CN113346480B - Opportunity constraint-based power system unit combination method - Google Patents

Opportunity constraint-based power system unit combination method Download PDF

Info

Publication number
CN113346480B
CN113346480B CN202110538629.1A CN202110538629A CN113346480B CN 113346480 B CN113346480 B CN 113346480B CN 202110538629 A CN202110538629 A CN 202110538629A CN 113346480 B CN113346480 B CN 113346480B
Authority
CN
China
Prior art keywords
power
renewable energy
constraint
model
unit combination
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
CN202110538629.1A
Other languages
Chinese (zh)
Other versions
CN113346480A (en
Inventor
李宝聚
孙勇
傅吉悦
吴文传
王彬
杨越
郭雷
刘畅
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.)
Tsinghua University
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jilin Electric Power Co Ltd
State Grid Jilin Electric Power Corp
Original Assignee
Tsinghua University
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jilin Electric Power Co Ltd
State Grid Jilin Electric Power Corp
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 Tsinghua University, State Grid Corp of China SGCC, Electric Power Research Institute of State Grid Jilin Electric Power Co Ltd, State Grid Jilin Electric Power Corp filed Critical Tsinghua University
Priority to CN202110538629.1A priority Critical patent/CN113346480B/en
Publication of CN113346480A publication Critical patent/CN113346480A/en
Application granted granted Critical
Publication of CN113346480B publication Critical patent/CN113346480B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a power system unit combination method based on opportunity constraint, and belongs to the technical field of power system operation control. Firstly, establishing an opportunity constraint-based power system unit combination model composed of an objective function and constraint conditions, then converting the model, writing opportunity constraints in the model into quantile forms, introducing relaxation variables, establishing the relaxed opportunity constraint-based power system unit combination model, and solving to obtain values of the relaxation variables; and obtaining constraint conditions with feasible solutions of the power system unit combination model based on opportunity constraint by using the values of the relaxation variables, and then sequentially establishing a renewable energy power curtailment scheduling model and a generator output optimization model and respectively solving the models so as to obtain a final unit combination result. The invention considers the fluctuation of renewable energy sources in the power system, ensures the safety of the power system, and is suitable for being applied to the power system unit combination scene with high renewable energy source permeability.

