CN117039872A - Multi-time-scale weak robust power supply scheduling method considering self-adaptive uncertain set - Google Patents

Multi-time-scale weak robust power supply scheduling method considering self-adaptive uncertain set Download PDF

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CN117039872A
CN117039872A CN202311015025.4A CN202311015025A CN117039872A CN 117039872 A CN117039872 A CN 117039872A CN 202311015025 A CN202311015025 A CN 202311015025A CN 117039872 A CN117039872 A CN 117039872A
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徐英
蔡钦钦
仪忠凯
徐佳宁
王恒震
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Harbin Institute of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • 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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
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    • 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
    • H02J2300/28—The renewable source being wind energy

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Abstract

The invention discloses a multi-time scale weak robust power supply scheduling method considering a self-adaptive uncertain set, which is used for making decisions for the on-off state of a coal-fired unit, the power pre-allocation problem of the unit and a positive and negative standby plan, wherein the daily self-adaptive uncertain set is constructed based on daily power prediction data and historical daily prediction errors of a wind-solar power supply in a daily scheduling stage; the scheduling target of the real-time scheduling stage is based on more accurate real-time prediction data, and the unit power is further adjusted to ensure the real-time power balance of the system; the task of the real-time scheduling stage is to make an optimization decision for the quick start-stop unit and the energy storage charge-discharge power of the pumped storage unit, the gas unit and the like, and make a power adjustment plan for the day-ahead power pre-allocation plan of the coal-fired unit; the invention can effectively relieve the influence of uncertain power on power grid dispatching by the coordination of the day-ahead dispatching stage and the real-time dispatching stage.

Description

Multi-time-scale weak robust power supply scheduling method considering self-adaptive uncertain set
Technical Field
The invention relates to a high-proportion wind-light energy power dispatching technology, in particular to a multi-time-scale weak robust power dispatching method considering a self-adaptive uncertain set.
Background
In order to adapt to development, a novel power system mainly comprising wind power and photovoltaic power generation needs to be constructed, wherein inherent inaccuracy of wind and light energy sources causes uncertainty of power supply power, and great challenges are brought to power grid dispatching.
The robust optimization method solves the optimal result under the worst scene, and is a typical representative method for coping with an optimal model containing uncertain random variables. The first stage model of the two-stage robust optimization method does not contain uncertain variables, but only contains uncertain variables in the second stage, and the total target value corresponding to the two decision stages in the worst scene is the smallest when the uncertain variables in the second stage are optimized in an iterative solution mode.
In a high-proportion wind-light energy power grid, a power balance plan is formulated in a day-ahead scheduling stage based on day-ahead predicted power, and real-time unbalanced power mainly from wind-light power supply uncertainty is balanced in a real-time scheduling stage. The day-ahead scheduling stage is coordinated with the real-time scheduling stage, so that the influence of uncertain power on power grid scheduling is further relieved.
The problem that the system operation is uneconomical due to the fact that an uncertainty set constructed by the existing two-stage robust optimization algorithm is too conservative in a model containing uncertainty variables is solved. In addition, in the two-stage robust optimization algorithm adopted in the prior art, the first stage is generally set to be a model for solving a day-ahead scheduling stage, and the second stage is set to be a model for solving a real-time scheduling stage, but the solving processes of the day-ahead scheduling and the real-time scheduling stages are not in the same time scale, so that the existing setting method is unreasonable. In contrast, in the day-ahead scheduling, not only the start-stop problem of the unit but also the power pre-allocation problem of the unit needs to be determined, namely, the problem of solving the 0-1 variable is solved, and the problem of solving the continuous variable is solved.
Disclosure of Invention
The invention aims to: the invention aims to solve the defects in the prior art and provide a multi-time-scale weak robust power supply scheduling method considering a self-adaptive uncertain set.
