CN112215433B - Virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price - Google Patents
Virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price Download PDFInfo
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Abstract
A virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price, comprising the following steps: a first step of: constructing an optimization target of day-ahead optimization scheduling of the virtual power plant; and a second step of: on the basis of an optimization target, establishing constraint conditions of day-ahead optimization scheduling of the virtual power plant; and a third step of: and solving a virtual power plant day-ahead optimization scheduling model considering the uncertainty of the power price of the power distribution market according to the optimization target and the constraint condition.
Description
Technical Field
The invention relates to the technical field of operation analysis of power systems, in particular to a day-ahead optimal scheduling method for a virtual power plant, which takes uncertainty of clear electricity prices of power distribution markets into consideration.
Background
The virtual power plant enables each distributed resource to form a cooperative relationship, and the scale benefit of the distributed resource is exerted. Meanwhile, the geographical position and composition of the distributed resources are not limited by the virtual power plant, an emerging management mode with high flexibility and adaptability is provided, and the virtual power plant brings wide attention to researchers at home and abroad.
The virtual power plant participates in the daily electric power market, can be used as a seller of the market to bid on the sales amount, and has the basic workflow as follows: the method comprises the steps that a prediction module predicts uncontrollable distributed power according to distributed resource historical data in a database in the future and predicts clearing prices of the market in the future; the optimization decision module performs bidding decision according to the prediction result; thereafter, the power market proceeds to the market in the day-ahead and issues bidding results to virtual power plants and other market participants.
However, in the current market, the virtual power plant is based on price prediction during day-ahead market decision and bidding, but the price of the power distribution market is influenced by multiple factors, the uncertainty of prediction data is large, and the internal income maximization cannot be realized.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a day-ahead optimal scheduling method for a virtual power plant, which considers the uncertainty of the clear electricity price of a power distribution market.
According to the invention, a virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price, which comprises the following steps:
A first step of: constructing an optimization target of day-ahead optimization scheduling of the virtual power plant;
And a second step of: on the basis of an optimization target, establishing constraint conditions of day-ahead optimization scheduling of the virtual power plant;
and a third step of: and solving a virtual power plant day-ahead optimization scheduling model considering the uncertainty of the power price of the power distribution market according to the optimization target and the constraint condition.
Preferably, in the first step, the maximum benefit of the virtual power plant is calculated as an optimization target of the future optimization scheduling of the virtual power plant based on the power benefit of the virtual power plant, the distributed power generation cost, the wind and light discarding cost, the energy storage operation cost and the demand response cost of the elastic load.
Preferably, in a first step, the maximum profit of the virtual power plant is calculated using the following formula:
max F＝F1-F2-F3-F4-F5；
Wherein F is the total income of the virtual power plant, F 1 is the electricity selling income of the virtual power plant, N T is the total daily period number, Price clearing for power distribution market at time t,/>The power output and clearing quantity of the virtual power plant at the moment t is the time interval delta t;
F 2 is the power generation cost of the gas turbine, N GT is the number of gas turbines in the virtual power plant, For the fixed cost after the ith gas turbine is started,/>For the working state of the ith gas turbine at the time t, N G is the linearization segmentation number, b j is the j-th section power generation cost slope,/>For the output of the ith gas turbine at time t on the jth segment,/>The start-stop cost of the ith gas turbine;
F 3 is the power generation cost of the distributed power supply (DG), N DG is the DG number, For the fixed cost of the ith distributed power supply,/>To compensate for the cost of discarding wind and discarding light,/>And/>The predicted power and the actual output power of the ith distributed power supply at the moment t are respectively;
f 4 is the charge-discharge cost of the energy storage battery, The unit charging cost at the moment t of the ith energy storage battery,For the charging power of the ith energy storage battery at the moment t,/>The unit discharge cost at the time t of the ith energy storage battery,The discharge power is the discharge power of the ith energy storage battery at the moment t;
F 5 is the demand side response cost, in which directly controllable interruptible loads are mainly considered, For the unit compensation of the ith interruptible load t moment,/>The power of interruption at the moment t of the ith interruptible load.