Description

Opportunity constraint-based power system unit combination method
Technical Field
The invention relates to a power system unit combination method based on opportunity constraint, and belongs to the technical field of power system operation control.
Background
The unit combination of the power system has an important guiding function on the operation of the power system, is responsible for making a starting plan of the generator set, provides reference for a subsequent power generation plan, and realizes economic operation on the premise of meeting power load and ensuring the safety of the power system.
The opportunity constraint unit combination is widely used for solving the problem of uncertainty caused by renewable energy sources. In the opportunity-constrained unit combination, the safety of the power system is ensured by limiting the risk to an allowable level. With the rapid increase of the permeability of renewable energy sources, under certain scenes, the situation that the standby of a power system is insufficient or transmission is blocked exists, and at the moment, the traditional opportunity constraint unit combination model may have the situation that the traditional opportunity constraint unit combination model cannot be solved. Therefore, in some scenarios, for power systems with high renewable energy penetration, the renewable energy output must be reduced in order to ensure system safety.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power system unit combination method based on opportunity constraint. The invention provides an effective solving method for sequentially determining the output of a traditional generator and the electricity abandonment amount of renewable energy sources so as to reduce the total operation cost to the maximum extent and limit the operation risk, and the method is suitable for being applied to the power system unit combination scene with high renewable energy permeability.
The invention provides an opportunity constraint-based power system unit combination method which is characterized in that the method comprises the steps of firstly establishing an opportunity constraint-based power system unit combination model composed of a target function and constraint conditions, then converting the model, writing opportunity constraints in the model into a quantile form, introducing relaxation variables, establishing a relaxed opportunity constraint-based power system unit combination model, and solving to obtain values of the relaxation variables; and obtaining constraint conditions with feasible solutions of a power system unit combination model based on opportunity constraint by using the values of all relaxation variables, then sequentially establishing a renewable energy power curtailment scheduling model and a generator output optimization model and respectively solving the models to obtain the curtailment power of each renewable energy source at each time interval and the active power and the start-stop state of each generator, thereby obtaining a final unit combination result. The method comprises the following steps:
1) Establishing an opportunity constraint-based power system unit combination model, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
1-1) determining an objective function of a power system unit combination model, wherein the expression is as follows:
Figure BDA0003070735440000021
wherein T is the number of time segments of the optimization cycle, N G In order to access the set of nodes of the generator,
Figure BDA0003070735440000022
for the active power of the ith generator during time period t,
Figure BDA0003070735440000023
for the power generation cost of the ith generator in the time period t,
Figure BDA0003070735440000024
for the startup cost of the ith generator during time period t,
Figure BDA0003070735440000025
cost of shutdown for ith generator in time period t, N R In order to access the set of nodes of the renewable energy source,
Figure BDA0003070735440000026
the active power output upper limit of the jth renewable energy source,
Figure BDA0003070735440000027
the electricity abandon penalty cost of the ith renewable energy source in the time period t;
wherein the content of the first and second substances,
Figure BDA0003070735440000028
wherein A is gi ,B gi ,C gi Generating cost coefficients of the ith generator are respectively;
Figure BDA0003070735440000029
therein su i For the start-up cost of the ith generator,
Figure BDA00030707354400000210
the starting and stopping state of the ith generator in a time period t is set;
Figure BDA00030707354400000211
wherein sd i The shutdown cost of the ith generator;
Figure BDA00030707354400000212
wherein, K j Penalty cost coefficient for jth renewable energy source, C j Is the jth oneThe maximum capacity of the renewable energy source,
Figure BDA00030707354400000213
a probability density function of available capacity for the jth renewable energy source over time period t;
1-2) determining constraint conditions of a power system unit combination model, comprising the following steps:
1-2-1) generator power constraint:
Figure BDA00030707354400000214
Figure BDA00030707354400000215
Figure BDA00030707354400000216
wherein the content of the first and second substances,
Figure BDA00030707354400000217
P i upper and lower active power limits, rup, of the ith generator, respectively i ,Rdown i The power limit of the ith generator is limited by upward climbing power and downward climbing power;
1-2-2) minimum start-stop time constraint of the generator:
Figure BDA00030707354400000218
Figure BDA0003070735440000031
Figure BDA0003070735440000032
Figure BDA0003070735440000033
wherein, UT i ,DT i Respectively the minimum startup time and the minimum shutdown time of the ith generator;
1-2-3) renewable energy power constraints:
Figure BDA0003070735440000034
Figure BDA0003070735440000035
wherein the content of the first and second substances,
Figure BDA0003070735440000036
scheduling power for the jth renewable energy source for time period t,
Figure BDA0003070735440000037
the predicted power for the jth renewable energy source over time period t,
Figure BDA0003070735440000038
the actual power for the jth renewable energy source during time period t,
Figure BDA0003070735440000039
available power for the jth renewable energy source for time period t;
1-2-4) power balance constraints:
Figure BDA00030707354400000310
wherein the content of the first and second substances,
Figure BDA00030707354400000311
for node k load demand over time period t, N D A node set for accessing the load;
1-2-5) affine control and backup constraints:
Figure BDA00030707354400000312
Figure BDA00030707354400000313
Figure BDA00030707354400000314
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00030707354400000315
actual power, beta, for the ith generator under consideration of affine control i Affine control factor, α, for the ith generator UR ,α DR Respectively the maximum allowable probability of the shortage of the up-regulation equipment and the maximum allowable probability of the shortage of the down-regulation equipment;