The technical scheme is as follows: the invention relates to a multi-time scale weak robust power supply scheduling method considering a self-adaptive uncertain set, which comprises a day-ahead scheduling stage and a real-time scheduling stage;
the day-ahead scheduling stage comprises the following steps:
step (11), calculating positive and negative prediction errors of power based on the day-ahead power prediction data and the historical day-ahead prediction errors of the wind-solar power supply; the actual value is larger than the predicted value by a positive prediction error; the actual value is smaller than the predicted value to be a negative prediction error;
then introducing probability variablesAnd->To construct the Xiyi set before date DA ；
Probability variableIndicating that the actual power is higher than the predicted value, +.>Indicating that the actual power is below the predicted value, +.> Mean value of prediction error; />And->Respectively, positive prediction error bandwidth and negative prediction error bandwidth;
step (12), solving a unit combination model (starting state of a unit) of the machine-controllable generator unit based on a two-stage robust optimization method, pre-distributing power and absorbing standby power; including stage 1 and stage 2;
step 1, determining the start-stop state of a thermal power controllable generator set, and representing the start-stop state by a matrix form (4);
x＝(u g,t ,v g,t ) Middle u g,t Corresponding to the binary variable of the starting state, v g,t Binary variables corresponding to the shutdown state; q (x, ζ) t ) Optimizing a target for day-ahead scheduling; c T x is a matrix representation of equation (5); ax-b.ltoreq.0 is a matrix representation of constraints (6) - (13);
the objective function is the start-stop cost of the unit (i.e. the fixed cost required for starting (stopping) once), expressed as formula (5):
in the method, in the process of the invention,for the start-up cost of the unit g in the period t, < >>The shutdown cost of the unit g in the period t is set;
stage 2, optimizing the objective function value Q (x, ζ) in worst scenario t ) I.e. scheduling periodThe internal targets are the running cost of the unit and the consumption standby cost;
step (13), issuing a day-ahead dispatch instruction
The scheduling target of the real-time scheduling stage is to adjust the unit power based on the real-time predicted power so as to ensure the real-time power balance of the system, and the method specifically comprises the following steps of;
step (21), calculating unbalanced power, namely the difference between the real-time predicted power and the predicted power before the day;
step (22), constructing a real-time uncertainty set based on the real-time prediction data and the historical real-time prediction errors;
step (23), solving a unit power adjustment plan based on a two-stage robust optimization method, calling a digestion reserve and an energy storage, and determining a wind-discarding light-discarding power and a load shedding; including stage 1 and stage II;
stage I, determining the states of a gas turbine capable of being started and stopped quickly, pumped storage and energy storage, wherein the states are expressed as formulas (26) - (29) in a matrix form; the objective function (26) is the transition cost of the energy storage charge-discharge state,
s.t. Wq≤ly (27)
By+Mq+Dξ＝h (28)
step II, optimizing an objective function value in the worst scene to obtain the running cost, the energy storage charging and discharging cost, the wind discarding and light discarding and the load discarding cost of the quick start-stop unit;
wherein z is e,t In charge-discharge state of energy storage, z e,t =1 is discharge state, z e,t =0 is the state of charge;the discharging and charging costs are respectively; />The discharging and charging power are respectively; />The unit cost of the discharging and charging power is respectively; />The load and wind discarding and light discarding power are respectively carried out; /> The unit cost of the load discarding and the wind discarding light discarding power is respectively;
and (24) issuing a real-time scheduling instruction.
Further, in the step (11), the positive and negative power prediction errors may be deterministic point prediction or probabilistic interval prediction, and the specific calculation method is as follows:
wind-solar power supply power is described asThe interval with confidence d is denoted asThe power prediction average value is +.>
Then dividing the historical prediction error into positive and negative errors, and respectively solving the cumulative distribution functions CDF of the positive and negative prediction errors to obtain the positive prediction error bandwidth under the confidence level dAnd negative prediction error bandwidth->
Finally obtaining the lower limit of the output of the power intervalUpper limit of output of power section +.>
Further, the method for constructing the uncertainty set before the day in the step (11) is as follows:
step (A), adjusting the conservation degree of the robust optimization method, and introducing probability variablesAnd->The uncertainty variable isWhen->When the wind power output reaches the upper limit, whenWhen the wind power output reaches the lower limit;
this avoids the worst scenarioOr->If probability variables are not introduced, the random variables only change at the two ends of the upper bound and the lower bound of the uncertainty set, and the true change range of the uncertainty power is difficult to reflect;
step (B), the larger the wind power level is, the larger the probability of positive error is; the smaller the wind power level, the larger the probability of negative error, and the probability variable is modifiedAnd->The upper and lower limits of (2) are as shown in formula (1);
in the method, in the process of the invention,the probability variable for controlling the wind power output to deviate upwards at the predicted value is controlled; />The probability variable is used for controlling the downward deviation of the wind power output at the predicted value; />And->According to the predicted value xi before the day t The corresponding output level grade at each moment; />Wind power output +.>When the error belongs to the class I, the probability of positive error and negative error appears in the history;is a probability variable +.>Upper and lower limits of (2); />Is a probability variable +.>Upper and lower limits of (2);
step (C), restraining the climbing rate of the wind-light power supply power in order to ensure that the output fluctuation of the wind-light power supply power variable is strong and more in line with the actual situation, wherein the climbing rate is shown in the formula (2):
in the method, in the process of the invention,respectively the minimum value and the maximum value of the forward fluctuation; />Respectively the minimum value and the maximum value of negative fluctuation; zeta type toy t-1 Is the wind-light power supply power at the moment t-1, xi t The power of the wind-solar power supply at the moment t;
step (D), based on the day-ahead power prediction data, combining the historical prediction error bandwidth and the occurrence probability of positive and negative prediction errors of different wind power levels, and describing a self-adaptive day-ahead uncertainty set of the wind-solar power supply as a formula (3);
in the formula (xi) DA The set of intervals is determined prior to the day for the resulting adaptation.
Further, the unit combination model of the controllable generator unit in the stage 1 comprises a startup process modeling and a shutdown process modeling;
the modeling of the starting-up process is shown in formulas (6) - (9);
the running time of the unit at the initial moment; equation (6) shows that the unit is already in the shutdown state before the scheduling>The period of time, and therefore from the initial moment, the unit g must be in a shutdown state of at least L g A time period; formula (7) is a minimum continuous operation time constraint of the unit,>for the minimum continuous operating period of the unit, in +.>Can be selectively started, (x) g,t -x g,t-1 ) The expression =1-0 indicates power on, and there is +.>Is a period of time; the formula (8) shows that the unit is at +.>Stage selection starting-up can meet the starting time H g But until the end of the scheduling period, the minimum run time cannot be met +.>Constraint; the formula (9) shows that the unit is at +.>Starting up in this range, the starting time H cannot be satisfied g And minimum run time->Constraint, the constraint should be allowed to be opened until the end of the scheduling period;
the shutdown process modeling is shown in formulas (10) - (13);
in the method, in the process of the invention,indicating that the unit must be in a stage of operation from the scheduled time; />For a minimum continuous run time of the unit; />Is the already running time of the unit at the initial moment.
Further, the matrix form in the stage 2 is expressed as:
s.t.Wx+Hy≤e (15)
Dy≤m (16)
Ky+Vξ＝L (17)
x≥0,y≥0 (18)
in the formula (xi) DA An uncertainty set for a day-ahead scheduling stage;a scheduling period of a day-ahead scheduling stage;
the operating costs and the costs of the digestion reserve for phase 2 are expressed as follows:
in the method, in the process of the invention,the output is the output of the machine set before the day; />The positive standby capacity of the unit is set; />Negative spare capacity for the unit; />The unit is the standby cost; />Negative standby cost per unit; f (·) is a variable force cost function of the unit, a quadratic function is adopted, and +.>
The constraint conditions of the stage 2 comprise unit output constraint, unit climbing constraint, line tide constraint and tide equation constraint;
in the method, in the process of the invention,P g the minimum output limit value and the maximum output limit value of the unit g are respectively; />The maximum ascending and descending climbing rates of the unit g in unit time are respectively; delta d For a scheduling time interval; GSF (GSF) n-l A branch power flow distribution factor which influences the active power of the branch l for the change of the active power of the node n; />Algebraic sum of injection power of n nodes; f (F) l Maximum transmission power for the first branch; zeta type toy t Is an uncertainty set; l (L) n,t Is the load demand of node n during time period t.
The real-time scheduling stage is based on more accurate real-time power prediction, and the uncertainty is further reduced by adopting a two-stage robust economic scheduling method in a shorter scheduling period; the constraint condition of the real-time scheduling in the step (23) is formulas (31) - (36);
equation (31) is the output adjustment amount of the real-time scheduling stage unit; the formula (32) is the output power of the real-time scheduling stage unit; equation (33) is the output power limit constraint of the real-time scheduling stage unit; equation (34) and equation (35) are climbing constraints of the output power of the real-time scheduling stage unit; equation (36) is the power balancing constraint of the real-time scheduling phase; wherein Δp g,t The output adjustment quantity of the unit in the real-time scheduling stage is adjusted;respectively reserving negative standby and positive standby for a day-ahead scheduling stage; />The output power of the real-time scheduling stage is obtained; />Uncertain power for a real-time scheduling phase;
the constraint condition of energy storage is formulas (37) - (40);
the formula (37) and the formula (38) are energy storage charging and discharging power constraint, the formula (39) is energy storage state of charge constraint, and the formula (40) is energy storage energy constraint;discharging and charging power for energy storage; soc e,t Is the state of charge of the stored energy; soc min 、soc max Respectively a minimum value and a maximum value of the energy storage charge state; η (eta) cha 、η dis Charging efficiency and discharging efficiency, respectively.
The beneficial effects are that: according to the method, the day-ahead scheduling plan and the real-time scheduling plan are solved based on the two-stage robust optimization method of the self-adaptive uncertain set, and the influence of uncertain power on power grid scheduling can be effectively relieved by considering different time scales through coordination of the day-ahead scheduling stage and the real-time scheduling stage.
Drawings
FIG. 1 is an overall flow chart of the present invention;
fig. 2 is a schematic diagram of power balance of a day-ahead schedule stage and an intra-day power adjustment stage according to an embodiment.
Detailed Description
The technical scheme of the present invention is described in detail below, but the scope of the present invention is not limited to the embodiments.
As shown in fig. 1, the multi-time scale weak robust power scheduling method considering the adaptive uncertainty set comprises a day-ahead scheduling stage and a real-time scheduling stage;
the day-ahead scheduling stage comprises the following steps:
step (11), calculating positive and negative prediction errors of power based on the day-ahead power prediction data and the historical day-ahead prediction errors of the wind-solar power supply;
then introducing probability variablesAnd->To construct the Xiyi set before date DA ；
Probability variableIndicating that the actual power is higher than the predicted value, +.>Indicating that the actual power is below the predicted value, +.> Mean value of prediction error; />And->Respectively, positive prediction error bandwidth and negative prediction error bandwidth;
step (12), solving a unit combination model of the machine-controllable generator unit based on a two-stage robust optimization method, pre-distributing power and absorbing standby power; including stage 1 and stage 2;
step 1, determining the start-stop state of a thermal power controllable generator set, and representing the start-stop state by a matrix form (4);
x＝(u g,t ,v g,t ) Middle u g,t Corresponding to the binary variable of the starting state, v g,t Binary variable corresponding to shutdown state, Q (x, ζ) t ) Optimizing a target for day-ahead scheduling; c T x is a matrix representation of equation (5); ax-b.ltoreq.0 is a matrix representation of constraints (6) - (13);
the objective function is the start-stop cost of the unit, expressed as formula (5):
in the method, in the process of the invention,for the start-up cost of the unit g in the period t, < >>The shutdown cost of the unit g in the period t is set;
stage 2, optimizing the objective function value Q (x, ζ) in worst scenario t ) I.e. scheduling periodThe internal targets are the running cost of the unit and the consumption standby cost;
step (13), issuing a day-ahead dispatch instruction
The scheduling target of the real-time scheduling stage is to adjust the unit power based on the real-time predicted power so as to ensure the real-time power balance of the system, and the method specifically comprises the following steps of;
step (21), the real-time predicted power and the predicted power before the day are subjected to difference to obtain unbalanced power;
step (22), constructing a real-time uncertainty set based on the real-time prediction data and the historical real-time prediction errors;
step (23), solving a unit power adjustment plan based on a two-stage robust optimization method, calling a digestion reserve and an energy storage, and determining a wind-discarding light-discarding power and a load shedding; including stage 1 and stage II;
stage I, determining the states of a gas turbine capable of being started and stopped quickly, pumped storage and energy storage, wherein the states are expressed as formulas (26) - (29) in a matrix form; the objective function (26) is the transition cost of the energy storage charge-discharge state,
s.t. Wq≤ly (27)
By+Mq+Dξ＝h (28)
in the above formula, q,W, y, l, B, M, D, h are all intermediate calculations; step II, optimizing an objective function value in the worst scene to obtain the running cost, the energy storage charging and discharging cost, the wind discarding and light discarding and the load discarding cost of the quick start-stop unit;
wherein z is e,t In charge-discharge state of energy storage, z e,t =1 is discharge state, z e,t =0 is the state of charge;the discharging and charging costs are respectively; />The discharging and charging power are respectively; />The unit cost of the discharging and charging power is respectively; />The load and wind discarding and light discarding power are respectively carried out; /> The unit cost of the load discarding and the wind discarding light discarding power is respectively;
and (24) issuing a real-time scheduling instruction.
Automatic power generation control can also be performed after day-ahead scheduling and real-time scheduling are completed.
Examples:
in the embodiment, by taking the power grid topological structure of the IEEE33 node as an example, a day-ahead interval uncertainty set is constructed by utilizing the day-ahead predicted power of wind power and the historical data of wind power of a certain real power grid, and is shown as a light green interval in fig. 2, a day-ahead scheduling model is firstly solved based on the day-ahead load demand power of the power grid, and a machine set start-up and shut-down state and a power pre-allocation result are obtained. Then, a real-time interval uncertainty set is constructed based on the real-time predicted power of wind power and the historical real-time power deviation data, and the real-time interval uncertainty set in the embodiment is shown in an orange color area in fig. 2. And then correcting the planned power of the thermal power generating unit in the future based on the real-time predicted power of wind power, and dispatching the power correction result and the stored energy of the thermal power generating unit, wherein the final dispatching result is shown in figure 2.
According to the analysis of fig. 2, compared with the future uncertainty set, the range of the future uncertainty set constructed based on the wind-light energy real-time power prediction data is smaller, so that the uncertainty degree of the wind-light energy is further reduced.

Claims (6)

1. A multi-time scale weak robust power supply scheduling method considering a self-adaptive uncertain set is characterized by comprising a day-ahead scheduling stage and a real-time scheduling stage;
the day-ahead scheduling stage comprises the following steps:
step (11), calculating positive and negative prediction errors of power based on the day-ahead power prediction data and the historical day-ahead prediction errors of the wind-solar power supply;
then introducing probability variablesAnd->To construct the Xiyi set before date DA ；
Probability variableIndicating that the actual power is higher than the predicted value, +.>Indicating that the actual power is below the predicted value, +.> Mean value of prediction error; />And->Respectively, positive prediction error bandwidth and negative prediction error bandwidth;
step (12), solving a unit combination model of the machine-controllable generator unit based on a two-stage robust optimization method, pre-distributing power and absorbing standby power; including stage 1 and stage 2;
step 1, determining the start-stop state of a thermal power controllable generator set, and representing the start-stop state by a matrix form (4);
x＝(u g,t ,v g,t ) Middle u g,t Corresponding to the binary variable of the starting state, v g,t Binary variable corresponding to shutdown state, Q (x, ζ) t ) Optimizing a target for day-ahead scheduling; c T x is a matrix representation of equation (5); ax-b.ltoreq.0 is a matrix representation of constraints (6) - (13);
the objective function is the start-stop cost of the unit, expressed as formula (5):
in the method, in the process of the invention,for the start-up cost of the unit g in the period t, < >>The shutdown cost of the unit g in the period t is set;
stage 2, optimizing the objective function value Q (x, ζ) in worst scenario t ) I.e. scheduling periodThe internal targets are the running cost of the unit and the spare cost；
Step (13), issuing a day-ahead dispatch instruction
The scheduling target of the real-time scheduling stage is to adjust the unit power based on the real-time predicted power so as to ensure the real-time power balance of the system, and the method specifically comprises the following steps of;
step (21), the real-time predicted power and the predicted power before the day are subjected to difference to obtain unbalanced power;
step (22), constructing a real-time uncertainty set based on the real-time prediction data and the historical real-time prediction errors;
step (23), solving a unit power adjustment plan based on a two-stage robust optimization method, calling a digestion reserve and an energy storage, and determining a wind-discarding light-discarding power and a load shedding; including stage 1 and stage II;
stage I, determining the states of a gas turbine capable of being started and stopped quickly, pumped storage and energy storage, wherein the states are expressed as formulas (26) - (29) in a matrix form; the objective function (26) is the transition cost of the energy storage charge-discharge state,
s.t.Wq≤ly(27)
By+Mq+Dξ＝h(28)
in the above formula, q,W, y, l, B, M, D, h are all intermediate calculations; step II, optimizing objective function values in the worst scene to obtain the running cost, the energy storage charging and discharging cost, the wind discarding and light discarding cost and the load discarding cost of the quick start-stop unit；
Wherein z is e,t In charge-discharge state of energy storage, z e,t =1 is discharge state, z e,t =0 is the state of charge;the discharging and charging costs are respectively; />The discharging and charging power are respectively; />The unit cost of the discharging and charging power is respectively; />The load and wind discarding and light discarding power are respectively carried out; /> The unit cost of the load discarding and the wind discarding light discarding power is respectively;
and (24) issuing a real-time scheduling instruction.
2. The multi-time scale weak robust power scheduling method considering adaptive uncertainty set according to claim 1, wherein the power positive and negative prediction error calculating method in step (11) is as follows:
wind-solar power supply power is firstly described as a section with a confidence degree of d and is recorded asThe power prediction average value is
Then dividing the historical prediction error into positive and negative errors, and respectively solving the cumulative distribution functions CDF of the positive and negative prediction errors to obtain the positive prediction error bandwidth under the confidence level dAnd negative prediction error bandwidth->
Finally obtaining the lower limit of the output of the power intervalUpper limit of output of power section +.>
3. The multi-time scale weak robust power scheduling method considering adaptive uncertainty set of claim 1, wherein the method of constructing the day-ahead uncertainty set in step (11) is:
step (A), introducing probability variablesAnd->The uncertainty variable is +.> When (when)When the wind power output reaches the upper limit, when +.>When the wind power output reaches the lower limit;
step (B), modifying probability variablesAnd->The upper and lower limits of (2) are as shown in formula (1);
in the method, in the process of the invention,the probability variable is used for controlling the wind-solar power supply output to deviate upwards at the predicted value; />The probability variable is used for controlling the downward deviation of the wind power output at the predicted value; />And->According to the predicted value xi before the day t The corresponding output level grade at each moment;wind power output +.>When the error belongs to the class I, the probability of positive error and negative error appears in the history;Γ up 、is a probability variable +.>Upper and lower limits of (2);Γ dn 、/>is a probability variable +.>Upper and lower limits of (2);
step (C), restraining the climbing rate of the wind-solar power supply power, wherein the climbing rate is shown in a formula (2):
in the method, in the process of the invention,respectively the minimum value and the maximum value of the forward fluctuation; />Respectively the minimum value and the maximum value of negative fluctuation; zeta type toy t-1 Is the wind-light power supply power at the moment t-1, xi t The power of the wind-solar power supply at the moment t;
step (D), based on the day-ahead power prediction data, combining the historical prediction error bandwidth and the occurrence probability of positive and negative prediction errors of different wind power levels, and describing a self-adaptive day-ahead uncertainty set of the wind-solar power supply as a formula (3);
in the formula (xi) DA The set of intervals is determined prior to the day for the resulting adaptation.
4. The multi-time scale weak robust power scheduling method considering adaptive uncertainty sets according to claim 1, wherein the unit combination model of the controllable generator unit in the stage 1 comprises a power-on process modeling and a power-off process modeling;
the modeling of the starting-up process is shown in formulas (6) - (9);
the running time of the unit at the initial moment; equation (6) shows that the unit is already in the shutdown state before the scheduling>The period of time, and therefore from the initial moment, the unit g must be in a shutdown state of at least L g A time period; formula (7) is a minimum continuous operation time constraint of the unit,>for the minimum continuous operating period of the unit, in +.>Can be selectively started, (x) g,t -x g,t-1 ) The expression of =1-0 indicates power-on, and there isIs a period of time; the formula (8) shows that the unit is at +.>Stage selection starting-up can meet the starting time H g But until the end of the scheduling period, the minimum run time cannot be met +.>Constraint; the formula (9) represents that if the unit isStarting up in this range, the starting time H cannot be satisfied g And minimum run time->Constraint, the constraint should be allowed to be opened until the end of the scheduling period;
the shutdown process modeling is shown in formulas (10) - (13);
in the method, in the process of the invention,indicating that the unit must be in a stage of operation from the scheduled time; />For a minimum continuous run time of the unit; />Is the already running time of the unit at the initial moment.
5. The multi-time scale weak robust power scheduling method considering adaptive uncertainty set of claim 1, wherein the matrix form in phase 2 is expressed as:
s.t.Wx+Hy≤e(15)
Dy≤m(16)
Ky+Vξ＝L(17)
x≥0,y≥0(18)
in the formula (xi) DA An uncertainty set for a day-ahead scheduling stage;a scheduling period of a day-ahead scheduling stage;
the operating costs and the costs of the digestion reserve for phase 2 are expressed as follows:
in the method, in the process of the invention,the output is the output of the machine set before the day; />The positive standby capacity of the unit is set; />Negative spare capacity for the unit; />The unit is the standby cost; />Negative standby cost per unit; f (·) is a variable force cost function of the unit, a quadratic function is adopted,
the constraint conditions of the stage 2 comprise unit output constraint, unit climbing constraint, line tide constraint and tide equation constraint;
in the method, in the process of the invention,P g the minimum output limit value and the maximum output limit value of the unit g are respectively; />The maximum ascending and descending climbing rates of the unit g in unit time are respectively; delta d For a scheduling time interval; GSF (GSF) n-l A branch power flow distribution factor which influences the active power of the branch l for the change of the active power of the node n; />Algebraic sum of injection power of n nodes; f (F) l Maximum transmission power for the first branch; zeta type toy t Is an uncertainty set; l (L) n,t Is the load demand of node n during time period t.
6. The multi-time scale weak robust power scheduling method considering adaptive uncertainty set as claimed in claim 1, wherein the constraint condition of the real-time scheduling in step (23) is formulas (31) - (36);
equation (31) is the output adjustment amount of the real-time scheduling stage unit; the formula (32) is the output power of the real-time scheduling stage unit; equation (33) is the output power limit constraint of the real-time scheduling stage unit; equation (34) and equation (35) are climbing constraints of the output power of the real-time scheduling stage unit; equation (36) is the power balancing constraint of the real-time scheduling phase; wherein Δp g,t The output adjustment quantity of the unit in the real-time scheduling stage is adjusted;respectively reserving negative standby and positive standby for a day-ahead scheduling stage; />The output power of the real-time scheduling stage is obtained; />Uncertain power for real-time scheduling phase；
The constraint condition of energy storage is formulas (37) - (40);
the formula (37) and the formula (38) are energy storage charging and discharging power constraint, the formula (39) is energy storage state of charge constraint, and the formula (40) is energy storage energy constraint;discharging and charging power for energy storage; soc e,t Is the state of charge of the stored energy; soc min 、soc max Respectively the minimum charge value and the maximum charge value of the stored energy charge state; η (eta) cha 、η dis Charging efficiency and discharging efficiency, respectively.
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