CN110414721A - A kind of power plant's daily trading planning decomposition method based on power spot market price - Google Patents
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Abstract
The invention discloses a kind of power plant's daily trading planning decomposition method based on power spot market price, comprising: obtain unit electric power data information, the unit electric power data information includes the monthly electricity plan of each unit, maintenance plan and Generating Cost Curve of Units；It establishes Optimized model and daily trading planning decomposition is carried out to the unit electric power data information, obtain Unit Combination in the moon of each unit and optimize data；Optimize data according to Unit Combination in the moon of each unit, exports each unit in the moon and generate electricity daily curve；The present invention is according to the monthly electricity plan of power plant, daily trading planning decomposition is carried out in conjunction with maintenance plan and Generating Cost Curve of Units, it is formulated in generation schedule with solving the prior art since experience is insufficient or accuracy in computation is not high, prepared generation schedule is caused the technical issues of deviation occur, it generates electricity daily curve data to which accurate calculation obtains each unit in the moon, and then generates profit maximization.
Description
Technical field
The present invention relates to generation schedules to decompose field more particularly to a kind of power plant based on power spot market price day hair
Electric plan itemizing method.
Background technique
Each power plant provides the energy by power generation production capacity for society and uses, and therefore, power plant must formulate relevant generation schedule,
To realize the profit maximization that generates electricity；But nowadays domestic power plant is all by the got formulation generation schedule of experience, due to experience
Insufficient or accuracy in computation is not high, causes prepared generation schedule deviation occur, can not generate profit maximization, and domestic
Temporarily without other power plant's daily trading planning decomposition methods.
Summary of the invention
The present invention provides a kind of power plant's daily trading planning decomposition method based on power spot market price, according to power plant
Monthly electricity plan, daily trading planning decomposition is carried out in conjunction with maintenance plan and Generating Cost Curve of Units, to solve existing skill
Art is formulated in generation schedule since experience is insufficient or accuracy in computation is not high, and prepared generation schedule is caused the skill of deviation occur
Art problem generates electricity curve data daily so that accurate calculation obtains each unit in the moon, and then generates profit maximization.
In order to solve the above-mentioned technical problem, the embodiment of the invention provides a kind of power plant based on power spot market price
Daily trading planning decomposition method, comprising:
Unit electric power data information is obtained, the unit electric power data information includes the monthly electricity plan of each unit, maintenance
Plan and Generating Cost Curve of Units；
It establishes Optimized model and daily trading planning decomposition is carried out to the unit electric power data information, obtain the moon of each unit
Interior Unit Combination optimizes data；
Optimize data according to Unit Combination in the moon of each unit, exports each unit in the moon and generate electricity daily curve.
Preferably, the unit electric power data information further include: the medium-term and long-term node electricity price of node where unit
Prediction data, unit essential attribute, generating set spare condition and the monthly transaction constraint condition of spare capacity ratio, unit, machine
Group start-up and shut-down costs and the booting of unit minimum and downtime.
Preferably, the Optimized model are as follows:
Wherein,
F (i, t)
={ ρLMP[P (i, t)-∑ PIt is medium-term and long-term(i, t)]+∑ ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)
+ρAuxiliaryPAssist electricity(i, t)-CI, cost of electricity-generating(P (i, t)+PAssist electricity(i, t))
-SThe number of starts(i, t) CI, start-up and shut-down costsI (i, t)
+{∑ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)-ρLMPBPurchase of electricity(i, t) } (1-I (i, t))
Wherein: t: time point；I (i, t): state, value 1 or 0 are indicated；ρLMP: the day node electricity price of prediction；P (i, t): the
The generated energy of i platform unit t moment；∑PIt is medium-term and long-term(i, t): electricity the sum of of the medium-term and long-term contract decomposition to i-th unit t moment；
ρIt is medium-term and long-term: medium-term and long-term contract electricity price；PAssist electricity(i, t): the electricity of i-th unit t moment ancillary service is spinning reserve capacity；
ρAuxiliary: the frequency modulation market guidance of ancillary service；CI, cost of electricity-generating: the cost of electricity-generating of i-th unit t moment；ρAuxiliary: the market of ancillary service
Electricity price；CI, start-up and shut-down costs: the start-up and shut-down costs of i-th unit；SThe number of starts(i, t): the start-stop time of i-th unit；BPurchase of electricity(i, t): from now
The electricity that goods market is bought.
Preferably, the Optimized model of establishing is to unit electric power data information progress daily trading planning decomposition
When, it further include being constrained according to preset constraint condition data are calculated.
