CN115115276A - Virtual power plant scheduling method and system considering uncertainty and privacy protection - Google Patents
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Abstract
The invention belongs to the field of power supply scheduling of power systems, and provides a virtual power plant scheduling method and system considering uncertainty and privacy protection, wherein the method is based on a historical data set and establishes a target function and constraint of a virtual power plant considering uncertainty in stages; combining an objective function and constraints to construct a single virtual power plant multi-scenario optimization problem, and decomposing the multi-scenario optimization problem into a plurality of scenario sub-problems and coupling constraints among the sub-problems; based on coupling constraints among the sub-problems, the ADMM iterative equation is adopted to continuously iteratively solve the sub-problems of the plurality of scenes until iteration precision is met, distributed cooperation is carried out among the plurality of virtual power plants, privacy protection is considered, a distributed method is adopted to solve system optimization problems among different virtual power plants to obtain an optimization result, and the virtual power plants schedule unit output according to the optimization result. Promote renewable energy's consumption, reduce economic cost, protect the privacy between the different virtual power plants simultaneously, prevent that the privacy from revealing.
Description
Technical Field
The invention belongs to the field of power supply scheduling of power systems, and particularly relates to a virtual power plant scheduling method and system considering uncertainty and privacy protection.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the reformation of electric power market, the proportion of renewable energy in the electric power market is further improved, and the renewable energy can fully utilize resources, reduce the long-distance power transmission cost and improve the power supply reliability through distributed power generation.
In recent years, renewable energy sources such as wind power and photovoltaic power are developed rapidly, but due to strong randomness and intermittency of output of the renewable energy sources, the output of the renewable energy sources is difficult to predict accurately, and difficulty is brought to scheduling and operation of a power system. In addition, distributed power supplies such as wind power and photovoltaic power supplies have the characteristics of small capacity, large quantity, scattered geographic positions and the like, so that the grid connection cost is high, and the management difficulty is high.
The Virtual Power Plant (VPP) generally comprises a traditional generator, distributed power generation equipment, flexible loads, energy storage equipment and the like, and integrates a plurality of units through a precise control mode and energy management to enable the units to be uniformly integrated into a whole, so that various flexible resources participate in the optimized operation of the system, and the complexity of a power distribution network is reduced. The virtual power plant can integrate distributed power supplies, and a relatively stable large power supply amount is output by installing a plurality of small power supplies.
The prior art has the following defects:
due to the fact that the problem of uncertainty of intermittent energy sources such as wind energy and solar energy and load prediction is further aggravated, the operation state and economy of a power grid are affected, and the scheduling and control of a power system are challenged.
In order to deal with the uncertainty of renewable energy, various renewable energy output scenes need to be considered, and the safe and stable operation of the system is ensured. In actual operation, however, different virtual power plants need to cooperate with each other to ensure that the safe operation and the overall economic benefit of the whole system are optimal. Because different virtual power plants belong to different operation subjects, detailed data information and structures cannot be exchanged between different subjects, and centralized solution is not practical.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the invention provides a virtual power plant scheduling method and system considering uncertainty and privacy protection.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a virtual power plant scheduling method considering uncertainty and privacy protection, which comprises the following steps:
acquiring historical data sets of wind power, photovoltaic output and load;
establishing an objective function and a constraint of the virtual power plant considering uncertainty in stages based on the historical data set;
combining an objective function and constraints to construct a single virtual power plant multi-scenario optimization problem, and decomposing the multi-scenario optimization problem into a plurality of scenario sub-problems and coupling constraints among the sub-problems;
based on coupling constraints among the sub-problems, a plurality of scene sub-problems are continuously solved in an iterative mode through an ADMM iterative equation until iteration precision is met, distributed cooperation is conducted among a plurality of virtual power plants, privacy protection is considered, a distributed method is adopted to solve system optimization problems among different virtual power plants to obtain an optimization result, and the virtual power plants dispatch unit output according to the optimization result.