1-2-6) line transmission capacity constraints:
Figure BDA00030707354400000316
Figure BDA00030707354400000317
wherein, N W Is a collection of lines, and is,
Figure BDA00030707354400000318
power transfer factors, P, of the line L with respect to the nodes i, j, k, respectively L For the maximum transmission capacity of the line L,
Figure BDA0003070735440000041
respectively the maximum allowable probability of forward out-of-limit and the maximum allowable probability of reverse out-of-limit of the transmission capacity of the line L;
2) Converting the model established in the step 1), establishing a relaxed opportunity constraint-based power system unit combination model and solving the model; the method comprises the following specific steps:
2-1) by substitution in formula (16)
Figure BDA0003070735440000042
Writing the opportunity constraints (17) - (20) in the model of step 1) into the form of quantiles as follows:
Figure BDA0003070735440000043
Figure BDA0003070735440000044
Figure BDA0003070735440000045
Figure BDA0003070735440000046
Figure BDA0003070735440000047
Figure BDA0003070735440000048
wherein M is a positive number, Q (ξ | p) is the p-quantile of the random variable ξ,
Figure BDA0003070735440000049
to account for the equivalent power transfer factor of line L with respect to node j under affine control, M L Correction coefficients for affine control to the branch L power transfer factors;
let the joint distribution of renewable energy power over time period t be as follows:
Figure BDA00030707354400000410
Figure BDA00030707354400000411
wherein the content of the first and second substances,
Figure BDA00030707354400000412
a random variation of the available power composition for each renewable energy source over time t,
Figure BDA00030707354400000413
random variables composed of power output of each renewable energy source in a time period t;
2-2) use
Figure BDA00030707354400000414
In alternative quantiles (21) to (24)
Figure BDA00030707354400000415
And introducing relaxation variables in the lower modulation reserve constraint (16) and the transmission power constraints (17) - (18) to obtain equations (29) - (32):
Figure BDA00030707354400000416
Figure BDA00030707354400000417
Figure BDA00030707354400000418
Figure BDA0003070735440000051
wherein, drs t Slack variables in the standby constraints are adjusted for a period of time t,
Figure BDA0003070735440000052
the relaxation variables in the forward constraint and the reverse constraint of the line transmission capacity in the t period are respectively;
2-3) establishing a relaxed opportunity constraint-based power system unit combination model and solving;
wherein the objective function of the model is formula (33), and the constraints include formulas (6) - (8), (13), (15), (29) - (32) and (34);
Figure BDA0003070735440000053
s.t. formulae (6) - (8), (13), (15), (, 29) - (32) and (34)
Wherein the content of the first and second substances,
Figure BDA0003070735440000054
wherein the content of the first and second substances,
Figure BDA0003070735440000055
respectively adjusting the standby weight coefficient and the section risk weight coefficient in t time;
solving the model to obtain
Figure BDA0003070735440000056
The optimal solution of (a);
3) Determining the electric quantity discarded by each renewable energy source and the active power of each generator to obtain a unit combination result of the power system; the method comprises the following specific steps:
3-1) utilizing the solving result of the model in the step 2-3) and relaxing the variable
Figure BDA0003070735440000057
Value of (1) establishing based on the chance constraintThe power system unit combination model has constraint conditions of feasible solution:
if the slack variable drs t After the renewable energy is abandoned, if the model in the step 1) has a feasible solution, the inequality constraint shown in the following formula is satisfied:
Figure BDA0003070735440000058
if the relaxation variable
Figure BDA0003070735440000059
If the value is not zero, the quantile in the line transmission capacity constraint equation (23) satisfies the inequality constraint represented by the following equation (36):
Figure BDA00030707354400000510
if the relaxation variable is changed
Figure BDA00030707354400000511
If the value is not zero, the quantile in the line transmission capacity constraint equation (24) satisfies an inequality constraint represented by the following equation (37):
Figure BDA00030707354400000512
the constraints (35) - (37) are written as shown in the following formula:
Figure BDA0003070735440000061
wherein Ω is a subscript set of renewable energy sources participating in electricity abandonment, c j ,c k Is a constant coefficient;
3-2) establishing a renewable energy power abandoning scheduling model and solving;
the objective function of the renewable energy power curtailment scheduling is shown as the following formula:
Figure BDA0003070735440000062
the first and second derivatives of equation (39) are as follows:
Figure BDA0003070735440000063
Figure BDA0003070735440000064
the curtailment penalty in equation (39) is approximated by a linear inequality shown in equation (42):
Figure BDA0003070735440000065
obtaining the electricity abandon quantity of each renewable energy source in each time period by solving the formula (42)
Figure BDA0003070735440000066
Then, based on the probability distribution of available power obtained by probability prediction and quantiles in the electric quantity abandoning calculation expressions (21) to (24) obtained by solving, the constraint expressions (21) to (24) become deterministic linear constraints;
3-3) establishing a generator output optimization model and solving by using the result of the step 3-2) to obtain the starting and stopping state of each generator at each time period
Figure BDA0003070735440000067
And active power
Figure BDA0003070735440000068
Thereby obtaining the unit combination optimization result of the generator;
the objective function of the optimization model is equation (43), and the constraint conditions include: formulae (6) to (13), formula (15), formulae (21) to (24), and formula (44), the expressions are as follows:
Figure BDA0003070735440000069
s.t. formulae (6) - (13), (15), (21) - (24) and (44)
Wherein the content of the first and second substances,
Figure BDA00030707354400000610
the invention provides an opportunity constraint-based power system unit combination method, which has the advantages that:
(1) The opportunity constraint-based power system unit combination method provided by the invention optimizes the output of the conventional generator and the renewable energy power-abandoning strategy at the same time, so as to reduce the total operation cost to the maximum extent and limit the operation risk.
(2) The invention provides an effective solving method for sequentially determining the output of a traditional generator and the electric quantity of renewable energy, establishes an easily-solved optimization model for each step, and is suitable for being applied to a power system unit combination scene with high renewable energy permeability.