Preferably, the second step comprises:
establishing an output constraint of the micro gas turbine:
In the method, in the process of the invention, For maximum output of the ith gas turbine on the jth section,/>The maximum starting times of the ith gas turbine per day;
establishing output constraint of a distributed power supply:
establishing operation constraint of the energy storage system:
In the method, in the process of the invention, For the i-th energy storage t moment, hold energy,/>And/>Respectively the upper and lower limits of the energy storage energy of the ith energy storage during charging and discharging, eta is the charging and discharging efficiency,/>Is the charge state variable of the ith energy storage battery at the moment t,/>Is a discharge state variable of the ith energy storage battery at the moment t;
establishing an operation constraint of the elastic load:
In the method, in the process of the invention, For the sign-up interruptible load maximum at the ith interruptible load t time,/>For the action state of the ith interruptible load T moment, T i DR,max is the interruptible maximum time of the ith interruptible load subscription,The ith interruptible maximum number of interruptible load subscriptions;
Establishing a virtual power plant overall operation constraint:
Establishing clear electricity price uncertainty constraint:
yvpp-(t)≤yvpp(t)≤yvpp+(t)。
preferably, the third step comprises:
For nonlinear functions AndPerforming linearization conversion;
Adding auxiliary 0-1 variable And/>The relation between the scheduling model variable and the scheduling model variable is as follows:
The auxiliary variables are themselves constrained to be:
nonlinear function
The linear converted response becomes:
Nonlinear constraint The constraint phase after linear conversion is as follows:
For non-linear constraint And carrying out linearization conversion.
Adding auxiliary 0-1 variableAnd/>The relation between the scheduling model variable and the scheduling model variable is as follows:
The auxiliary variables are themselves constrained to be:
Nonlinear constraint The constraint phase after linear conversion is as follows:
Through linear conversion, the linear conversion is formed by max F=F 1-F2-F3-F4-F5, And forming a virtual power plant day-ahead optimal scheduling model considering the uncertainty of the clear electricity price of the distribution market.
Preferably, the third step further comprises: the clearing price is initialized for each period before the day in the clearing price constraint space:
In the method, in the process of the invention, The initial value of the clear price vector is obtained for each time period before the day when the virtual power plant day before optimizing and scheduling model is solved,And (5) clearing the price initial value for the time t.
Preferably, the third step further comprises: converting the equality constraint into the inequality constraint
Wherein M is a positive number.
Preferably, the third step further comprises: introducing a dual variable lambda and an auxiliary variable sigma, and solving a relaxed minimization problem;
And (3) making:
Wherein, P i GT is the power of each period before the day of the ith micro gas turbine, P i DG is the power of each period before the day of the ith distributed power supply, P i B,ch is the charging power of each period before the day of the ith energy storage battery, P i B,dis is the discharging power of each period before the day of the ith energy storage battery, P i DR is the interrupt power of each period before the day of the ith interruptible load, For the working state of each period before the day of the ith gas turbine,/>And/>Respectively, the start and stop states of the ith gas turbine in each period before the day,/>For the charge state variable of each period before the day of the ith energy storage battery,/>To be the discharge state variable of the ith energy storage battery in each period before the day,For the motion state of each period before the ith interruptible load day,/>And/>The auxiliary variable states of the ith interruptible load in each period before the day are respectively;
Order the Representing inequality constraints;
Converting the minimization problem into:
minσ；
λ≥0；
Wherein lambda is the sum of Dual variable vectors of the same dimension;
Solving the above-mentioned minimization problem to obtain nth calculation result lambda n、σn and
Preferably, the third step further comprises: solving the maximization problem:
Yvpp-(t)≤Yvpp(t)≤Yvpp+(t)；
Solving to obtain
Preferably, the third step further comprises: checking whether the algorithm is finished;
wherein, the calculation is performed and it is judged if:
Wherein epsilon is a positive number;
Then the solution converges, and virtual power plant day-ahead dispatch data under the condition of uncertain price of the distribution market is obtained
The invention is different from the prior art in that uncertainty of the market price is considered in an optimization model of the virtual power plant, and the method is characterized in that when the model is built, the price is not used singly for a daily forecast value, but the price is limited in a certain fluctuation range, and how the virtual power plant obtains an optimal scheduling scheme which is beneficial to the virtual power plant when the price fluctuates in the range is considered. Thus, the model of the present invention directly determines that the solution thereafter is a relaxed, maximized, minimized iterative solution process, model and solution for a pulse-like phase.
In addition, the start-stop times limitation of the micro gas turbine and the elastic load is mainly considered in the model, the constraint is a nonlinear constraint of the equipment state 0-1 variable in the model, and the nonlinear constraint is difficult to solve in practice, so that the model introduces an auxiliary variable to the equipment state 0-1 variable, converts the nonlinear constraint into a linear constraint, and can be optimized and solved together with other linear constraints.
