Sort the average user time-based electricity consumption matrix J to obtain the sorted average user time-based electricity consumption vector J:

[0086]

[0087] Among them, J is the average user time-based electricity consumption matrix; J(:) is the flattening operation of matrix J, that is, the operation of converting the multi-dimensional matrix J into a one-dimensional vector, which flattens the average user time-based electricity consumption matrix J with a matrix size of A*C into a vector with a length of A*C; sort(J(:)) is a sorting operation, which sorts the elements in the vector J(:) from small to large; in the python program, the operation procedure described in this step is

[0088] In the program, the operation procedure described in step 1 is or

[0089] Step 2: Define the independent variable ladder electricity price matrix N and the independent variable time-phase ladder electricity quantity vector Q, and calculate the total electricity price expectation M;

[0090] In step 2, the independent variable step electricity price matrix N is defined:

[0091]

[0092] Where N is the ladder electricity price matrix, and its matrix size is H*C; N h,c ∈N,N h,cis the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period, h∈[1,H], c∈[1,C]; and satisfies E≤N 1,c ≤N 2,c ≤…≤N H,c ≤F, c=1,2,..,C; E is the lowest standard electricity price; F is the highest standard electricity price; H is the number of electricity price tiers;

[0093] Define the independent variable step power consumption vector Q:

[0094] Q=(Q1,Q2,..,Q H+1 )

[0095] Among them, Q is the step power consumption vector, and its vector length is H+1; Q h ∈Q,Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step, h∈[1,H]; and satisfies Q1≤Q2≤…≤Q H+1 ; Q1 = 0; H = 3 is the number of electricity price steps;

[0096] According to the number of users A, the number of daily time periods C, the average user time-based electricity consumption matrix J, the step electricity price matrix N, and the step electricity consumption vector Q, the expected total electricity price M is calculated as:

[0097]

[0098] Where M is the total electricity price expectation, A = 100 is the number of users; C = 24 is the number of daily time periods; H = 3 is the number of electricity price steps; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; Q h+1 is the h+1th element of the step power consumption vector Q, which means the power consumption of the h+1th power consumption step; min{J a,c ,Q h+1} to get J a,c and Qh+1, max{min{J a,c ,Q h+1}-Q h ,0} is to take min{J a,c ,Q h+1}-Q h The larger value between 0 and 0 is calculated.

[0099] Step 3: According to the average user time-based electricity consumption matrix J, the step electricity consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H, establish and solve the equation group of the electricity consumption fluctuation matrix O caused by electricity price changes, and calculate the total pressure expectation P according to the power supply vector I of the power grid in different time periods, the electricity consumption fluctuation O caused by electricity price changes, and the average user time-based electricity consumption matrix J;

[0100] In step 3, the equation group of the power consumption fluctuation matrix O caused by the change in electricity price is established according to the average user time-divided power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H:

[0101]

[0102] Among them, f(x) is the piecewise function related to the variable x; J is the average user power consumption matrix in different time periods, is to predict the user's electricity consumption, which is an intermediate variable; J a,c is the element in the ath row and cth column of the matrix, which means the average daily electricity consumption of the ath user in the cth period; H is the number of electricity price steps; Q is the step electricity consumption vector, Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step; Q h+1 is the power consumption of the h+1th power consumption level; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period; A is the number of users; C is the number of daily time periods; is the standard step electricity price matrix, whose vector length is the number of electricity price steps H. is the standard tiered electricity price matrix The element of the nth row and cth column of is the standard electricity price of the nth electricity consumption ladder in the cth period; O is the electricity consumption fluctuation matrix caused by the change of electricity price, and its matrix size is A*C=100*24, O a,c is the a-th row and c-th column element of the electricity consumption fluctuation matrix O caused by electricity price changes, which means the average daily electricity consumption fluctuation caused by the a-th user in the c-th period due to the influence of electricity price. This set of equations can be efficiently solved by Matlab program.

[0103] The total pressure expectation P is calculated based on the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user power consumption matrix J in different time periods:

[0104]

[0105] Where P is the total pressure expectation, I is the power supply vector of the power grid in different time periods, and the vector length of the power supply vector I of the power grid in different time periods is C=24, and its cth element Ic The meaning is the upper limit of the power supply of the power grid in the cth period of each day.

[0106] Step 4: Establish an optimization problem and solve it to obtain the ladder electricity price planning scheme, namely the ladder electricity consumption vector Q and the ladder electricity price matrix N.

