CN112288173B - Peak load adjustment method considering time-of-use electricity price and excitation compensation - Google Patents

Peak load adjustment method considering time-of-use electricity price and excitation compensation Download PDF

Info

Publication number
CN112288173B
CN112288173B CN202011195967.1A CN202011195967A CN112288173B CN 112288173 B CN112288173 B CN 112288173B CN 202011195967 A CN202011195967 A CN 202011195967A CN 112288173 B CN112288173 B CN 112288173B
Authority
CN
China
Prior art keywords
time
peak
load
formula
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.)
Active
Application number
CN202011195967.1A
Other languages
Chinese (zh)
Other versions
CN112288173A (en
Inventor
杨贺钧
张新宇
马英浩
张大波
吴红斌
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 CN202011195967.1A priority Critical patent/CN112288173B/en
Publication of CN112288173A publication Critical patent/CN112288173A/en
Application granted granted Critical
Publication of CN112288173B publication Critical patent/CN112288173B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • G06Q30/0283—Price estimation or determination
    • 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
    • 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
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Tourism & Hospitality (AREA)
  • Game Theory and Decision Science (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Accounting & Taxation (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Quality & Reliability (AREA)
  • Educational Administration (AREA)
  • Finance (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Algebra (AREA)
  • Public Health (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a peak load adjusting method considering time-of-use electricity price and excitation compensation, which comprises the following steps: 1, establishing a time-of-use electricity price model considering objective functions of a power grid side and a user side to obtain an optimal time-of-use electricity price; 2, time interval division is carried out again based on the load under the time-of-use electricity price, and a peak time interval is further divided through fuzzy clustering; 3, determining a peak load adjustment strategy based on a proportion sharing principle; 4, solving the load transfer rate through the price elastic matrix and the load curve under the time-of-use electricity price; and 5, calculating a peak incentive price based on the power shortage cost of the user. According to the invention, through researching the influence of different demand side response strategies on the user load, a peak load adjustment model is established on the basis of time-of-use electricity price, so that the peak load is further reduced on the premise of ensuring the satisfaction of the user, and the system reliability is improved.

Description

Peak load adjustment method considering time-of-use electricity price and excitation compensation
Technical Field
The invention relates to the field of response of a demand side of a power system, in particular to a peak load adjusting method considering time-of-use electricity price and excitation compensation.
Background
The demand response refers to the behavior of the power consumer to shift or shed the load in response to the electricity rate or the stimulus signal. Time of use electricity price (TOU) is an important component of a demand response strategy, and the implementation of the TOU electricity price strategy can delay the investment of a power grid and improve the operation stability of the system, so that the TOU electricity price strategy is widely applied to the power market. However, for a particular period, even if peak-to-valley time-of-use electricity prices are implemented, the peak-to-time load (i.e., peak load) in the peak period is still high. The existence of the peak load not only reduces the utilization rate of the power equipment, but also has adverse effects on the safe and reliable operation of the power system, so that research needs to be carried out on a demand response strategy of the peak load, and the peak load is transferred or reduced through a demand response means, so as to improve the reliability of the operation of the system. At present, the research on peak load regulation and control at home and abroad is still in an exploration stage, and the research combining multiple demand-side regulation and control modes is in a starting stage.
For the problem of regulation and control of peak load, at present, domestic and foreign researches are mainly implemented in a free bidding mode based on a completely open power market environment. However, the development of domestic electric power markets is still in a semi-open stage, and the demand side regulation and control mode is mainly based on time-of-use electricity price. Therefore, how to establish a reasonably feasible peak load adjusting mechanism on the basis of time-sharing electricity price to be made into a problem to be solved urgently in demand side response research.
Disclosure of Invention
The invention aims to avoid the defects of the prior art and provides the peak load adjusting method considering the time-of-use electricity price and the excitation compensation, so that the peak load can be regulated and controlled again on the basis of the time-of-use electricity price, the peak load and the peak-valley difference of the power grid are reduced, and the reliability and the stability of the operation of the power grid are improved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to a peak load adjusting method considering time-of-use electricity price and excitation compensation, which is characterized by comprising the following steps of;
step one, establishing a time-of-use electricity price optimization model considering objective functions of a power grid side and a user side:
step 1.1, respectively establishing a power grid side objective function by using the formula (1) and the formula (2):
Figure GDA0003444175310000011
Figure GDA0003444175310000012
in the formulae (1) and (2)，F1(. and F)2(. to) denotes the minimum peak load and minimum peak-to-valley difference, q'iTo optimize the load capacity at time ip、pfAnd pvThe electricity prices at the peak time, the flat time and the valley time are respectively, and T is duration;
step 1.2, respectively establishing a user side objective function by using the formula (3) and the formula (4):
F3(pp,pf,pv)＝max(K) (3)
F4(pp,pf,pv)＝max(S) (4)
in formulae (3) and (4), F3(. and F)4(. cndot.) represents the maximum electricity utilization similarity K and the maximum user satisfaction S, respectively, and has:
Figure GDA0003444175310000021
Figure GDA0003444175310000022
in the formulae (5) and (6), qiRepresenting the load capacity at time i before optimization,
Figure GDA0003444175310000023
and
Figure GDA0003444175310000024
respectively representing the average electric quantity p at each moment before and after optimization0Is the initial price of electricity, p, before optimizationiRepresenting the time-of-use electricity price at the moment i;
step 1.3, establishing a constraint function in the electricity price optimization process:
step 1.3.1, establishing Power supplier constraint S using equation (7)1：
Figure GDA0003444175310000025
In the formula (7), s represents a certain period of time, p, f, v represent peak, flat, and valley periods, respectively, and QsRepresenting the total electric quantity before the time-of-use electricity price in the s period; q'sRepresenting the electric quantity of s time period after the time-of-use electricity price; p is a radical ofsRepresenting the electricity price s period after the time-of-use electricity price;
step 1.3.2, establishing user side constraint S by using formula (8)2：
Figure GDA0003444175310000026
In the formula (8), λ represents an adjustment coefficient;
step 1.3.3, respectively establishing peak-to-average valence constraint S by using formula (9) and formula (10)3Flat valley price constraint S4：
S3＝pp-pf＞0 (9)
S4＝pf-pv＞0 (10)
Step 1.3.4, establishing marginal cost constraint S by using formula (11)5：
S5＝pv-pd＞0 (11)
In the formula (11), pdRepresents a marginal cost;
step two, establishing a peak time interval division model based on the time-of-use electricity price:
step 2.1, calculating load electric quantity q 'of optimized time i at time-of-use electricity price by using formula (12)'i：
q′i＝qi+Δqi (12)
In the formula (12), Δ qiIs the load variation at time i; and comprises the following components:
Figure GDA0003444175310000031
in the formula (13a), plIs the time-of-use electricity price corresponding to the time l;klis the number of times in the period of time l; i and l are both time; e.g. of the typeilIs the third order elastic coefficient corresponding to the time period of time i and time l; and comprises the following components:
Figure GDA0003444175310000032
in the formula (13b), Δ ps＝{p,f,v}And Δ Qs＝{p,f,v}Respectively the electricity price change and the load change in a period s before and after the time-of-use electricity price, and making k, j equal to p, f, v and the electricity price elastic coefficient ekjRepresenting the effect of the change in electricity price for time period j on the load for time period k;
step 2.2, calculating peak time interval membership U of time i by using the formula (14)i：
Figure GDA0003444175310000033
In the formula (14), qminAnd q ismaxRespectively the minimum load and the maximum load under the time-of-use electricity price;
step 2.3, establishing a peak, flat and valley movement variable m by using the formula (15)s＝{p,f,v}Let m bes∈[0,1]And initializing:
Figure GDA0003444175310000034
in the formula (15), Δ m represents an iteration step; and comprises the following components:
Figure GDA0003444175310000035
in formula (16), N represents the number of iteration steps;
step 2.4, to the mobile variable msStep iteration is carried out according to the iteration step length delta m, and the membership degree U is calculated by using the formula (17)iAnd a movement variable msOf (2) exponential similarity ris：
Figure GDA0003444175310000036
Step 2.5, establishing an optimized objective function F divided by peak-to-valley time periods by using the formula (18)5：
Figure GDA0003444175310000037
In the formula (18), GsRepresenting a set of time instants contained in the time interval s, and adding a mobile variable position constraint and a time interval length constraint by using an equation (19) and an equation (20), respectively:
Figure GDA0003444175310000041
Figure GDA0003444175310000042
in formula (20), card (G)s) Representing the number of times in the time period s,/minAnd lmaxRespectively representing the maximum and minimum lengths of the time period;
step 2.6, the membership degree U of the peak time period in the optimal time period division result is comparediSum peak shift variable mpIf U is concernedi≥mpDividing the time i into peak time periods, otherwise, remaining the peak time periods;
step three, determining a peak load adjustment strategy based on a proportion apportionment principle:
step 3.1, assuming the total load is unchanged, the peak load adjustment is represented by equation (21):
Figure GDA0003444175310000043
q 'in the formula (21)'s＝{cp,p,f,v}And Q "s＝{cp,p,f,v}Respectively representing the total load of each time interval before and after adjustment, and cp representing a peak time interval; thetacp，θcfAnd thetacvLoad transfer rates for peak-to-peak, peak-to-average and peak-to-valley periods, respectively, and having a value of θcp+θcf+θcv＝1；
Step 3.2, obtaining the load q at each moment after the peak load is adjusted by using a formula (22) according to a proportion sharing principle "i：
Figure GDA0003444175310000044
Step 3.3, obtaining the total load Delta Q of the peak period reduction through the formula (23)cp：
Figure GDA0003444175310000045
In formula (23), q'cp,minMinimum load, q', representing the spike period before load adjustment "p,maxDenotes the maximum load during the peak period after load adjustment, gamma denotes the load reduction rate during the peak period, and Δ qcp,minA minimum load reduction amount representing a peak period;
step four, obtaining the load transfer rate through the electricity price elastic matrix and the load curve under the time-of-use electricity price:
step 4.1, assuming that the load adjusting effect of peak excitation and peak time electricity price on the flat and valley time sections is the same, obtaining the peak-flat load transfer rate theta by using the formula (24)cfAnd peak-to-valley load transfer rate θcvThe relation of (1):
Figure GDA0003444175310000051
step 4.2, set thetacp:θcf:θcv＝a1:a2:a3Then, the peak-to-average proportionality coefficient a is obtained by using the formula (25)2And the peak-to-valley proportionality coefficient a3：
Figure GDA0003444175310000052
Similarly, the peak-to-peak proportionality coefficient a is obtained by using the load change relationship between different periods under the time-of-use electricity price represented by the formula (26)1：
Figure GDA0003444175310000053
Step 4.3 obtaining the Peak-to-Peak load transfer Rate θ by Using the formula (27)cpPeak-to-flat load transfer rate thetacfAnd peak-to-valley load transfer θcv：
Figure GDA0003444175310000054
Step five, calculating peak excitation through the power shortage cost of the user:
step 5.1, establishing the power shortage loss C (delta q) of the user by utilizing the formula (28)cp,i) And load reduction amount Δ q at peak period time icp,iThe relation of (1):
C(Δqcp,i)＝k1Δqcp,i 2+k2Δqcp,i-k2Δqcp,iτi (28)
in the formula (28), τiA user type parameter, k, representing the time i1And k2Is a constant coefficient;
step 5.2, obtaining user reduced load delta q by using formula (29)cp,iThe gain C' (Δ q) obtained aftercp,i)：
C′(Δqcp,i)＝(pi+bi)Δqcp,i (29)
In formula (29), C' (q)i)＝C(qi)；biIs a spike excitation at time i and has:
Figure GDA0003444175310000055
step 5.3, when delta qcp,i≠0,bi＝k1Δqcp,iThen, the daily peak incentive price a is obtained using equation (31):
Figure GDA0003444175310000056
in formula (31), GcpA set of times representing a spike period;
and 5.4, taking the peak period load reduction rate gamma and the daily peak incentive price A as a peak load adjusting scheme.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, a time-of-use electricity price optimization model considering benefits of the power grid side and the user side is established, the optimal time-of-use electricity price is obtained through multi-objective optimization, the peak clipping and valley filling requirements are met, and meanwhile the electricity utilization similarity and the electricity utilization satisfaction of the user are guaranteed;
2. according to the invention, by researching the user response curve under the time-of-use electricity price, the response condition of the user to the time-of-use electricity price is effectively analyzed, and the optimized load is divided into time periods again. Compared with the traditional method, the method reduces clustering time, reduces the influence of mobile variables on the distribution of other time intervals by adopting preprocessing based on fuzzy membership ranking, and adds length constraint to time interval division results, thereby improving the intra-class aggregation degree and inter-class dispersion degree of the clustering results and being beneficial to improving the accuracy of power grid load analysis and prediction;
3. according to the method, a peak load adjustment strategy is determined based on a proportion sharing principle, peak loads can be reduced to the maximum on the basis of not changing a time period division result, the load transfer relation between different time periods is quantized, the load variation at each moment is determined through the load reduction rate, the maximum peak load and the load peak-valley difference are effectively reduced, the operation pressure of a power grid is reduced, and the reliability is improved;
4. according to the method, the demand side response effect of the peak excitation is analyzed and predicted through the electricity price elastic matrix, the peak excitation is solved from the angle of electricity price change, two regulation and control modes are combined, and the accuracy of load response under the peak excitation is improved;
5. according to the method, the peak excitation is researched from the aspect of power shortage cost of a user, and the functional relation between peak load adjustment and the peak excitation is deduced, so that reasonable pricing is achieved according to the peak load reduction, and the operation safety of the power grid is improved through the peak excitation.
Drawings
Fig. 1 is a schematic flow chart of a peak load adjustment method considering time-of-use electricity price and excitation compensation according to the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, a peak load adjustment method considering time-of-use electricity prices and excitation compensation is that, first, an optimal time-of-use electricity price is solved based on a time-of-use electricity price optimization model considering interests of a power grid side and a user side, and a load curve under the time-of-use electricity price is solved through a third-order elastic matrix; secondly, dividing the time interval of the load under the time-of-use electricity price again by a fuzzy clustering method, and dividing a peak time interval; finally, determining a peak load adjustment measure based on a proportion apportionment principle, analyzing the power shortage cost of a user to determine a peak incentive price, and providing technical support for the actual peak load demand side management; specifically, the method comprises the following steps:
step one, establishing a time-of-use electricity price optimization model considering objective functions of a power grid side and a user side:
step 1.1, establishing a power grid side objective function by using the formula (1) and the formula (2):
Figure GDA0003444175310000061
Figure GDA0003444175310000062
in the formulae (1) and (2), F1(. and F)2(. to) denotes the minimum peak load and minimum peak-to-valley difference, q'iTo optimize the load capacity at time ip、pfAnd pvThe electricity prices at the peak time, the flat time and the valley time are respectively, and T is duration;
step 1.2, establishing a user side objective function by using the formula (3) and the formula (4):
F3(pp,pf,pv)＝max(K) (3)
F4(pp,pf,pv)＝max(S) (4)
in formulae (3) and (4), F3(. and F)4(. cndot.) represents the maximum electricity utilization similarity K and the maximum user satisfaction S, respectively, and has:
Figure GDA0003444175310000071
Figure GDA0003444175310000072
in formulae (5) and (6), qiRepresenting the load capacity at time i before optimization,
Figure GDA0003444175310000073
and
Figure GDA0003444175310000074
respectively representing the average electric quantity p at each moment before and after optimization0Is the initial price of electricity, p, before optimizationiRepresenting the time-of-use electricity price at the moment i;
step 1.3, establishing a constraint function in the electricity price optimization process:
step 1.3.1, establishing Power supplier constraint S using equation (7)1：
Figure GDA0003444175310000075
In the formula (7), s represents a certain period of time, p, f, v represent peak, flat, and valley periods, respectively, and QsTo representThe total electric quantity before the time-of-use electricity price in s time period; q'sRepresenting the electric quantity of s time period after the time-of-use electricity price; p is a radical ofsRepresenting the electricity price s period after the time-of-use electricity price;
step 1.3.2, establishing user side constraint S by using formula (8)2：
Figure GDA0003444175310000076
In the formula (8), λ represents an adjustment coefficient;
step 1.3.3, respectively establishing peak-to-average valence constraint S by using formula (9) and formula (10)3Flat valley price constraint S4：
S3＝pp-pf＞0 (9)
S4＝pf-pv＞0 (10)
Step 1.3.4, establishing marginal cost constraint S by using formula (11)5：
S5＝pv-pd＞0 (11)
In the formula (11), pdRepresents a marginal cost;
step two, establishing a peak time interval division model based on the time-of-use electricity price:
step 2.1, calculating load electric quantity q 'of optimized time i at time-of-use electricity price by using formula (12)'i：
q′i＝qi+Δqi (12)
In the formula (12), Δ qiIs the load variation at time i; and comprises the following components:
Figure GDA0003444175310000081
in the formula (13a), plIs the time-of-use electricity price corresponding to the time l; k is a radical oflIs the number of times in the period of time l; i and l are both time; e.g. of the typeilIs the third order elastic coefficient corresponding to the time period of time i and time l; and comprises the following components:
Figure GDA0003444175310000082
in the formula (13b), Δ ps＝{p,f,v}And Δ Qs＝{p,f,v}The electricity price change and the load change in the s period before and after the time-of-use electricity price and the electricity price elastic coefficient ekj(k, j ═ p, f, v) represents the effect of a change in electricity price for time period j on the load for time period k;
step 2.2, calculating peak time interval membership U of time i by using the formula (14)i：
Figure GDA0003444175310000083
In the formula (14), qminAnd q ismaxRespectively the minimum load and the maximum load under the time-of-use electricity price;
step 2.3, establishing a peak, flat and valley movement variable m by using the formula (15)s＝{p,f,v}Let m bes∈[0,1]And initializing:
Figure GDA0003444175310000084
in the formula (15), Δ m represents an iteration step; and comprises the following components:
Figure GDA0003444175310000085
in formula (16), N represents the number of iteration steps;
step 2.4, to the mobile variable msStep iteration is carried out according to the iteration step length delta m, and the membership degree U is calculated by using the formula (17)iAnd a movement variable msOf (2) exponential similarity ris：
Figure GDA0003444175310000086
Step (ii) of2.5, establishing an optimized objective function F of peak-to-valley time interval division by using an equation (18)5：
Figure GDA0003444175310000091
In the formula (18), GsRepresenting a set of time instants contained in the time interval s, and adding a mobile variable position constraint and a time interval length constraint by using an equation (19) and an equation (20), respectively:
Figure GDA0003444175310000092
Figure GDA0003444175310000093
in formula (20), card (G)s) Representing the number of times in the time period s,/minAnd lmaxRespectively representing the maximum and minimum lengths of the time period;
step 2.6, the membership degree U of the peak time period in the optimal time period division result is comparediSum peak shift variable mpIf U is concernedi≥mpDividing the time i into peak time periods, otherwise, remaining the peak time periods;
step three, determining a peak load adjustment strategy based on a proportion apportionment principle:
step 3.1, assuming the total load is unchanged, the peak load adjustment is represented by equation (21):
Figure GDA0003444175310000094
q 'in the formula (21)'s＝{cp,p,f,v}And Q "s＝{cp,p,f,v}Respectively representing the total load of each time interval before and after adjustment, and cp representing a peak time interval; thetacp，θcfAnd thetacvLoad transfer rates for peak-to-peak, peak-to-average and peak-to-valley periods, respectively, and having a value of θcp+θcf+θcv＝1；
Step 3.2, obtaining the load q at each moment after the peak load is adjusted by using a formula (22) according to a proportion sharing principle "i：
Figure GDA0003444175310000095
Step 3.3, obtaining the total load Delta Q of the peak period reduction through the formula (23)cp：
Figure GDA0003444175310000096
In formula (23), q'cp,minMinimum load, q', representing the spike period before load adjustment "p,maxDenotes the maximum load during the peak period after load adjustment, gamma denotes the load reduction rate during the peak period, and Δ qcp,minA minimum load reduction amount representing a peak period;
step four, obtaining the load transfer rate through the electricity price elastic matrix and the load curve under the time-of-use electricity price:
step 4.1, assuming that the load adjusting effect of peak excitation and peak time electricity price on the flat and valley time sections is the same, obtaining the peak-flat load transfer rate theta by using the formula (24)cfAnd peak-to-valley load transfer rate θcvThe relation of (1):
Figure GDA0003444175310000101
step 4.2, set thetacp:θcf:θcv＝a1:a2:a3Then, the peak-to-average proportionality coefficient a is obtained by using the formula (25)2And the peak-to-valley proportionality coefficient a3：
Figure GDA0003444175310000102
Similarly, the negative value at different time intervals at the time of electricity price represented by the formula (26) is usedObtaining a peak-peak proportionality coefficient a by the charge variation relation1：
Figure GDA0003444175310000103
Step 4.3 obtaining the peak-to-peak, peak-to-average and peak-to-valley load transfer rates θ by using equation (27)cp，θcfAnd thetacv：
Figure GDA0003444175310000104
Step five, calculating peak excitation through the power shortage cost of the user:
step 5.1, establishing the power shortage loss C (delta q) of the user by utilizing the formula (28)cp,i) And load reduction amount Δ q at peak period time icp,iThe relation of (1):
C(Δqcp,i)＝k1Δqcp,i 2+k2Δqcp,i-k2Δqcp,iτi (28)
in the formula (28), τiA user type parameter, k, representing the time i1And k2Is a constant coefficient;
step 5.2, obtaining user reduced load delta q by using formula (29)cp,iThe gain C' (Δ q) obtained aftercp,i)：
C′(Δqcp,i)＝(pi+bi)Δqcp,i (29)
In formula (29), C' (q)i)＝C(qi)；biIs a spike excitation at time i and has:
Figure GDA0003444175310000105
step 5.3, when delta qcp,i≠0,bi＝k1Δqcp,iThen, the daily peak incentive price a is obtained using equation (31):
Figure GDA0003444175310000106
in formula (31), GcpA set of times representing a spike period;
and 5.4, taking the peak time interval load reduction rate gamma obtained by the formula (23) and the daily peak incentive price A obtained by the formula (31) as a peak load adjustment scheme, and performing the adjustment by signing an intention contract with the user.

Claims (1)

1. A peak load adjusting method considering time-of-use electricity price and excitation compensation is characterized by comprising the following steps of;
step one, establishing a time-of-use electricity price optimization model considering objective functions of a power grid side and a user side:
step 1.1, respectively establishing a power grid side objective function by using the formula (1) and the formula (2):
Figure FDA0003444175300000011
Figure FDA0003444175300000012
in the formulae (1) and (2), F1(. and F)2(. to) denotes the minimum peak load and minimum peak-to-valley difference, q'iTo optimize the load capacity at time ip、pfAnd pvThe electricity prices at the peak time, the flat time and the valley time are respectively, and T is duration;
step 1.2, respectively establishing a user side objective function by using the formula (3) and the formula (4):
F3(pp,pf,pv)＝max(K) (3)
F4(pp,pf,pv)＝max(S) (4)
in formulae (3) and (4), F3(. and F)4(. to) represents the maximum power consumption similarity K andmaximum user satisfaction S, and has:
Figure FDA0003444175300000013
Figure FDA0003444175300000014
in the formulae (5) and (6), qiRepresenting the load capacity at time i before optimization,
Figure FDA0003444175300000015
and
Figure FDA0003444175300000016
respectively representing the average electric quantity p at each moment before and after optimization0Is the initial price of electricity, p, before optimizationiRepresenting the time-of-use electricity price at the moment i;
step 1.3, establishing a constraint function in the electricity price optimization process:
step 1.3.1, establishing Power supplier constraint S using equation (7)1：
Figure FDA0003444175300000017
In the formula (7), s represents a certain period of time, p, f, v represent peak, flat, and valley periods, respectively, and QsRepresenting the total electric quantity before the time-of-use electricity price in the s period; q'sRepresenting the electric quantity of s time period after the time-of-use electricity price; p is a radical ofsRepresenting the electricity price s period after the time-of-use electricity price;
step 1.3.2, establishing user side constraint S by using formula (8)2：
Figure FDA0003444175300000018
In the formula (8), λ represents an adjustment coefficient;
step 1.3.3, respectively establishing peak-to-average valence constraint S by using formula (9) and formula (10)3Flat valley price constraint S4：
S3＝pp-pf＞0 (9)
S4＝pf-pv＞0 (10)
Step 1.3.4, establishing marginal cost constraint S by using formula (11)5：
S5＝pv-pd＞0 (11)
In the formula (11), pdRepresents a marginal cost;
step two, establishing a peak time interval division model based on the time-of-use electricity price:
step 2.1, calculating load electric quantity q 'of optimized time i at time-of-use electricity price by using formula (12)'i：
q′i＝qi+Δqi (12)
In the formula (12), Δ qiIs the load variation at time i; and comprises the following components:
Figure FDA0003444175300000021
in the formula (13a), plIs the time-of-use electricity price corresponding to the time l; k is a radical oflIs the number of times in the period of time l; i and l are both time; e.g. of the typeilIs the third order elastic coefficient corresponding to the time period of time i and time l; and comprises the following components:
Figure FDA0003444175300000022
in the formula (13b), Δ ps＝{p,f,v}And Δ Qs＝{p,f,v}Respectively the electricity price change and the load change in a period s before and after the time-of-use electricity price, and making k, j equal to p, f, v and the electricity price elastic coefficient ekjRepresenting the effect of the change in electricity price for time period j on the load for time period k;
step 2.2, calculate the peak at time i using equation (14)Time interval membership degree Ui：
Figure FDA0003444175300000023
In the formula (14), qminAnd q ismaxRespectively the minimum load and the maximum load under the time-of-use electricity price;
step 2.3, establishing a peak, flat and valley movement variable m by using the formula (15)s＝{p,f,v}Let m bes∈[0,1]And initializing:
Figure FDA0003444175300000024
in the formula (15), Δ m represents an iteration step; and comprises the following components:
Figure FDA0003444175300000025
in formula (16), N represents the number of iteration steps;
step 2.4, to the mobile variable msStep iteration is carried out according to the iteration step length delta m, and the membership degree U is calculated by using the formula (17)iAnd a movement variable msOf (2) exponential similarity ris：
Figure FDA0003444175300000031
Step 2.5, establishing an optimized objective function F divided by peak-to-valley time periods by using the formula (18)5：
Figure FDA0003444175300000032
In the formula (18), GsRepresenting a set of time instants contained in the time interval s, and adding a mobile variable position constraint and a time interval length constraint by using an equation (19) and an equation (20), respectively:
Figure FDA0003444175300000033
Figure FDA0003444175300000034
in formula (20), card (G)s) Representing the number of times in the time period s,/minAnd lmaxRespectively representing the maximum and minimum lengths of the time period;
step 2.6, the membership degree U of the peak time period in the optimal time period division result is comparediSum peak shift variable mpIf U is concernedi≥mpDividing the time i into peak time periods, otherwise, remaining the peak time periods;
step three, determining a peak load adjustment strategy based on a proportion apportionment principle:
step 3.1, assuming the total load is unchanged, the peak load adjustment is represented by equation (21):
Figure FDA0003444175300000035
q 'in the formula (21)'s＝{cp,p,f,v}And Q "s＝{cp,p,f,v}Respectively representing the total load of each time interval before and after adjustment, and cp representing a peak time interval; thetacp，θcfAnd thetacvLoad transfer rates for peak-to-peak, peak-to-average and peak-to-valley periods, respectively, and having a value of θcp+θcf+θcv＝1；
Step 3.2, obtaining the load q at each moment after the peak load is adjusted by using a formula (22) according to a proportion sharing principle "i：
Figure FDA0003444175300000036
Step 3.3, obtaining by the formula (23)Reducing total load Δ Q by spike periodcp：
Figure FDA0003444175300000041
In formula (23), q'cp,minMinimum load, q', representing the spike period before load adjustment "p,maxDenotes the maximum load during the peak period after load adjustment, gamma denotes the load reduction rate during the peak period, and Δ qcp,minA minimum load reduction amount representing a peak period;
step four, obtaining the load transfer rate through the electricity price elastic matrix and the load curve under the time-of-use electricity price:
step 4.1, assuming that the load adjusting effect of peak excitation and peak time electricity price on the flat and valley time sections is the same, obtaining the peak-flat load transfer rate theta by using the formula (24)cfAnd peak-to-valley load transfer rate θcvThe relation of (1):
Figure FDA0003444175300000042
step 4.2, set thetacp:θcf:θcv＝a1:a2:a3Then, the peak-to-average proportionality coefficient a is obtained by using the formula (25)2And the peak-to-valley proportionality coefficient a3：
Figure FDA0003444175300000043
Similarly, the peak-to-peak proportionality coefficient a is obtained by using the load change relationship between different periods under the time-of-use electricity price represented by the formula (26)1：
Figure FDA0003444175300000044
Step 4.3 obtaining the Peak-to-Peak load transfer Rate θ by Using the formula (27)cpPeak-to-flat load transfer rate thetacfAnd peak-to-valley load transfer θcv：
Figure FDA0003444175300000045
Step five, calculating peak excitation through the power shortage cost of the user:
step 5.1, establishing the power shortage loss C (delta q) of the user by utilizing the formula (28)cp,i) And load reduction amount Δ q at peak period time icp,iThe relation of (1):
C(Δqcp,i)＝k1Δqcp,i 2+k2Δqcp,i-k2Δqcp,iτi (28)
in the formula (28), τiA user type parameter, k, representing the time i1And k2Is a constant coefficient;
step 5.2, obtaining user reduced load delta q by using formula (29)cp,iThe gain C' (Δ q) obtained aftercp,i)：
C′(Δqcp,i)＝(pi+bi)Δqcp,i (29)
In formula (29), C' (q)i)＝C(qi)；biIs a spike excitation at time i and has:
Figure FDA0003444175300000051
step 5.3, when delta qcp,i≠0,bi＝k1Δqcp,iThen, the daily peak incentive price a is obtained using equation (31):
Figure FDA0003444175300000052
in formula (31), GcpA set of times representing a spike period;
and 5.4, taking the peak period load reduction rate gamma and the daily peak incentive price A as a peak load adjusting scheme.
CN202011195967.1A 2020-10-30 2020-10-30 Peak load adjustment method considering time-of-use electricity price and excitation compensation Active CN112288173B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011195967.1A CN112288173B (en) 2020-10-30 2020-10-30 Peak load adjustment method considering time-of-use electricity price and excitation compensation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011195967.1A CN112288173B (en) 2020-10-30 2020-10-30 Peak load adjustment method considering time-of-use electricity price and excitation compensation

Publications (2)

Publication Number Publication Date
CN112288173A CN112288173A (en) 2021-01-29
CN112288173B true CN112288173B (en) 2022-03-22

Family

ID=74354154

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011195967.1A Active CN112288173B (en) 2020-10-30 2020-10-30 Peak load adjustment method considering time-of-use electricity price and excitation compensation

Country Status (1)

Country Link
CN (1) CN112288173B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113569405A (en) * 2021-07-23 2021-10-29 国网安徽省电力有限公司经济技术研究院 Energy storage scheduling benefit potential evaluation method and equipment considering multiple time-of-use electricity prices
CN113972699B (en) * 2021-10-22 2023-07-18 国网安徽省电力有限公司经济技术研究院 Power grid peak load optimization method
CN114154900A (en) * 2021-12-09 2022-03-08 国网电子商务有限公司 Power supply control method and device
CN115375091B (en) * 2022-07-15 2023-05-02 国网宁夏电力有限公司营销服务中心(国网宁夏电力有限公司计量中心) Load adjustable capacity quantification method based on price elastic coefficient
CN117077368B (en) * 2023-07-07 2024-02-06 华中科技大学 Comprehensive energy system crowd target planning method considering industrial comprehensive demand response
CN117611227B (en) * 2024-01-23 2024-04-09 中科三清科技有限公司 Air quality-based electricity price adjustment method and device, electronic equipment and medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340525A (en) * 2020-02-03 2020-06-26 中国电力科学研究院有限公司 Method and system for determining time-sharing electricity price peak electricity price

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130332327A1 (en) * 2009-09-11 2013-12-12 Masdar Institute Of Science And Technology Hybrid Energy Market and Currency System for Total Energy Management
US9721278B2 (en) * 2010-09-27 2017-08-01 Economemetics Ab Method, system and computer program product for dynamically pricing perishable goods
CN110705792A (en) * 2019-09-30 2020-01-17 重庆大学 Dynamic demand response solving method considering time-sharing pricing
CN111062506A (en) * 2019-12-10 2020-04-24 贵州电网有限责任公司 Time-of-use electricity price time interval division and price making method based on user behaviors
CN111242702B (en) * 2020-02-29 2021-08-06 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
CN113112056A (en) * 2021-03-18 2021-07-13 四川大学 Electricity vendor dynamic bidding decision optimization method considering multi-type demand response

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340525A (en) * 2020-02-03 2020-06-26 中国电力科学研究院有限公司 Method and system for determining time-sharing electricity price peak electricity price

Also Published As

Publication number Publication date
CN112288173A (en) 2021-01-29

Similar Documents

Publication Publication Date Title
CN112288173B (en) Peak load adjustment method considering time-of-use electricity price and excitation compensation
Kamyab et al. Demand response program in smart grid using supply function bidding mechanism
CN106655246B (en) Solving method of robust double-layer optimization model based on wind power prediction and demand response
CN103679555A (en) Time-of-use electricity price determining method based on load characteristic classification
CN110705792A (en) Dynamic demand response solving method considering time-sharing pricing
CN104156581B (en) Peak-valley time-of-use power price determining method taking both system reliability and power purchase risk into consideration
CN111695943B (en) Optimization management method considering floating peak electricity price
CN107231001B (en) Building microgrid online energy management method based on improved grey prediction
CN110675275A (en) Demand side response power load regulation and control method and system of virtual power plant
CN111724210A (en) Power demand response regulation and control method based on price elastic coefficient matrix
Li et al. The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation
CN113610351A (en) User demand response capability assessment method, system, terminal and medium
CN111047097A (en) Day-to-day rolling optimization method for comprehensive energy system
CN113746089A (en) Multi-user-oriented multi-time-scale power package and family energy optimization method
JP6698375B2 (en) Surplus power purchase price determination system, surplus power purchase unit price determination system, and standard purchase unit price setting system
CN116914847A (en) Aggregation complementation and optimal scheduling method for multiple generator sets in virtual power plant
Tan et al. Time-of-use price optimizing model and fuzzy solving method
CN115829268A (en) Resident-oriented demand response evaluation method and device, terminal and storage medium
Hou et al. Low-carbon demand response strategy of buildings considering load rebound
Chen et al. Available transfer capability calculations considering demand response
CN115375091A (en) Load adjustability quantification method based on price elastic coefficient
Hong et al. Long-Term Load Forecasting of Integrated Energy Systems Considering Source-Load Evolution Game
Su et al. Price Demand Response Model Based on Consumer Psychology
CN104573871A (en) Demand response project selecting optimum method based on entropy weight method and minimum distance method
Jiawei et al. Green time-sharing electricity price mechanism and optimization based on demand response

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