Therefore, the invention innovates the three aspects of model self improvement, nonlinear constraint conversion into linear constraint and iterative solution. Moreover, the present invention can be used in terms of cost, benefits and operational constraints for various types of equipment without adding new burden.
Drawings
The invention will be more fully understood and its attendant advantages and features will be more readily understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, in which:
FIG. 1 schematically illustrates an overall flow chart of a virtual power plant day-ahead optimization scheduling method based on market price uncertainty in reserve prices in accordance with a preferred embodiment of the present invention.
It should be noted that the drawings are for illustrating the invention and are not to be construed as limiting the invention. Note that the drawings representing structures may not be drawn to scale. Also, in the drawings, the same or similar elements are denoted by the same or similar reference numerals.
Detailed Description
In order that the invention may be more readily understood, a detailed description of the invention is provided below along with specific embodiments and accompanying figures.
The virtual power plant is based on price prediction during day-ahead market decision and bidding, but the price of the power distribution market is influenced by multiple factors, and the uncertainty of prediction data is large. How to consider such uncertainty, then excavate the internal regulation and control ability of virtual power plant, realize that inside income is maximized, is the problem that this patent is to solve.
The invention focuses on the optimization decision-making module in the virtual power plant to make bidding decision according to the prediction result, considers certain uncertainty factors when predicting the clear electricity price, and makes power decision of each time period of the next day on the distributed resources in the virtual power plant under the condition so as to realize the maximum overall benefit of the virtual power plant.
FIG. 1 schematically illustrates an overall flow chart of a virtual power plant day-ahead optimization scheduling method based on market price uncertainty in reserve prices in accordance with a preferred embodiment of the present invention.
As shown in fig. 1, the method for optimizing and scheduling the virtual power plant day before based on uncertainty of the market price of electricity clearing according to the preferred embodiment of the invention comprises the following steps:
a first step S1: constructing an optimization target of day-ahead optimization scheduling of the virtual power plant;
specifically, for example, the maximum virtual power plant profit is taken as an optimization target, wherein the electricity profit of the virtual power plant, the distributed power generation cost, the wind discarding and light discarding cost, the energy storage operation cost and the demand response cost of the elastic load are comprehensively considered. In other words, the maximum benefit of the virtual power plant is calculated based on the power benefit of the virtual power plant, the distributed power generation cost, the wind and light abandoning cost, the energy storage operation cost and the demand response cost of the elastic load, and the maximum benefit is used as an optimization target for the daily optimization scheduling of the virtual power plant.
max F＝F1-F2-F3-F4-F5 (1)
Wherein F is the total income of the virtual power plant, F 1 is the electricity selling income of the virtual power plant, N T is the total daily period number,Price clearing for power distribution market at time t,/>The power output and clearing quantity of the virtual power plant at the moment t is the time interval delta t;
F 2 is the power generation cost of the gas turbine, N GT is the number of gas turbines in the virtual power plant, For the fixed cost after the ith gas turbine is started,/>For the working state of the ith gas turbine at the time t, N G is the linearization segmentation number, b j is the j-th section power generation cost slope,/>For the output of the ith gas turbine at time t on the jth segment,/>Is the start-stop cost of the ith gas turbine.
F 3 is the power generation cost of the distributed power supply (DG), N DG is the DG number,For the fixed cost of the ith distributed power supply,/>To compensate for the cost of discarding wind and discarding light,/>And/>The predicted power and the actual output power of the ith distributed power supply at the moment t are respectively.
F 4 is the charge-discharge cost of the energy storage battery,The unit charging cost at the moment t of the ith energy storage battery,For the charging power of the ith energy storage battery at the moment t,/>For the unit discharge cost of the ith energy storage battery at the t moment,/>The discharge power at the moment t of the ith energy storage battery.
F 5 is the demand side response cost, in which directly controllable interruptible loads are mainly considered,For the unit compensation of the ith interruptible load t moment,/>The power of interruption at the moment t of the ith interruptible load.
A second step S2: based on the optimization target, constraint conditions of day-ahead optimization scheduling of the virtual power plant are established.
(I): an output constraint of the micro gas turbine is established.
In the method, in the process of the invention,For maximum output of the ith gas turbine on the jth section,/>The maximum number of starts per day for the ith gas turbine.
(II): and establishing the output constraint of the distributed power supply.
(III): and establishing operation constraint of the energy storage system.