[0107] In step 4, an optimization problem is established:

[0108]

[0109] stE≤N 1,c ≤N 2,c ≤..≤N H,c ≤F,c=1,2,..,C

[0110] Q1≤Q2≤..≤Q H+1

[0111] Q1=0

[0112] Among them, α = 0.7 is the total electricity price expectation weight parameter, β = 0.3 is the total pressure expectation weight parameter, M is the total electricity price expectation, P is the total pressure expectation, Q is the step electricity consumption vector, N is the step electricity price matrix, and H = 3 is the number of electricity price steps. This optimization problem can be efficiently solved by the cvx module in the matlab program.

[0113] Among them, the following data are obtained through simulation calculation, and the step power consumption vector Q is: 0 7.3973 13.1507

[0115] The ladder electricity price matrix N is (transposed for convenience):

[0116]

[0117] Embodiment 2:

[0118] See also Figures 2 to 3 , a time-based gradient electricity price planning system associated with user power supply, including: a data acquisition and analysis module, a total electricity price expectation calculation module, a total pressure expectation calculation module and an optimization solution module;

[0119] The data collection and analysis module is used to calculate the average user time-based electricity consumption matrix J based on the user's daily time-based electricity consumption matrix D, and sort it to obtain the sorted average user time-based electricity consumption vector

[0120] In the data collection and analysis module, the average user time-based electricity consumption matrix J is calculated based on the user's daily time-based electricity consumption matrix D:

[0121]

[0122] Among them, the matrix size of the average user time-divided electricity consumption matrix J is A*C; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; A is the number of users; B is the number of days of power consumption records; C is the number of daily periods; D is the user's daily power consumption matrix by time period; D a,b,c is the element of the ath row, bth column, and cth layer of the user's daily time-divided electricity consumption matrix D, which means the electricity consumption of the ath user in the cth period of day b;

[0123] Sort the average user time-based electricity consumption matrix J to obtain the sorted average user time-based electricity consumption vector

[0124]

[0125] Among them, J is the average user time-based electricity consumption matrix; J(:) is the flattening operation of the matrix J, that is, the operation of converting the multi-dimensional matrix J into a one-dimensional vector, which flattens the average user time-based electricity consumption matrix J with a matrix size of A*C into a vector with a length of A*C; sort(J(:)) is a sorting operation, which sorts the elements in the vector J(:) from small to large.

[0126] The total electricity price expectation calculation module is used to define the independent variable ladder electricity price matrix N and the independent variable time-division ladder electricity quantity vector Q, and calculate the total electricity price expectation M;

[0127] In the total electricity price expectation calculation module, the independent variable ladder electricity price matrix N is defined:

[0128]

[0129] Where N is the ladder electricity price matrix, and its matrix size is H*C; N h,c ∈N,N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period, h∈[1,H], c∈[1,C]; and satisfies E≤N 1,c ≤N 2,c ≤…≤N H,c ≤F, c=1,2,..,C; E is the lowest standard electricity price; F is the highest standard electricity price; H is the number of electricity price tiers;

[0130] Define the independent variable step power consumption vector Q:

[0131] Q=(Q1,Q2,..,Q H+1 )

[0132] Among them, Q is the step power consumption vector, and its vector length is H+1; Q h ∈Q,Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step, h∈[1,H]; and satisfies Q1≤Q2≤…≤Q H+1 ; Q1 = 0; H is the number of electricity price steps;

[0133] According to the number of users A, the number of daily time periods C, the average user time-based electricity consumption matrix J, the step electricity price matrix N, and the step electricity consumption vector Q, the expected total electricity price M is calculated as:

[0134]

[0135] Among them, M is the total electricity price expectation, A is the number of users; C is the number of daily time periods; H is the number of electricity price tiers; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; Q h+1 is the h+1th element of the step power consumption vector Q, which means the power consumption of the h+1th power consumption step; min{J a,c ,Q h+1} to get J a,c and Qh+1, max{min{J a,c ,Q h+1}-Q h ,0} is to take min{J a,c ,Q h+1}-Q h The larger value between 0 and 0 is calculated.

