CN112100564A - Master-slave game robust energy management method for community multi-microgrid system - Google Patents

Master-slave game robust energy management method for community multi-microgrid system Download PDF

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CN112100564A
CN112100564A CN202010879821.2A CN202010879821A CN112100564A CN 112100564 A CN112100564 A CN 112100564A CN 202010879821 A CN202010879821 A CN 202010879821A CN 112100564 A CN112100564 A CN 112100564A
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王栋
徐晓春
蔡康林
丁波
李毅
徐智
范广博
戴欣
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HuaiAn Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention relates to the technical field of coordinated scheduling, and discloses a master-slave game robust energy management method for a community multi-microgrid system, which comprises the following steps: obtaining an operation cost parameter and an operation limit value parameter of a power supplier, and constructing an optimized scheduling model of which the power supplier is a leader; acquiring operation cost parameters of each user microgrid and operation limit parameters of each device, and constructing a double-layer robust coordinated scheduling model with the user microgrid as a follower; converting the double-layer robust coordination scheduling model into a single-layer mathematical programming model through a strong dual theory; solving a master-slave game robust energy management model: and iteratively solving the optimal scheduling problem of the power supplier and the user microgrid by using a distributed algorithm to obtain a master-slave game scheduling plan of the community multi-microgrid system. The method realizes game balanced scheduling of the power supplier microgrid and the user microgrid under the renewable energy power generation uncertainty, ensures the robustness of a balanced solution to the uncertainty, and provides guidance and help for formulating an energy management plan of a community multi-microgrid system.

Description

Master-slave game robust energy management method for community multi-microgrid system
Technical Field
The invention relates to the technical field of coordination scheduling and energy management of micro-grids, in particular to a master-slave game robust energy management method for a community multi-micro-grid system.
Background
With the development of power technology, more and more users in the power grid are configured with local renewable energy sources to generate electricity to improve the electricity utilization benefits of the users, and the users have certain demand side response capability, so that the power supply and electricity utilization benefits of the power grid system can be further improved through reasonable load regulation, and a foundation is laid for the users to participate in the coordinated operation of the superior power grid. However, considering that the capacity of a single user is small and distributed in a distributed manner in the power system, it is difficult to independently participate in the energy management operation of the system, and therefore, the demand-side response regulation capability is often exerted by a way that a power provider aggregates a plurality of micro-grids.
The renewable energy power generation in the user microgrid is influenced by the natural environment, so that strong uncertainty exists, accurate prediction of the power generation power is difficult to realize in practice, and great challenge is brought to coordinated operation of the power grid. However, the existing master-slave game scheduling method does not consider uncertainty, and it is difficult to ensure that a system manager can obtain a new equilibrium point on the basis of a deterministic game scheduling plan after the uncertainty occurs, so that the scheduling plan has poor robustness and practicability. In addition, the information interaction scenes of the multi-level main bodies in the master-slave game model are different, but the difference is ignored by the traditional distributed algorithm, the model calculation efficiency is greatly reduced through multi-level nested iterative calculation optimization solutions, and how to realize the efficient solution of the multi-level coordination planning model in the complex scene is still to be deeply researched.
Disclosure of Invention
The technical purpose is as follows: in order to overcome the defects of the prior art, the invention provides a master-slave game robust energy management method for a community multi-microgrid system, which considers the game balance scheduling problem of a power supplier microgrid and a user microgrid under the renewable energy power generation uncertainty, ensures the robustness of a scheduling balance solution to the uncertainty, and provides guidance and help for formulating an energy management plan of the community multi-microgrid system.
The technical scheme is as follows: the invention discloses a master-slave game robust energy management method for a community multi-microgrid system, which comprises the following steps of:
step 10), obtaining operation cost parameters and operation limit parameters of a power supplier in the community multi-microgrid system, and constructing a max-form optimized scheduling model with the power supplier as a leading person;
step 20), obtaining operation cost parameters and operation limit parameters of each user microgrid in the community multi-microgrid system, and constructing a max-max form double-layer robust coordination scheduling model with the user microgrid as a follower;
step 30), converting the max-max form double-layer robust coordination scheduling model of the user microgrid into a single-layer mathematical programming model containing balance constraint by using a strong dual theory;
and step 40), solving a master-slave game robust energy management model which is formed in the community multi-microgrid system and takes a power supplier as a leading person and a user microgrid as a following person, namely, iteratively solving the optimization scheduling problem of the power supplier and the user microgrid by utilizing a two-stage distributed optimization algorithm to obtain a master-slave game scheduling plan of the community multi-microgrid system.