A second aspect of the invention provides a virtual power plant scheduling system that accounts for uncertainty and privacy protection, comprising:
the data acquisition module is used for acquiring historical data sets of wind power, photovoltaic output and load;
the optimization problem construction module is used for establishing an objective function and constraint of the virtual power plant considering uncertainty in stages based on the historical data set; combining an objective function and constraints to construct a single virtual power plant multi-scenario optimization problem, and decomposing the multi-scenario optimization problem into a plurality of scenario sub-problems and coupling constraints among the sub-problems;
and the virtual power plant scheduling module is used for solving the sub-problems of the plurality of scenes by adopting an ADMM iterative equation continuously and iteratively based on the coupling constraints among the sub-problems until the iteration precision is met, performing distributed cooperation among the plurality of virtual power plants, considering privacy protection, solving the system optimization problem among different virtual power plants by adopting a distributed method to obtain an optimization result, and scheduling the unit output by the virtual power plants according to the optimization result.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the virtual plant scheduling method taking uncertainty and privacy protection into account as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the virtual plant scheduling method taking into account uncertainty and privacy protection as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a virtual power plant multi-scene scheduling method considering uncertainty and privacy protection. On one hand, the output scene of renewable energy sources is considered, the uncertainty of the renewable energy sources is coped with, and the safe and stable operation of the system is ensured. On the other hand, the cooperation among different virtual power plants is guaranteed in actual operation, so that the safe operation of the whole system and the optimal overall economic benefit are guaranteed, and meanwhile, the privacy among different virtual power plants is guaranteed.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart diagram of a virtual power plant scheduling method considering uncertainty and privacy protection according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention aims to deal with uncertainty of renewable energy output in a virtual power plant and guarantee privacy of different virtual power plants in the virtual power plant, and provides a virtual power plant multi-scene scheduling method considering uncertainty and privacy protection. The uncertainty of renewable energy sources can be dealt with, the output scene of various renewable energy sources is considered, the safe and stable operation of the system is ensured, and meanwhile the privacy of different virtual power plants in the virtual power plants is ensured.
Example one
As shown in fig. 1, the present embodiment provides a virtual power plant scheduling method considering uncertainty and privacy protection, including the following steps:
step 1: acquiring historical data sets of wind power, photovoltaic output and load;
in one or more embodiments, the data of the present embodiment is collected by a state-aware technique, and short-term prediction is performed based on the collected data set to predict the new energy contribution and load that may occur in the future.
Screening a typical new energy output and load data set by adopting a scene reduction technology to serve as a multi-scene considered by the second stage constraint in the step 2;
and (3) taking the scene mean value data of the new energy output as the upper limit value of the fan and photovoltaic load shedding operation set value (namely the upper limit parameter of wind and photovoltaic output in the virtual power plant constraint) in the first stage in the step (2).
And determining cost coefficients of the exchange power between the virtual power plants, the wind and light abandonment, the unit operation and the reserve capacity in the objective function according to the historical market transaction price.
Step 2: and establishing an objective function of the virtual power plant.
The virtual plant objective function is the total cost of two phases:
equation (1) represents the total cost of virtual plant operation, where the first stage cost includes the cost C of injecting power from the outside of the area to the virtual plant ch Operating cost C of distributed generator set (DG) DG Spare cost C R Wind cost of wind power generation C W Light abandon cost of photovoltaic power generation C PV (ii) a The second stage cost includes the expected costs of DG power regulation, curtailment, and load shedding under the considered scenario, where the cost of ω under the scenario is represented as follows respectively
P ω Omega represents the set of all uncertainty scenarios, for the probability of the considered uncertainty scenario omega occurring.
And step 3: and establishing virtual power plant constraints. The virtual power plant constraint is divided into two stages of constraint; the method comprises the following specific steps:
first stage constraint:
in the formula:
respectively representing the output, the upward reserve capacity and the downward reserve capacity of the ith DG at the time t; p i Gmax ,P i Gmin Respectively representing the maximum and minimum output limits of the ith DG;
respectively representing the maximum and minimum climbing rate limits of the ith DG;
respectively representing the planned output and the predicted maximum output of the ith controllable fan at the moment t;
respectively representing the planned output and the predicted maximum output at the ith controllable photovoltaic time t;
representing the power injected into the virtual power plant at the region boundary node i, which can be positive or negative;
representing the net load at the t moment at a node i in the control range of the virtual power plant, wherein the net load comprises the uncontrollable new energy output; n is a radical of ch ,N DG ,N W ,N PV ,N n And respectively representing the collection of the nodes in the control range of the regional boundary node, the DG set, the controllable wind power set, the photovoltaic set and the virtual power plant.
The formula (2) and the formula (3) respectively represent the output power, the reserve capacity limit and the climbing power limit of the DG, the formula (4) represents the output power limit of the controllable wind power and the controllable photovoltaic unit, and the formula (5) represents the power balance constraint of the first stage.
Second stage constraints:
in the formula (I), the compound is shown in the specification,
respectively representing the upward and downward power adjustment amount and the total output power of the ith DG under the scene omega;
respectively representing the maximum output and the maximum output of the ith controllable fan at the moment t under the scene omega;
respectively representing the output and the maximum output at the ith controllable photovoltaic t moment under the scene omega;
representing the net load at the t moment of a node i in the control range of the virtual power plant under a scene omega, wherein the net load comprises the uncontrollable new energy output;
representing the amount of load shedding at time t at node i under scene ω.
Equations (6) and (7) show that DG output adjustment is limited by the first-stage reserve capacity and the total output climbing, equation (8) shows the output power limitation of the controllable wind power and photovoltaic units under the scene omega, and equation (9) shows the power balance constraint under the second-stage scene omega.
By using
To represent
Form a column vector x, all of which
Forming a column vector y, the first-stage constraint described above can be written in abstract form as follows: ax + By is less than or equal to b;
all will be
Form a column vector z ω The second stage constraints described above can be written in the abstract form: dx + Ey + Fz ω ≤h ω 。
Abstracting an objective function as c 1 x+c 2 y，c 1 ,c 2 Is the corresponding cost coefficient vector.
Thus, the optimization problem for a single virtual plant VPP can be expressed as follows:
and 4, step 4: firstly, aiming at the optimization problem of a single virtual power plant with a large scale, an ADMM algorithm is adopted for scene decomposition, so that the parallel calculation of each scene subproblem is realized, and the solving time is reduced.
The decomposition steps are as follows:
step 401: the single VPP optimization problem in equation (10) is decomposed into | Ω | scenario subproblems, each scenario subproblem is expressed as equation (11), and the coupling constraint between each subproblem is expressed as equation (12), which is equivalent to equation (13).
x 1 ＝···＝x |Ω| ,y 1 ＝···＝y |Ω| (12)
In the formula: x is the number of ω ,y ω And respectively representing the injection power and DG, controllable wind power and photovoltaic output values of the first stage in the scene omega subproblem.
Step 402: constructing an augmented Lagrangian function as shown in a formula (14) through formulas (11) to (13):
in the formula: mu.s ω ,λ ω Dual variables respectively representing two types of equations of formula (13); rho x ,ρ y The penalty coefficients and the square regular terms respectively represent the injection power and the output values of DG, controllable wind power and photovoltaic in the first stage
It is the corresponding penalty term.
Step 403: setting the iteration times k to zero, setting the penalty term and the dual variable to zero, solving the subproblems in the formula (11) in sequence to obtain x ω ,y ω Is initialized.
Step 404: solving the ADMM iterative equation shown in the formula (15) in turn
In the formula:
i.e. the lagrangian augmentation function in step 402,
representing the variable x in the subproblem except for the scene ω ω ,y ω And the other variables are values at the k iteration.
Step 405: according to the result in step 404Computing
And updating the dual variable value as formula (16).
Step 406: the iteration convergence accuracy is calculated as shown in equation (17) if g k+1 If not more than epsilon, executing the step 5, and turning to outer layer circulation; wherein, g k+1 For the error of each iteration, ε is the convergence criterion; otherwise, the iteration number k is k +1, and the process returns to step 404 to continue the iteration.
And 4, obtaining an optimization result which is the exchange power of the single virtual power plant and the outside and the planned power of the internal distributed power source, wherein the exchange power with the outside is used as a boundary constraint of cooperation among different virtual power plants in the step 5.
And 5: the virtual power plants belong to different operation main bodies, detailed data information and structures cannot be exchanged among the different main bodies, and distributed cooperation needs to be carried out among the virtual power plants.
Taking two virtual power plants as an example, the coordination steps are as follows:
step 501: the boundary condition of the two virtual power plants is that the sum of the injected power of the two virtual power plants is 0, i.e. the power balance of the whole system is maintained.
The specific expression is as follows:
H 1 x 1 +H 2 x 2 ＝0 (18)
in the formula: h 1 ,H 2 Respectively representing the boundary coupling matrixes of the 1 st and 2 nd power plants.
The collaborative expression between the two virtual power plants is:
step 502: for the expression in step 501, an augmented lagrange function is constructed,
in the formula:
a dual variable representing a coupling equation; rho VPP Penalty factor of representation, squared regular term
It is a penalty term.
Step 503: setting the iteration number k as 0 and setting an iteration initial value x 1 (k)＝x 2 (k)＝λ VPP (k)＝0。
Step 504: the virtual power plant solves the following sub-problems in sequence:
step 505: update dual multipliers in ADMM:
λ VPP (k+1)＝λ VPP (k)+ρ VPP (H 1 x 1 (k+1)+H 2 x 2 (k+1)) (22)
step 506: calculating an iterative residual:
r VPP (k+1)＝H 1 x 1 (k+1)+H 2 x 2 (k+1) (23)
if r VPP (k+1)|| 2 ≤ε VPP Iterative convergence is carried out, and the virtual power plants are completed cooperatively; otherwise, the iteration number k is k +1, and the process returns to step 504 to continue the iteration.
Step 6: after step 5 convergence, the last iteration value x is added 1 (k+1)，x 2 (k +1) Global optimal Unit output of different virtual Power plants
And the virtual power plant issues an instruction according to the optimization result and schedules the output of the unit.
In this embodiment, in solving the system optimization problem between different virtual power plants by using a distributed method, the distributed method is an alternative direction multiplier method.
The technical scheme has the advantages that the uncertainty of the renewable energy output is coped with by adopting a multi-scene scheduling and ADMM decomposition method, and then the privacy among different virtual power plants is protected by adopting the ADMM decomposition method. The multi-scene scheduling method of the virtual power plant, which is provided by the invention and considers uncertainty and privacy protection, can improve the consumption of renewable energy sources, reduce certain economic cost, save resources, protect the privacy among different virtual power plants and prevent privacy disclosure.
Example two
The embodiment provides a virtual power plant scheduling system considering uncertainty and privacy protection, which comprises:
the data acquisition module is used for acquiring historical data sets of wind power, photovoltaic output and load;
the optimization problem construction module is used for establishing an objective function and constraint of the virtual power plant considering uncertainty in stages based on the historical data set; combining an objective function and constraints to construct a single virtual power plant multi-scenario optimization problem, and decomposing the multi-scenario optimization problem into a plurality of scenario sub-problems and coupling constraints among the sub-problems;
and the virtual power plant scheduling module is used for solving a plurality of scene sub-problems continuously in an iterative manner by adopting an ADMM iterative equation based on coupling constraints among the sub-problems until the iteration precision is met, performing distributed cooperation among a plurality of virtual power plants, considering privacy protection, solving system optimization problems among different virtual power plants by adopting a distributed method to obtain an optimization result, and scheduling the unit output by the virtual power plants according to the optimization result.
EXAMPLE III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps in the virtual plant scheduling method considering uncertainty and privacy protection as described above.
Example four
The present embodiment provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the virtual plant scheduling method considering uncertainty and privacy protection as described above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The virtual power plant scheduling method considering uncertainty and privacy protection is characterized by comprising the following steps of:
acquiring historical data sets of wind power, photovoltaic output and load;
establishing an objective function and a constraint of the virtual power plant considering uncertainty in stages based on the historical data set;
combining an objective function and constraints to construct a single virtual power plant multi-scenario optimization problem, and decomposing the multi-scenario optimization problem into a plurality of scenario sub-problems and coupling constraints among the sub-problems;
based on coupling constraints among the sub-problems, a plurality of scene sub-problems are continuously solved in an iterative mode through an ADMM iterative equation until iteration precision is met, distributed cooperation is conducted among a plurality of virtual power plants, privacy protection is considered, a distributed method is adopted to solve system optimization problems among different virtual power plants to obtain an optimization result, and the virtual power plants dispatch unit output according to the optimization result.
2. The virtual power plant scheduling method taking uncertainty and privacy protection into account of claim 1, wherein the objective function of the virtual power plant is:
wherein the first stage cost comprises a cost C of injecting power from outside the area to the virtual power plant ch Operating cost C of distributed generator set DG Spare cost C R Wind cost of wind power generation C W Light abandon cost of photovoltaic power generation C PV (ii) a The second stage cost includes the expected costs of DG power regulation, curtailment, and load shedding under the considered scenario, where the cost of ω under the scenario is represented as follows respectively
P ω Omega represents the set of all uncertainty scenarios, for the probability of the considered uncertainty scenario omega occurring.
3. The virtual power plant scheduling method taking uncertainty and privacy into account as recited in claim 1, wherein the phased establishing of constraints taking uncertainty into account comprises:
based on the output power of the DG, the standby capacity limit and the climbing power limit, the controllable wind power, the output power limit of the photovoltaic set and the power balance constraint of the first stage as constraint conditions of the first stage;
and adjusting the output based on DG, wherein the output adjustment is subject to the limitation of the first-stage reserve capacity and the climbing limitation of the total output, the output power limitation of the controllable wind power and the photovoltaic set under the scene, and the power balance constraint under the second-stage scene as second-stage constraint conditions.
4. The virtual power plant scheduling method taking uncertainty and privacy protection into account as claimed in claim 3, wherein the expression of the first stage constraint is:
in the formula (I), the compound is shown in the specification,
respectively representing the output, the upward reserve capacity and the downward reserve capacity of the ith DG at the time t;
respectively representing the maximum and minimum output limits of the ith DG;
respectively representing the maximum and minimum climbing rate limits of the ith DG;
respectively representing the planned output and the predicted maximum output of the ith controllable fan at the moment t;
respectively representing the planned output and the predicted maximum output at the ith controllable photovoltaic time t;
representing the power injected into the virtual power plant at a region boundary node i;
representing the net load at the t moment at a node i in the control range of the virtual power plant, wherein the net load comprises the uncontrollable new energy output; n is a radical of ch ,N DG ,N W ,N PV ,N n And respectively representing the collection of the nodes in the control range of the regional boundary node, the DG set, the controllable wind power set, the photovoltaic set and the virtual power plant.
5. The virtual power plant scheduling method taking uncertainty and privacy protection into account as claimed in claim 3, wherein the expression of the second stage constraint is:
in the formula (I), the compound is shown in the specification,
respectively representing the upward and downward power adjustment amount and the total output power of the ith DG under the scene omega;
respectively representing the maximum output and the maximum output of the ith controllable fan at the moment t under the scene omega;
respectively representing the output and the maximum output at the ith controllable photovoltaic t moment under the scene omega;
representing the net load at the t moment of a node i in the control range of the virtual power plant under a scene omega, wherein the net load comprises the uncontrollable new energy output;
representing the amount of load shedding at time t at node i under scene omega.
6. The virtual power plant scheduling method taking uncertainty and privacy protection into account of claim 1, wherein after obtaining historical data sets of wind power, photovoltaic output, and load, preprocessing the data comprises:
screening a typical new energy output and load data set by adopting a scene reduction technology to serve as a multi-scene considered by the second stage constraint;
taking scene mean value data of new energy output as upper limit parameters of wind and photovoltaic output in the constraint of the virtual power plant at the first stage;
and determining cost coefficients of the exchange power between the virtual power plants, the wind and light abandoning, the unit operation and the reserve capacity in the objective function according to the historical market transaction price.
7. The virtual power plant scheduling method taking uncertainty and privacy protection into account of claim 1 wherein the distributed method employs an alternating direction multiplier method.
8. Virtual power plant scheduling system considering uncertainty and privacy protection is characterized by comprising:
the data acquisition module is used for acquiring historical data sets of wind power, photovoltaic output and load;
the optimization problem construction module is used for establishing an objective function and constraint of the virtual power plant considering uncertainty in stages based on the historical data set; combining an objective function and constraints to construct a single virtual power plant multi-scenario optimization problem, and decomposing the multi-scenario optimization problem into a plurality of scenario sub-problems and coupling constraints among the sub-problems;
and the virtual power plant scheduling module is used for solving the sub-problems of the plurality of scenes by adopting an ADMM iterative equation continuously and iteratively based on the coupling constraints among the sub-problems until the iteration precision is met, performing distributed cooperation among the plurality of virtual power plants, considering privacy protection, solving the system optimization problem among different virtual power plants by adopting a distributed method to obtain an optimization result, and scheduling the unit output by the virtual power plants according to the optimization result.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for virtual plant scheduling taking account of uncertainty and privacy protection as claimed in any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the steps in the virtual plant scheduling method taking uncertainty and privacy protection into account as claimed in any one of claims 1-7.
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