[0136] The total pressure expectation calculation module is used to establish and solve the equation group of the power consumption fluctuation matrix O caused by the change of electricity price according to the average user's time-sharing power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H, and calculate the total pressure expectation P according to the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user's time-sharing power consumption matrix J;

[0137] In the total pressure expectation calculation module, the equation group of the power consumption fluctuation matrix O caused by the change in electricity price is established according to the average user time-sharing power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H:

[0138]

[0139] Among them, f(x) is the piecewise function related to the variable x; J is the average user power consumption matrix in different time periods, is to predict the user's electricity consumption, which is an intermediate variable; J a,c is the element in the ath row and cth column of the matrix, which means the average daily electricity consumption of the ath user in the cth period; H is the number of electricity price steps; Q is the step electricity consumption vector, Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step; Q h+1 is the power consumption of the h+1th power consumption level; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period; A is the number of users; C is the number of daily time periods; is the standard step electricity price matrix, whose vector length is the number of electricity price steps H. is the standard tiered electricity price matrix The element of the nth row and cth column of is the standard electricity price of the nth electricity consumption ladder in the cth period; O is the electricity consumption fluctuation matrix caused by the change of electricity price, and its matrix size is A*C, O a,c is the a-th row and c-th column element of the electricity consumption fluctuation matrix O caused by electricity price changes, which means the average daily electricity consumption fluctuation caused by the a-th user in the c-th period due to the influence of electricity prices;

[0140] The total pressure expectation P is calculated based on the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user power consumption matrix J in different time periods:

[0141]

[0142] Where P is the total pressure expectation, I is the power supply vector of the power grid in different time periods, and the vector length of the power supply vector I of the power grid in different time periods is C, and its cth element I c The meaning is the upper limit of the power supply of the power grid in the cth period of each day.

[0143] The optimization solution module is used to establish and solve the optimization problem to obtain the ladder electricity price planning scheme, namely the ladder electricity consumption vector Q and the ladder electricity price matrix N.

[0144] In the optimization solution module, an optimization problem is established:

[0145]

[0146] stE≤N 1,c ≤N 2,c ≤..≤N H,c ≤F,c=1,2,..,C

[0147] Q1≤Q2≤..≤QH+1

[0148] Q1=0

[0149] Among them, α is the expected weight parameter of the total electricity price, β is the expected weight parameter of the total pressure, M is the expected total electricity price, P is the expected total pressure, Q is the step electricity consumption vector, N is the step electricity price matrix, and H is the number of electricity price steps.

[0150] Embodiment 3:

[0151] See also Figure 4 , a time-based gradient electricity price planning device for associated user power supply, comprising a memory and a processor, wherein the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the time-based gradient electricity price planning method for associated user power supply described in Example 1 according to the instructions in the computer program code.

[0152] Embodiment 4:

[0153] A computer program product includes a computer program, which, when executed by a processor, implements the steps of the time-based gradient electricity price planning method for associated user power supply as described in Example 1.

Claims

1. A time-based gradient electricity price planning method for associated user power supply, characterized in that: The steps include: Step 1: Calculate the average user time-based electricity consumption matrix J according to the user's daily time-based electricity consumption matrix D, and sort it to obtain the sorted average user time-based electricity consumption vector J; Step 2: Define the independent variable ladder electricity price matrix N and the independent variable time-phase ladder electricity quantity vector Q, and calculate the total electricity price expectation M; Step 3: According to the average user time-based electricity consumption matrix J, the step electricity consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H, establish and solve the equation group of the electricity consumption fluctuation matrix O caused by electricity price changes, and calculate the total pressure expectation P according to the power supply vector I of the power grid in different time periods, the electricity consumption fluctuation O caused by electricity price changes, and the average user time-based electricity consumption matrix J; Step 4: Establish an optimization problem and solve it to obtain a tiered electricity price planning scheme. That is, the step electricity consumption vector Q and the step electricity price matrix N.

2. A time-based gradient electricity price planning method for associated user power supply according to claim 1, characterized in that: In step 1, the average user time-division power consumption matrix J is calculated based on the user's daily time-division power consumption matrix D: Among them, the matrix size of the average user time-divided electricity consumption matrix J is A*C; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; A is the number of users; B is the number of days of power consumption record; C is the number of daily periods; D is the daily power consumption matrix of users in different periods; D a,b,c is the element of the ath row, bth column, and cth layer of the user's daily time-divided electricity consumption matrix D, which means the electricity consumption of the ath user in the cth time period on the bth day; Sort the average user time-based electricity consumption matrix J to obtain the sorted average user time-based electricity consumption vector Among them, J is the average user time-based electricity consumption matrix; J(:) is the flattening operation of the matrix J, that is, the operation of converting the multi-dimensional matrix J into a one-dimensional vector, which flattens the average user time-based electricity consumption matrix J with a matrix size of A*C into a vector with a length of A*C; sort(J(:)) is a sorting operation, which sorts the elements in the vector J(:) from small to large.

3. The method for planning time-based gradient electricity prices for associated user power supply according to claim 1, characterized in that: In step 2, the independent variable step electricity price matrix N is defined: Where N is the ladder electricity price matrix, and its matrix size is H*C; N h,c ∈N,N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period, h∈[1,H], c∈[1,C]; and satisfies E≤N 1,c ≤N 2,c ≤…≤N H,c ≤F, c=1,2,..,C; E is the lowest standard electricity price; F is the highest standard electricity price; H is the number of electricity price tiers; Define the independent variable step power consumption vector Q: Q=(Q1,Q2,..,Q H+1 ) Among them, Q is the step power consumption vector, and its vector length is H+1; Q h ∈Q,Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step, h∈[1,H]; and satisfies Q1≤Q2≤…≤Q H+1 ; Q1 = 0; H is the number of electricity price steps; According to the number of users A, the number of daily time periods C, the average user time-based electricity consumption matrix J, the step electricity price matrix N, and the step electricity consumption vector Q, the expected total electricity price M is calculated as: Among them, M is the total electricity price expectation, A is the number of users; C is the number of daily time periods; H is the number of electricity price tiers; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; Q h+1 is the h+1th element of the step power consumption vector Q, which means the power consumption of the h+1th power consumption step; min{J a,c ,Q h+1 } to get J a,c and Qh+1, max{min{J a,c ,Q h+1 }-Q h ,0} is to take min{J a,c ,Q h+1 }-Q h The larger value between 0 and 0 is calculated.

4. The method for planning time-based gradient electricity prices for associated user power supply according to claim 1, characterized in that: In step 3, the equation group of the power consumption fluctuation matrix O caused by the change in electricity price is established according to the average user time-divided power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H: Among them, f(x) is the piecewise function related to the variable x; J is the average user power consumption matrix in different time periods, is to predict the user's electricity consumption, which is an intermediate variable; J a,c is the element in the ath row and cth column of the matrix, which means the average daily electricity consumption of the ath user in the cth period; H is the number of electricity price steps; Q is the step electricity consumption vector, Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step; Q h+1 is the power consumption of the h+1th power consumption level; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; A is the number of users; C is the number of daily periods; N is the standard step electricity price matrix, and its vector length is the number of electricity price steps H, and its N n,c is the element of the nth row and cth column of the standard step electricity price matrix N, which means the standard electricity price of the nth electricity consumption step in the cth period; O is the electricity consumption fluctuation matrix caused by the change of electricity price, and its matrix size is A*C, O a,c is the a-th row and c-th column element of the electricity consumption fluctuation matrix O caused by electricity price changes, which means the average daily electricity consumption fluctuation caused by the a-th user in the c-th period due to the influence of electricity prices; The total pressure expectation P is calculated based on the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user power consumption matrix J in different time periods: Where P is the total pressure expectation, I is the power supply vector of the power grid in different time periods, and the vector length of the power supply vector I of the power grid in different time periods is C, and its cth element I c The meaning is the upper limit of the power supply of the power grid in the cth period of each day.

5. The method for planning time-based gradient electricity prices for associated user power supply according to claim 1, characterized in that: In step 4, an optimization problem is established: Among them, α is the expected weight parameter of the total electricity price, β is the expected weight parameter of the total pressure, M is the expected total electricity price, P is the expected total pressure, Q is the step electricity consumption vector, N is the step electricity price matrix, and H is the number of electricity price steps.

6. A time-based gradient electricity price planning system associated with user power supply, characterized in that: include: Data collection and analysis module, total electricity price expectation calculation module, total pressure expectation calculation module and optimization solution module; The data collection and analysis module is used to calculate the average user time-based electricity consumption matrix J based on the user's daily time-based electricity consumption matrix D, and sort it to obtain the sorted average user time-based electricity consumption vector J; The total electricity price expectation calculation module is used to define the independent variable ladder electricity price matrix N and the independent variable time-division ladder electricity quantity vector Q, and calculate the total electricity price expectation M; The total pressure expectation calculation module is used to establish and solve the equation group of the power consumption fluctuation matrix O caused by the change of electricity price according to the average user's time-sharing power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H, and calculate the total pressure expectation P according to the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user's time-sharing power consumption matrix J; The optimization solution module is used to establish and solve the optimization problem to obtain the ladder electricity price planning scheme, namely the ladder electricity consumption vector Q and the ladder electricity price matrix N.

7. A time-based gradient electricity price planning system for associated user power supply according to claim 6, characterized in that: In the data collection and analysis module, the average user time-based electricity consumption matrix J is calculated based on the user's daily time-based electricity consumption matrix D: Among them, the matrix size of the average user time-divided electricity consumption matrix J is A*C; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; A is the number of users; B is the number of days of power consumption records; C is the number of daily periods; D is the user's daily power consumption matrix by time period; D a,b,c is the element of the ath row, bth column, and cth layer of the user's daily time-divided electricity consumption matrix D, which means the electricity consumption of the ath user in the cth time period on the bth day; Sort the average user time-based electricity consumption matrix J to obtain the sorted average user time-based electricity consumption vector Among them, J is the average user time-based electricity consumption matrix; J(:) is the flattening operation of the matrix J, that is, the operation of converting the multi-dimensional matrix J into a one-dimensional vector, which flattens the average user time-based electricity consumption matrix J with a matrix size of A*C into a vector with a length of A*C; sort(J(:)) is a sorting operation, which sorts the elements in the vector J(:) from small to large.

8. A time-based gradient electricity price planning system for associated user power supply according to claim 6, characterized in that: In the total electricity price expectation calculation module, the independent variable ladder electricity price matrix N is defined: Where N is the ladder electricity price matrix, and its matrix size is H*C; N h,c ∈N,N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period, h∈[1,H], c∈[1,C]; and satisfies E≤N 1,c ≤N 2,c ≤…≤N H,c ≤F, c=1,2,..,C; E is the lowest standard electricity price; F is the highest standard electricity price; H is the number of electricity price tiers; Define the independent variable step power consumption vector Q: Q=(Q1,Q2,..,Q H+1 ) Among them, Q is the step power consumption vector, and its vector length is H+1; Q h ∈Q,Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step, h∈[1,H]; and satisfies Q1≤Q2≤…≤Q H+1 ; Q1 = 0; H is the number of electricity price steps; According to the number of users A, the number of daily time periods C, the average user time-based electricity consumption matrix J, the step electricity price matrix N, and the step electricity consumption vector Q, the expected total electricity price M is calculated as: Among them, M is the total electricity price expectation, A is the number of users; C is the number of daily time periods; H is the number of electricity price tiers; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth period; J a,c is the element in the ath row and cth column of the matrix, which means the average daily power consumption of the ath user in the cth period; Q h+1 is the h+1th element of the step power consumption vector Q, which means the power consumption of the h+1th power consumption step; min{J a,c ,Q h+1 } to get J a,c and Qh+1, max{min{J a,c ,Q h+1 }-Q h ,0} is to take min{J a,c ,Q h+1 }-Q h The larger value between 0 and 0 is calculated.

9. A time-based gradient electricity price planning system for associated user power supply according to claim 6, characterized in that: In the total pressure expectation calculation module, the equation group of the power consumption fluctuation matrix O caused by the change in electricity price is established according to the average user time-sharing power consumption matrix J, the step power consumption vector Q, the step electricity price matrix N, and the number of electricity price steps H: Among them, f(x) is the piecewise function related to the variable x; J is the average user power consumption matrix in different time periods, is to predict the user's electricity consumption, which is an intermediate variable; J a,c is the element in the ath row and cth column of the matrix, which means the average daily electricity consumption of the ath user in the cth period; H is the number of electricity price steps; Q is the step electricity consumption vector, Q h is the hth element of the step power consumption vector Q, which means the power consumption of the hth power consumption step; Q h+1 is the power consumption of the h+1th power consumption level; N h,c is the element of the hth row and cth column of the step electricity price matrix N, which means the electricity price of the hth electricity consumption step in the cth time period; A is the number of users; C is the number of daily time periods; is the standard step electricity price matrix, whose vector length is the number of electricity price steps H. is the standard tiered electricity price matrix The element of the nth row and cth column of is the standard electricity price of the nth electricity consumption ladder in the cth period; O is the electricity consumption fluctuation matrix caused by the change of electricity price, and its matrix size is A*C, O a,c is the a-th row and c-th column element of the electricity consumption fluctuation matrix O caused by electricity price changes, which means the average daily electricity consumption fluctuation caused by the a-th user in the c-th period due to the influence of electricity prices; The total pressure expectation P is calculated based on the power supply vector I of the power grid in different time periods, the power consumption fluctuation O caused by the change of electricity price, and the average user power consumption matrix J in different time periods: Where P is the total pressure expectation, I is the power supply vector of the power grid in different time periods, and the vector length of the power supply vector I of the power grid in different time periods is C, and its cth element I c The meaning is the upper limit of the power supply of the power grid in the cth period of each day.

10. The time-based gradient electricity price planning system for associated user power supply according to claim 6, characterized in that: In the optimization solution module, an optimization problem is established: Among them, α is the expected weight parameter of the total electricity price, β is the expected weight parameter of the total pressure, M is the expected total electricity price, P is the expected total pressure, Q is the step electricity consumption vector, N is the step electricity price matrix, and H is the number of electricity price steps.

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