Further, in the step 10), the operation cost coefficient and the operation limit parameter of the power provider include a cost parameter and an operation limit related to the power generation and supply prices of the power provider, and the obtained cost coefficient and the obtained operation limit are substituted into the following formula to establish a max-form optimized scheduling model with the power provider as a dominant one:
the objective function of the power supplier optimized scheduling model is as follows:
Figure BDA0002653770330000021
in the formula of USRepresenting the total power supply income of the power supplier in the whole scheduling period for the objective function of the power supplier model; lambda [ alpha ]tA price of power supplied to the power provider; dtThe net load requirements of all the micro grids of the users in the time period t under the reference scene are set; a ist、btAnd ctCost factor for power provider generation; and T is the total time period number of one scheduling period.
The constraint conditions of the power supplier optimized scheduling model are as follows:
Figure BDA0002653770330000022
Figure BDA0002653770330000023
equation (2) represents the power balance constraint for the supplier supply, Dn,tThe net load demand of the nth user microgrid in the time period t under the reference scene is set, and N represents the total number of the user microgrids in the community multi-microgrid system; equation (3) is the power supply price constraint of the power supplier, the first term represents that the sum of the power supply prices is constant in the whole scheduling period, the second term defines the range of the power supply prices of the power supplier in any scheduling period, C is the constant average power supply price,
Figure BDA0002653770330000024
and
Figure BDA0002653770330000025
respectively the minimum and maximum power supply rates for the t period.
Further, in the step 20), the operation cost parameters and the operation limit parameters of each device in the microgrid users include all operation cost coefficients and operation limit parameters related to renewable energy power generation and loads in each microgrid user, uncertainty of renewable energy power generation is calculated, and the obtained cost coefficients and limit parameters are substituted into the following formula to establish a max-max form double-layer robust coordination scheduling model for the microgrid users as followers:
the upper layer model objective function of the user microgrid double-layer robust coordinated scheduling model is as follows:
Figure DEST_PATH_GDA0002737958510000026
in the formula (I), the compound is shown in the specification,
Figure BDA0002653770330000031
the total power utilization benefit of the microgrid users under the scene with renewable energy power generation as a reference is expressed for an upper model objective function of the nth microgrid users; l isn,tThe load power of the nth user microgrid in the time period t under the reference scene is obtained; alpha is alphan,tAnd betan,tThe power utilization yield coefficient of the nth user microgrid in the time period t is obtained; gamma rayn,tPunishing a cost coefficient for the net load deviation of the nth user microgrid in the t period; gn,tThe net load demand of the nth user microgrid in the period of t in the worst scene is met.
The constraint conditions of an upper layer model of the user microgrid double-layer robust coordinated scheduling model are as follows:
Dn,t＝Ln,t-Pn,t,Dn,t≥0 (5)
Figure BDA0002653770330000032
the work of the nth microgrid of users under the scene with the formula (5) as the referenceRate balance constraint, Pn,tGenerating power for a reference scene of renewable energy sources in the nth microgrid during a time period t; the formula (6) is load power constraint of the nth microgrid of users under a reference scene, the former term represents that the total power consumption of the load in the scheduling period is constant, and the latter term represents that upper and lower limits exist in the load power at any time interval; enRepresenting the constant amount of power consumed by the nth microgrid;
Figure BDA0002653770330000033
and
Figure BDA0002653770330000034
representing the minimum and maximum load power values of the nth user during the t period.
The lower layer model objective function of the user microgrid double-layer robust coordinated scheduling model is as follows:
Figure DEST_PATH_GDA0002737958510000035
in the formula (I), the compound is shown in the specification,
Figure BDA0002653770330000036
the model is a lower-layer model objective function of the nth microgrid user, and the total power utilization benefit of the microgrid user under the worst scene of renewable energy power generation is represented; kn,tThe load power of the nth microgrid is the load power of the nth microgrid during the time period t in the worst scene.
