CN113224752A - Peak-valley time interval dividing method considering reliability loss under constant time-of-use electricity price - Google Patents

Peak-valley time interval dividing method considering reliability loss under constant time-of-use electricity price Download PDF

Info

Publication number
CN113224752A
CN113224752A CN202110489839.6A CN202110489839A CN113224752A CN 113224752 A CN113224752 A CN 113224752A CN 202110489839 A CN202110489839 A CN 202110489839A CN 113224752 A CN113224752 A CN 113224752A
Authority
CN
China
Prior art keywords
time
peak
formula
valley
electricity price
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.)
Granted
Application number
CN202110489839.6A
Other languages
Chinese (zh)
Other versions
CN113224752B (en
Inventor
杨贺钧
高原
马英浩
王井寅
刘志博
张大波
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.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
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 Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN202110489839.6A priority Critical patent/CN113224752B/en
Publication of CN113224752A publication Critical patent/CN113224752A/en
Application granted granted Critical
Publication of CN113224752B publication Critical patent/CN113224752B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a peak-valley time interval dividing method considering power supply reliability under constant time-of-use electricity price, which comprises the following steps: 1, acquiring a typical daily hour load curve; 2, establishing a load-reliability analysis relation model based on the BP neural network; 3, establishing a power demand ratio before and after time-of-use electricity price based on the electricity price elastic matrix, and calculating hour load values divided in different time periods; and 4, constructing a time interval division optimization objective function according to corresponding indexes, and performing peak-valley time interval division optimization by using a dynamic load point method. The invention can improve the power supply reliability of the power system, smoothen the load curve, guide the reasonable power utilization of users, and improve the peak clipping and valley filling capacity of the power grid, thereby realizing the safe and reliable operation of the power grid.