Preferably, the constraint condition includes the monthly transaction constraint of unit:
P (i, t)+BPurchase of electricity(i, t) >=∑ PIt is medium-term and long-term(i, t)
0≤BPurchase of electricity(i, t)≤∑ PIt is medium-term and long-term(i, t)
Preferably, the constraint condition includes that unit generation amount and auxiliary power generation amount constrain:
PminI (i, t)≤P (i, t)+PAssist electricity(i, t)≤PmaxI (i, t)
0≤PAssist electricity(i, t)≤0.15*Pmax
Preferably, the constraint condition includes: that month generated energy is equal to generation schedule, and adds penalty coefficient:
∑ P (i, t)+s-≤ moon electricity plan+∑ | B (i, t) |-∑ P0(i, t)
Wherein, B (i, t) is to buy electricity, P from spot market0(i, t) is the electricity of medium-term and long-term contract business, sells and is positive,
It buys in and is negative.
Preferably, the constraint condition includes the constraint of unit climbing rate:
P (i, t)-P (i, t-1)≤UR (i)
P (i, t-1)-P (i, t)≤DR (i)
Wherein, i-th unit per minute adjusts power output maximum value, UR (i), DR (i) respectively indicate it is upward, adjust downwards
Maximum value.
Preferably, the constraint condition includes: the minimum available machine time constraint of unit, minimum unused time constraint:
Y (i, t)-Z (i, t)≤1；
Y (i, t)-Z (i, t)=I (i, t)-I (i, t-1)；
Wherein:
Y (i, t): i-th unit booting factor, Y (i, t)=1 indicate that t moment unit has start-up operation, the table of Y (i, t)=0
Show t moment unit without start-up operation；
Z (i, t): i-th compressor emergency shutdown factor, Z (i, t)=1 indicate that t moment unit has shutdown operation, the table of Z (i, t)=0
Show that t moment unit no shutdown operates；
I (i, t): i-th set state variable, I (i, t)=1 indicate that t moment unit is open state, the table of I (i, t)=0
Show that t moment unit is shutdown status；
Ton(i): i-th minimum available machine time；Toff(i): i-th minimum downtime.
Preferably, the constraint condition includes repair time constraint.
Compared with the prior art, the embodiment of the present invention has the following beneficial effects:
The present invention carries out day power generation in conjunction with maintenance plan and Generating Cost Curve of Units according to the monthly electricity plan of power plant
Plan itemizing is formulated in generation schedule since experience is insufficient or accuracy in computation is not high with solving the prior art, causes to be formulated
Generation schedule there is the technical issues of deviation, generate electricity daily curve data so that accurate calculation obtains each unit in the moon, in turn
Generate profit maximization.
Detailed description of the invention
Fig. 1: it is patrolled for power plant's daily trading planning decomposition method based on power spot market price in the embodiment of the present invention
Collect schematic diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 is please referred to, the preferred embodiment of the present invention provides a kind of power plant based on power spot market price day power generation
Plan itemizing method, comprising:
S1, obtains unit electric power data information, and the unit electric power data information includes the monthly electricity plan of each unit, inspection
Repair plan and Generating Cost Curve of Units；In the present embodiment, the unit electric power data information further include: node where unit
Medium-term and long-term node electricity price prediction data, unit essential attribute, generating set spare condition and spare capacity ratio, unit it is monthly
Constraint condition, Unit Commitment cost and the unit minimum of trading are switched on and downtime.
S2 establishes Optimized model and carries out daily trading planning decomposition to the unit electric power data information, obtains each unit
The moon in Unit Combination optimize data；In the present embodiment, it is described establish Optimized model to the unit electric power data information into
It further include being constrained according to preset constraint condition data are calculated when row daily trading planning decomposes.
In the present embodiment, the Optimized model are as follows:
Wherein,
F (i, t)
={ ρLMP[P (i, t)-∑ PIt is medium-term and long-term(i, t)]+∑ ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)
+ρAuxiliaryPAssist electricity(i, t)-CI, cost of electricity-generating(P (i, t)+PAssist electricity(i, t))
-SThe number of starts(i, t) CI, start-up and shut-down costsI (i, t)
+{∑ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)-ρLMPBPurchase of electricity(i, t) } (1-I (i, t))
Wherein: t: time point；I (i, t): state, value 1 or 0 are indicated；ρLMP: the day node electricity price of prediction；P (i, t): the
The generated energy of i platform unit t moment；∑PIt is medium-term and long-term(i, t): electricity the sum of of the medium-term and long-term contract decomposition to i-th unit t moment；
ρIt is medium-term and long-term: medium-term and long-term contract electricity price；PAssist electricity(i, t): the electricity of i-th unit t moment ancillary service is spinning reserve capacity；
ρAuxiliary: the frequency modulation market guidance of ancillary service；CI, cost of electricity-generating: the cost of electricity-generating of i-th unit t moment；ρAuxiliary: the market of ancillary service
Electricity price；CI, start-up and shut-down costs: the start-up and shut-down costs of i-th unit；SThe number of starts(i, t): the start-stop time of i-th unit；BPurchase of electricity(i, t): from now
The electricity that goods market is bought.