Detailed Description
The invention provides an opportunity constraint-based power system unit combination method, which comprises the steps of firstly establishing an opportunity constraint-based power system unit combination model composed of a target function and constraint conditions, then converting the model, writing opportunity constraints in the model into quantiles and introducing relaxation variables, establishing a relaxed opportunity constraint-based power system unit combination model, and solving to obtain values of the relaxation variables; and obtaining constraint conditions with feasible solutions of the power system unit combination model based on opportunity constraint by using the values of the relaxation variables, then sequentially establishing a renewable energy power curtailment scheduling model and a generator output optimization model and respectively solving the models to obtain the power curtailment of the renewable energy sources in each time period and the active power and the start-stop state of each generator, thereby obtaining a final unit combination result. The method comprises the following steps:
1) Establishing an opportunity constraint-based power system unit combination model, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
1-1) determining an objective function of a power system unit combination model;
the objective of the power system unit combination is to minimize the total cost, which includes the cost of generating power, the cost of starting and stopping the generator, and the cost of the renewable energy power abandonment penalty, and the objective function is shown as the following formula:
Figure BDA0003070735440000071
wherein T is the number of time segments of the optimization cycle, N G In order to access the set of nodes of the generator,
Figure BDA0003070735440000072
for the active power of the ith generator during time period t,
Figure BDA0003070735440000073
for the power generation cost of the ith generator in the time period t,
Figure BDA0003070735440000074
for the startup cost of the ith generator during time period t,
Figure BDA0003070735440000075
cost of shutdown for ith generator in time period t, N R In order to access the set of nodes of the renewable energy source,
Figure BDA0003070735440000076
the active power output upper limit of the jth renewable energy source,
Figure BDA0003070735440000077
charging penalty cost for the electricity abandon of the ith renewable energy source in the time period t;
wherein the power generation cost can be represented by a quadratic function of the following formula:
Figure BDA0003070735440000078
wherein A is gi ,B gi ,C gi The coefficients are the power generation cost coefficients of the ith generator.
The starting cost of the generator is shown as follows:
Figure BDA0003070735440000079
therein, su i For the start-up cost of the ith generator,
Figure BDA00030707354400000710
the starting and stopping states of the ith generator in the time period t are shown (the variable is 0-1).
The cost of the generator shutdown is shown as follows:
Figure BDA0003070735440000081
wherein sd i The shutdown cost of the ith generator.
The penalty charge for electricity abandonment of renewable energy sources is shown as follows:
Figure BDA0003070735440000082
wherein, K j Penalty cost coefficient for jth renewable energy source, C j Is the maximum capacity of the jth renewable energy source,
Figure BDA0003070735440000083
a probability density function of available capacity for the jth renewable energy source over time period t;
1-2) determining constraint conditions of a power system unit combination model, comprising the following steps:
1-2-1) generator power constraint:
Figure BDA0003070735440000084
Figure BDA0003070735440000085
Figure BDA0003070735440000086
wherein the content of the first and second substances,
Figure BDA0003070735440000087
P i upper and lower active power limits, rup, of the ith generator, respectively i ,Rdown i The power limit of the ith generator is limited by the upward climbing power and the downward climbing power respectively.
1-2-2) minimum start-stop time constraint of the generator:
Figure BDA0003070735440000088
Figure BDA0003070735440000089
Figure BDA00030707354400000810
Figure BDA00030707354400000811
wherein, UT i ,DT i Respectively the minimum startup time and the minimum shutdown time of the ith generator;
1-2-3) renewable energy power constraints:
Figure BDA00030707354400000812
Figure BDA00030707354400000813
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00030707354400000814
the power is scheduled for the jth renewable energy source for time period t,
Figure BDA00030707354400000815
the predicted power for the jth renewable energy source over time period t,
Figure BDA00030707354400000816
the actual power for the jth renewable energy source during time period t,
Figure BDA00030707354400000817
available power for the jth renewable energy source during time period t.
1-2-4) power balance constraints:
Figure BDA0003070735440000091
wherein the content of the first and second substances,
Figure BDA0003070735440000092
for node k load demand over time period t, N D Is the set of nodes accessing the load.
1-2-5) affine control and backup constraints:
Figure BDA0003070735440000093
Figure BDA0003070735440000094
Figure BDA0003070735440000095
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003070735440000096
actual power, beta, for the ith generator under consideration of affine control i Affine control participation factor, alpha, for the i-th generator UR ,α DR The maximum allowable probability of under-provisioning and the maximum allowable probability of under-provisioning are respectively up-provisioning and down-provisioning.
1-2-6) line transmission capacity constraints:
Figure BDA0003070735440000097
Figure BDA0003070735440000098
wherein N is W In order to be a set of lines,
Figure BDA0003070735440000099
the power transfer factors of the line L with respect to the nodes i, j, k,
Figure BDA00030707354400000910
for the maximum transmission capacity of the line L,
Figure BDA00030707354400000911
respectively, a maximum allowed probability of the transmission capacity of the line L being out of limit in the forward direction and a maximum allowed probability of the transmission capacity being out of limit in the reverse direction.
2) Detecting infeasible constraints and renewable energy power curtailment, converting the model established in the step 1), establishing a relaxed power system unit combination model based on opportunity constraints, and solving; the method comprises the following specific steps:
2-1) by substitution in formula (16)
Figure BDA00030707354400000912
Writing the opportunistic constraints (17) - (20) in the model of step 1) into the form of quantiles as follows:
Figure BDA00030707354400000913
Figure BDA00030707354400000914
Figure BDA00030707354400000915
Figure BDA00030707354400000916
Figure BDA0003070735440000101
Figure BDA0003070735440000102
where M is a large positive number, in this example 10, Q (ξ | p) is the p-quantile of the random variable ξ,
Figure BDA0003070735440000103