Description

Peak-valley time interval dividing method considering reliability loss under constant time-of-use electricity price
Technical Field
The invention relates to the field of time-sharing electricity price and time-period division in demand side response, in particular to a peak-valley time-period division method considering power supply reliability under constant time-sharing electricity price.
Background
The formulation of the electricity price type demand response strategy not only relates to the design of time interval electricity prices (namely time-of-use electricity prices) but also is closely related to the division of time intervals, and both the design and the division of the time interval electricity prices and the time-of-use electricity prices have direct influence on the peak clipping and valley filling effects of a load curve, so that the influences on the aspects of power grid investment, power failure loss, power supply reliability and the like are generated. The interval of the peak-valley period can be adjusted, so that the user can be effectively guided to adjust the power utilization mode of the user, the peak clipping and valley filling effects are achieved, the power utilization efficiency of the power grid is improved, and the resource allocation is optimized. However, under the condition of constant time-of-use electricity price, the load curve peak-valley difference and the power supply reliability of the power system are greatly influenced by changing the peak-valley time period division, so that the safe and reliable operation of a power grid is influenced.
Disclosure of Invention
The invention aims to avoid the defects of the prior art and provides a peak-valley time interval division method considering the power supply reliability under the condition of constant time-of-use electricity price so as to reduce the peak-valley difference of a load curve and improve the power supply reliability of a power system, thereby realizing the safe and reliable operation of a power grid.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the peak-valley time interval dividing method considering the power supply reliability under the constant time-of-use electricity price is characterized by comprising the following steps of;
step one, obtaining a typical daily hour load curve PLt＝{PL1,PL2,…,PLt,…,PL24Where t is 1,2, …,24 is hours; PLtRepresents a load value at the t-th hour;
step two, establishing a load-reliability analysis relation model based on the BP neural network:
step 2.1, determining the number of nodes and basic parameters of each layer of the BP neural network, comprising the following steps: maximum iteration number K, connection weight w, a system drawn-up allowable error epsilon, and a network total error E (K) of the kth iteration;
acquiring X training samples which take a load value as input and a reliability index RI as output by using an analytical method;
step 2.2, initializing a connection weight w;
step 2.3, initializing k to be 1;
step 2.4, initializing x to be 1;
2.5, sequentially inputting X training samples into the BP neural network of the kth iteration;
step 2.6, setting the current input to be the x-th training sample, and calculating the output and back propagation error of each layer of the k-th iteration;
step 2.7, if X is less than X, assigning X +1 to X, and returning to the step 2.5; otherwise, executing step 2.8;
step 2.8, calculating and adjusting the connection weight w of each layer of the kth iteration by using a weight adjustment formula;
step 2.9, calculating output and back propagation errors of each layer of the kth iteration by using the updated connection weight of the kth iteration, and calculating a total error E (k) of the kth iteration;
step 2.10, if E (K) is less than epsilon or K is more than K, terminating iteration, and taking the finally obtained BP neural network as a load-reliability analysis relation model; otherwise, assigning k +1 to k, and executing the step 2.4;
step three, establishing a power demand ratio before and after the time-of-use electricity price based on the electricity price elastic matrix, and calculating hour load values under different time interval divisions:
step 3.1, calculating the electricity price elastic coefficient by using the formula (1) and the formula (2) so as to form an electricity price elastic matrix
Figure BDA0003051619400000021
Figure BDA0003051619400000022
Figure BDA0003051619400000023
In the formulae (1) and (2), lambdaiiExpressing the self-elasticity coefficient, i belongs to { p, f, v } and expresses different peak, flat and valley periods; lambda [ alpha ]ijThe coefficient of mutual elasticity is represented, j belongs to { p, f, v } and represents different peak, flat and valley time periods, and i is not equal to j; delta EiThe electricity demand variation quantity of the i time period before and after the implementation of the time-of-use electricity price; eiThe electricity demand in the period i before the time-of-use electricity price is implemented; p0Represents an initial electricity rate before the time-of-use electricity rate is not implemented; delta Pi、ΔPjThe electricity price variation amounts of the i time period and the j time period before and after the implementation of the time-of-use electricity price are respectively;
step 3.2, calculating the time-interval electric quantity variable quantity under the time-of-use electricity price by using the formula (3):
Figure BDA0003051619400000024
in formula (3), E'p、E′f、E′vRespectively the electricity demand of the time-of-use electricity price in the later peak, flat and valley periods; ep、Ef、EvRespectively implementing the electricity consumption demand of the time-of-use electricity price in the front peak period, the flat period and the valley period; delta Pp、ΔPfAnd Δ PvRespectively carrying out the electricity price variation of the time-of-use electricity price in the front and back peak, flat and valley periods;
step 3.3, defining the power demand ratio before and after the time-of-use electricity price under the constant time-of-use electricity price by using the formula (4):
Figure BDA0003051619400000031
in the formula (4), Kp、Kf、KvRespectively representing the electricity demand ratios of the front and rear peak, flat and valley time periods of the time-of-use electricity price;
and 3.4, according to the formula (3) and the formula (4), expressing the electricity demand ratio in the i time period after the time-of-use electricity price by using a formula (5):
Figure BDA0003051619400000032
and 3.5, calculating the hour load value divided by considering different time intervals by using the formula (6):
PLt(T)＝Ki×PLt (6)
in formula (6), T ═ Tp,Tf,TvThe different peak, flat and valley time interval division is represented; PLtIs a typical daily hour load curve (PL)1,PL2,…,PLt,…,PL24The load value at the t hour in (1); PLt(T) the load value of the T hour divided by different time periods is considered after the time-of-use electricity price;
fourthly, constructing a time interval division optimized objective function according to corresponding indexes, and performing peak-valley time interval division optimization by using a dynamic load point method:
step 4.1, constructing a time interval division optimized objective function according to corresponding indexes:
step 4.1.1, defining the global peak-to-valley difference O by using the formula (7)pvd：
Figure BDA0003051619400000033
In formula (7), PL'tRepresents a load curve { PL by day and hour1,PL2,…,PLt,…,PL24} Loading sequence { PL 'obtained after ascending order'1,PL′2,...,PL′t,...,PL′24The load value at the t hour;
step 4.1.2, establishing the minimum peak load F by using the formula (8) and the formula (9) respectively1(T) and minimizing the global peak-to-valley difference F2(T)：
Figure BDA0003051619400000034
Figure BDA0003051619400000035
In formula (8), PL't(T) represents a load value at the tth hour of the day-hour load curve in ascending order at a constant time-of-use electricity rate and at different time intervals;
step 4.1.3, establishing a minimum reliability index objective function F by using the formula (10)3(T)：
Figure BDA0003051619400000041
Step 4.2, considering an objective function in the time interval division optimization process:
step 4.2.1, establishing a time period constraint by using the formula (11):
Figure BDA0003051619400000042
in formula (11), TminRepresenting the minimum number of hours that the lower peak-to-valley period is divided over different periods;
and 4.2.2, establishing a total expenditure electric charge constraint of the user by using the formula (12):
Pbefore-Pafter(T)≥0 (12)
in the formula (12), PbeforeRepresenting that the total electricity fee is paid by the user before the time-of-use electricity price is not implemented; pafter(T) paying a total electricity rate by the user at a constant time-of-use electricity rate and under different time-of-day division conditions;
and 4.2.3, establishing revenue constraint of the power supply company by using the formula (13):
Pafter(T)-(1-δ)Pbefore≥0 (13)
in the formula (13), δ represents an odds coefficient;
step 4.2.4, establishing peak-to-valley period electrovalence constraint by using the formula (14):
Pp＞Pf＞Pv＞Pc (14)
in formula (14), Pp represents the peak period electrovalence; pfRepresenting the electricity price in the ordinary time period; pv represents the electricity price in the valley period; pc represents a marginal cost price;
step 4.3, converting the time-interval optimization multi-objective function under the constant time-of-use electricity price into a single objective function by using the weight coefficient, and calculating the corresponding objective function by using the formula (15) and the formula (16):
F12(T)＝αF1(T)+βF2(T) (15)
F(T)＝F12(T)+γF3(T) (16)
in equations (15) and (16), α is a weight coefficient for minimizing the peak load by the objective function; β is a weight coefficient of the objective function to minimize the global peak-to-valley difference; gamma is an objective function of the objective function minimum reliability indicator; f12(T) is an objective function that takes into account the minimized peak load and the minimized global peak-to-valley difference; f (T) is an objective function that takes into account the minimized peak load, the minimized global peak-to-valley difference, and the minimized reliability index;
step 4.4, optimizing the peak-to-valley period division by using a dynamic charge point method:
step 4.4.1, initializing number k of load points in valley period1＝Tmin；
Step 4.4.2, initializing peak and ordinary period load point number k2＝Tmin，k3＝24-k1-k2；
Step 4.4.3, calculating the objective function F under different time interval divisions12(T)；
Step 4.4.4, calculating an objective function F by utilizing the load-reliability analysis relation model3(T) and an objective function F (T);
step 4.4.5, updating the number of load points: will k2+1 assignment to k2Will k is3-1 assignment to k3；
Step 4.4.6, if k2＜24-Tmin-k1If not, executing step 4.4.3, otherwise, executing step 4.4.7;
step 4.4.7, if k1＜24-2TminThen k will be1+1 assignment to k1Executing step 4.4.2, otherwise, executing step 4.4.8;
and 4.4.8, outputting the target functions F (T) divided in all different periods, solving the minimum value, and taking the number of the load points in the peak period, the number of the load points in the ordinary period and the number of the load points in the valley period corresponding to the minimum value as the optimal peak-valley period.
Compared with the prior art, the invention has the beneficial effects that:
1. compared with the traditional reliability assessment method, the load-reliability analysis relation model based on the BP neural network is provided, the time-consuming assessment process is replaced by the analysis model, the repeated assessment process in the optimization process is avoided, and therefore the power supply reliability computation efficiency is remarkably improved.
2. The invention utilizes the concept of the electricity demand ratio under the constant time-of-use electricity price and can consider the original hourly load value, thereby depicting the change of the hourly load value under different time interval divisions more visually and accurately.
3. According to the method, the time interval division states are enumerated by using a dynamic load point method, compared with a traditional optimization algorithm, the algorithm complexity is obviously reduced, the calculation efficiency is improved, the global peak-valley difference replaces the traditional peak-valley difference, and the smoothness of a load curve is obviously improved.
Drawings
Fig. 1 is a schematic diagram of the peak-valley period division optimization process considering power supply reliability under a constant time-of-use electricity price.
Detailed Description
In this embodiment, as shown in fig. 1, a peak-valley period dividing method considering reliability loss under a constant time-of-use electricity price is to first obtain a typical daily-hour load curve; secondly, establishing a load-reliability analysis relation model based on the BP neural network; then, under the constant time-of-use electricity price, establishing a power demand ratio before and after the time-of-use electricity price so as to depict the change of the hourly load values divided at different periods; finally, the time interval division state is enumerated based on a dynamic load point method, the calculation efficiency is improved, and the global peak-valley difference is adopted to replace the traditional peak-valley difference, so that the load curve smoothness is improved. Specifically, the method comprises the following steps:
step one, obtaining a typical daily hour load curve PLt＝{PL1,PL2,…,PLt,…,PL24Where t is 1,2, …,24 is hours; PLtRepresents a load value at the t-th hour;
step two, establishing a load-reliability analysis relation model based on the BP neural network:
step 2.1, determining the number of nodes and basic parameters of each layer of the BP neural network, comprising the following steps: maximum iteration number K, connection weight w, a system drawn-up allowable error epsilon, and a network total error E (K) of the kth iteration;
acquiring X training samples which take a load value as input and a reliability index RI as output by using an analytical method;
and 2.2, initializing a connection weight w, and initializing the weight w by using normal distribution with the mean value of 0 and the variance of 1.
Step 2.3, initializing k to be 1;
step 2.4, initializing x to be 1;
2.5, sequentially inputting X training samples into the BP neural network of the kth iteration;
step 2.6, setting the current input to be the x-th training sample, and calculating the output and back propagation error of each layer of the k-th iteration;
step 2.7, if X is less than X, namely the training sample is not completely trained, assigning X +1 to X, and returning to the step 2.5; otherwise, executing step 2.8;
step 2.8, calculating and adjusting the connection weight w of each layer of the kth iteration by using a weight adjustment formula;
step 2.9, calculating output and back propagation errors of each layer of the kth iteration by using the updated connection weight of the kth iteration, and calculating a total error E (k) of the kth iteration;
step 2.10, if E (K) is less than epsilon or K is more than K, terminating iteration, and taking the finally obtained BP neural network as a load-reliability analysis relation model; otherwise, assigning k +1 to k, and executing the step 2.4;
step three, establishing a power demand ratio before and after the time-of-use electricity price based on the electricity price elastic matrix, and calculating hour load values under different time interval divisions:
step 3.1, calculating the electricity price elastic coefficient by using the formula (1) and the formula (2), wherein the electricity price elastic coefficient represents the relation between electricity price change and electric quantity demand change, and therefore an electricity price elastic matrix is formed
Figure BDA0003051619400000061
Figure BDA0003051619400000062
Figure BDA0003051619400000063
In the formulae (1) and (2), lambdaiiExpressing the self-elasticity coefficient, i belongs to { p, f, v } and expresses different peak, flat and valley periods; lambda [ alpha ]ijThe coefficient of mutual elasticity is represented, j belongs to { p, f, v } and represents different peak, flat and valley time periods, and i is not equal to j; delta EiThe electricity demand variation quantity of the i time period before and after the implementation of the time-of-use electricity price; eiThe electricity demand in the period i before the time-of-use electricity price is implemented; p0Represents an initial electricity rate before the time-of-use electricity rate is not implemented; delta Pi、ΔPjThe electricity price variation amounts of the i time period and the j time period before and after the implementation of the time-of-use electricity price are respectively;
step 3.2, calculating the time-interval electric quantity variable quantity under the time-of-use electricity price by using the formula (3):
Figure BDA0003051619400000071
in formula (3), E'p、E′f、E′vRespectively the electricity demand of the time-of-use electricity price in the later peak, flat and valley periods; ep、Ef、EvRespectively implementing the electricity consumption demand of the time-of-use electricity price in the front peak period, the flat period and the valley period; delta Pp、ΔPfAnd Δ PvRespectively the front peak time, the rear peak time, the flat time and the valley time of the time-of-use electricity priceElectricity price variation of the segment;
step 3.3, defining the power demand ratio before and after the time-of-use electricity price under the constant time-of-use electricity price by using the formula (4):
Figure BDA0003051619400000072
in the formula (4), Kp、Kf、KvRespectively representing the electricity demand ratios of the front and rear peak, flat and valley time periods of the time-of-use electricity price;
and 3.4, according to the formula (3) and the formula (4), expressing the electricity demand ratio in the i time period after the time-of-use electricity price by using a formula (5):
Figure BDA0003051619400000073
step 3.5, under the constant time-of-use electricity price, the electricity demand ratio K before and after the time-of-use electricity pricei(i.e. K)p、Kf、Kv) For the fixed value, the hourly load values divided in consideration of different periods are calculated using equation (6):
PLt(T)＝Ki×PLt (6)
in formula (6), T ═ Tp,Tf,TvThe different peak, flat and valley time interval division is represented; PLtIs a typical daily hour load curve (PL)1,PL2,…,PLt,…,PL24The load value at the t hour in (1); PLt(T) the load value of the T hour divided by different time periods is considered after the time-of-use electricity price;
fourthly, constructing a time interval division optimized objective function according to corresponding indexes, and performing peak-valley time interval division optimization by using a dynamic load point method:
step 4.1, constructing a time interval division optimized objective function according to corresponding indexes:
step 4.1.1, the global peak-valley difference is adopted as one of the objective functions, the smoothness of the load curve after time interval division can be obviously improved, the peak-valley difference of the load curve can be reduced, and the global peak-valley difference O is defined by using a formula (7)pvd：
Figure BDA0003051619400000081
In formula (7), PL'tRepresents a load curve { PL by day and hour1,PL2,…,PLt,…,PL24} Loading sequence { PL 'obtained after ascending order'1,PL′2,...,PL′t,...,PL′24The load value at the t hour;
step 4.1.2, establishing the minimum peak load F by using the formula (8) and the formula (9) respectively1(T) and minimizing the global peak-to-valley difference F2(T)：
Figure BDA0003051619400000082
Figure BDA0003051619400000083
In formula (8), PL't(T) represents a load value at the tth hour of the day-hour load curve in ascending order at a constant time-of-use electricity rate and at different time intervals;
step 4.1.3, establishing a minimum reliability index objective function F by using the formula (10)3(T)：
Figure BDA0003051619400000084
Step 4.2, considering an objective function in the time interval division optimization process:
step 4.2.1, in practical application, in order to implement convenience and technical feasibility, it is not desirable to divide each time interval of the peak, the valley and the peak too small, and a time interval constraint is established by using the formula (11):
Figure BDA0003051619400000085
in formula (11), TminRepresenting the minimum number of hours that the lower peak-to-valley period is divided over different periods;
and 4.2.2, establishing a total expenditure electric charge constraint of the user by using the formula (12):
PbeforePafter(T)≥0 (12)
in the formula (12), PbeforeRepresenting that the total electricity fee is paid by the user before the time-of-use electricity price is not implemented; pafter(T) paying a total electricity rate by the user at a constant time-of-use electricity rate and under different time-of-day division conditions;
step 4.2.3, under the condition of different time interval division, the income of the power supply company is not reduced, and the income constraint of the power supply company is established by using the formula (13):
Pafter(T)(1δ)Pbefore≥0 (13)
in the formula (13), δ represents an odds coefficient;
step 4.2.4, establishing peak-to-valley period electrovalence constraint by using the formula (14):
Pp＞Pf＞Pv＞Pc (14)
in formula (14), PpRepresenting peak period electricity prices; pfRepresenting the electricity price in the ordinary time period; pvIndicating the electricity price in the valley period; pcRepresenting a marginal cost price;
step 4.3, converting the time-interval optimization multi-objective function under the constant time-of-use electricity price into a single objective function by using the weight coefficient, and calculating the corresponding objective function by using the formula (15) and the formula (16):
F12(T)＝αF1(T)+βF2(T) (15)
F(T)＝F12(T)+γF3(T) (16)
in equations (15) and (16), α is a weight coefficient for minimizing the peak load by the objective function; β is a weight coefficient of the objective function to minimize the global peak-to-valley difference; gamma is an objective function of the objective function minimum reliability indicator; f12(T) is an objective function that takes into account the minimized peak load and the minimized global peak-to-valley difference; f (T) is a minimum peak load, maximumMinimizing a global peak-to-valley difference and minimizing a target function of a reliability index;
step 4.4, optimizing the peak-to-valley period division by using a dynamic charge point method:
for the ascending cargo sequence { PL'1,PL′2,...,PL′t,...,PL′24Where each load point is referred to simply as a "load point", where k is3、k2、k1Respectively represent the "number of load points" of the peak, plateau, and valley periods, and k1+k2+k324. If the "number of load points" for the peak, plateau, and valley periods is known, i.e., k1、k2、k3The corresponding peak, flat and valley time interval division can be determined.
Step 4.4.1, initializing number k of load points in valley period1＝Tmin；
Step 4.4.2, initializing peak and ordinary period load point number k2＝Tmin，k3＝24-k1-k2；
Step 4.4.3, calculating the objective function F under different time interval divisions12(T)；
Step 4.4.4, calculating an objective function F by utilizing the load-reliability analysis relation model3(T) and an objective function F (T);
step 4.4.5, updating the number of load points: will k2+1 assignment to k2Will k is3-1 assignment to k3；
Step 4.4.6, if k2＜24-Tmin-k1If not, executing step 4.4.3, otherwise, executing step 4.4.7;
step 4.4.7, if k1＜24-2TminThen k will be1+1 assignment to k1Executing step 4.4.2, otherwise, executing step 4.4.8;
and 4.4.8, outputting the target functions F (T) divided in all different periods, solving the minimum value, and taking the number of the load points in the peak period, the number of the load points in the ordinary period and the number of the load points in the valley period corresponding to the minimum value as the optimal peak-valley period.

Claims (1)

1. A peak-valley time interval dividing method considering power supply reliability under constant time-of-use electricity price is characterized by comprising the following steps;
step one, obtaining a typical daily hour load curve PLt＝{PL1,PL2,…,PLt,…,PL24Where t is 1,2, …,24 is hours; PLtRepresents a load value at the t-th hour;
step two, establishing a load-reliability analysis relation model based on the BP neural network:
step 2.1, determining the number of nodes and basic parameters of each layer of the BP neural network, comprising the following steps: maximum iteration number K, connection weight w, a system drawn-up allowable error epsilon, and a network total error E (K) of the kth iteration;
acquiring X training samples which take a load value as input and a reliability index RI as output by using an analytical method;
step 2.2, initializing a connection weight w;
step 2.3, initializing k to be 1;
step 2.4, initializing x to be 1;
2.5, sequentially inputting X training samples into the BP neural network of the kth iteration;
step 2.6, setting the current input to be the x-th training sample, and calculating the output and back propagation error of each layer of the k-th iteration;
step 2.7, if X is less than X, assigning X +1 to X, and returning to the step 2.5; otherwise, executing step 2.8;
step 2.8, calculating and adjusting the connection weight w of each layer of the kth iteration by using a weight adjustment formula;
step 2.9, calculating output and back propagation errors of each layer of the kth iteration by using the updated connection weight of the kth iteration, and calculating a total error E (k) of the kth iteration;
step 2.10, if E (K) is less than epsilon or K is more than K, terminating iteration, and taking the finally obtained BP neural network as a load-reliability analysis relation model; otherwise, assigning k +1 to k, and executing the step 2.4;
step three, establishing a power demand ratio before and after the time-of-use electricity price based on the electricity price elastic matrix, and calculating hour load values under different time interval divisions:
step 3.1, calculating the electricity price elastic coefficient by using the formula (1) and the formula (2) so as to form an electricity price elastic matrix
Figure FDA0003051619390000011
Figure FDA0003051619390000012
Figure FDA0003051619390000013
In the formulae (1) and (2), lambdaiiExpressing the self-elasticity coefficient, i belongs to { p, f, v } and expresses different peak, flat and valley periods; lambda [ alpha ]ijThe coefficient of mutual elasticity is represented, j belongs to { p, f, v } and represents different peak, flat and valley time periods, and i is not equal to j; delta EiThe electricity demand variation quantity of the i time period before and after the implementation of the time-of-use electricity price; eiThe electricity demand in the period i before the time-of-use electricity price is implemented; p0Represents an initial electricity rate before the time-of-use electricity rate is not implemented; delta Pi、ΔPjThe electricity price variation amounts of the i time period and the j time period before and after the implementation of the time-of-use electricity price are respectively;
step 3.2, calculating the time-interval electric quantity variable quantity under the time-of-use electricity price by using the formula (3):
Figure FDA0003051619390000021
in formula (3), E'p、E′f、E′vRespectively the electricity demand of the time-of-use electricity price in the later peak, flat and valley periods; ep、Ef、EvRespectively implementing the electricity consumption demand of the time-of-use electricity price in the front peak period, the flat period and the valley period; delta Pp、ΔPfAnd Δ PvRespectively carrying out the electricity price variation of the time-of-use electricity price in the front and back peak, flat and valley periods;
step 3.3, defining the power demand ratio before and after the time-of-use electricity price under the constant time-of-use electricity price by using the formula (4):
Figure FDA0003051619390000022
in the formula (4), Kp、Kf、KvRespectively representing the electricity demand ratios of the front and rear peak, flat and valley time periods of the time-of-use electricity price;
and 3.4, according to the formula (3) and the formula (4), expressing the electricity demand ratio in the i time period after the time-of-use electricity price by using a formula (5):
Figure FDA0003051619390000023
and 3.5, calculating the hour load value divided by considering different time intervals by using the formula (6):
PLt(T)＝Ki×PLt (6)
in formula (6), T ═ Tp,Tf,TvThe different peak, flat and valley time interval division is represented; PLtIs a typical daily hour load curve (PL)1,PL2,…,PLt,…,PL24The load value at the t hour in (1); PLt(T) the load value of the T hour divided by different time periods is considered after the time-of-use electricity price;
fourthly, constructing a time interval division optimized objective function according to corresponding indexes, and performing peak-valley time interval division optimization by using a dynamic load point method:
step 4.1, constructing a time interval division optimized objective function according to corresponding indexes:
step 4.1.1, defining the global peak-to-valley difference O by using the formula (7)pvd：
Figure FDA0003051619390000031
In formula (7), PL'tRepresents a load curve { PL by day and hour1,PL2,…,PLt,…,PL24} Loading sequence { PL 'obtained after ascending order'1,PL′2,...,PL′t,...,PL′24The load value at the t hour;
step 4.1.2, establishing the minimum peak load F by using the formula (8) and the formula (9) respectively1(T) and minimizing the global peak-to-valley difference F2(T)：
Figure FDA0003051619390000032
Figure FDA0003051619390000033
In formula (8), PL't(T) represents a load value at the tth hour of the day-hour load curve in ascending order at a constant time-of-use electricity rate and at different time intervals;
step 4.1.3, establishing a minimum reliability index objective function F by using the formula (10)3(T)：
Figure FDA0003051619390000034
Step 4.2, considering an objective function in the time interval division optimization process:
step 4.2.1, establishing a time period constraint by using the formula (11):
Figure FDA0003051619390000035
in formula (11), TminRepresenting the minimum number of hours that the lower peak-to-valley period is divided over different periods;
and 4.2.2, establishing a total expenditure electric charge constraint of the user by using the formula (12):
Pbefore-Pafter(T)≥0 (12)
in the formula (12), PbeforeRepresenting that the total electricity fee is paid by the user before the time-of-use electricity price is not implemented; pafter(T) paying a total electricity rate by the user at a constant time-of-use electricity rate and under different time-of-day division conditions;
and 4.2.3, establishing revenue constraint of the power supply company by using the formula (13):
Pafter(T)-(1-δ)Pbefore≥0 (13)
in the formula (13), δ represents an odds coefficient;
step 4.2.4, establishing peak-to-valley period electrovalence constraint by using the formula (14):
Pp＞Pf＞Pv＞Pc (14)
in formula (14), PpRepresenting peak period electricity prices; pfRepresenting the electricity price in the ordinary time period; pvIndicating the electricity price in the valley period; pcRepresenting a marginal cost price;
step 4.3, converting the time-interval optimization multi-objective function under the constant time-of-use electricity price into a single objective function by using the weight coefficient, and calculating the corresponding objective function by using the formula (15) and the formula (16):
F12(T)＝αF1(T)+βF2(T) (15)
F(T)＝F12(T)+γF3(T) (16)
in equations (15) and (16), α is a weight coefficient for minimizing the peak load by the objective function; β is a weight coefficient of the objective function to minimize the global peak-to-valley difference; gamma is an objective function of the objective function minimum reliability indicator; f12(T) is an objective function that takes into account the minimized peak load and the minimized global peak-to-valley difference; f (T) is an objective function that takes into account the minimized peak load, the minimized global peak-to-valley difference, and the minimized reliability index;
step 4.4, optimizing the peak-to-valley period division by using a dynamic charge point method:
step 4.4.1, initializing number k of load points in valley period1＝Tmin；
Step 4.4.2, initializing peak and ordinary period load point number k2＝Tmin，k3＝24-k1-k2；
Step 4.4.3, calculating the objective function F under different time interval divisions12(T)；
Step 4.4.4, calculating an objective function F by utilizing the load-reliability analysis relation model3(T) and an objective function F (T);
step 4.4.5, updating the number of load points: will k2+1 assignment to k2Will k is3-1 assignment to k3；
Step 4.4.6, if k2＜24-Tmin-k1If not, executing step 4.4.3, otherwise, executing step 4.4.7;
step 4.4.7, if k1＜24-2TminThen k will be1+1 assignment to k1Executing step 4.4.2, otherwise, executing step 4.4.8;
and 4.4.8, outputting the target functions F (T) divided in all different periods, solving the minimum value, and taking the number of the load points in the peak period, the number of the load points in the ordinary period and the number of the load points in the valley period corresponding to the minimum value as the optimal peak-valley period.
CN202110489839.6A 2021-05-06 2021-05-06 Peak-valley period dividing method considering reliability loss under constant time-of-use electricity price Active CN113224752B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110489839.6A CN113224752B (en) 2021-05-06 2021-05-06 Peak-valley period dividing method considering reliability loss under constant time-of-use electricity price

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110489839.6A CN113224752B (en) 2021-05-06 2021-05-06 Peak-valley period dividing method considering reliability loss under constant time-of-use electricity price

Publications (2)

Publication Number Publication Date
CN113224752A true CN113224752A (en) 2021-08-06
CN113224752B CN113224752B (en) 2022-09-23

Family

ID=77090990

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110489839.6A Active CN113224752B (en) 2021-05-06 2021-05-06 Peak-valley period dividing method considering reliability loss under constant time-of-use electricity price

Country Status (1)

Country Link
CN (1) CN113224752B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117060425A (en) * 2023-10-12 2023-11-14 国网浙江省电力有限公司宁波供电公司 Distribution network peak-valley difference self-adaptive control method and system based on reinforcement learning

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202596A (en) * 2015-05-08 2016-12-07 贵州电网公司电力调度控制中心 A kind of segmentation method of tou power price
CN107944630A (en) * 2017-12-01 2018-04-20 华北电力大学 A kind of seasonality tou power price optimization formulating method
CN111242702A (en) * 2020-02-29 2020-06-05 贵州电网有限责任公司 Method for formulating power grid peak-valley time-of-use electricity price considering minimum system peak-valley difference
US20210044117A1 (en) * 2019-08-09 2021-02-11 Changsha University Of Science & Technology Method for dynamically and economically dispatching power system based on optimal load transfer ratio and optimal grid connection ratio of wind power and photovoltaic power

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202596A (en) * 2015-05-08 2016-12-07 贵州电网公司电力调度控制中心 A kind of segmentation method of tou power price
CN107944630A (en) * 2017-12-01 2018-04-20 华北电力大学 A kind of seasonality tou power price optimization formulating method
US20210044117A1 (en) * 2019-08-09 2021-02-11 Changsha University Of Science & Technology Method for dynamically and economically dispatching power system based on optimal load transfer ratio and optimal grid connection ratio of wind power and photovoltaic power
CN111242702A (en) * 2020-02-29 2020-06-05 贵州电网有限责任公司 Method for formulating power grid peak-valley time-of-use electricity price considering minimum system peak-valley difference

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117060425A (en) * 2023-10-12 2023-11-14 国网浙江省电力有限公司宁波供电公司 Distribution network peak-valley difference self-adaptive control method and system based on reinforcement learning
CN117060425B (en) * 2023-10-12 2024-04-09 国网浙江省电力有限公司宁波供电公司 Distribution network peak-valley difference self-adaptive control method and system based on reinforcement learning

Also Published As

Publication number Publication date
CN113224752B (en) 2022-09-23

Similar Documents

Publication Publication Date Title
CN110137942B (en) Multi-time scale flexible load rolling scheduling method and system based on model predictive control
CN108429288B (en) Off-grid type microgrid energy storage optimization configuration method considering demand response
WO2021213192A1 (en) Load prediction method and load prediction system employing general distribution
CN115693757A (en) Photovoltaic energy optimization regulation and control method based on digital twinning technology
CN104598986A (en) Big data based power load prediction method
CN107565585B (en) Energy storage device peak regulation report-back time prediction technique and its model creation method
CN105405061A (en) Evaluation method of power supply reliability of distribution network based on demand response
CN113224752B (en) Peak-valley period dividing method considering reliability loss under constant time-of-use electricity price
CN104598985A (en) Power load forecasting method
CN106485093A (en) Based on the solar irradiance time series synthetic method for improving Markov chain
CN112508287A (en) Energy storage optimization configuration method based on user side BESS full life cycle
CN115511541A (en) Time-period electricity price calculation method based on supply and demand situations and system cost
Yang et al. Optimal modification of peak-valley period under multiple time-of-use schemes based on dynamic load point method considering reliability
CN114971090A (en) Electric heating load prediction method, system, equipment and medium
CN108683174B (en) Network loss optimization method based on multi-time scale demand response model
CN109861236B (en) Optimal time-of-use electricity price obtaining method considering power flow constraint of power distribution network
CN109615151B (en) Load energy storage double-optimization prediction method, device and medium
CN111371091A (en) Calculation method of photovoltaic power generation intelligent prediction system based on market benefit driving
CN115829141A (en) Energy storage system optimal configuration method based on short-term intelligent ammeter data
CN113779861B (en) Photovoltaic Power Prediction Method and Terminal Equipment
CN116341681A (en) Low-voltage photovoltaic user power generation load model training and predicting method
CN115864461A (en) Control method of electric power energy storage system
CN112883588B (en) Method, system and device for simulating sub-industry load curve
CN111079966B (en) Generalized load space prediction method and system
CN112510678A (en) Time sequence simulation-based power selling company distributed power capacity configuration method

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
GR01 Patent grant
GR01 Patent grant