The lower layer model constraint conditions of the user microgrid double-layer robust coordinated scheduling model are as follows:
Gn,t＝Kn,t-Pn,t+τn,t+υn,t n (8)
Figure BDA0002653770330000037
τn,t,υn,t≥0,ζn,t≥1 (10)
Figure BDA0002653770330000038
the equation (8) is the power balance constraint of the nth microgrid of users in the worst scene; equations (9) - (10) are dual parameter constraints on the uncertainty of renewable energy generation in the nth microgrid of users, τn,t、υn,tAnd ζn,tRespectively, are dual variables of the uncertainty constraint,nbudgeting parameters for periods of renewable energy generation uncertainty,
Figure BDA0002653770330000039
and
Figure BDA00026537703300000310
an upper deviation value and a lower deviation value of the power generation uncertainty of the renewable energy source; and the equation (11) is the load operation power constraint of the nth microgrid of users in the worst scene.
Further, the specific content of the step 30) includes:
step 301): writing the user microgrid double-layer robust coordination scheduling model constructed in the step 20) into the following form:
Figure BDA0002653770330000041
s.t.Hy≤m (13)
Figure BDA0002653770330000042
s.t.Kz≤j (15)
in the formula, the optimized variables of each layer of model correspond to the model constructed in the step 20), and the objective function and the constraint condition are uniformly written into a matrix form; x, y and z respectively represent an optimization variable set corresponding to the formulas (1), (4) and (7); f. ofm xyzAnd
Figure BDA0002653770330000043
are respectively provided withExpressing the objective functions corresponding to the expressions (4) and (7); equations (13) and (15) represent matrix forms of the constraints of equations (5) to (6) and (8) to (11), respectively.
Step 302): based on the model in the step 301), converting a max-max form double-layer robust coordination scheduling model of the user microgrid into a single-layer mathematical programming model containing balance constraint through a strong dual theory:
Figure BDA0002653770330000044
in the formula (I), the compound is shown in the specification,
Figure BDA0002653770330000045
represents the partial derivative of the function pair z; u is a dual variable of formula (15); and obtaining the single-layer mathematical programming model containing the balance constraint, which is easy to directly solve, through the dual equivalence.
Further, in the step 40), the two-stage distributed optimization algorithm is used to iteratively solve the optimization scheduling problem of the power provider and the user microgrid, so as to obtain a master-slave game scheduling plan of the community multi-microgrid system, and the specific contents of the master-slave game scheduling plan include:
step 401): the number of iterations v is set to 0, and the convergence threshold τ is set.
Step 402): supplier initialization lambdat *Is a feasible solution and is used as the result x of the solution of the v-th iteration of the power supplier modelvAnd the data is transmitted to each user microgrid.
Step 403): each user microgrid will xvAnd solving the equivalent single-layer mathematical programming model containing the balance constraint as the known quantity substitution to obtain an optimization result (y)v,zv) And uploading to the power supplier.
Step 404): supplier general (y)v,zv) Substituting the known quantity, solving the self-optimization scheduling model and obtaining an optimization result xv+1。
Step 405): determine | xv+1-xv|/xvIf tau is established, the final optimization result (x) is outputv,yv,zv) Ending the solution; otherwise v ═ v +1, return to step 403).
Has the advantages that:
the invention provides a master-slave game robust energy management method for a community multi-microgrid system, and realizes robust balanced scheduling of community power grid multiple subjects under renewable energy power generation uncertainty. The method further considers the influence of uncertainty on system game scheduling, ensures that the operation cost of the micro-grid of the user is balanced when the uncertainty is responded, simultaneously ensures that the game scheduling strategies of the power supplier and the micro-grid of the user have robustness when the uncertainty of renewable energy power generation is responded, improves the calculation efficiency of the complex multilayer model through two-stage distributed iterative optimization solution, and provides effective, safe and stable guarantee for coordinated operation of the multi-micro-grid system of the community under the uncertainty.
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FIG. 1 is a flow chart of an embodiment of the present invention;
fig. 2 is a topology structure diagram of the community multi-microgrid system in the embodiment of the present invention.
Detailed Description
The technical solution of the embodiment of the present invention is further described below with reference to the accompanying drawings.
The invention provides a master-slave game robust energy management method for a community multi-microgrid system, which considers the game balance scheduling problem of power suppliers and user microgrids under the renewable energy power generation uncertainty, ensures the robustness of a scheduling balance solution to the uncertainty, adopts a two-stage distributed optimization method to iteratively solve a multistage coupled optimization scheduling model, avoids nested optimization calculation, ensures the solving efficiency of the model to meet the actual requirement, and provides guidance and help for formulating an energy management plan of the community multi-microgrid system.
As shown in fig. 1, the embodiment of the present invention employs a master-slave game robust energy management method for a community multi-piconet system, and a topology structure of the community multi-piconet system is shown in fig. 2. The method comprises the following steps:
and step 10) obtaining the operation cost parameter and the operation limit value parameter of a power supplier in the community multi-microgrid system, and constructing a max-form optimized scheduling model with the power supplier as a leading person.
The operation cost parameters and the operation limit parameters of the power supplier comprise cost parameters and operation limits related to the power generation and supply prices of the power supplier, and the obtained cost parameters and the obtained operation limits are substituted into the following formula to establish a max-form optimized dispatching model taking the power supplier as a leader:
the objective function of the power supplier optimized scheduling model is as follows:
Figure BDA0002653770330000051
in the formula of USRepresenting the total power supply income of the power supplier in the whole scheduling period for the objective function of the power supplier model; lambda [ alpha ]tA price of power supplied to the power provider; dtThe net load requirements of all the micro grids of the users in the time period t under the reference scene are set; a ist、btAnd ctCost factor for power provider generation; and T is the total time period number of one scheduling period.
The constraint conditions of the power supplier optimized scheduling model are as follows:
Figure BDA0002653770330000061
Figure BDA0002653770330000062
equation (2) represents the power balance constraint for the supplier supply, Dn,tThe net load demand of the nth user microgrid in the time period t under the reference scene is set, and N represents the total number of the user microgrids in the community multi-microgrid system; equation (3) is the power supply price constraint of the power supplier, the first term represents that the sum of the power supply prices is constant in the whole scheduling period, the second term defines the range of the power supply prices of the power supplier in any scheduling period, C is the constant average power supply price,
Figure BDA0002653770330000063
and
Figure BDA0002653770330000064
respectively the minimum and maximum power supply rates for the t period.
And 20) obtaining the operation cost parameters and the operation limit parameters of each user microgrid in the community multi-microgrid system, and constructing a max-max form double-layer robust coordination scheduling model of the user microgrid as a follower.
The operation cost parameters and the operation limit parameters of each device of the user microgrid comprise all operation cost coefficients and operation limit parameters related to renewable energy power generation and load in each user microgrid, uncertainty of the renewable energy power generation is calculated, and the obtained cost coefficients and limit parameters are substituted into the following formula to establish a max-max form double-layer robust coordination scheduling model for the user microgrid as a follower:
the upper layer model objective function of the user microgrid double-layer robust coordinated scheduling model is as follows:
Figure 255566DEST_PATH_GDA0002737958510000026
in the formula (I), the compound is shown in the specification,
Figure BDA0002653770330000066
the total power utilization benefit of the microgrid users under the scene with renewable energy power generation as a reference is expressed for an upper model objective function of the nth microgrid users; l isn,tThe load power of the nth user microgrid in the time period t under the reference scene is obtained; alpha is alphan,tAnd betan,tThe power utilization yield coefficient of the nth user microgrid in the time period t is obtained; gamma rayn,tPunishing a cost coefficient for the net load deviation of the nth user microgrid in the t period; gn,tThe net load demand of the nth user microgrid in the period of t in the worst scene is met.
The constraint conditions of an upper layer model of the user microgrid double-layer robust coordinated scheduling model are as follows:
Dn,t＝Ln,t-Pn,t,Dn,t≥0 (5)
Figure BDA0002653770330000067
equation (5) is the power balance constraint of the nth microgrid of users under the reference scene, Pn,tGenerating power for a reference scene of renewable energy sources in the nth microgrid during a time period t; the formula (6) is load power constraint of the nth microgrid of users under a reference scene, the former term represents that the total power consumption of the load in the scheduling period is constant, and the latter term represents that upper and lower limits exist in the load power at any time interval; enRepresenting the constant amount of power consumed by the nth microgrid;
Figure BDA0002653770330000068
and
Figure BDA0002653770330000069
representing the minimum and maximum load power values of the nth user during the t period.
The lower layer model objective function of the user microgrid double-layer robust coordinated scheduling model is as follows:
Figure 172706DEST_PATH_GDA0002737958510000035
in the formula (I), the compound is shown in the specification,
Figure BDA0002653770330000072
the model is a lower-layer model objective function of the nth microgrid user, and the total power utilization benefit of the microgrid user under the worst scene of renewable energy power generation is represented; kn,tThe load power of the nth microgrid is the load power of the nth microgrid during the time period t in the worst scene.
The lower layer model constraint conditions of the user microgrid double-layer robust coordinated scheduling model are as follows:
Gn,t＝Kn,t-Pn,t+τn,t+υn,t n (8)
Figure BDA0002653770330000073
τn,t,υn,t≥0,ζn,t≥1 (10)
Figure BDA0002653770330000074
the equation (8) is the power balance constraint of the nth microgrid of users in the worst scene; equations (9) - (10) are dual parameter constraints on the uncertainty of renewable energy generation in the nth microgrid of users, τn,t、υn,tAnd ζn,tRespectively, are dual variables of the uncertainty constraint,nbudgeting parameters for periods of renewable energy generation uncertainty,
Figure BDA0002653770330000075
and
Figure BDA0002653770330000076
an upper deviation value and a lower deviation value of the power generation uncertainty of the renewable energy source; and the equation (11) is the load operation power constraint of the nth microgrid of users in the worst scene.
And step 30), converting the max-max form double-layer robust coordination scheduling model of the user microgrid into a single-layer mathematical programming model containing balance constraint by using a strong dual theory. The concrete content comprises:
step 301): writing the user microgrid double-layer robust coordination scheduling model constructed in the step 20) into the following form:
Figure BDA0002653770330000077
s.t.Hy≤m (13)
Figure BDA0002653770330000078
s.t.Kz≤j (15)
in the formula, the optimized variables of each layer of model correspond to the model constructed in the step 20), and the objective function and the constraint condition are uniformly written into a matrix form; x, y and z respectively represent an optimization variable set corresponding to the formulas (1), (4) and (7); f. ofm xyzAnd
Figure BDA0002653770330000079
respectively representing the target functions corresponding to the expressions (4) and (7); equations (13) and (15) represent matrix forms of the constraints of equations (5) to (6) and (8) to (11), respectively.
Step 302): based on the model in the step 301), converting a max-max form double-layer robust coordination scheduling model of the user microgrid into a single-layer mathematical programming model containing balance constraint through a strong dual theory:
Figure BDA0002653770330000081
in the formula (I), the compound is shown in the specification,
Figure BDA0002653770330000082
represents the partial derivative of the function pair z; u is a dual variable of formula (15); and obtaining the single-layer mathematical programming model containing the balance constraint, which is easy to directly solve, through the dual equivalence.
And step 40), solving a master-slave game robust energy management model which is formed in the community multi-microgrid system and takes a power supplier as a leading person and a user microgrid as a following person, namely, iteratively solving the optimization scheduling problem of the power supplier and the user microgrid by utilizing a two-stage distributed optimization algorithm to obtain a master-slave game scheduling plan of the community multi-microgrid system.
The optimization scheduling problem of the power supplier and the user microgrid is solved by utilizing a two-stage distributed optimization algorithm in an iterative manner, and a master-slave game scheduling plan of the community multi-microgrid system is obtained, wherein the specific contents of the master-slave game scheduling plan comprise:
step 401): the number of iterations v is set to 0, and the convergence threshold τ is set.
Step 402): supplier initialization lambdat *Is a feasible solution and is used as the result x of the solution of the v-th iteration of the power supplier modelvAnd the data is transmitted to each user microgrid.
Step 403): each user microgrid will xvAnd solving the equivalent single-layer mathematical programming model containing the balance constraint as the known quantity substitution to obtain an optimization result (y)v,zv) And uploading to the power supplier.
Step 404): supplier general (y)v,zv) Substituting the known quantity, solving the self-optimization scheduling model and obtaining an optimization result xv+1。
Step 405): determine | xv+1-xv|/xvIf tau is established, the final optimization result (x) is outputv,yv,zv) Ending the solution; otherwise, v +1, return to step 403).
The method provided by the embodiment of the invention provides a master-slave game robust energy management method for a community multi-microgrid system, and the method considers the renewable energy power generation uncertainty to establish a master-slave game balanced scheduling model taking a power supplier as a leading person and a user microgrid as a following person, so that the robustness of a system scheduling plan to the uncertainty is ensured; the single-layer equivalence of the user microgrid double-layer scheduling model is realized by using a strong dual theory, so that the efficient calculation of the model is facilitated; the two-stage distributed optimization method is adopted to solve the multi-stage coupled optimization scheduling model, nested iterative optimization is avoided, model solving efficiency is guaranteed to meet actual requirements, and guidance and help are provided for formulating an energy management plan of the community multi-microgrid system.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the claims and their equivalents.

Claims (5)

1. A master-slave game robust energy management method for a community multi-microgrid system is characterized by comprising the following steps:
step 10) obtaining an operation cost parameter and an operation limit value parameter of a power supplier in the community multi-microgrid system, and constructing a max-form optimized scheduling model with the power supplier as a leading person;
step 20) obtaining operation cost parameters and operation limit parameters of each user microgrid in the community multi-microgrid system, and constructing a max-max form double-layer robust coordination scheduling model with the user microgrid as a follower;
step 30) converting the max-max form double-layer robust coordination scheduling model of the user microgrid into a single-layer mathematical programming model containing balance constraint by using a strong dual theory;
and step 40) solving a master-slave game robust energy management model which is formed in the community multi-microgrid system and takes a power supplier as a leading person and a user microgrid as a following person, namely, iteratively solving the optimization scheduling problem of the power supplier and the user microgrid by utilizing a two-stage distributed optimization algorithm to obtain a master-slave game scheduling plan of the community multi-microgrid system.
2. The master-slave game robust energy management method for the community multi-microgrid system according to claim 1, wherein in the step 10), the operation cost parameters and the operation limit parameters of the power provider include cost parameters and operation limits related to power generation and supply prices of the power provider, and the obtained cost parameters and operation limits are substituted into the following formula to establish a max-form optimization scheduling model for the power provider as a dominant person:
the objective function of the power supplier optimized scheduling model is as follows:
Figure FDA0002653770320000011
in the formula of USRepresenting the total power supply income of the power supplier in the whole scheduling period for the objective function of the power supplier model; lambda [ alpha ]tA price of power supplied to the power provider; dtThe net load requirements of all the micro grids of the users in the time period t under the reference scene are set; a ist、btAnd ctCost factor for power provider generation; t is the total time period number of a scheduling period;
the constraint conditions of the power supplier optimized scheduling model are as follows:
Figure FDA0002653770320000012
Figure FDA0002653770320000013
equation (2) represents the power balance constraint for the supplier supply, Dn,tThe net load demand of the nth user microgrid in the time period t under the reference scene is set, and N represents the total number of the user microgrids in the community multi-microgrid system; equation (3) is the power supply price constraint of the power supplier, the first term represents that the sum of the power supply prices is constant in the whole scheduling period, the second term defines the range of the power supply prices of the power supplier in any scheduling period, C is the constant average power supply price,
Figure FDA0002653770320000014
and
Figure FDA0002653770320000015
respectively the minimum and maximum power supply rates for the t period.
3. The master-slave game robust energy management method for the community multi-microgrid system according to claim 2, wherein in the step 20), the operation cost parameters and the operation limit parameters of each device of the user microgrid comprise all operation cost coefficients and operation limit parameters related to renewable energy power generation and load in each user microgrid, uncertainty of the renewable energy power generation is calculated, and the obtained operation cost coefficients and operation limit parameters are substituted into the following formula to establish a max-max form double-layer robust coordination scheduling model for the user microgrid as a follower:
the upper layer model objective function of the user microgrid double-layer robust coordinated scheduling model is as follows:
Figure DEST_PATH_FDA0002737958500000021
in the formula (I), the compound is shown in the specification,
Figure FDA0002653770320000022
the total power utilization benefit of the microgrid users under the scene with renewable energy power generation as a reference is expressed for an upper model objective function of the nth microgrid users; l isn,tThe load power of the nth user microgrid in the time period t under the reference scene is obtained; alpha is alphan,tAnd betan,tThe power utilization yield coefficient of the nth user microgrid in the time period t is obtained; gamma rayn,tPunishing a cost coefficient for the net load deviation of the nth user microgrid in the t period; gn,tThe net load requirement of the nth user microgrid in the period of t in the worst scene is met;
the constraint conditions of an upper layer model of the user microgrid double-layer robust coordinated scheduling model are as follows:
Dn,t＝Ln,t-Pn,t,Dn,t≥0 (5)
Figure FDA0002653770320000023
equation (5) is the power balance constraint of the nth microgrid of users under the reference scene, Pn,tGenerating power for a reference scene of renewable energy sources in the nth microgrid during a time period t; the formula (6) is load power constraint of the nth microgrid of users under a reference scene, the former term represents that the total power consumption of the load in the scheduling period is constant, and the latter term represents that upper and lower limits exist in the load power at any time interval; enRepresenting the constant amount of power consumed by the nth microgrid;
Figure FDA0002653770320000024
and
Figure FDA0002653770320000025
representing the minimum value and the maximum value of the load power of the nth user in the t period;
the lower layer model objective function of the user microgrid double-layer robust coordinated scheduling model is as follows:
Figure DEST_PATH_FDA0002737958500000026
in the formula (I), the compound is shown in the specification,
Figure FDA0002653770320000027
the model is a lower-layer model objective function of the nth microgrid user, and the total power utilization benefit of the microgrid user under the worst scene of renewable energy power generation is represented; kn,tThe load power of the nth user microgrid in the worst scene in the time period t is obtained;
the lower layer model constraint conditions of the user microgrid double-layer robust coordinated scheduling model are as follows:
Gn,t＝Kn,t-Pn,t+τn,t+υn,t n (8)
Figure FDA0002653770320000031
τn,t,υn,t≥0,ζn,t≥1 (10)
Figure FDA0002653770320000032
the equation (8) is the power balance constraint of the nth microgrid of users in the worst scene; equations (9) - (10) are dual parameter constraints on the uncertainty of renewable energy generation in the nth microgrid of users, τn,t、υn,tAnd ζn,tRespectively, are dual variables of the uncertainty constraint,nbudgeting parameters for periods of renewable energy generation uncertainty,
Figure FDA0002653770320000033
and
Figure FDA0002653770320000034
an upper deviation value and a lower deviation value of the power generation uncertainty of the renewable energy source; and the equation (11) is the load operation power constraint of the nth microgrid of users in the worst scene.
4. The master-slave gaming robust energy management method for the community multi-microgrid system according to claim 3, wherein the specific content of the step 30) includes:
step 301): writing the user microgrid double-layer robust coordination scheduling model constructed in the step 20) into the following form:
Figure FDA0002653770320000035
s.t.Hy≤m (13)
Figure FDA0002653770320000036
s.t.Kz≤j (15)
in the formula, the optimized variables of each layer of model correspond to the model constructed in the step 20), and the objective function and the constraint condition are uniformly written into a matrix form; x, y and z respectively represent an optimization variable set corresponding to the formulas (1), (4) and (7); f. ofm xyzAnd
Figure FDA0002653770320000037
respectively representing the target functions corresponding to the expressions (4) and (7); equations (13) and (15) represent matrix forms of the constraints of equations (5) - (6) and (8) - (11), respectively;
step 302): based on the model in the step 301), converting a max-max form double-layer robust coordination scheduling model of the user microgrid into a single-layer mathematical programming model containing balance constraint through a strong dual theory:
Figure FDA0002653770320000038
in the formula (I), the compound is shown in the specification,
Figure FDA0002653770320000039
represents the partial derivative of the function pair z; u is a dual variable of formula (15); and obtaining the single-layer mathematical programming model containing the balance constraint, which is easy to directly solve, through the dual equivalence.
5. The master-slave gaming robust energy management method for the community multi-microgrid system according to claim 4, wherein the specific contents in the step 40) include:
step 401): setting the iteration number v as 0, and setting a convergence threshold tau;
step 402): power supplier initialization
Figure FDA0002653770320000041
Is a feasible solution and is used as the result x of the solution of the v-th iteration of the power supplier modelvThe data is transmitted to each user microgrid;
step 403): each user microgrid will xvAnd solving the equivalent single-layer mathematical programming model containing the balance constraint as the known quantity substitution to obtain an optimization result (y)v,zv) Uploading the data to a power supplier;
step 404): supplier general (y)v,zv) Substituting the known quantity, solving the self-optimization scheduling model and obtaining an optimization result xv+1；
Step 405): determine | xv+1-xv|/xvIf tau is established, the final optimization result (x) is outputv,yv,zv) Ending the solution; otherwise, v +1, return to step 403).
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