to account for the equivalent power transfer factor of line L relative to node j in affine control, M L And controlling a correction coefficient of the power transfer factor of the branch L for affine control.
Assuming that the joint distribution of renewable energy power over time period t is known, as follows:
Figure BDA0003070735440000104
Figure BDA0003070735440000105
wherein the content of the first and second substances,
Figure BDA0003070735440000106
a random variation of the power available for each renewable energy source over a period of time t,
Figure BDA0003070735440000107
random variables composed of power output of each renewable energy source in a time period t;
actual renewable energy power in traditional opportunity constrained unit combination without considering renewable energy power abandon
Figure BDA0003070735440000108
Equal to available renewable energy power
Figure BDA0003070735440000109
However, the calculation of the quantiles in equations (22) - (24) is based on the assumption that renewable energy sources do not have a power dump, which may result in conventional generator power
Figure BDA00030707354400001010
There is no feasible solution.
2-2) renewable energy power can be cut down by reducing the quantile on the right of the equations (22) - (24), thereby reducing the risk of down-regulation of backup shortages and transmission blockages. By using
Figure BDA00030707354400001011
In alternative quantiles (21) to (24)
Figure BDA00030707354400001012
And introducing relaxation variables in and among the lower modulation use constraint (16) and the transmission power constraints (17) to (18), the following equations (29) to (32) can be obtained:
Figure BDA00030707354400001013
Figure BDA00030707354400001014
Figure BDA00030707354400001015
Figure BDA00030707354400001016
wherein, drs t Slack variables in the standby constraints are adjusted for a period of time t,
Figure BDA00030707354400001017
the relaxation variables in the forward constraint and the reverse constraint of the line transmission capacity in the t period are respectively;
2-3) establishing a relaxed opportunity constraint-based power system unit combination model and solving;
wherein the objective function of the model is equation (33), which is a weighted sum of the minimized relaxation variables to account for generator power
Figure BDA00030707354400001018
There is no feasible solution case. In the calculation of linear combination
Figure BDA00030707354400001019
After the quantiles are divided, the following relaxed opportunity constraint-based power system unit combination model can be directly solved by linear programming.
Figure BDA0003070735440000111
s.t. formulae (6) - (8), (13), (15), (, 29) - (32) and (34)
Wherein the content of the first and second substances,
Figure BDA0003070735440000112
wherein the content of the first and second substances,
Figure BDA0003070735440000113
and adjusting the standby weight coefficient and the section risk weight coefficient in the time period t respectively.
Solving the model to obtain
Figure BDA0003070735440000114
The optimal solution of (2);
3) Determining the electric quantity discarded by each renewable energy source and the active power of each generator to obtain a unit combination result of the power system; the method comprises the following specific steps:
3-1) after solving the electric power system unit combination model based on the opportunity constraint after the relaxation in the step 2-3), the relaxation variables can be passed
Figure BDA0003070735440000115
Obtaining the constraint condition that the opportunity constraint-based power system unit combination model established in the step 1) has a feasible solution:
if the slack variable drs in the backup constraint is adjusted downward t After the renewable energy is abandoned, in order to make the model of step 1) feasible, the inequality constraint shown in the following formula must be satisfied:
Figure BDA0003070735440000116
if the relaxation variable is changed
Figure BDA0003070735440000117
If the value is not zero, the quantile in the line transmission capacity constraint equation (23) satisfies an inequality constraint represented by the following equation (36):
Figure BDA0003070735440000118
if the relaxation variable
Figure BDA0003070735440000119
If the number of quantiles is not zero, the quantile in the line transmission capacity constraint equation (24) satisfies an inequality constraint represented by the following equation (37):
Figure BDA00030707354400001110
the constraints (35) - (37) can be written as shown in the following formula:
Figure BDA00030707354400001111
wherein Ω is a subscript set of renewable energy sources participating in electricity abandonment, c j ,c k Is a constant coefficient.
3-2) establishing a renewable energy power abandoning scheduling model and solving;
the objective function of renewable energy power curtailment scheduling is to minimize the overall power curtailment penalty cost as shown in the following formula:
Figure BDA00030707354400001112
the quantile in equation (38) and the objective function in equation (39) are related to the upper limit of renewable energy power
Figure BDA0003070735440000121
The linear approximation of equations (38) and (39) can be used to obtain a solvable renewable energy curtailment scheduling model whose first and second derivatives of the objective function equation (39) are as follows:
Figure BDA0003070735440000122
Figure BDA0003070735440000123
since the second derivative of the objective function (39) is equal to or greater than zero, the power curtailment penalty is related to the upper power limit of the renewable energy source
Figure BDA0003070735440000124
A convex function of (a). The curtailment penalty in equation (39) is approximated by a linear inequality shown in equation (42) below, according to the nature of the convex function:
Figure BDA0003070735440000125
by solving the formula (42), the electricity abandon quantity of each renewable energy source in each time period can be obtained
Figure BDA0003070735440000126
Then, based on the probability distribution of the available power obtained by the probability prediction and the quantiles in the calculation expressions (21) to (24) obtained by solving the expression (42), the constraint expressions (21) to (24) become deterministic linear constraints.
3-3) obtaining the starting and stopping states of each generator in each time period by solving the generator output optimization model shown in the following step 3-2) according to the result of the step 3-2)
Figure BDA0003070735440000127
And active power
Figure BDA0003070735440000128
Thereby obtaining the unit combination optimization result of the generator.
The objective function of the optimization model is equation (43), and the constraint equation (44) is used to limit the scheduled output of the renewable energy source to be lower than the upper power limit thereof.
Figure BDA0003070735440000129
s.t. formulae (6) - (13), (15), (21) - (24) and (44)
Wherein the content of the first and second substances,
Figure BDA00030707354400001210