In the present embodiment, the constraint condition includes the monthly transaction constraint of unit:
P (i, t)+BPurchase of electricity(i, t) >=∑ PIt is medium-term and long-term(i, t)
0≤BPurchase of electricity(i, t)≤∑ PIt is medium-term and long-term(i, t)
In the present embodiment, the constraint condition includes that unit generation amount and auxiliary power generation amount constrain:
PminI (i, t)≤P (i, t)+PAssist electricity(i, t)≤PmaxI (i, t)
0≤PAssist electricity(i, t)≤0.15*Pmax
In the present embodiment, the constraint condition includes: that month generated energy is equal to generation schedule, and adds penalty coefficient:
∑ P (i, t)+s-≤ moon electricity plan+∑ | B (i, t) |-∑ P0(i, t)
Wherein, B (i, t) is to buy electricity, P from spot market0(i, t) is the electricity of medium-term and long-term contract business, sells and is positive,
It buys in and is negative.
In the present embodiment, the constraint condition includes the constraint of unit climbing rate:
P (i, t)-P (i, t-1)≤UR (i)
P (i, t-1)-P (i, t)≤DR (i)
Wherein, i-th unit per minute adjusts power output maximum value, UR (i), DR (i) respectively indicate it is upward, adjust downwards
Maximum value.
In the present embodiment, the constraint condition includes: the minimum available machine time constraint of unit, minimum unused time constraint:
Y (i, t)-Z (i, t)≤1；
Y (i, t)-Z (i, t)=I (i, t)-I (i, t-1)；
Wherein:
Y (i, t): i-th unit booting factor, Y (i, t)=1 indicate that t moment unit has start-up operation, the table of Y (i, t)=0
Show t moment unit without start-up operation；
Z (i, t): i-th compressor emergency shutdown factor, Z (i, t)=1 indicate that t moment unit has shutdown operation, the table of Z (i, t)=0
Show that t moment unit no shutdown operates；
I (i, t): i-th set state variable, I (i, t)=1 indicate that t moment unit is open state, the table of I (i, t)=0
Show that t moment unit is shutdown status；
Ton(i): i-th minimum available machine time；Toff(i): i-th minimum downtime.
In the present embodiment, the constraint condition includes repair time constraint.
S3 optimizes data according to Unit Combination in the moon of each unit, exports each unit in the moon and generates electricity daily curve.
The present invention is according to the monthly electricity plan of power plant, in conjunction with maintenance plan and Generating Cost Curve of Units, with the benefit that generates electricity
Profit maximum turns to target, by obtaining the daily optimum combination mode of unit to Unit Combination model optimizing, realizes day power generation meter
The establishment drawn.
Combined with specific embodiments below, the present invention is described in detail.
Input data:
I. (data are obtained the monthly electricity plan of each unit by the monthly electricity plan that emulation provides, this data user can repair
Change)；
Ii. maintenance plan (data are obtained by the maintenance plan that emulation provides, this data user can modify)；
Iii. (data pass through Market Simulation simulation software simulation and prediction for the medium-term and long-term node electricity price prediction of node where unit
Acquisition or the historical price data actually occurred by market make regression analysis and are fitted and predict)；
Iv. unit essential attribute (the unit account information acquisition that data are inputted by user)；
V. (voluntarily typing or Market Simulation simulation software provide user Generating Cost Curve of Units, describe unit each moment
Cost of electricity-generating)；
Vi. generating set spare condition, spare capacity ratio (user's voluntarily typing)；
Vii. the monthly transaction constraint of unit (system default, constraint condition is depending on market rules)；
Viii. Unit Commitment cost, unit minimum is switched on and downtime (user's voluntarily typing)；
Output data:
I. 24 daily power curves of each unit in the moon；
Algorithm introduction:
I. in monthly progress daily trading planning decomposition, with power plant, overall profit is up to optimization aim daily, according to machine
Group built-up pattern obtains Unit Combination in the moon of each unit and optimizes situation.Specific Optimized model are as follows:
Wherein,
F (i, t)
={ ρLMP[P (i, t)-∑ PIt is medium-term and long-term(i, t)]+∑ ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)
+ρAuxiliaryPAssist electricity(i, t)-CI, cost of electricity-generating(P (i, t)+PAssist electricity(i, t))
-SThe number of starts(i, t) CI, start-up and shut-down costsI (i, t)
+{∑ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)-ρLMPBPurchase of electricity(i, t) } (1-I (i, t))
Formula explanation: formula is divided by state variable I (i, t) by two states, state 1 indicates to calculate power plant by opening
Machine power generation day gross profit obtained；State 2 indicates that power plant shuts down, and the electricity day obtained by buying spot market is always sharp
Profit.
Parameter declaration:
T: time point (with 1 hour for interval, totally 24), the period not comprising maintenance,
I (i, t): value 1 or 0,1 value 1 of state, 2 value 0 of state,
ρLMP: the day node electricity price of prediction,
The generated energy of P (i, t): i-th unit t moment,
∑PIt is medium-term and long-term(i, t): the sum of the electricity of medium-term and long-term contract decomposition to i-th unit t moment,
ρIt is medium-term and long-term: medium-term and long-term contract electricity price,
PAssist electricity(i, t): the electricity of i-th unit t moment ancillary service is spinning reserve capacity,
ρAuxiliary: the frequency modulation market guidance of ancillary service,
CI, cost of electricity-generating: the cost of electricity-generating of i-th unit t moment,
ρAuxiliary: the market guidance of ancillary service,
CI, start-up and shut-down costs: the start-up and shut-down costs of i-th unit,
SThe number of starts(i, t): the start-stop time of i-th unit,
BPurchase of electricity(i, t): the electricity bought from spot market.
Constraint condition:
1. the monthly transaction constraint of unit:
P (i, t)+BPurchase of electricity(i, t) >=∑ PIt is medium-term and long-term(i, t)
0≤BPurchase of electricity(i, t)≤∑ PIt is medium-term and long-term(i, t)
2. unit generation amount and auxiliary power generation amount constrain:
PminI (i, t)≤P (i, t)+PAssist electricity(i, t)≤PmaxI (i, t)
0≤PAssist electricity(i, t)≤0.15*Pmax
3. month generated energy is equal to generation schedule, and adds penalty coefficient:
∑ P (i, t)+s-≤ moon electricity plan+∑ | B (i, t) |-∑ P0(i, t)
B (i, t) is to buy electricity, P from spot market0(i, t) is that the electricity of medium-term and long-term contract business (is sold and is positive, buy in
It is negative)
4. unit climbing rate constrains:
P (i, t)-P (i, t-1)≤UR (i)
P (i, t-1)-P (i, t)≤DR (i)
I-th unit adjusts power output maximum value per minute, and UR (i), DR (i) respectively indicate maximum that is upward, adjusting downwards
Value
5. unit minimum available machine time constraint, minimum unused time constraint:
Y (i, t)-Z (i, t)≤1
Formula explanation: unit cannot exist simultaneously startup and shutdown operation；
Y (i, t)-Z (i, t)=I (i, t)-I (i, t-1)
Formula illustrates: the value of the unit starting factor and the shutdown factor has to comply with the variation of unit startup-shutdown state；
Formula explanation: unit has start-up operation in t moment, then needs to guarantee unit in t+TonPeriod is in booting shape
State；
Formula explanation: unit has shutdown operation in t moment, then needs to guarantee unit in t+ToffPeriod, which is in, shuts down shape
State；
Parameter declaration:
Y (i, t): i-th unit booting factor.Y (i, t)=1 indicates that t moment unit has start-up operation, the table of Y (i, t)=0
Show t moment unit without start-up operation；
Z (i, t): i-th compressor emergency shutdown factor.Z (i, t)=1 indicates that t moment unit has shutdown operation, the table of Z (i, t)=0
Show that t moment unit no shutdown operates；
I (i, t): i-th set state variable.I (i, t)=1 indicates that t moment unit is open state, the table of I (i, t)=0
Show that t moment unit is shutdown status；
Ton(i): i-th minimum available machine time；
Toff(i): i-th minimum downtime；
6. the repair time constrains；
Using MILP (mixed integer linear programming) solver, calculates each unit and generate electricity daily curve.
Particular embodiments described above has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that the above is only a specific embodiment of the present invention, the protection being not intended to limit the present invention
Range.It particularly points out, to those skilled in the art, all within the spirits and principles of the present invention, that is done any repairs
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of power plant's daily trading planning decomposition method based on power spot market price characterized by comprising
Unit electric power data information is obtained, the unit electric power data information includes the monthly electricity plan of each unit, maintenance plan
And Generating Cost Curve of Units；
It establishes Optimized model and daily trading planning decomposition is carried out to the unit electric power data information, obtain machine in the moon of each unit
Group Combinatorial Optimization data；
Optimize data according to Unit Combination in the moon of each unit, exports each unit in the moon and generate electricity daily curve.
2. power plant's daily trading planning decomposition method based on power spot market price, feature exist as described in claim 1
In the unit electric power data information further include: the medium-term and long-term node electricity price prediction data of node, unit belong to substantially where unit
Property, generating set spare condition and the monthly transaction constraint condition of spare capacity ratio, unit, Unit Commitment cost and unit it is minimum
Booting and downtime.
3. power plant's daily trading planning decomposition method based on power spot market price, feature exist as described in claim 1
In the Optimized model are as follows:
Wherein,
F (i, t)={ ρLMP[P (i, t)-∑ PIt is medium-term and long-term(i, t)]+∑ ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)+ρAuxiliaryPAssist electricity(i, t)-CI, cost of electricity-generating(P
(i, t)+PAssist electricity(i, t))-SThe number of starts(i, t) CI, start-up and shut-down costsI (i, t)+{ ∑ ρIt is medium-term and long-termPIt is medium-term and long-term(i, t)-ρLMPBPurchase of electricity(i, t) } (1-I
(i, t))
Wherein: t: time point；I (i, t): state, value 1 or 0 are indicated；ρLMP: the day node electricity price of prediction；P (i, t): i-th
The generated energy of unit t moment；∑PIt is medium-term and long-term(i, t): electricity the sum of of the medium-term and long-term contract decomposition to i-th unit t moment；ρIt is medium-term and long-term:
Medium-term and long-term contract electricity price；PAssist electricity(i, t): the electricity of i-th unit t moment ancillary service is spinning reserve capacity；ρAuxiliary: it is auxiliary
Help the frequency modulation market guidance of service；CI, cost of electricity-generating: the cost of electricity-generating of i-th unit t moment；ρAuxiliary: the market guidance of ancillary service；
CI, start-up and shut-down costs: the start-up and shut-down costs of i-th unit；SThe number of starts(i, t): the start-stop time of i-th unit；BPurchase of electricity(i, t): from stock city
The electricity that field is bought.
4. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 3
In, it is described when establishing Optimized model and carrying out daily trading planning decomposition to the unit electric power data information, it further include according to default
Constraint condition to calculate data constrain.
5. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 4
In the constraint condition includes the monthly transaction constraint of unit:
P (i, t)+BPurchase of electricity(i, t) >=∑ PIt is medium-term and long-term(i, t)
0≤BPurchase of electricity(i, t)≤∑ PIt is medium-term and long-term(i, t)
6. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 4
In the constraint condition includes that unit generation amount and auxiliary power generation amount constrain:
PminI (i, t)≤P (i, t)+PAssist electricity(i, t)≤PmaxI (i, t)
0≤PAssist electricity(i, t)≤0.15*Pmax
7. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 4
In the constraint condition includes: that month generated energy is equal to generation schedule, and adds penalty coefficient:
∑ P (i, t)+s-≤ moon electricity plan+∑ | B (i, t) |-∑ P0(i, t)
Wherein, B (i, t) is to buy electricity, P from spot market0(i, t) is the electricity of medium-term and long-term contract business, sells and is positive, buys in
It is negative.
8. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 4
In the constraint condition includes the constraint of unit climbing rate:
P (i, t)-P (i, t-1)≤UR (i)
P (i, t-1)-P (i, t)≤DR (i)
Wherein, i-th unit adjusts power output maximum value per minute, and UR (i), DR (i) respectively indicate maximum that is upward, adjusting downwards
Value.
9. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 4
In the constraint condition includes: the minimum available machine time constraint of unit, minimum unused time constraint:
Y (i, t)-Z (i, t)≤1；
Y (i, t)-Z (i, t)=I (i, t)-I (i, t-1)；
Wherein:
Y (i, t): i-th unit booting factor, Y (i, t)=1 indicate that t moment unit has start-up operation, and Y (i, t)=0 indicates t
Moment, unit was without start-up operation；
Z (i, t): i-th compressor emergency shutdown factor, Z (i, t)=1 indicate that t moment unit has shutdown operation, and Z (i, t)=0 indicates t
Moment unit no shutdown operation；
I (i, t): i-th set state variable, I (i, t)=1 indicate that t moment unit is open state, and I (i, t)=0 indicates t
Moment unit is shutdown status；
Ton(i): i-th minimum available machine time；Toff(i): i-th minimum downtime.
10. power plant's daily trading planning decomposition method based on power spot market price, feature exist as claimed in claim 4
In the constraint condition includes repair time constraint.
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