In the method, in the process of the invention,For the i-th energy storage t moment, hold energy,/>And/>Respectively the upper and lower limits of the energy storage energy of the ith energy storage during charging and discharging, eta is the charging and discharging efficiency,/>Is the charge state variable of the ith energy storage battery at the moment t,/>Is the discharge state variable of the ith energy storage battery at the moment t.
(IV): an operational constraint of the elastic load is established.
In the method, in the process of the invention,For the sign-up interruptible load maximum at the ith interruptible load t time,/>For the i-th operational state at which the load t can be interrupted,/>The interruptible maximum time for the ith interruptible load subscription,The i-th interruptible maximum number of interruptible load subscriptions.
(V): and establishing the overall operation constraint of the virtual power plant.
(VI): establishing uncertainty constraint of clear electricity price, and finally having
yvpp-(t)≤yvpp(t)≤yvpp+(t) (23)
Third step S3: and solving a virtual power plant day-ahead optimal scheduling model considering the uncertainty of the clear electricity price of the distribution market.
Step 3-1: and (3) carrying out linearization conversion on the nonlinear functions (3) and (9).
Adding auxiliary 0-1 variableAnd/>The relation between the scheduling model variable and the scheduling model variable is as follows:
The auxiliary variables are themselves constrained to be:
The nonlinear function (3) becomes linearly converted correspondingly:
the constraint phase after the nonlinear constraint (9) linear conversion is as follows:
A nonlinear constraint (24) is linearized.
Adding auxiliary 0-1 variableAnd/>The relation between the scheduling model variable and the scheduling model variable is as follows:
The auxiliary variables are themselves constrained to be:
the constraint phase after the nonlinear constraint (24) linear conversion is as follows:
/>
The virtual power plant day-ahead optimal scheduling model taking the uncertainty of the electricity clearing price of the electricity distribution market into consideration is composed of formulas (1), (2), (4) - (6), (8), (10) - (16), (18), (19), (21) and (23) - (36) through linear conversion.
Step 3-2: and initializing the clearing price of each day-ahead period in the clearing price constraint space.
In the method, in the process of the invention,The initial value of the clear price vector is obtained for each time period before the day when the virtual power plant day before optimizing and scheduling model is solved,And (5) clearing the price initial value for the time t.
Step 3-3: the equality constraint is converted to an inequality constraint.
Formulas (7), (17) and (22) can be converted into:
where M selects a smaller positive number.
Step 3-4: and introducing a dual variable lambda and an auxiliary variable sigma, and solving the relaxed minimization problem.
And (3) making:
Wherein, P i GT is the power of each period before the day of the ith micro gas turbine, P i DG is the power of each period before the day of the ith distributed power supply, P i B,ch is the charging power of each period before the day of the ith energy storage battery, P i B,dis is the discharging power of each period before the day of the ith energy storage battery, P i DR is the interrupt power of each period before the day of the ith interruptible load, For the working state of each period before the day of the ith gas turbine,/>And/>Respectively, the start and stop states of the ith gas turbine in each period before the day,/>For the charge state variable of each period before the day of the ith energy storage battery,/>To be the discharge state variable of the ith energy storage battery in each period before the day,For the motion state of each period before the ith interruptible load day,/>And/>The auxiliary variable states of the ith interruptible load day-ahead time periods are respectively set.
Order theThe inequality constraints represented by formulas (8), (11), (14) - (16), (18), (19), (24), (25), (29) - (32), (35), (36), (38) - (40) are expressed.
The minimization problem translates into:
minσ (42)
λ≥0 (44)
And formulas (12) - (14), (16), (17), (25), (30), (31).
Wherein lambda is the sum ofThe same dimension as the dual variable vector.
Solving the above-mentioned minimization problem to obtain nth calculation result lambda n、σn and
Step 3-5: solving the maximization problem.
And formula (27).
Solving to obtain
Step 3-6: checking whether the algorithm is finished.
If the following holds:
Where ε is a small positive number.
Then the solution converges, and virtual power plant day-ahead dispatch data under the condition of uncertain price of the distribution market is obtained
If the formula (41) is not satisfied, the obtained product is thenTurning to step 3-4, the minimization problem is recalculated.
The invention is different from the prior art in that uncertainty of the market price is considered in an optimization model of the virtual power plant, and the method is characterized in that when the model is built, the price is not used singly for a daily forecast value, but the price is limited in a certain fluctuation range, and how the virtual power plant obtains an optimal scheduling scheme which is beneficial to the virtual power plant when the price fluctuates in the range is considered. Thus, the model of the present invention directly determines that the solution thereafter is a relaxed, maximized, minimized iterative solution process, model and solution for a pulse-like phase.
In addition, the start-stop times limitation of the micro gas turbine and the elastic load is mainly considered in the model, the constraint is a nonlinear constraint of the equipment state 0-1 variable in the model, and the nonlinear constraint is difficult to solve in practice, so that the model introduces an auxiliary variable to the equipment state 0-1 variable, converts the nonlinear constraint into a linear constraint, and can be optimized and solved together with other linear constraints.
Therefore, the invention innovates the three aspects of model self improvement, nonlinear constraint conversion into linear constraint and iterative solution. Moreover, the present invention can be used in terms of cost, benefits and operational constraints for various types of equipment without adding new burden.
In summary, the invention establishes a virtual power plant day-ahead optimal scheduling model considering uncertain factors of the electricity clearing price of the distribution market, and the model fully considers distributed resources in the virtual power plants, including output and adjustment cost, self-safety operation constraint, state transition constraint and the like, wherein the distributed resources comprise adjustable distributed power sources, micro gas turbines, only limited distributed power sources, energy storage batteries capable of realizing bidirectional flexible adjustment and interruptible loads capable of being regulated according to contracts. The uncertainty of clear price is characterized in the model in an interval mode, and a solution method for relaxation and iteration of the day-ahead optimal scheduling model is provided. The invention provides a linear conversion method by utilizing auxiliary variables for nonlinear functions of the maximum start-stop times and the maximum interruption times of interruptible loads of a miniature gas turbine, and provides a solution method for relaxation and iteration of a day-ahead optimal scheduling model aiming at interval mode characterization clear price uncertainty. Therefore, the virtual power plant day-ahead optimal scheduling method based on uncertainty of the market electricity price can achieve maximum internal income.
It should be noted that, unless specifically stated otherwise, the terms "first," "second," "third," and the like in the specification are used merely as a distinction between various components, elements, steps, etc. in the specification, and are not used to denote a logical or sequential relationship between various components, elements, steps, etc.
It will be appreciated that although the invention has been described above in terms of preferred embodiments, the above embodiments are not intended to limit the invention. Many possible variations and modifications of the disclosed technology can be made by anyone skilled in the art without departing from the scope of the technology, or the technology can be modified to be equivalent. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present invention still fall within the scope of the technical solution of the present invention.
Claims (8)
1. A virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price, which is characterized by comprising the following steps:
A first step of: constructing an optimization target of day-ahead optimization scheduling of the virtual power plant;
In the first step, calculating the maximum profit of the virtual power plant based on the electric power profit of the virtual power plant, the power generation cost of the distributed power supply, the wind and light discarding cost, the energy storage operation cost and the demand response cost of the elastic load, and taking the maximum profit as an optimization target of the daily optimization scheduling of the virtual power plant;
in a first step, the maximum profit of the virtual power plant is calculated using the following formula:
maxF＝F1-F2-F3-F4-F5；
Wherein F is the total income of the virtual power plant, F 1 is the electricity selling income of the virtual power plant, N T is the total daily period number, Price clearing for power distribution market at time t,/>The power output and clearing quantity of the virtual power plant at the moment t is the time interval delta t;
F 2 is the power generation cost of the gas turbine, N GT is the number of gas turbines in the virtual power plant, For the fixed cost after the ith gas turbine is started,/>For the working state of the ith gas turbine at the time t, N G is the linearization segmentation number, b j is the j-th section power generation cost slope,/>For the output of the ith gas turbine at time t on the jth segment,/>The start-stop cost of the ith gas turbine;
F 3 is the power generation cost of the distributed power source DG, N DG is the DG number, For the fixed cost of the ith distributed power supply,/>To compensate for the cost of discarding wind and discarding light,/>And/>The predicted power and the actual output power of the ith distributed power supply at the moment t are respectively;
f 4 is the charge-discharge cost of the energy storage battery, For the unit charging cost of the ith energy storage battery at time t,/>For the charging power of the ith energy storage battery at the moment t,/>For the unit discharge cost of the ith energy storage battery at the t moment,/>The discharge power at the moment t of the ith energy storage battery;
F 5 is the cost of demand side response, directly controllable interruptible load, For the unit compensation of the ith interruptible load t moment,/>The interruption power at the moment of t of the ith interruptible load;
And a second step of: on the basis of an optimization target, establishing constraint conditions of day-ahead optimization scheduling of the virtual power plant;
and a third step of: and solving a virtual power plant day-ahead optimization scheduling model considering the uncertainty of the power price of the power distribution market according to the optimization target and the constraint condition.
2. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out electricity prices according to claim 1, wherein the second step comprises:
establishing an output constraint of the micro gas turbine:
In the method, in the process of the invention, For maximum output of the ith gas turbine on the jth section,/>The maximum starting times of the ith gas turbine per day;
establishing output constraint of a distributed power supply:
establishing operation constraint of the energy storage system:
In the method, in the process of the invention, For the i-th energy storage t moment, hold energy,/>And/>Respectively the upper and lower limits of the energy storage energy of the ith energy storage during charging and discharging, eta is the charging and discharging efficiency,/>Is the charge state variable of the ith energy storage battery at the moment t,The discharge state variable at the moment t of the ith energy storage battery;
establishing an operation constraint of the elastic load:
In the method, in the process of the invention, For the sign-up interruptible load maximum at the ith interruptible load t time,/>For the action state of the ith interruptible load T moment, T i DR,max is the interruptible maximum time of the ith interruptible load subscription,/>The ith interruptible maximum number of interruptible load subscriptions;
Establishing a virtual power plant overall operation constraint:
Establishing clear electricity price uncertainty constraint:
yvpp-(t)≤yvpp(t)≤yvpp+(t)。
3. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out electricity prices according to claim 1, wherein the third step comprises:
For nonlinear functions AndPerforming linearization conversion;
Adding auxiliary 0-1 variable And/>The relation between the scheduling model variable and the scheduling model variable is as follows:
The auxiliary variables are themselves constrained to be:
nonlinear function
The linear converted response becomes:
Nonlinear constraint The constraint phase after linear conversion is as follows:
For non-linear constraint Performing linearization conversion;
Adding auxiliary 0-1 variable And/>The relation between the scheduling model variable and the scheduling model variable is as follows:
The auxiliary variables are themselves constrained to be:
Nonlinear constraint The constraint phase after linear conversion is as follows:
through linear conversion, the linear conversion is carried out by maxF =F 1-F2-F3-F4-F5,
And/>And forming a virtual power plant day-ahead optimal scheduling model considering the uncertainty of the clear electricity price of the distribution market.
4. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out power price according to claim 1, wherein the third step further comprises: the clearing price is initialized for each period before the day in the clearing price constraint space:
In the method, in the process of the invention, The initial value of the clear price vector is obtained for each time period before the day when the virtual power plant day before optimizing and scheduling model is solved,And (5) clearing the price initial value for the time t.
5. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out power price according to claim 1, wherein the third step further comprises: converting the equality constraint into the inequality constraint
Wherein M is a positive number.
6. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out power price according to claim 1, wherein the third step further comprises: introducing a dual variable lambda and an auxiliary variable sigma, and solving a relaxed minimization problem;
And (3) making:
Wherein, P i GT is the power of each period before the day of the ith micro gas turbine, P i DG is the power of each period before the day of the ith distributed power supply, P i B,ch is the charging power of each period before the day of the ith energy storage battery, P i B,dis is the discharging power of each period before the day of the ith energy storage battery, P i DR is the interrupt power of each period before the day of the ith interruptible load, For the working state of each period before the day of the ith gas turbine,/>And/>Respectively, the start and stop states of the ith gas turbine in each period before the day,/>For the charge state variable of each period before the day of the ith energy storage battery,/>For the discharge state variable of each period before the day of the ith energy storage battery,/>For the motion state of each period before the ith interruptible load day,/>And/>The auxiliary variable states of the ith interruptible load in each period before the day are respectively;
Order the Representing inequality constraints;
Converting the minimization problem into:
minσ；
λ≥0；
Wherein lambda is the sum of Dual variable vectors of the same dimension;
Solving the above-mentioned minimization problem to obtain nth calculation result lambda n、σn and
7. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out power price according to claim 1, wherein the third step further comprises: solving the maximization problem:
Yvpp-(t)≤Yvpp(t)≤Yvpp+(t)；
Solving to obtain
8. The virtual power plant day-ahead optimal scheduling method based on uncertainty of market-out power price according to claim 1, wherein the third step further comprises: checking whether the algorithm is finished;
wherein, the calculation is performed and it is judged if:
Wherein epsilon is a positive number;
Then the solution converges, and virtual power plant day-ahead dispatch data under the condition of uncertain price of the distribution market is obtained
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