Claims (1)

1. the method is characterized in that firstly, an opportunity constraint-based power system unit combination model composed of a target function and constraint conditions is established, then the model is converted, opportunity constraints in the model are written into quantile forms and slack variables are introduced, the relaxed power system unit combination model based on opportunity constraints is established, and values of the slack variables are obtained through solving; the method comprises the following steps of obtaining constraint conditions with feasible solutions of a power system unit combination model based on opportunity constraint by using values of relaxation variables, then sequentially establishing a renewable energy power curtailment scheduling model and a generator output optimization model and respectively solving the models to obtain power curtailment of renewable energy sources at each time interval and active power and start-stop states of generators, and further obtaining a final unit combination result, wherein the method comprises the following steps:
1) Establishing an opportunity constraint-based power system unit combination model, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
1-1) determining an objective function of a power system unit combination model, wherein the expression is as follows:
Figure FDA0003926135260000011
wherein T is the number of time segments of the optimization cycle, N G In order to access the set of nodes of the generator,
Figure FDA0003926135260000012
for the active power of the ith generator during time period t,
Figure FDA0003926135260000013
for the power generation cost of the ith generator in the time period t,
Figure FDA0003926135260000014
for the startup cost of the ith generator at time period t,
Figure FDA0003926135260000015
cost of shutdown for ith generator in time period t, N R In order to access the collection of nodes of the renewable energy source,
Figure FDA0003926135260000016
the active power output upper limit for the jth renewable energy source,
Figure FDA0003926135260000017
the electricity abandon penalty cost of the ith renewable energy source in the time period t;
wherein the content of the first and second substances,
Figure FDA0003926135260000018
wherein A is gi ,B gi ,C gi Generating cost coefficients of the ith generator are respectively;
Figure FDA0003926135260000019
therein su i For the start-up cost of the ith generator,
Figure FDA00039261352600000110
starting and stopping states of an ith generator in a time period t;
Figure FDA00039261352600000111
wherein sd i The shutdown cost of the ith generator;
Figure FDA00039261352600000112
wherein, K j Penalty cost coefficient for the jth renewable energy source, C j Is the maximum capacity of the jth renewable energy source,
Figure FDA00039261352600000113
an available capacity probability density function for the jth renewable energy source at time period t;
1-2) determining constraint conditions of a power system unit combination model, comprising the following steps:
1-2-1) generator power constraint:
Figure FDA0003926135260000021
Figure FDA0003926135260000022
Figure FDA0003926135260000023
wherein the content of the first and second substances,
Figure FDA0003926135260000024
P i the upper limit and the lower limit of active power, rup, of the ith generator i ,Rdown i The power limit of the ith generator is limited by upward climbing power and downward climbing power;
1-2-2) minimum start-stop time constraint of the generator:
Figure FDA0003926135260000025
Figure FDA0003926135260000026
Figure FDA0003926135260000027
Figure FDA0003926135260000028
wherein, UT i ,DT i Respectively the minimum startup time and the minimum shutdown time of the ith generator;
1-2-3) renewable energy power constraints:
Figure FDA0003926135260000029
Figure FDA00039261352600000210
wherein the content of the first and second substances,
Figure FDA00039261352600000211
the power is scheduled for the jth renewable energy source for time period t,
Figure FDA00039261352600000212
the predicted power for the jth renewable energy source over time period t,
Figure FDA00039261352600000213
the actual power for the jth renewable energy source during time period t,
Figure FDA00039261352600000214
available power for the jth renewable energy source for time period t;
1-2-4) power balance constraints:
Figure FDA00039261352600000215
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00039261352600000216
for the load demand of node k in time period t, N D A node set for accessing the load;
1-2-5) affine control and backup constraints:
Figure FDA00039261352600000217
Figure FDA00039261352600000218
Figure FDA0003926135260000031
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003926135260000032
actual power, beta, in consideration of affine control for the ith generator i Affine control participation factor, alpha, for the i-th generator UR ,α DR Respectively the maximum allowable probability of the shortage of the up-regulation equipment and the maximum allowable probability of the shortage of the down-regulation equipment;
1-2-6) line transmission capacity constraints:
Figure FDA0003926135260000033
Figure FDA0003926135260000034
wherein N is W In order to be a set of lines,
Figure FDA0003926135260000035
the power transfer factors of the line L with respect to the nodes i, j, k,
Figure FDA0003926135260000036
for the maximum transmission capacity of the line L,
Figure FDA0003926135260000037
respectively the maximum allowable probability of forward out-of-limit and the maximum allowable probability of reverse out-of-limit of the transmission capacity of the line L;
2) Converting the model established in the step 1), establishing a relaxed opportunity constraint-based power system unit combination model and solving the model; the method comprises the following specific steps:
2-1) by substitution in formula (16)
Figure FDA0003926135260000038
Writing the opportunity constraints (17) - (20) in the model of step 1) into the form of quantiles as follows:
Figure FDA0003926135260000039
Figure FDA00039261352600000310
Figure FDA00039261352600000311
Figure FDA00039261352600000312
Figure FDA00039261352600000313
Figure FDA00039261352600000314
wherein M is a positive number, Q (ξ | p) is the p-quantile of the random variable ξ,
Figure FDA00039261352600000315
to account for the equivalent power transfer factor of line L relative to node j in affine control, M L A correction coefficient for affine control to the branch L power transfer factor;
let the joint distribution of renewable energy power over time period t be as follows:
Figure FDA00039261352600000316
Figure FDA00039261352600000317
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003926135260000041
a random variation of the power available for each renewable energy source over a period of time t,
Figure FDA0003926135260000042
random variables composed of power output of each renewable energy source in a time period t;
2-2) use
Figure FDA0003926135260000043
In alternative quantiles (21) to (24)
Figure FDA0003926135260000044
And introducing relaxation variables in the lower modulation reserve constraint (16) and the transmission power constraints (17) - (18) to obtain equations (29) - (32):
Figure FDA0003926135260000045
Figure FDA0003926135260000046
Figure FDA0003926135260000047
Figure FDA0003926135260000048
wherein, drs t Slack variables in the standby constraints are adjusted for a period of time t,
Figure FDA0003926135260000049
the slack variable in the forward constraint and the slack variable in the reverse constraint of the line transmission capacity in the t period are respectively;
2-3) establishing a relaxed opportunity constraint-based power system unit combination model and solving;
wherein the objective function of the model is equation (33), and the constraints include equations (6) - (8), (13), (15), (29) - (32) and (34);
Figure FDA00039261352600000410
s.t. formulae (6) - (8), (13), (15), (29) - (32) and (34)
Wherein the content of the first and second substances,
Figure FDA00039261352600000411
wherein the content of the first and second substances,
Figure FDA00039261352600000412
respectively adjusting the standby weight coefficient and the cross section risk weight coefficient in t time period;
solving the model to obtain
Figure FDA00039261352600000413
The optimal solution of (a);
3) Determining the electric quantity discarded by each renewable energy source and the active power of each generator to obtain a unit combination result of the power system; the method comprises the following specific steps:
3-1) utilizing the solving result of the model in the step 2-3) and relaxing the variable
Figure FDA00039261352600000414
Obtaining the constraint condition that the opportunity constraint-based power system unit combination model established in the step 1) has a feasible solution:
if the slack variable drs t After the renewable energy is abandoned, if the model in the step 1) has a feasible solution, the inequality constraint shown in the following formula is satisfied:
Figure FDA00039261352600000415
if the relaxation variable is changed
Figure FDA0003926135260000051
If not zero, the quantile in the line transmission capacity constraint equation (23) satisfies the inequality constraint expressed by the following equation (36)Bundling:
Figure FDA0003926135260000052
if the relaxation variable is changed
Figure FDA0003926135260000053
If the value is not zero, the quantile in the line transmission capacity constraint equation (24) satisfies an inequality constraint represented by the following equation (37):
Figure FDA0003926135260000054
the constraints (35) - (37) are written as shown in the following formula:
Figure FDA0003926135260000055
wherein Ω is a subscript set of renewable energy sources participating in electricity abandonment, c j ,c k Is a constant coefficient;
3-2) establishing a renewable energy power abandoning scheduling model and solving;
the objective function of the renewable energy power curtailment scheduling is shown as the following formula:
Figure FDA0003926135260000056
the first and second derivatives of equation (39) are as follows:
Figure FDA0003926135260000057
Figure FDA0003926135260000058
the curtailment penalty in equation (39) is approximated by a linear inequality shown in equation (42):
Figure FDA0003926135260000059
Figure FDA00039261352600000510
obtaining the electricity abandon quantity of each renewable energy source in each time period by solving the formula (42)
Figure FDA00039261352600000511
Then, based on the probability distribution of available power obtained by probability prediction and quantiles in electric quantity abandonment calculation formulas (21) to (24) obtained by solving, the constraint formulas (21) to (24) become deterministic linear constraints;
3-3) establishing a generator output optimization model and solving by using the result of the step 3-2) to obtain the starting and stopping state of each generator at each time period
Figure FDA00039261352600000512
And active power
Figure FDA00039261352600000513
Thereby obtaining the unit combination optimization result of the generator;
the objective function of the optimization model is equation (43), and the constraint conditions include: formulae (6) to (13), formula (15), formulae (21) to (24), and formula (44), the expressions are as follows:
Figure FDA0003926135260000061
s.t. formulae (6) - (13), (15), (21) - (24) and (44)
Wherein the content of the first and second substances,
Figure FDA0003926135260000062
CN202110538629.1A 2021-05-18 2021-05-18 Opportunity constraint-based power system unit combination method Active CN113346480B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110538629.1A CN113346480B (en) 2021-05-18 2021-05-18 Opportunity constraint-based power system unit combination method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110538629.1A CN113346480B (en) 2021-05-18 2021-05-18 Opportunity constraint-based power system unit combination method

Publications (2)

Publication Number Publication Date
CN113346480A CN113346480A (en) 2021-09-03
CN113346480B true CN113346480B (en) 2022-12-13

Family

ID=77469049

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110538629.1A Active CN113346480B (en) 2021-05-18 2021-05-18 Opportunity constraint-based power system unit combination method

Country Status (1)

Country Link
CN (1) CN113346480B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5715165A (en) * 1994-12-23 1998-02-03 The University Of Connecticut Method and system for scheduling using a facet ascending algorithm or a reduced complexity bundle method for solving an integer programming problem
CN106251027A (en) * 2016-08-17 2016-12-21 合肥工业大学 Electric load probability density Forecasting Methodology based on fuzzy support vector quantile estimate
CN109657898A (en) * 2018-10-19 2019-04-19 云南电网有限责任公司 A kind of renewable energy stochastic and dynamic economic load dispatching method based on convex relaxation
CN109728578A (en) * 2019-02-19 2019-05-07 清华大学 Electric system stochastic and dynamic Unit Combination method based on Newton Algorithm quantile
CN111313475A (en) * 2018-12-11 2020-06-19 华北电力大学（保定） Power system scheduling method considering prediction error uncertain variable through power balance constraint

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112039058A (en) * 2020-08-18 2020-12-04 国网山东省电力公司临沂供电公司 Unit combination method, system, medium and device based on wind power prediction interval

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5715165A (en) * 1994-12-23 1998-02-03 The University Of Connecticut Method and system for scheduling using a facet ascending algorithm or a reduced complexity bundle method for solving an integer programming problem
CN106251027A (en) * 2016-08-17 2016-12-21 合肥工业大学 Electric load probability density Forecasting Methodology based on fuzzy support vector quantile estimate
CN109657898A (en) * 2018-10-19 2019-04-19 云南电网有限责任公司 A kind of renewable energy stochastic and dynamic economic load dispatching method based on convex relaxation
CN111313475A (en) * 2018-12-11 2020-06-19 华北电力大学（保定） Power system scheduling method considering prediction error uncertain variable through power balance constraint
CN109728578A (en) * 2019-02-19 2019-05-07 清华大学 Electric system stochastic and dynamic Unit Combination method based on Newton Algorithm quantile

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
输电网规划模型中失负荷概率约束的嵌入及转化方法研究;徐谦等;《电网技术》;20180630;第6卷(第42期);第1760-1767页 *

Also Published As

Publication number Publication date
CN113346480A (en) 2021-09-03

Similar Documents

Publication Publication Date Title
CN112104007B (en) Centralized control method for generalized source storage system scheduling
CN104362673B (en) Based on the wind-electricity integration coordinated scheduling optimization method of peak regulation nargin
CN109103924B (en) Analysis method of renewable energy consumption capability key factors based on flexible analysis
CN109361242B (en) Automatic voltage control method for photovoltaic power generation
CN108321853B (en) Power grid active real-time control method giving consideration to both regulation characteristic and economic and environment-friendly performance
CN113193547A (en) Day-ahead-day cooperative scheduling method and system for power system considering uncertainty of new energy and load interval
CN114336599B (en) Independent micro-grid dynamic optimization scheduling method
CN111786417A (en) Distributed new energy consumption-oriented active power distribution network multi-target interval optimization scheduling method
CN115204443B (en) Wind-solar-fire-storage combined market bidding optimal clear scheduling method
CN110783927B (en) Multi-time scale AC/DC power distribution network scheduling method and device
CN115528674A (en) Offshore wind farm virtual inertia optimal distribution method and system based on load shedding operation
CN102255337A (en) Calculating method for wind power field receiving capability of power grid
CN109657898B (en) Renewable energy random dynamic economic dispatching method based on convex relaxation
CN113346480B (en) Opportunity constraint-based power system unit combination method
CN117060396A (en) Day-ahead optimal operation method of wind-solar-fire-storage multi-energy power system
CN113346479B (en) Opportunity constraint-based economic dispatching method for power system
CN110994695A (en) Intraday optimal scheduling method and system for power distribution network
CN115800276A (en) Power system emergency scheduling method considering unit climbing
CN115833203A (en) Power grid peak regulation method based on multi-time interval optimal power flow and energy storage battery
CN110417002B (en) Optimization method of island micro-grid energy model
CN109255102B (en) Photovoltaic power generation maximum grid-connected capacity evaluation method based on convex relaxation
CN112039057B (en) Low-voltage treatment method based on two-stage scheduling
Gou et al. A New Method for Optimal Dispatch Considering System Flexibility in Wind Farm
Gao et al. Research on Distributed Photovoltaic Source-load-storage Cooperative Control Strategy
CN114548522A (en) Two-step system optimization method and device suitable for clearing and checking of provincial standby market

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
TA01 Transfer of patent application right

Effective date of registration: 20211214

Address after: 130021 No. 10388, Renmin Street, Chaoyang District, Changchun City, Jilin Province

Applicant after: STATE GRID JILIN ELECTRIC POWER SUPPLY Co.

Applicant after: TSINGHUA University

Applicant after: STATE GRID CORPORATION OF CHINA

Applicant after: JILIN PROVINCE ELECTRIC POWER RESEARCH INSTITUTE OF JILIN ELECTRIC POWER Co.,Ltd.

Address before: 130021 No. 10388, Renmin Street, Chaoyang District, Changchun City, Jilin Province

Applicant before: STATE GRID JILIN ELECTRIC POWER SUPPLY Co.

Applicant before: Tsinghua University

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant