CN113807882A - Electricity price optimization method and system based on load prediction and user satisfaction - Google Patents

Electricity price optimization method and system based on load prediction and user satisfaction Download PDF

Info

Publication number
CN113807882A
CN113807882A CN202110913813.XA CN202110913813A CN113807882A CN 113807882 A CN113807882 A CN 113807882A CN 202110913813 A CN202110913813 A CN 202110913813A CN 113807882 A CN113807882 A CN 113807882A
Authority
CN
China
Prior art keywords
peak
electricity
user
valley
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110913813.XA
Other languages
Chinese (zh)
Inventor
王素
张铭
郭明星
吕冉
傅晨
吴恩琦
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Shanghai Electric Power Co Ltd
Original Assignee
State Grid Shanghai Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Shanghai Electric Power Co Ltd filed Critical State Grid Shanghai Electric Power Co Ltd
Priority to CN202110913813.XA priority Critical patent/CN113807882A/en
Publication of CN113807882A publication Critical patent/CN113807882A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Development Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Finance (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Accounting & Taxation (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Game Theory and Decision Science (AREA)
  • Public Health (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to a method and a system for optimizing electricity price based on load prediction and user satisfaction, wherein the method comprises the following steps: dividing a day into a plurality of time intervals, and obtaining the predicted power consumption of each time interval after the peak-valley time-of-use electricity price is implemented through the load prediction step; and solving the optimal peak-valley electricity price through the satisfaction degree target function. Compared with the prior art, the method has the advantages that the electricity price at the peak valley time period is optimized, the peak clipping and valley filling are balanced, and the satisfaction degree of a user is maximized.

Description

Electricity price optimization method and system based on load prediction and user satisfaction
Technical Field
The invention relates to the technical field of electric power, in particular to a method and a system for optimizing electricity price based on load prediction and user satisfaction.
Background
In recent years, time-of-use electricity price is taken as one of effective demand response modes based on price, and price signals for properly increasing electricity price and properly reducing electricity price in a load peak period and a load valley period are used for guiding a user to make a reasonable electricity utilization plan, so that partial load in the peak period is transferred to an underestimation period, the purposes of peak clipping, valley filling and load balancing are achieved, the contradiction between power supply and demand can be effectively relieved, the load rate is improved, and the consumption of new energy resources such as wind and light is promoted. The peak, flat and valley electricity prices have large difference, so that the production mode of a user is greatly changed, the production and the life of the user are not facilitated, and meanwhile, the peak, flat and valley electricity prices have large difference, so that the extreme conditions that the load of the user is too small under the peak electricity price and the load of the user is too large under the valley electricity price are easily caused. Therefore, time interval division is particularly important for ensuring that benefits of a power grid and users are not damaged and the time-of-use electricity price effect is more effectively played.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a power price optimization method and a power price optimization system based on load prediction and user satisfaction, and aims to optimize the power price at the time of peak valley and normal time, balance peak clipping and valley filling and maximize the user satisfaction.
The purpose of the invention can be realized by the following technical scheme:
a power price optimization method based on load prediction and user satisfaction comprises the following steps:
dividing one day into a plurality of time intervals, and obtaining predicted electricity consumption Q 'of each time interval after peak-valley time-of-use electricity price implementation through a load prediction step'*；
Solving the optimal peak-valley electricity price through a satisfaction target function, wherein the satisfaction target function expression is as follows:
Figure BDA0003204897190000011
wherein Q is the user electricity consumption in each time period before the peak-valley time-of-use electricity price is carried out, maxQ and minQ respectively represent the maximum user load and the minimum user load in all time periods before the peak-valley electricity price is carried out, and lambda1、λ2And λ3In order to set the weight coefficient, R is the comprehensive satisfaction, and the calculation formula is as follows:
R＝γ1ε+γ2θ
wherein epsilon is the satisfaction degree of the electricity utilization mode of the user, theta is the satisfaction degree of the electricity expense of the user, and gamma is1And gamma2To set the weight coefficient, γ1+γ2＝1；
γ1And gamma2Different values can be set for different users to reflect different users' attention degrees to electricity utilization mode change and electricity fee expenditure, for example, for users whose electricity fee accounts for a great proportion of enterprise production cost, gamma is set2For larger users with strict requirements on production time and operation procedures, gamma is1Is large;
the satisfaction target function takes the electricity price at the peak-valley and the ordinary time as a variable, and achieves the purposes of peak clipping and valley filling and maximizing the user satisfaction in a balanced manner, namely achieving the purposes of minimizing the maximum peak load of a daily load curve and minimizing the peak-valley difference of the daily load curve, and achieving the maximization of the user satisfaction at the same time.
Further, the calculation formula of the user electricity utilization mode satisfaction epsilon is as follows:
Figure BDA0003204897190000021
wherein n is the number of time segments, PP，PS，PVElectricity prices at peak, plateau and valley periods, respectively, fi,t(PP,PS,PV) To implement the user load at time t after the peak-valley time-of-use price, ft(Pi) The load of the user at the t moment in the ith time period when the peak-valley time-of-use electricity price is not carried out;
before the peak-valley electricity price is not implemented, the user arranges the electricity utilization mode according to the production mode which is most suitable for the user, and the satisfaction degree of the electricity utilization mode of the user is the maximum at the moment. After the peak-valley electricity price is implemented, the user responds and changes the electricity utilization mode to pursue a smaller electricity price increase. At this time, the electricity consumption is recombined on the time axis to form a new user load curve, and the satisfaction degree of the user electricity utilization mode is established on the basis of the difference value of the adjusted electricity consumption and the original load curve, so that the change of the user electricity utilization mode and the change situation of the user electricity consumption are reflected.
Further, the expression of the user electricity expense satisfaction degree θ is as follows:
Figure BDA0003204897190000022
wherein, C (P)P,PS,PV) To implement the electricity fee expenditure of the user at peak-valley time-of-use electricity rates, C (P)0) The electricity fee expenditure of the user is not realized when the peak-valley time-of-use electricity price is not realized;
in order to ensure that the total level of the electricity price after the peak-valley time-sharing electricity price is implemented is unchanged, the electricity price in the peak time period and the electricity price in the valley time period are equal, when the electricity quantity in the peak time period is greater than the electricity quantity in the valley time period, the total level of the electricity price is increased to exceed the original electricity price level, when the electricity quantity in the valley time period is greater than the electricity quantity in the peak time period, the total level of the electricity price is reduced to be lower than the original electricity price level, in the actual work, the condition that the electricity quantities in the peak-valley time period are just equal is difficult to achieve, and the electricity quantity in the peak-valley time period is unbalanced, the electricity consumption in the peak time period is greater than the electricity consumption in the valley time period, therefore, if the electricity consumption is not arranged according to the peak-valley electricity price ratio, the electricity expense can be greatly increased, and the variation of the electricity expense of the user can be measured according to the satisfaction degree of the electricity expense of the user.
Further, the load prediction step comprises:
calculating a first predicted power consumption Q at the ith time period after the peak-valley time-of-use electricity price is executedi'；
Calculating a second predicted power consumption Qi'*As the final predicted power consumption, the calculation formula is:
Figure BDA0003204897190000031
wherein Q isFiIs the ith timeFixed load of the section, QmaxiIs the maximum user load for the ith period.
Further, the first predicted power consumption QiThe formula for calculation of' is:
calculating the electrovalence elastic coefficient rhoiiObtaining an electrovalence elastic matrix E, rhoiiThe calculation formula is as follows:
Figure BDA0003204897190000032
ΔQi＝∫[fi,t(PP,PS,PV)-ft(Pi)]dt
ΔPi＝PTOU,i-Pi
wherein, is Δ QiFor implementing the electricity consumption change value Q of the user in the ith time period before and after the peak-valley time-of-use electricity priceiFor implementing user power consumption, delta P, in the ith period before peak-valley time-of-use electricity priceiTo implement the change value of electricity price before and after the time-of-use electricity price at peak and valley, if Δ PiWhen the value is 0, the self-elasticity coefficient of the user in the ith time period is 0, PiAnd PTOU,iRespectively, before and after the peak-valley time-of-use electricity price, PP，PS，PVThe electricity prices of the peak time period, the flat time period and the valley time period respectively, and the expression of an electricity price elastic matrix E is as follows:
Figure BDA0003204897190000033
calculating a first predicted power consumption Q at the ith time period after the peak-valley time-of-use electricity price is executedi', the calculation formula is:
Figure BDA0003204897190000034
Figure BDA0003204897190000041
a power price optimization system based on load prediction and user satisfaction comprises a load prediction module and a power price optimization module;
the load prediction module divides one day into a plurality of time intervals, and predicted electricity consumption Q 'of each time interval after peak-valley time-of-use electricity price is realized is obtained through the load prediction step'*；
The electricity price optimization module solves the optimal peak-valley electricity price through a satisfaction degree target function, and the satisfaction degree target function expression is as follows:
Figure BDA0003204897190000042
wherein Q is the user electricity consumption in each time period before the peak-valley time-of-use electricity price is carried out, maxQ and minQ respectively represent the maximum user load and the minimum user load in all time periods before the peak-valley electricity price is carried out, and lambda1、λ2And λ3In order to set the weight coefficient, R is the comprehensive satisfaction, and the calculation formula is as follows:
R＝γ1ε+γ2θ
wherein epsilon is the satisfaction degree of the electricity utilization mode of the user, theta is the satisfaction degree of the electricity expense of the user, and gamma is1And gamma2To set the weight coefficient, γ1+γ2＝1；
γ1And gamma2Different values can be set for different users to reflect different users' attention degrees to electricity utilization mode change and electricity fee expenditure, for example, for users whose electricity fee accounts for a great proportion of enterprise production cost, gamma is set2For larger users with strict requirements on production time and operation procedures, gamma is1Is large;
the satisfaction target function takes the electricity price at the peak-valley and the ordinary time as a variable, and achieves the purposes of peak clipping and valley filling and maximizing the user satisfaction in a balanced manner, namely achieving the purposes of minimizing the maximum peak load of a daily load curve and minimizing the peak-valley difference of the daily load curve, and achieving the maximization of the user satisfaction at the same time.
Further, the calculation formula of the user electricity utilization mode satisfaction epsilon is as follows:
Figure BDA0003204897190000043
wherein n is the number of time segments, PP，PS，PVElectricity prices at peak, plateau and valley periods, respectively, fi,t(PP,PS,PV) To implement the user load at time t after the peak-valley time-of-use price, ft(Pi) The load of the user at the t moment in the ith time period when the peak-valley time-of-use electricity price is not carried out;
before the peak-valley electricity price is not implemented, the user arranges the electricity utilization mode according to the production mode which is most suitable for the user, and the satisfaction degree of the electricity utilization mode of the user is the maximum at the moment. After the peak-valley electricity price is implemented, the user responds and changes the electricity utilization mode to pursue a smaller electricity price increase. At this time, the electricity consumption is recombined on the time axis to form a new user load curve, and the satisfaction degree of the user electricity utilization mode is established on the basis of the difference value of the adjusted electricity consumption and the original load curve, so that the change of the user electricity utilization mode and the change situation of the user electricity consumption are reflected.
Further, the expression of the user electricity expense satisfaction degree θ is as follows:
Figure BDA0003204897190000051
wherein, C (P)P,PS,PV) To implement the electricity fee expenditure of the user at peak-valley time-of-use electricity rates, C (P)0) The electricity fee expenditure of the user is not realized when the peak-valley time-of-use electricity price is not realized;
in order to ensure that the total level of the electricity price after the peak-valley time-sharing electricity price is implemented is unchanged, the electricity price in the peak time period and the electricity price in the valley time period are equal, when the electricity quantity in the peak time period is greater than the electricity quantity in the valley time period, the total level of the electricity price is increased to exceed the original electricity price level, when the electricity quantity in the valley time period is greater than the electricity quantity in the peak time period, the total level of the electricity price is reduced to be lower than the original electricity price level, in the actual work, the condition that the electricity quantities in the peak-valley time period are just equal is difficult to achieve, and the electricity quantity in the peak-valley time period is unbalanced, the electricity consumption in the peak time period is greater than the electricity consumption in the valley time period, therefore, if the electricity consumption is not arranged according to the peak-valley electricity price ratio, the electricity expense can be greatly increased, and the variation of the electricity expense of the user can be measured according to the satisfaction degree of the electricity expense of the user.
Further, the load prediction step comprises:
the load prediction module calculates a first predicted power consumption Q in the ith time period after peak-valley time-of-use electricity price implementationi'；
The load prediction module calculates second predicted power consumption Qi'*As the final predicted power consumption, the calculation formula is:
Figure BDA0003204897190000052
wherein Q isFiIs a fixed load for the i-th period, QmaxiIs the maximum user load for the ith period.
Further, the first predicted power consumption QiThe formula for calculation of' is:
the load forecasting module calculates the electrovalence elastic coefficient rhoiiObtaining an electrovalence elastic matrix E, rhoiiThe calculation formula is as follows:
Figure BDA0003204897190000053
ΔQi＝∫[fi,t(PP,PS,PV)-ft(Pi)]dt
ΔPi＝PTOU,i-Pi
wherein, is Δ QiFor implementing the electricity consumption change value Q of the user in the ith time period before and after the peak-valley time-of-use electricity priceiFor implementing user power consumption, delta P, in the ith period before peak-valley time-of-use electricity priceiTo implement the change value of electricity price before and after the time-of-use electricity price at peak and valley, if Δ Pi＝0，The self-elastic coefficient of the user in the ith period is 0, PiAnd PTOU,iRespectively, before and after the peak-valley time-of-use electricity price, PP，PS，PVThe electricity prices of the peak time period, the flat time period and the valley time period respectively, and the expression of an electricity price elastic matrix E is as follows:
Figure BDA0003204897190000061
the load prediction module calculates a first predicted power consumption Q in the ith time period after peak-valley time-of-use electricity price implementationi', the calculation formula is:
Figure BDA0003204897190000062
Figure BDA0003204897190000063
compared with the prior art, the invention has the following beneficial effects:
(1) the method comprises the steps of dividing a day into a plurality of time intervals, and obtaining the predicted power consumption of each time interval after peak-valley time-of-use electricity price implementation through a load prediction step; the optimal peak-valley electricity price is solved through a satisfaction objective function taking the peak-valley electricity price as a variable, and the purposes of peak clipping and valley filling and maximizing the user satisfaction are achieved in a balanced manner, namely the purposes of minimizing the maximum peak load of a daily load curve and minimizing the peak-valley difference of the daily load curve are achieved, and meanwhile, the satisfaction maximization of the user is achieved;
(2) the comprehensive satisfaction degree is the weighted average of the satisfaction degree of the power utilization mode of the user and the satisfaction degree of the power expenditure of the user, different weight values are set for different users to reflect the difference of the change of the power utilization mode of the different users and the attention degree of the power expenditure, if the power expenditure accounts for a great proportion of the users in the production cost of an enterprise, the weight coefficient of the satisfaction degree of the power expenditure of the user is larger, and if the requirements on the production time and the operation process are strict, the weight coefficient of the satisfaction degree of the power utilization mode of the user is larger, the calculation is simple and convenient, and the application range is wide;
(3) the price elasticity matrix is used for expressing the price demand elasticity of the user, the price elasticity of the peak-valley time-of-use electricity price refers to the electricity quantity change caused by the change of the price of each peak-valley flat time period, namely the ratio of the electricity consumption change rate to the corresponding electricity price change rate in a certain time period, and the electricity price elasticity matrix can quantify the response of the user to the peak-valley time-of-use electricity price and realize the predicted electricity consumption of the user after the peak-valley time-of-use electricity price is implemented.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
A method for optimizing electricity prices based on load prediction and user satisfaction, as shown in fig. 1, comprising:
1) dividing one day into a plurality of time intervals, and obtaining predicted electricity consumption Q 'of each time interval after peak-valley time-of-use electricity price implementation through a load prediction step'*；
2) And solving the optimal peak-valley electricity price through the satisfaction degree target function.
The satisfaction target function expression is:
Figure BDA0003204897190000071
wherein Q is the user electricity consumption in each time period before the peak-valley time-of-use electricity price is carried out, maxQ and minQ respectively represent the maximum user load and the minimum user load in all time periods before the peak-valley electricity price is carried out, and lambda1、λ2And λ3In order to set the weight coefficient, R is the comprehensive satisfaction, and the calculation formula is as follows:
R＝γ1ε+γ2θ
wherein epsilon is the satisfaction degree of the electricity utilization mode of the user, theta is the satisfaction degree of the electricity expense of the user, and gamma is1And gamma2To set the weight coefficient, γ1+γ2＝1；
γ1And gamma2Different values can be set for different users to reflect different users' attention degrees to electricity utilization mode change and electricity fee expenditure, for example, for users whose electricity fee accounts for a great proportion of enterprise production cost, gamma is set2For larger users with strict requirements on production time and operation procedures, gamma is1Is large;
the satisfaction objective function takes the electricity price at the peak-valley and the ordinary time as a variable, and the purposes of peak clipping and valley filling and maximizing the user satisfaction are achieved in a balanced mode, namely the purposes of minimizing the maximum peak load of a daily load curve and minimizing the peak-valley difference of the daily load curve are achieved, and meanwhile the satisfaction maximization of the user is achieved.
The calculation formula of the satisfaction degree epsilon of the user power utilization mode is as follows:
Figure BDA0003204897190000081
wherein n is the number of time segments, PP，PS，PVElectricity prices at peak, plateau and valley periods, respectively, fi,t(PP,PS,PV) To implement the user load at time t after the peak-valley time-of-use price, ft(Pi) The load of the user at the t moment in the ith time period when the peak-valley time-of-use electricity price is not carried out;
before the peak-valley electricity price is not implemented, the user arranges the electricity utilization mode according to the production mode which is most suitable for the user, and the satisfaction degree of the electricity utilization mode of the user is the maximum at the moment. After the peak-valley electricity price is implemented, the user responds and changes the electricity utilization mode to pursue a smaller electricity price increase. At this time, the electricity consumption is recombined on the time axis to form a new user load curve, and the satisfaction degree of the user electricity utilization mode is established on the basis of the difference value of the adjusted electricity consumption and the original load curve, so that the change of the user electricity utilization mode and the change situation of the user electricity consumption are reflected.
The expression of the user electricity expense satisfaction degree theta is as follows:
Figure BDA0003204897190000082
wherein, C (P)P,PS,PV) To implement the electricity fee expenditure of the user at peak-valley time-of-use electricity rates, C (P)0) The electricity fee expenditure of the user is not realized when the peak-valley time-of-use electricity price is not realized;
in order to ensure that the total level of the electricity price after the peak-valley time-sharing electricity price is implemented is unchanged, the electricity price in the peak time period and the electricity price in the valley time period are equal, when the electricity quantity in the peak time period is greater than the electricity quantity in the valley time period, the total level of the electricity price is increased to exceed the original electricity price level, when the electricity quantity in the valley time period is greater than the electricity quantity in the peak time period, the total level of the electricity price is reduced to be lower than the original electricity price level, in the actual work, the situation that the electricity quantities in the peak-valley time period are just equal is difficult to achieve, and the electricity quantity in the peak-valley time period is unbalanced, and the electricity consumption in the peak time period is greater than the electricity consumption in the valley time period, therefore, if the electricity consumption is not arranged according to the electricity price ratio in the peak-valley time period, the electricity expense can be greatly increased, and the variation of the electricity expense of the user can be measured according to the satisfaction degree of the electricity expense of the user.
The load prediction step comprises the following steps:
calculating a first predicted power consumption Q at the ith time period after the peak-valley time-of-use electricity price is executedi'；
Calculating a second predicted power consumption Qi'*As the final predicted power consumption, the calculation formula is:
Figure BDA0003204897190000083
wherein Q isFiIs a fixed load for the i-th period, QmaxiThe maximum user load in the ith time period;
the second predicted power consumption is used for correction in consideration of the practical constraint that the load of the user cannot be adjusted freely in a certain period of time, a certain fixed ratio exists, and the upper limit of the load exists due to the limitation of the operation capacity of the equipment.
First predicted power consumption QiThe formula for calculation of' is:
calculating the electrovalence elastic coefficient rhoiiObtaining an electrovalence elastic matrix E, rhoiiThe calculation formula is as follows:
Figure BDA0003204897190000091
ΔQi＝∫[fi,t(PP,PS,PV)-ft(Pi)]dt
ΔPi＝PTOU,i-Pi
wherein, is Δ QiFor implementing the electricity consumption change value Q of the user in the ith time period before and after the peak-valley time-of-use electricity priceiFor implementing user power consumption, delta P, in the ith period before peak-valley time-of-use electricity priceiTo implement a change in electricity price before and after the peak-valley time-of-use electricity price, PiAnd PTOU,iRespectively, before and after the peak-valley time-of-use electricity price, PP，PS，PVThe electricity prices of the peak time period, the flat time period and the valley time period respectively, and the expression of an electricity price elastic matrix E is as follows:
Figure BDA0003204897190000092
calculating a first predicted power consumption Q at the ith time period after the peak-valley time-of-use electricity price is executedi', the calculation formula is:
Figure BDA0003204897190000093
Figure BDA0003204897190000094
example 2
A power price optimization system based on load prediction and user satisfaction comprises a load prediction module and a power price optimization module;
the load forecasting module divides a day into a plurality of time intervals, and forecasted electricity consumption in the ith time interval after peak-valley time-of-use electricity price is carried out is obtained through the load forecasting step;
the electricity price optimization module solves the optimal peak-valley electricity price through a satisfaction degree target function, and the satisfaction degree target function expression is as follows:
Figure BDA0003204897190000101
wherein maxQ and minQ denote maximum and minimum user loads, λ, respectively, in all periods before peak-to-valley electricity prices are carried out1、λ2And λ3In order to set the weight coefficient, R is the comprehensive satisfaction, and the calculation formula is as follows:
R＝γ1ε+γ2θ
wherein epsilon is the satisfaction degree of the electricity utilization mode of the user, theta is the satisfaction degree of the electricity expense of the user, and gamma is1And gamma2To set the weight coefficient, γ1+γ2＝1；
γ1And gamma2Different values can be set for different users to reflect different users' attention degrees to electricity utilization mode change and electricity fee expenditure, for example, for users whose electricity fee accounts for a great proportion of enterprise production cost, gamma is set2For larger users with strict requirements on production time and operation procedures, gamma is1Is large;
the satisfaction objective function takes the electricity price at the peak-valley and the ordinary time as a variable, and the purposes of peak clipping and valley filling and maximizing the user satisfaction are achieved in a balanced mode, namely the purposes of minimizing the maximum peak load of a daily load curve and minimizing the peak-valley difference of the daily load curve are achieved, and meanwhile the satisfaction maximization of the user is achieved.
The calculation formula of the satisfaction degree epsilon of the user power utilization mode is as follows:
Figure BDA0003204897190000102
wherein n is the number of time segments, PP，PS，PVElectricity prices at peak, plateau and valley periods, respectively, fi,t(PP,PS,PV) To implement the user load at time t after the peak-valley time-of-use price, ft(Pi) The load of the user at the t moment in the ith time period when the peak-valley time-of-use electricity price is not carried out;
before the peak-valley electricity price is not implemented, the user arranges the electricity utilization mode according to the production mode which is most suitable for the user, and the satisfaction degree of the electricity utilization mode of the user is the maximum at the moment. After the peak-valley electricity price is implemented, the user responds and changes the electricity utilization mode to pursue a smaller electricity price increase. At this time, the electricity consumption is recombined on the time axis to form a new user load curve, and the satisfaction degree of the user electricity utilization mode is established on the basis of the difference value of the adjusted electricity consumption and the original load curve, so that the change of the user electricity utilization mode and the change situation of the user electricity consumption are reflected.
The expression of the user electricity expense satisfaction degree theta is as follows:
Figure BDA0003204897190000103
wherein, C (P)P,PS,PV) To implement the electricity fee expenditure of the user at peak-valley time-of-use electricity rates, C (P)0) The electricity fee expenditure of the user is not realized when the peak-valley time-of-use electricity price is not realized;
in order to ensure that the total level of the electricity price after the peak-valley time-sharing electricity price is implemented is unchanged, the electricity price in the peak time period and the electricity price in the valley time period are equal, when the electricity quantity in the peak time period is greater than the electricity quantity in the valley time period, the total level of the electricity price is increased to exceed the original electricity price level, when the electricity quantity in the valley time period is greater than the electricity quantity in the peak time period, the total level of the electricity price is reduced to be lower than the original electricity price level, in the actual work, the situation that the electricity quantities in the peak-valley time period are just equal is difficult to achieve, and the electricity quantity in the peak-valley time period is unbalanced, and the electricity consumption in the peak time period is greater than the electricity consumption in the valley time period, therefore, if the electricity consumption is not arranged according to the electricity price ratio in the peak-valley time period, the electricity expense can be greatly increased, and the variation of the electricity expense of the user can be measured according to the satisfaction degree of the electricity expense of the user.
The load prediction step comprises the following steps:
the load prediction module calculates a first predicted power consumption Q in the ith time period after peak-valley time-of-use electricity price implementationi'；
The load prediction module calculates a second predicted power consumption Qi'*As the final predicted power consumption, the calculation formula is:
Figure BDA0003204897190000111
wherein Q isFiIs a fixed load for the i-th period, QmaxiThe maximum user load in the ith time period;
the second predicted power consumption is used for correction in consideration of the practical constraint that the load of the user cannot be adjusted freely in a certain period of time, a certain fixed ratio exists, and the upper limit of the load exists due to the limitation of the operation capacity of the equipment.
First predicted power consumption QiThe formula for calculation of' is:
the load forecasting module calculates the electrovalence elastic coefficient rhoiiObtaining an electrovalence elastic matrix E, rhoiiThe calculation formula is as follows:
Figure BDA0003204897190000112
ΔQi＝∫[fi,t(PP,PS,PV)-ft(Pi)]dt
ΔPi＝PTOU,i-Pi
wherein, is Δ QiFor implementing the electricity consumption change value Q of the user in the ith time period before and after the peak-valley time-of-use electricity priceiFor implementing user power consumption, delta P, in the ith period before peak-valley time-of-use electricity priceiTo implement a change in electricity price before and after the peak-valley time-of-use electricity price, PiAnd PTOU,iRespectively before and after the peak-valley time-of-use electricity priceValence, PP，PS，PVThe electricity prices of the peak time period, the flat time period and the valley time period respectively, and the expression of an electricity price elastic matrix E is as follows:
Figure BDA0003204897190000113
the load prediction module calculates a first predicted power consumption Q in the ith time period after peak-valley time-of-use electricity price implementationi', the calculation formula is:
Figure BDA0003204897190000121
Figure BDA0003204897190000122
embodiments 1 and 2 provide a method and a system for optimizing electricity prices based on load prediction and user satisfaction, which solve the optimal peak-valley electricity prices through a satisfaction objective function taking the peak-valley electricity prices as variables, balance the purposes of peak clipping and valley filling and maximizing the user satisfaction, namely, the purposes of minimizing the maximum peak-to-charge of a daily load curve and minimizing the peak-to-valley difference of the daily load curve, and simultaneously maximize the user satisfaction.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A power price optimization method based on load prediction and user satisfaction is characterized by comprising the following steps:
dividing one day into several time intervals, and obtaining peak-valley time-of-use electricity price through the load prediction stepPredicted electric power consumption Q 'of each period'*；
Solving the optimal peak-valley electricity price through a satisfaction target function, wherein the satisfaction target function expression is as follows:
Figure FDA0003204897180000011
wherein Q is the user electricity consumption in each time period before the peak-valley time-of-use electricity price is carried out, maxQ and minQ respectively represent the maximum user load and the minimum user load in all time periods before the peak-valley electricity price is carried out, and lambda1、λ2And λ3In order to set the weight coefficient, R is the comprehensive satisfaction, and the calculation formula is as follows:
R＝γ1ε+γ2θ
wherein epsilon is the satisfaction degree of the electricity utilization mode of the user, theta is the satisfaction degree of the electricity expense of the user, and gamma is1And gamma2To set the weight coefficient, γ1+γ2＝1。
2. The method for optimizing electricity rates based on load forecasting and user satisfaction according to claim 1, wherein the calculation formula of the user electricity utilization mode satisfaction epsilon is as follows:
Figure FDA0003204897180000012
wherein n is the number of time segments, PP，PS，PVElectricity prices at peak, plateau and valley periods, respectively, fi,t(PP,PS,PV) To implement the user load at time t after the peak-valley time-of-use price, ft(Pi) The user load at the t-th time in the i-th time period when the peak-valley time-of-use electricity rate is not carried out.
3. The method as claimed in claim 1, wherein the user electricity rate expenditure satisfaction degree θ is expressed as:
Figure FDA0003204897180000013
wherein, C (P)P,PS,PV) To implement the electricity fee expenditure of the user at peak-valley time-of-use electricity rates, C (P)0) The user's electricity fee is paid when the peak-valley time-of-use electricity price is not implemented.
4. The method of claim 1, wherein the load prediction step comprises:
calculating a first predicted power consumption Q at the ith time period after the peak-valley time-of-use electricity price is executedi'；
Calculating a second predicted power consumption Qi'*As the final predicted power consumption, the calculation formula is:
Figure FDA0003204897180000021
wherein Q isFiIs a fixed load for the i-th period, QmaxiIs the maximum user load for the ith period.
5. The method as claimed in claim 4, wherein the first predicted power consumption Q is a predicted power consumptioniThe formula for calculation of' is:
calculating the electrovalence elastic coefficient rhoiiObtaining an electrovalence elastic matrix E, rhoiiThe calculation formula is as follows:
Figure FDA0003204897180000022
Figure FDA0003204897180000023
ΔPi＝PTOU,i-Pi
wherein, is Δ QiFor implementing the electricity consumption change value Q of the user in the ith time period before and after the peak-valley time-of-use electricity priceiFor implementing user power consumption, delta P, in the ith period before peak-valley time-of-use electricity priceiTo implement a change in electricity price before and after the peak-valley time-of-use electricity price, PiAnd PTOU,iRespectively, before and after the peak-valley time-of-use electricity price, PP，PS，PVThe electricity prices of the peak time period, the flat time period and the valley time period respectively, and the expression of an electricity price elastic matrix E is as follows:
Figure FDA0003204897180000024
calculating a first predicted power consumption Q at the ith time period after the peak-valley time-of-use electricity price is executedi', the calculation formula is:
Figure FDA0003204897180000025
Figure FDA0003204897180000026
6. a power price optimization system based on load prediction and user satisfaction is characterized by comprising a load prediction module and a power price optimization module;
the load prediction module divides one day into a plurality of time intervals, and predicted electricity consumption Q 'of each time interval after peak-valley time-of-use electricity price is realized is obtained through the load prediction step'*；
The electricity price optimization module solves the optimal peak-valley electricity price through a satisfaction degree target function, and the satisfaction degree target function expression is as follows:
Figure FDA0003204897180000031
wherein Q is the user electricity consumption in each time period before the peak-valley time-of-use electricity price is carried out, maxQ and minQ respectively represent the maximum user load and the minimum user load in all time periods before the peak-valley electricity price is carried out, and lambda1、λ2And λ3In order to set the weight coefficient, R is the comprehensive satisfaction, and the calculation formula is as follows:
R＝γ1ε+γ2θ
wherein epsilon is the satisfaction degree of the electricity utilization mode of the user, theta is the satisfaction degree of the electricity expense of the user, and gamma is1And gamma2To set the weight coefficient, γ1+γ2＝1。
7. The system of claim 6, wherein the user satisfaction epsilon is calculated by the formula:
Figure FDA0003204897180000032
wherein n is the number of time segments, PP，PS，PVElectricity prices at peak, plateau and valley periods, respectively, fi,t(PP,PS,PV) To implement the user load at time t after the peak-valley time-of-use price, ft(Pi) The user load at the t-th time in the i-th time period when the peak-valley time-of-use electricity rate is not carried out.
8. The system according to claim 6, wherein the user electricity rate expenditure satisfaction degree θ is expressed as:
Figure FDA0003204897180000033
wherein, C (P)P,PS,PV) To implement the electricity fee expenditure of the user at peak-valley time-of-use electricity rates, C (P)0) The user's electricity fee is paid when the peak-valley time-of-use electricity price is not implemented.
9. The system of claim 6, wherein the load prediction step comprises:
the load prediction module calculates a first predicted power consumption Q in the ith time period after peak-valley time-of-use electricity price implementationi'；
The load prediction module calculates second predicted power consumption Qi'*As the final predicted power consumption, the calculation formula is:
Figure FDA0003204897180000041
wherein Q isFiIs a fixed load for the i-th period, QmaxiIs the maximum user load for the ith period.
10. The system of claim 9, wherein the first predicted power usage Q is based on load prediction and user satisfactioniThe formula for calculation of' is:
the load forecasting module calculates the electrovalence elastic coefficient rhoiiObtaining an electrovalence elastic matrix E, rhoiiThe calculation formula is as follows:
Figure FDA0003204897180000042
Figure FDA0003204897180000043
ΔPi＝PTOU,i-Pi
wherein, is Δ QiFor implementing the electricity consumption change value Q of the user in the ith time period before and after the peak-valley time-of-use electricity priceiFor implementing user power consumption, delta P, in the ith period before peak-valley time-of-use electricity priceiTo implement a change in electricity price before and after the peak-valley time-of-use electricity price, PiAnd PTOU,iRespectively, before and after the peak-valley time-of-use electricity price, PP，PS，PVThe electricity prices of the peak time period, the flat time period and the valley time period respectively, and the expression of an electricity price elastic matrix E is as follows:
Figure FDA0003204897180000044
the load prediction module calculates a first predicted power consumption Q in the ith time period after peak-valley time-of-use electricity price implementationi', the calculation formula is:
Figure FDA0003204897180000045
Figure FDA0003204897180000046
CN202110913813.XA 2021-08-10 2021-08-10 Electricity price optimization method and system based on load prediction and user satisfaction Pending CN113807882A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110913813.XA CN113807882A (en) 2021-08-10 2021-08-10 Electricity price optimization method and system based on load prediction and user satisfaction

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110913813.XA CN113807882A (en) 2021-08-10 2021-08-10 Electricity price optimization method and system based on load prediction and user satisfaction

Publications (1)

Publication Number Publication Date
CN113807882A true CN113807882A (en) 2021-12-17

Family

ID=78943031

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110913813.XA Pending CN113807882A (en) 2021-08-10 2021-08-10 Electricity price optimization method and system based on load prediction and user satisfaction

Country Status (1)

Country Link
CN (1) CN113807882A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106855960A (en) * 2016-12-27 2017-06-16 国网福建省电力有限公司 A kind of charging electric vehicle load forecasting method under Peak-valley TOU power price guiding
CN107798625A (en) * 2017-09-19 2018-03-13 东南大学 A kind of time-of-use tariffs optimization method for considering user satisfaction
CN107944630A (en) * 2017-12-01 2018-04-20 华北电力大学 A kind of seasonality tou power price optimization formulating method
CN110705792A (en) * 2019-09-30 2020-01-17 重庆大学 Dynamic demand response solving method considering time-sharing pricing
CN113283640A (en) * 2021-04-29 2021-08-20 国网浙江省电力有限公司湖州供电公司 Peak-valley time-of-use electricity price decision model construction method based on user response and satisfaction

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106855960A (en) * 2016-12-27 2017-06-16 国网福建省电力有限公司 A kind of charging electric vehicle load forecasting method under Peak-valley TOU power price guiding
CN107798625A (en) * 2017-09-19 2018-03-13 东南大学 A kind of time-of-use tariffs optimization method for considering user satisfaction
CN107944630A (en) * 2017-12-01 2018-04-20 华北电力大学 A kind of seasonality tou power price optimization formulating method
CN110705792A (en) * 2019-09-30 2020-01-17 重庆大学 Dynamic demand response solving method considering time-sharing pricing
CN113283640A (en) * 2021-04-29 2021-08-20 国网浙江省电力有限公司湖州供电公司 Peak-valley time-of-use electricity price decision model construction method based on user response and satisfaction

Similar Documents

Publication Publication Date Title
CN109713666B (en) K-means clustering-based distributed energy storage economy regulation and control method in power market
CN104376385A (en) Microgrid power price optimizing method
CN110994694A (en) Microgrid source load-storage coordination optimization scheduling method considering differentiated demand response
CN107565585B (en) Energy storage device peak regulation report-back time prediction technique and its model creation method
CN111864758B (en) Energy storage system operation scheduling method considering load demand difference
CN112803495A (en) 5G base station microgrid optical storage system capacity optimal configuration method based on energy sharing
CN115994656A (en) Virtual power plant economic dispatching method considering excitation demand response under time-of-use electricity price
CN111525624A (en) Household distributed energy scheduling method based on storage battery energy storage system
CN109271678B (en) Storage battery charge-discharge scheduling optimization method based on photovoltaic micro-grid operation cost
CN114154790A (en) Industrial park light storage capacity configuration method based on demand management and flexible load
CN109245143B (en) Energy storage peak regulation power station optimized operation method considering lithium ion battery service life
CN112801343A (en) Energy storage system capacity planning method considering multi-meteorological-scene adaptive cost
CN113807882A (en) Electricity price optimization method and system based on load prediction and user satisfaction
CN116108981A (en) Capacity optimization configuration method of virtual power plant electrochemical energy storage power station considering time-of-use electricity price
CN115099489B (en) Industrial and commercial energy storage system capacity configuration method based on optimal economic measurement and calculation
CN110224397A (en) It is a kind of scene access background under user side battery energy storage cost effectiveness analysis method
CN115204944A (en) Energy storage optimal peak-to-valley price difference measuring and calculating method and device considering whole life cycle
CN112861376B (en) Evaluation method and device based on unit scheduling model
CN113690925B (en) Energy interaction optimization method and system based on micro-grid
CN113780638B (en) Comprehensive energy system optimization scheduling method considering energy storage life attenuation
CN115081838A (en) Dynamic electricity price-based source-charge coordination scheduling method for wind and light absorption of heat accumulating type electric heating
CN109378844A (en) A kind of Optimal Configuration Method of distributed energy storage system
CN113471993B (en) Robust optimization-based user side hybrid energy storage technology operation optimization method
CN114462676A (en) User side energy storage optimization method and system
CN111313478A (en) Renewable energy storage optimization configuration method based on power smoothing

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination