CN113159366A - Multi-time scale self-adaptive optimization scheduling method for multi-microgrid system - Google Patents

Multi-time scale self-adaptive optimization scheduling method for multi-microgrid system Download PDF

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CN113159366A
CN113159366A CN202011638375.2A CN202011638375A CN113159366A CN 113159366 A CN113159366 A CN 113159366A CN 202011638375 A CN202011638375 A CN 202011638375A CN 113159366 A CN113159366 A CN 113159366A
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张后谊
林呈辉
高吉普
古庭赟
徐玉韬
代奇迹
刘斌
王宇
赵健
祝健杨
冯成
李博文
冯起辉
李鑫卓
张俊杰
唐赛秋
张历
范强
陈敦辉
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Abstract

The invention discloses a multi-time scale self-adaptive optimization scheduling method for a multi-microgrid system, which comprises the following steps: constructing a multi-time scale scheduling framework of the multi-microgrid system and an adaptive framework for optimizing scheduling of the multi-microgrid system; constructing a multi-microgrid system self-adaptive collaborative optimization scheduling model under the multi-time scale based on a multi-time scale scheduling framework and a self-adaptive framework; adopting an alternate direction multiplier method to realize model solution to obtain a scheduling result considering dynamic access or exit of the sub-microgrid to the multi-microgrid system; the model solution comprises a day-ahead optimization scheduling model solution and a day-in optimization scheduling model solution; the method can adaptively adjust the scheduling target, reduce the influence of real-time dynamic link behaviors or fault events in the multi-microgrid system on the cooperative optimization operation of the multi-microgrid system, and improve the economic toughness and the safety and stability of the operation of the multi-microgrid system.

Description

Multi-time scale self-adaptive optimization scheduling method for multi-microgrid system
Technical Field
The invention belongs to a micro-grid scheduling technology, and particularly relates to a multi-time scale self-adaptive optimization scheduling method for a multi-micro-grid system.
Background
With the large-scale access of a micro-grid containing high-permeability renewable energy to a power distribution network, a plurality of adjacent micro-grid clusters in a certain area are bound to form a multi-micro-grid system. The consumption and control capability of renewable energy sources can be effectively improved by optimizing the operation of the sub-microgrid clusters in the multi-microgrid system; in the dispatching process of the multi-microgrid system, the dynamic link behavior of the sub-microgrid during planned or unplanned dynamic access or quit from the multi-microgrid system and internal faults of the multi-microgrid system bring challenges to the operation management of the multi-microgrid system, and how to effectively manage the dynamic link behavior of the sub-microgrid in the multi-microgrid system has practical significance for effectively managing the multi-microgrid system and improving the consumption rate of renewable distributed energy. Because the optimization problem of the multi-microgrid system is larger in scale and higher in complexity compared with that of a single microgrid, how to effectively manage the operation of the multi-microgrid system is one of the problems to be solved urgently in the research of the multi-microgrid system.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the multi-time scale self-adaptive optimization scheduling method for the multi-micro-grid system is provided, so that the capability of the multi-micro-grid system for adaptively responding to dynamic link behaviors and faults of sub-micro-grid access or quit under the multi-time scale is improved, and the consumption rate of renewable distributed energy is improved.
The technical scheme adopted by the invention is as follows:
a multi-time scale self-adaptive optimization scheduling method for a multi-microgrid system comprises the following steps:
step 1, constructing a multi-time scale scheduling framework of a multi-microgrid system and an adaptive framework for optimizing scheduling of the multi-microgrid system;
step 2, constructing a multi-microgrid system self-adaptive collaborative optimization scheduling model under multi-time scale based on a multi-time scale scheduling framework and a self-adaptive framework, wherein the multi-microgrid system self-adaptive collaborative optimization scheduling model under multi-time scale comprises a day-ahead optimization scheduling model and an intra-day optimization scheduling model;
step 3, adopting an alternate direction multiplier method to realize model solution to obtain a scheduling result considering dynamic access or exit of the sub-microgrid to the multi-microgrid system; the model solution comprises day-ahead optimization scheduling model solution and day-in optimization scheduling model solution.
The method for constructing the multi-time scale scheduling framework of the multi-microgrid system constructed in the step 1 comprises the following steps: based on the advantages of model predictive control, in order to process uncertainty of renewable energy sources and load output in the multi-microgrid system, a multi-microgrid system day-ahead and day-in multi-time scale optimization framework based on a model predictive control method is constructed according to the characteristic that the output power prediction error of a renewable distributed power supply is reduced along with shortening of a prediction time scale.
The optimization target of the multi-microgrid system day-ahead multi-time scale optimization framework comprises the following steps: the overall operation cost of the multi-microgrid system is minimized, and a single sub-microgrid system can independently control the local load demand of the local distributed renewable energy supply and maintain the overall supply and demand balance of the interconnected multi-microgrid system; the day-ahead optimization scheduling is carried out according to the output power predicted value of the renewable distributed power supply and the load of the multi-micro-grid system in the day-ahead; the daily multi-time scale optimization framework adopts a dynamic self-adaptive scheduling method based on model predictive control, and each sub-microgrid needs to meet the following targets: the ultra-short-term predicted values of the renewable distributed power supply and the load are taken as references, and the renewable distributed power supply and the load are cooperated with other sub-micro-grids; tracking a day-ahead dispatch plan; and when the uncertainty of supply and demand is processed and when the sub-micro-grid exits from the multi-micro-grid system or fails, each sub-micro-grid can adaptively adjust the scheduling plan in the day to deal with the real-time dynamic link behavior or failure event of the multi-micro-grid system.
The method for constructing the self-adaptive architecture for optimizing and scheduling the multi-microgrid system comprises the following steps: in order to effectively deal with the dynamic link behavior that a sub-microgrid in a multi-microgrid system is not planned to exit or access the multi-microgrid system in real time, the self-adaptive management of optimized dispatching of the multi-microgrid is realized, and a self-adaptive dynamic framework of the multi-microgrid system is established; MG-EMC is a controller of each sub-microgrid, IEP is an information interaction center between each sub-microgrid, and the sub-microgrid performs information interaction through the IEP to minimize the overall operation cost of the multi-microgrid system; describing physical links of the multi-microgrid system by using an undirected connected graph (M (t), L (t)), L (t) representing an adjacency matrix of the physical links of the multi-microgrid system, M (t) {1,2, …, n } representing a sub-microgrid set forming the multi-microgrid, and l (t) } representing a sub-microgrid setij(t)＝1,lij(t) E L (t), i, j E M (t) indicates that there is a physical link between sub-grids i and j, lii(t) ═ 1 indicates that the sub-microgrid i is physically linked with the power distribution network, and the information link matrix e (t) ═ e1(t),…,en(t)]Description of ei(t) ═ 1 is the information link between the sub-microgrid i and the IEP;
during rolling optimization in the day, the real-time dynamic link behavior of the multi-microgrid is added or withdrawn from the sub-microgrid in an unplanned manner, so that the corresponding elements of the physical and information link matrix of the multi-microgrid are increased, decreased and changed, and a dynamic link matrix Y is defined for describing the real-time dynamic link behavior of the multi-microgrid system
Figure BDA0002879239350000031
Scheduling t as t in the current day0When N sub-micro-grids are added into the multi-micro-grid at any moment, the original physical and information link matrix form can be ensured to be unchanged, corresponding rows and columns are only needed to be added to the original augmented dynamic link matrix, the augmented dynamic matrix for adding the N sub-micro-grids is obtained as shown in the formula (2), and the N sub-micro-grids are used
Figure BDA0002879239350000032
Denotes that t is t0The case of physical links to multiple micro-grids at the time, wherein,
Figure BDA0002879239350000033
Figure BDA0002879239350000034
if there is an intra-day schedule t ═ t1The sub-microgrid i exits the multi-microgrid system at any moment, and only row and column elements where the sub-microgrid i is located in the dynamic link matrix need to be deleted
Figure BDA0002879239350000041
Y in the formula (3)(n)(t1) For multiple micro-grid systems t ═ t1Physical linking of time of day.
The construction method of the day-ahead optimization scheduling model comprises the following steps:
the day-ahead scheduling aims at minimizing the overall operation cost of the multi-microgrid system, and according to day-ahead load and a predicted value of wind-solar renewable energy output power, the interaction power among the sub-microgrid systems and the power purchasing and selling rates of the sub-microgrid systems and the power distribution network are determined by optimizing and scheduling the output of energy storage and controllable distributed power sources and loads, so that the overall operation cost of the multi-microgrid system is the lowest;
under the dynamic self-adaptive architecture of energy management of the multi-microgrid system, each sub-microgrid considers the day-ahead objective function of power interaction with other sub-microgrids as
Figure BDA0002879239350000042
In the formula: cDG,i(t) operating cost of the controllable distributed power supply of the sub-microgrid i in the period t, CBE,i(t) energy storage operating cost, CDN,i(t) cost of electricity purchase and sale from sub-microgrid i to power distribution network system, CDR,i(t) demand side response cost of the i load of the sub-microgrid; the scheduling model and the operation constraint conditions of each sub-microgrid system unit are as follows:
the cost of the controllable distributed power supply is as follows:
CDG,i(t)＝(aDG,iPDG,i(t)+bDG,i)Δt (5)
in the formula: a isDG,iAnd bDG,iTo transportThe cost coefficient is calculated, and the operation constraint of the controllable distributed power supply is as follows:
Figure BDA0002879239350000043
in the formula:
Figure BDA0002879239350000044
and
Figure BDA0002879239350000045
the maximum and minimum power constraints of the controllable distributed power supply of the micro-grid i are set;
the energy storage system model is
CBE,i(t)＝cBE,i(Pc,i(t)ηi+Pd,i(t)/ηi)Δt (7)
In the formula: c. CBE,iFor the unit charge-discharge cost, P, of the I energy storage system of the sub-microgridc,i(t) and Pd,i(t) is the charge and discharge power, etaiFor the energy storage charge-discharge efficiency, the constraints met in the operation process are as follows:
Figure BDA0002879239350000051
Figure BDA0002879239350000052
Figure BDA0002879239350000053
Figure BDA0002879239350000054
in the formula:
Figure BDA0002879239350000055
and
Figure BDA0002879239350000056
the maximum charge-discharge power allowed by the energy storage of the sub-microgrid i,
Figure BDA0002879239350000057
and
Figure BDA0002879239350000058
the allowable minimum and maximum residual capacity of the energy storage in the operation process of the sub-microgrid i; equations (8) and (9) are energy storage sufficient electric power constraints of the sub-microgrid i, and equations (10) and (11) are energy storage residual capacity constraints of the sub-microgrid i;
demand side response model for translatable loads
Each sub-microgrid system has a translatable load, and the compensation cost for users due to load translation is as follows
Figure BDA0002879239350000059
In the formula (12) cDR,iCost per unit compensation for translatable loads, PL,iThe actual scheduled power for the sub-microgrid i,
Figure BDA00028792393500000510
for the expected scheduling power of the sub-microgrid i in the period t, the constraint conditions met by the demand side response are as follows:
Figure BDA00028792393500000511
Figure BDA00028792393500000512
the formula (13) ensures that the power consumption of the user is unchanged after the response of the demand side is considered, the formula (14) is the minimum/large power consumption constraint of each time period, and a is the maximum proportion of the output of the translatable load to the total load;
power interaction and electricity purchasing and selling model among sub-micro grids
CDN,i(t)＝(cb,i(t)Pb,i(t)-cs,i(t)Ps,i(t))Δt (15)
In the formula: c. Cb,i(t) and cs,i(t) is the purchase and sale price of the sub-microgrid i in the time period t, Pb,i(t) and Ps,i(t) the power purchasing and selling of the sub-microgrid i in the time period t, and the power interaction among the sub-microgrid and the power purchasing and selling meeting the operation constraint conditions are
Figure BDA0002879239350000061
Figure BDA0002879239350000062
Figure BDA0002879239350000063
ui(t)+vi(t)≤1 (19)
Pij(t)+Pji(t)＝0 (20)
In the formula:
Figure BDA0002879239350000065
purchasing the maximum power for selling electricity from the sub-micro-grid i to the distribution network system,
Figure BDA0002879239350000066
the maximum value of the power interaction of the sub-micro-grids i and j is obtained; u. ofi(t) and viAnd (t) is 0/1 variable of the power purchasing and selling state of the sub-microgrid, and 1 cannot be simultaneously selected. The formulas (16) - (17) are maximum power purchasing and selling constraints of the sub-microgrid i, and the formulas (18) - (20) are power interaction constraints of the sub-microgrid i and j;
the power balance constraint of the sub-microgrid i is as follows:
Pb,i(t)-Ps,i(t)+Pwt,i(t)+Ppv,i(t)+Pij(t)+PDG,i(t)-Pc,i(t)+Pd,i(t)＝PL,i(t) (21)
in the formula: pwt,i(t)、Ppv,i(t) with PL,iAnd (t) respectively predicting output of the fan, the photovoltaic and the load of the sub-microgrid i in a time period t.
The construction method of the intraday optimal scheduling model comprises the following steps:
in-day optimized scheduling, the length t of the rolling time of each sub-microgrid is { k ═ k1,k1+1,…,k1The aim of minimizing the overall operation cost of the multi-microgrid system in + M is to regulate and control internal energy storage, translatable load and controllable distributed power supply to maintain stable operation of each microgrid system according to predicted values of short-term fans, photovoltaic and load in the day, and because the energy storage and the controllable load of each sub-microgrid need to arrange an output plan in a long time scale, the energy storage and the translatable load of each sub-microgrid in rolling optimization scheduling in the day track a predetermined power output track in the day, so that safe and stable operation of the multi-microgrid system is realized, and the comprehensive benefit in a scheduling period of the multi-microgrid system is improved;
the intra-day rolling optimization of each sub-microgrid aims at minimizing the overall operation cost of the multi-microgrid system, energy storage and translational load output of each sub-microgrid in the day ahead are tracked, and intra-day energy storage and translational load output deviation of each sub-microgrid is used as a penalty function, and the specific model is as follows:
Figure BDA0002879239350000064
the constraints are:
Figure BDA0002879239350000071
in the formula
Figure BDA0002879239350000072
Energy storage and translational load meter for day-ahead micro-grid iDividing the value of the tone ug(k) Is a Boolean state quantity, ug(k) And 0 represents that the multi-microgrid system has real-time dynamic link behavior or fault events.
The day-ahead optimization scheduling model solving method comprises the following steps:
firstly, IEP collects physical link matrixes (M (t), L (t)) and information link matrixes E (t) of a plurality of micro-grid systems in the day ahead;
secondly, decoupling by adopting an alternative direction multiplier method to realize distributed solution of each sub-microgrid, wherein the specific method comprises the following steps: in the sub-microgrid layer, the sub-microgrid i receives the reference interactive power of the IEP in the kth iteration after receiving the physical and information link matrix of the multiple microgrids before the IEP
Figure BDA0002879239350000073
And lagrange multiplier
Figure BDA0002879239350000074
Solving a day-ahead scheduling plan of each sub-microgrid in the kth iteration;
Figure BDA0002879239350000075
the IEP receives the power interaction value of the kth iteration of each sub-microgrid
Figure RE-GDA0003094965530000077
Figure BDA0002879239350000077
The constraint conditions are as follows:
Figure BDA0002879239350000078
lagrange multiplier update:
Figure BDA0002879239350000079
t∈{1,2,...,T}
when the condition is satisfied
Figure BDA00028792393500000710
And then, iteratively converging, and solving to obtain the day-ahead scheduling plan of each sub-microgrid.
The solution method of the intraday optimal scheduling model comprises the following steps:
the intra-day rolling optimization tracks a day-ahead scheduling plan according to short-term prediction of a fan, a photovoltaic and a load in a day, adaptively adjusts and optimizes a scheduling objective function to maintain stable operation of the multi-microgrid system when a real-time dynamic link behavior occurs, and the intra-day rolling optimization specifically comprises the following steps:
first, at t ═ k1At the moment, the IEP collects the physical and information link information of the multi-microgrid system to obtain a real-time augmented dynamic link matrix Y (k)1) Variable u linked dynamically to flags or whether failure has occurredg(k1) At a rolling period t ═ k1,k1+1,…,k1+ M physical and information linking matrix with t ═ k1Time-wise augmented dynamic link matrix Y (k)1) The decomposed physical link is the same as the information link matrix;
secondly, each sub-microgrid receives a real-time dynamic link matrix Y (k) from the IEP1) Variable u for marking dynamic link behavior or fault occurrenceg(k1) (ii) a Solving the formula (22) by adopting an ADMM algorithm, and solving the formula according to the current t-k1Predicted rolling period t ═ k1,k1+1,…,k1+ M, predicting the short-term predicted power of the fan, the photovoltaic and the load of each sub-microgrid in the multi-microgrid system, and calculating a scheduling plan of each sub-microgrid in the multi-microgrid system in a rolling optimization period;
at the mth iteration, the sub-microgrid system i receives the reference interaction power from the IEP
Figure BDA0002879239350000081
And lagrange multiplier
Figure BDA0002879239350000082
Solving a scheduling plan in the rolling scheduling duration of each sub-microgrid in the mth iteration;
Figure BDA0002879239350000083
at the interaction layer of the multi-microgrid system, the IEP receives the power interaction value of the mth iteration of each sub-microgrid
Figure BDA0002879239350000084
Solving the reference interactive power and the Lagrange multiplier of the (m + 1) th iteration;
Figure BDA0002879239350000085
the constraint conditions are as follows:
Figure BDA0002879239350000086
lagrange multiplier update:
Figure BDA0002879239350000087
t∈{k1,k1+1,...,k1+M}
when the condition is satisfied
Figure BDA0002879239350000088
And then, iteratively converging, and solving to obtain { k ] in the rolling period of each sub-microgrid system1,k1+1,…,k1+ M scheduling plan, and k1The time of day schedule is implemented to the control system.
Finally, at k1At +1 moment, the real-time augmented dynamic link matrix Y (k) is updated1) Variable u with flag dynamic linking is or is not occurringg(k1) And repeating the rolling optimization steps in the day, carrying out a new round of optimization, and finishing the iteration to obtain the optimized scheduling result of the multiple micro-grid systems in the day.
The invention has the beneficial effects that:
in order to deal with the uncertainty of fan power generation, photovoltaic power generation and load output, the invention constructs a multi-time scale scheduling framework of a multi-microgrid system based on a model prediction control method according to the characteristic that the source-load output power prediction error is reduced along with the shortening of the time scale. In order to deal with real-time dynamic link behaviors and fault events, a multi-microgrid self-adaptive dynamic framework is established, on the basis, a multi-time scale self-adaptive scheduling strategy of the multi-microgrid system is provided, the strategy can adaptively adjust a scheduling target to reduce the influence of the real-time dynamic link behaviors or the fault events in the multi-microgrid system on the cooperative optimization operation of the multi-microgrid system, and the economic toughness and the safety stability of the operation of the multi-microgrid system are improved; the capacity of the multi-micro-grid system for adaptively responding to dynamic link behaviors and faults of access or exit of the sub-micro-grid under the multi-time scale is improved.
Drawings
FIG. 1 is a multi-time scale scheduling framework for a multi-microgrid system of the present invention;
FIG. 2 is a multi-microgrid system adaptive dynamic architecture of the present invention;
fig. 3 is a schematic diagram of a day-ahead and in-day adaptive optimization scheduling model of multiple micro-grids according to the present invention.
Detailed Description
The invention comprises the following steps:
step 1) constructing a multi-time scale scheduling framework of the multi-microgrid system and establishing a self-adaptive framework for optimizing scheduling of the multi-microgrid system.
The step 1) of constructing the multi-time scale scheduling framework of the multi-microgrid system specifically comprises the following steps: based on the advantages of model predictive control, in order to process uncertainty of renewable energy sources and load output in the multi-microgrid system, a multi-microgrid system day-ahead and day-inside multi-time scale optimization framework based on a model predictive control method is constructed according to the characteristic that the output power prediction error of a renewable distributed power supply is reduced along with shortening of a prediction time scale, and the specific practical process is shown in fig. 1.
The optimization objective of the day-ahead multi-timescale optimization framework includes three aspects (see fig. 1): 1) the overall operation cost of the multi-microgrid system is minimized; 2) the single sub-microgrid system can independently control the local distributed renewable energy to supply local load requirements; 3) and the overall supply and demand balance of the interconnected multi-microgrid system is maintained. According to the output power preset value of renewable distributed power sources and loads of the multi-microgrid system in the day, the day-ahead optimized scheduling obtains a day-ahead scheduling plan of controllable distributed power sources, stored energy, purchased power and power interaction quantity with other sub-microgrid power sources of each sub-microgrid through optimized scheduling, and scheduling reference is provided for rolling optimization in the day.
The multi-time scale optimization framework in the day adopts a dynamic self-adaptive scheduling strategy based on model predictive control, and each sub-microgrid needs to meet the following targets: 1) tracking a day-ahead scheduling plan by taking the ultra-short-term predicted values of the reproducible distributed power supply and the load as references and through cooperative cooperation with other sub-micro-grids; 2) handling supply and demand uncertainty; 3) when the sub-micro-grid exits from the multi-micro-grid system or fails, each sub-micro-grid can adaptively adjust the scheduling plan in the day to deal with real-time dynamic link behaviors or failure events of the multi-micro-grid system. The day-in rolling optimization mainly adopts the latest information, the latest renewable energy and the latest predicted output power of the load rolling time domain are obtained through a prediction model, online optimization is realized while the day-ahead scheduling value is tracked, the optimized scheduling instruction of each sub-micro-grid in the rolling time domain is obtained, the first instruction is implemented into the control, and the process is repeated at the next moment.
Step 1) establishing a self-adaptive framework for optimizing and scheduling of the multi-microgrid system. The method comprises the following steps: in order to effectively deal with the dynamic link behavior of the multi-microgrid system in which the sub-microgrid is not planned to exit or access in real time, the self-adaptive management of the optimized dispatching of the multi-microgrid is realized, and a self-adaptive dynamic architecture of the multi-microgrid system is established (see figure 2).
MG-EMC is a controller of each sub-microgrid, IEP is an information interaction center between each sub-microgrid, and the sub-microgrid performs information interaction through the IEP to minimize the overall operation cost of the multi-microgrid system (see figure 2).
Describing physical links of the multi-microgrid system by using an undirected connected graph (M (t), L (t)), L (t) representing an adjacency matrix of the physical links of the multi-microgrid system, M (t) {1,2, …, n } representing a sub-microgrid set forming the multi-microgrid, and l (t) }ij(t)＝1,lij(t) E L (t), i, j E M (t) indicates that there is a physical link between sub-grids i and j, lii(t) ═ 1 indicates that the sub-microgrid i is physically linked with the power distribution network, and the information link matrix e (t) ═ e1(t),…,en(t)]Description of eiAnd (t) ═ 1 represents that the sub-microgrid i is linked with IEP presence information.
During rolling optimization in a day, the sub-microgrid is not planned to join or leave the real-time dynamic link behavior of the multi-microgrid, so that the corresponding elements of the physical and information link matrix of the multi-microgrid are increased, decreased and changed, and a dynamic link matrix Y is defined and used for describing the real-time dynamic link behavior of the multi-microgrid system.
Figure BDA0002879239350000111
Scheduling t as t in the current day0When N sub-micro-grids are added into the multi-micro-grid at any moment, the original physical and information link matrix can be ensured to be unchanged, corresponding rows and columns are only needed to be added to the original augmented dynamic link matrix, the augmented dynamic matrix for adding the N sub-micro-grids is obtained as shown in the formula (2), and the N sub-micro-grids are used
Figure BDA0002879239350000112
Denotes t ═ t0The case of physical links to multiple micro-grids at the time, wherein,
Figure BDA0002879239350000113
Figure BDA0002879239350000114
if there is an intra-day schedule t ═ t1And (4) the sub-microgrid i exits the multi-microgrid system at the moment, and only row and column elements where the sub-microgrid i of the augmented dynamic link matrix is located are deleted.
Figure BDA0002879239350000115
Y in the formula (3)(n)(t1) For multiple micro-grid systems t ═ t1Physical linking of time of day.
The step 2) comprises the following steps: establishing a multi-microgrid system self-adaptive collaborative optimization scheduling model under multiple time scales, mainly a day-ahead optimization scheduling model and an intra-day optimization scheduling model.
In step 2), the day-ahead optimization scheduling model specifically comprises:
the day-ahead scheduling aims at minimizing the overall operation cost of the multi-microgrid system, and the overall operation cost of the multi-microgrid system is the lowest by optimizing and scheduling the output of the energy storage, the controllable distributed power supply and the load according to the day-ahead load and the wind-solar renewable energy output power predicted value and determining the interactive power among the sub-microgrid and the power purchasing and selling rates of the sub-microgrid and the power distribution network.
Under the dynamic self-adaptive architecture of energy management of the multi-microgrid system, each sub-microgrid considers the day-ahead objective function of power interaction with other sub-microgrids as
Figure BDA0002879239350000121
In the formula: cDG,i(t) operating cost of the controllable distributed power supply of the sub-microgrid i in the period t, CBE,i(t) energy storage operating cost, CDN,i(t) cost of electricity purchase and sale from sub-microgrid i to power distribution network system, CDR,i(t) demand side response cost of sub-microgrid i-load. The scheduling model and the operation constraint condition of each sub-microgrid system unit are as follows:
the cost of the controllable distributed power supply is as follows:
CDG,i(t)＝(aDG,iPDG,i(t)+bDG,i)Δt (5)
in the formula: a isDG,iAnd bDG,iFor the operation cost factor, the controllable distributed power supply operation constraints are as follows:
Figure BDA0002879239350000122
in the formula:
Figure BDA0002879239350000123
and
Figure BDA0002879239350000124
are the controllable distributed power supply maximum and minimum power constraints of the microgrid i.
Energy storage system model
CBE,i(t)＝cBE,i(Pc,i(t)ηi+Pd,i(t)/ηi)Δt (7)
In the formula: c. CBE,iFor the unit charge-discharge cost, P, of the I energy storage system of the sub-microgridc,i(t) and Pd,i(t) is the charge-discharge power, etaiFor the energy storage charge-discharge efficiency, the constraints met in the operation process are as follows:
Figure BDA0002879239350000125
Figure BDA0002879239350000126
Figure BDA0002879239350000131
Figure BDA0002879239350000132
in the formula:
Figure BDA0002879239350000133
and
Figure BDA0002879239350000134
the maximum charge-discharge power allowed by the energy storage of the sub-microgrid i,
Figure BDA0002879239350000135
and
Figure BDA0002879239350000136
and (4) the allowable minimum and maximum residual capacity of the energy storage in the operation process of the sub-micro-grid i. Equations (8) and (9) are the energy storage sufficient electric power constraints of the sub-microgrid i, and equations (10) and (11) are the energy storage residual capacity constraints of the sub-microgrid i.
Demand side response model for translatable loads
Each sub-microgrid system has a translatable load, and the compensation cost for users due to load translation is as follows
Figure BDA0002879239350000137
In the formula (12) cDR,iCost per unit compensation for translatable loads, PL,iThe power is actually scheduled for the sub-microgrid i,
Figure BDA0002879239350000138
for the expected scheduling power of the sub-microgrid i in the period t, the constraint conditions met by the demand side response are as follows:
Figure BDA0002879239350000139
Figure BDA00028792393500001310
and (3) the equation (13) ensures that the power consumption of the user is unchanged after the demand side response is considered, the equation (14) is the minimum/large power consumption constraint of each time period, and a is the maximum proportion of the output of the translatable load to the total load.
Power interaction and electricity purchasing and selling model among sub-micro grids
CDN,i(t)＝(cb,i(t)Pb,i(t)-cs,i(t)Ps,i(t))Δt (15)
In the formula: c. Cb,i(t) and cs,i(t) is the purchase and sale price of the sub-microgrid i in the time period t, Pb,i(t) and Ps,i(t) the power purchasing and selling of the sub-microgrid i in the time period t, and the power interaction among the sub-microgrids and the power purchasing and selling meeting the operation constraint conditions are
Figure BDA00028792393500001311
Figure BDA00028792393500001312
Figure BDA00028792393500001313
ui(t)+vi(t)≤1 (19)
Pij(t)+Pji(t)＝0 (20)
In the formula:
Figure BDA0002879239350000141
purchasing the maximum power for selling electricity from the sub-micro-grid i to the distribution network system,
Figure BDA0002879239350000142
and the maximum value of the power interaction of the sub-micro-grids i and j is shown. u. ofi(t) and viAnd (t) is 0/1 variable of the power purchasing and selling state of the sub-microgrid, and 1 cannot be simultaneously selected. The maximum power purchasing and selling constraint of the sub-micro-grid i is represented by the formulas (16) - (17), and the maximum power purchasing and selling constraint of the sub-micro-grid i is represented by the formulas (18) - (20)And (5) carrying out power interaction constraint on the micro-grids i and j.
The power balance constraint of the sub-microgrid i is as follows:
Pb,i(t)-Ps,i(t)+Pwt,i(t)+Ppv,i(t)+Pij(t)+PDG,i(t)-Pc,i(t)+Pd,i(t)＝PL,i(t) (21)
in the formula: pwt,i(t)、Ppv,i(t) with PL,iAnd (t) respectively predicting the fan, photovoltaic and load output of the sub-microgrid i in the period t.
In step 2), the intraday optimization scheduling model specifically comprises:
in the day-optimized scheduling, each sub-microgrid has a rolling time length t ═ k1,k1+1,…,k1And the aim of minimizing the overall operation cost of the multiple micro-grids is fulfilled in + M, internal energy storage, load translation and controllable distributed power supply are regulated and controlled according to predicted values of short-term fans, photovoltaic and load in the day, and stable operation of each micro-grid system is maintained.
The intra-day rolling optimization of each sub-microgrid aims at minimizing the overall operation cost of a plurality of microgrid systems, energy storage and translational load output of each sub-microgrid before the day are tracked, and intra-day energy storage and translational load output deviation of each sub-microgrid is taken as a penalty function, and the specific model is as follows:
Figure BDA0002879239350000143
the constraints are:
Figure BDA0002879239350000144
and a controllable distributed power supply operation constraint formula (6), energy storage charging and discharging constraint formulas (8) and (9), power interaction among the sub-micro grids, and power purchasing and selling constraint formulas (16) - (20) and a power balance constraint formula (21).
In the formula (22)
Figure BDA0002879239350000151
Respectively the energy storage and translational load plan regulation values u of the day-ahead sub-microgrid ig(k) Is a Boolean state quantity, ug(k) The real-time dynamic link behavior or fault event of the multi-microgrid system is represented as 0, and frequent dynamic link behavior or fault event may occur in the real-time scheduling process, so that the adaptive scheduling strategy changes the operation objective function to enhance the mutual support of the power among the microgrid, maintains the power balance of the multi-microgrid under the dynamic link behavior or fault event, and improves the toughness of the multi-microgrid system.
A day-ahead and day-inside adaptive scheduling framework of the multi-microgrid system, wherein each day-ahead sub-microgrid MG-EMC is subjected to interactive solution with other microgrid information according to a predicted value of source-load output through IEP to obtain a scheduling plan of energy storage and load translation of each sub-microgrid, and the day-inside IEP is used for acquiring a real-time augmentation dynamic link matrix Y (k) in the day1) Linking behavior with real-time dynamic and fault event flag ug(k) And transmitting the output predicted value of short-term wind, light and load in a rolling time domain to each sub-microgrid, realizing information interaction with other microgrids by each sub-microgrid MG-EMC according to the output predicted value of short-term wind, light and load in the rolling time domain, solving a daily optimization objective function to obtain a rolling time domain scheduling plan of each sub-microgrid, implementing a first value of the scheduling plan into scheduling control of each sub-microgrid system, and repeating the steps at the next moment.
And 3) adopting an alternate direction multiplier method to realize model solution to obtain a scheduling result considering dynamic access or exit of the sub-microgrid to the multi-microgrid system, wherein the scheduling result comprises day-ahead optimization scheduling model solution and day-inside optimization scheduling model solution.
In step 3), solving the day-ahead optimization scheduling model specifically comprises the following steps:
firstly, the IEP collects the physical link matrix (m (t), l (t)) and the information link matrix e (t) of the multiple micro-grid systems at the day before.
Secondly, decoupling of the formula (20) by adopting an Alternative Direction Multiplier Method (ADMM) to realize distributed solution of each sub-microgrid, wherein the specific flow is as follows:
in the sub-microgrid layer, the sub-microgrid i receives the reference interactive power of the IEP in the kth iteration after receiving the physical and information link matrix of the multiple microgrids before the IEP
Figure BDA0002879239350000161
And lagrange multiplier
Figure BDA0002879239350000162
And solving the day-ahead scheduling plan of each sub-microgrid in the kth iteration.
Figure BDA0002879239350000163
The constraint conditions are as follows: formula (6), formula (8) -formula (11), formula (13) -formula (14), formula (16) -formula (19) and formula (21).
The IEP receives the power interaction value of the kth iteration of each sub-microgrid
Figure RE-GDA0003094965530000164
And solving the reference interaction power and the Lagrange multiplier of the (k + 1) th iteration.
Figure BDA0002879239350000166
The constraint conditions are as follows:
Figure BDA0002879239350000167
lagrange multiplier update:
Figure BDA0002879239350000168
t∈{1,2,...,T}
when the condition is satisfied
Figure BDA0002879239350000169
And then, iteratively converging, and solving to obtain the day-ahead scheduling plan of each sub-microgrid.
In step 3), solving the intraday optimization scheduling model specifically comprises the following steps:
the intra-day rolling optimization tracks the day-ahead scheduling according to short-term prediction of a fan, a photovoltaic and a load in the day, adaptively adjusts and optimizes a scheduling objective function to maintain stable operation of the multi-microgrid system when a real-time dynamic link behavior occurs, and the intra-day rolling optimization comprises the following specific steps:
first, at t ═ k1At the moment, the IEP collects the physical and information link information of the multi-microgrid system to obtain a real-time augmented dynamic link matrix Y (k)1) Variable u linked dynamically to flags or whether failure has occurredg(k1) The proposed model is in a rolling period t ═ k1,k1+1,…,k1+ M physical and information linking matrix with t ═ k1Time of day augmented dynamic link matrix Y (k)1) The decomposed physical links are the same as the information link matrix.
Second, each sub-microgrid accepts a real-time dynamic link matrix Y (k) from the IEP1) Variable u for marking whether dynamic link action or fault occursg(k1). Solving the formula (22) by adopting an ADMM algorithm according to the current t-k1Predicted rolling period t ═ k1,k1+1,…,k1And+ M, short-term predicted power of each sub-microgrid fan, photovoltaic and load in the microgrid, and calculating a scheduling plan of each sub-microgrid in the multi-microgrid system in a rolling optimization period.
At the mth iteration, the sub-microgrid system i receives the reference interaction power from the IEP
Figure BDA0002879239350000171
And lagrange multiplier
Figure BDA0002879239350000172
And solving the scheduling plan in the rolling scheduling duration of each sub-microgrid in the mth iteration.
Figure BDA0002879239350000173
The constraint conditions are as follows: formula (6), formula (8) -formula (9), (16) -formula (19), formula (21) and formula (23).
At the interaction layer of the multi-microgrid system, the IEP receives the power interaction value of the mth iteration of each sub-microgrid
Figure BDA0002879239350000174
Figure BDA0002879239350000175
And solving the reference interactive power and the Lagrange multiplier of the (m + 1) th iteration.
Figure BDA0002879239350000176
The constraint conditions are as follows:
Figure BDA0002879239350000177
lagrange multiplier update:
Figure BDA0002879239350000178
t∈{k1,k1+1,...,k1+M}
when the condition is satisfied
Figure BDA0002879239350000179
And then, iteratively converging, and solving to obtain { k ] in the rolling period of each sub-microgrid system1,k1+1,…,k1+ M scheduling plan, anWill k1The time of day schedule is implemented to the control system.
Finally, at k1At +1 moment, the real-time augmented dynamic link matrix Y (k) is updated1) Variable u with flag dynamic linking is or is not occurringg(k1) And repeating the rolling optimization steps in the day, carrying out a new round of optimization, and finishing iteration to obtain the optimized scheduling result of the multiple micro-grid systems in the day.

Claims (8)

1. A multi-time scale self-adaptive optimization scheduling method for a multi-microgrid system comprises the following steps:
step 1, constructing a multi-time scale scheduling framework of a multi-microgrid system and an adaptive framework for optimizing scheduling of the multi-microgrid system;
step 2, constructing a multi-microgrid system self-adaptive collaborative optimization scheduling model under multi-time scale based on a multi-time scale scheduling framework and a self-adaptive framework, wherein the multi-microgrid system self-adaptive collaborative optimization scheduling model under multi-time scale comprises a day-ahead optimization scheduling model and a day-interior optimization scheduling model;
step 3, adopting an alternate direction multiplier method to realize model solution to obtain a scheduling result considering dynamic access or quit of the sub-microgrid to the multi-microgrid system; the model solution comprises day-ahead optimization scheduling model solution and day-in optimization scheduling model solution.
2. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 1, characterized in that: the method for constructing the multi-time scale scheduling framework of the multi-microgrid system constructed in the step 1 comprises the following steps: based on the advantages of model predictive control, in order to process uncertainty of renewable energy sources and load output in the multi-microgrid system, a multi-microgrid system day-ahead and day-inside multi-time scale optimization framework based on a model predictive control method is constructed according to the characteristic that the output power prediction error of a renewable distributed power supply is reduced along with shortening of a prediction time scale.
3. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 2, characterized in that: the optimization target of the multi-microgrid system day-ahead multi-time scale optimization framework comprises the following steps: the overall operation cost of the multi-microgrid system is minimized, and a single sub-microgrid system can independently control the local load demand of the local distributed renewable energy supply and maintain the overall supply and demand balance of the interconnected multi-microgrid system; the day-ahead optimization scheduling is carried out according to the output power predicted value of the renewable distributed power supply and the load of the multi-micro-grid system in the day-ahead; the daily multi-time scale optimization framework adopts a dynamic self-adaptive scheduling method based on model predictive control, and each sub-microgrid needs to meet the following targets: the ultra-short-term predicted values of the renewable distributed power supply and the load are taken as references, and the renewable distributed power supply and the load are cooperated with other sub-micro-grids; tracking a day-ahead dispatch plan; and when the uncertainty of supply and demand is processed and when the sub-micro-grid exits from the multi-micro-grid system or fails, each sub-micro-grid can adaptively adjust the scheduling plan in the day to deal with the real-time dynamic link behavior or failure event of the multi-micro-grid system.
4. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 1, characterized in that: the method for constructing the self-adaptive architecture for optimizing and scheduling the multi-microgrid system comprises the following steps: in order to effectively deal with the dynamic link behavior of the multi-microgrid system in which the sub-microgrid is not planned to exit or access in real time, the self-adaptive management of the optimized dispatching of the multi-microgrid is realized, and a self-adaptive dynamic framework of the multi-microgrid system is established; MG-EMC is a controller of each sub-microgrid, IEP is an information interaction center between each sub-microgrid, and the sub-microgrid performs information interaction through the IEP to minimize the overall operation cost of the multi-microgrid system; describing physical links of the multi-microgrid system by using an undirected connected graph (M (t), L (t)), L (t) representing an adjacency matrix of the physical links of the multi-microgrid system, M (t) {1,2, …, n } representing a sub-microgrid set forming the multi-microgrid, and l (t) }ij(t)＝1,lij(t) E L (t), i, j E M (t) indicates that there is a physical link between sub-grids i and j, lii(t) ═ 1 indicates that the sub-microgrid i is physically linked with the power distribution network, and the information link matrix e (t) ═ e1(t),…,en(t)]Description of ei(t) ═ 1 is the link between the sub-microgrid i and the IEP presence information;
during rolling optimization in the day, the sub-microgrid is not planned to join or leave the real-time dynamic link behavior of the multi-microgrid, so that the corresponding elements of the physical and information link matrix of the multi-microgrid are increased, decreased and changed, and the dynamic link matrix Y is defined and used for describing the real-time dynamic link behavior of the multi-microgrid system
Figure FDA0002879239340000021
Scheduling t as t in the current day0When N sub-micro-grids are added into the multi-micro-grid at any moment, the original physical and information link matrix form can be ensured to be unchanged, corresponding rows and columns are only needed to be added to the original augmented dynamic link matrix, the augmented dynamic matrix for adding the N sub-micro-grids is obtained as shown in the formula (2), and the N sub-micro-grids are used
Figure FDA0002879239340000022
Denotes t ═ t0The case of physical links to multiple micro-grids at the time, wherein,
Figure FDA0002879239340000023
Figure FDA0002879239340000031
if there is an intra-day schedule t ═ t1The sub-microgrid i exits the multi-microgrid system at any moment, and only row and column elements where the sub-microgrid i of the augmented dynamic link matrix is located are deleted
Figure FDA0002879239340000032
Y in the formula (3)(n)(t1) For multiple micro-grid systems t ═ t1Physical linking of time of day.
5. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 1, characterized in that: the construction method of the day-ahead optimization scheduling model comprises the following steps:
the day-ahead scheduling aims at minimizing the overall operation cost of the multi-microgrid system, and the overall operation cost of the multi-microgrid system is minimized by optimizing and scheduling the output of energy storage and controllable distributed power sources and loads and determining the interaction power among the sub-microgrid and the electricity purchasing and selling power of the sub-microgrid and the power distribution network according to day-ahead load and wind-solar renewable energy output power predicted values;
under the dynamic self-adaptive architecture of energy management of the multi-microgrid system, each sub-microgrid considers the day-ahead objective function of power interaction with other sub-microgrids as
Figure FDA0002879239340000033
In the formula: cDG,i(t) operating cost of the controllable distributed power supply of the sub-microgrid i in the period t, CBE,i(t) energy storage operating cost, CDN,i(t) cost of electricity purchase and sale from sub-microgrid i to power distribution network system, CDR,i(t) demand side response cost of the i load of the sub-microgrid; the scheduling model and the operation constraint condition of each sub-microgrid system unit are as follows:
the cost of the controllable distributed power supply is as follows:
CDG,i(t)＝(aDG,iPDG,i(t)+bDG,i)Δt (5)
in the formula: a isDG,iAnd bDG,iFor the operation cost factor, the controllable distributed power supply operation constraints are as follows:
Figure FDA0002879239340000041
in the formula:
Figure FDA0002879239340000042
and
Figure FDA0002879239340000043
the maximum and minimum power constraints of the controllable distributed power supply of the micro-grid i are set;
the energy storage system model is
CBE,i(t)＝cBE,i(Pc,i(t)ηi+Pd,i(t)/ηi)Δt (7)
In the formula: c. CBE,iFor the unit charge-discharge cost, P, of the I energy storage system of the sub-microgridc,i(t) and Pd,i(t) is the charge-discharge power, etaiFor the energy storage charge-discharge efficiency, the constraints met in the operation process are as follows:
Figure FDA0002879239340000044
Figure FDA0002879239340000045
Figure FDA0002879239340000046
Figure FDA0002879239340000047
in the formula:
Figure FDA0002879239340000048
and
Figure FDA0002879239340000049
the maximum charge-discharge power allowed by the energy storage of the sub-microgrid i,
Figure FDA00028792393400000410
and
Figure FDA00028792393400000411
the allowable minimum and maximum residual capacity of the energy storage in the operation process of the sub-microgrid i; equations (8) and (9) are energy storage sufficient electric power constraints of the sub-microgrid i, and equations (10) and (11) are energy storage residual capacity constraints of the sub-microgrid i;
demand side response model for translatable loads
Each sub-microgrid system has a translatable load, and the compensation cost for users due to load translation is as follows
Figure FDA00028792393400000412
In the formula (12) cDR,iCost per unit compensation for translatable loads, PL,iThe actual scheduled power for the sub-microgrid i,
Figure FDA00028792393400000413
for the expected scheduling power of the sub-microgrid i in the period t, the constraint conditions met by the demand side response are as follows:
Figure FDA00028792393400000414
Figure FDA00028792393400000415
the formula (13) ensures that the power consumption of the user is unchanged after the response of the demand side is considered, the formula (14) is the minimum/large power consumption constraint of each time period, and a is the maximum proportion of the output of the translatable load to the total load;
power interaction and electricity purchasing and selling model among sub-micro grids
CDN,i(t)＝(cb,i(t)Pb,i(t)-cs,i(t)Ps,i(t))Δt (15)
In the formula: c. Cb,i(t) and cs,i(t) is the purchase and sale price of the sub-microgrid i in the time period t, Pb,i(t) and Ps,i(t) is the power for buying and selling electricity of the sub-microgrid i in the time period t, and the power interaction among the sub-microgrids and the power for buying and selling electricity meet the operation constraint condition that P is more than or equal to 0b,i(t)≤ei(t)lii(t)ui(t)Pi max (16)
0≤Ps,i(t)≤ei(t)lii(t)vi(t)Pi max (17)
Figure FDA0002879239340000051
ui(t)+vi(t)≤1 (19)
Pij(t)+Pji(t)＝0 (20)
In the formula:
Figure FDA0002879239340000052
purchasing the maximum power for selling electricity from the sub-micro-grid i to the distribution network system,
Figure FDA0002879239340000053
the maximum value of the power interaction of the sub-micro-grids i and j is obtained; u. ofi(t) and viAnd (t) is 0/1 variable of the power purchasing and selling state of the sub-microgrid, and 1 cannot be simultaneously selected. The formulas (16) - (17) are maximum power purchasing and selling constraints of the sub-microgrid i, and the formulas (18) - (20) are power interaction constraints of the sub-microgrid i and j;
the power balance constraint of the sub-microgrid i is as follows:
Pb,i(t)-Ps,i(t)+Pwt,i(t)+Ppv,i(t)+Pij(t)+PDG,i(t)-Pc,i(t)+Pd,i(t)＝PL,i(t) (21)
in the formula: pwt,i(t)、Ppv,i(t) with PL,iAnd (t) respectively predicting the fan, photovoltaic and load output of the sub-microgrid i in the period t.
6. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 1, characterized in that: the construction method of the intraday optimal scheduling model comprises the following steps:
in-day optimized scheduling, the length t of the rolling time of each sub-microgrid is { k ═ k1,k1+1,…,k1The aim of minimizing the overall operation cost of the multiple micro-grids is fulfilled in + M, internal energy storage, translatable load and controllable distributed power supply are regulated and controlled according to predicted values of short-term fans, photovoltaic and load in the day to maintain stable operation of each micro-grid system, and because the energy storage and the controllable load of each sub-micro-grid need to arrange a power output plan in a long time scale, the energy storage and the translatable load of each sub-micro-grid in rolling optimization scheduling in the day track the power output track preset in the day, safe and stable operation of the multiple micro-grid systems is realized, and the comprehensive benefit in the scheduling period of the multiple micro-grids is improved;
the intra-day rolling optimization of each sub-microgrid aims at minimizing the overall operation cost of the multi-microgrid system, energy storage and translational load output of each sub-microgrid in the day ahead are tracked, and intra-day energy storage and translational load output deviation of each sub-microgrid is used as a penalty function, and the specific model is as follows:
Figure FDA0002879239340000061
the constraints are:
Figure FDA0002879239340000062
in the formula
Figure FDA0002879239340000063
Respectively the energy storage and translational load plan regulation values u of the day-ahead sub-microgrid ig(k) Is a Boolean state quantity, ug(k) And 0 represents that the multi-microgrid system has real-time dynamic link behavior or fault events.
7. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 1, characterized in that: the day-ahead optimization scheduling model solving method comprises the following steps:
firstly, IEP collects physical link matrixes (M (t), L (t)) and information link matrixes E (t)) of a plurality of micro-grid systems in the day ahead;
secondly, decoupling by adopting an alternative direction multiplier method to realize distributed solution of each sub-microgrid, wherein the specific method comprises the following steps: in the sub-microgrid layer, the sub-microgrid i receives the reference interactive power of the IEP in the kth iteration after receiving the physical and information link matrix of the multiple microgrids before the IEP
Figure RE-FDA0003094965520000064
And lagrange multiplier
Figure RE-FDA0003094965520000065
Solving a day-ahead scheduling plan of each sub-microgrid in the kth iteration;
Figure RE-FDA0003094965520000066
the IEP receives the power interaction value of the kth iteration of each sub-microgrid
Figure RE-FDA0003094965520000071
Solving the reference interactive power and the Lagrange multiplier of the (k + 1) th iteration;
Figure RE-FDA0003094965520000072
the constraint conditions are as follows:
Figure RE-FDA0003094965520000073
lagrange multiplier update:
Figure RE-FDA0003094965520000074
t∈{1,2,...,T}
when the condition is satisfied
Figure RE-FDA0003094965520000075
And then, iteratively converging, and solving to obtain the day-ahead scheduling plan of each sub-microgrid.
8. The multi-time scale adaptive optimization scheduling method for the multi-microgrid system as claimed in claim 1, characterized in that: the solution method of the intraday optimal scheduling model comprises the following steps:
the rolling optimization in the day tracks a day-ahead scheduling plan according to short-term prediction of a day-ahead fan, photovoltaic and load, a scheduling objective function is adaptively adjusted and optimized to maintain stable operation of the multi-microgrid system when a real-time dynamic link behavior occurs, and the rolling optimization in the day specifically comprises the following steps:
first, at t ═ k1At the moment, the IEP collects the physical and information link information of the multi-microgrid system to obtain a real-time augmented dynamic link matrix Y (k)1) Variable u linked dynamically to flags or whether failure has occurredg(k1) At a rolling period t ═ k1,k1+1,…,k1+ M physical and information linking matrix with t ═ k1Time of day augmented dynamic link matrix Y (k)1) The decomposed physical link is the same as the information link matrix;
secondly, each sub-microgrid receives a real-time dynamic link matrix Y (k) from the IEP1) Variable u for marking whether dynamic link action or fault occursg(k1) (ii) a Solving the formula (22) by adopting an ADMM algorithm, and solving the formula according to the current t-k1Predicted rolling period t ═ k1,k1+1,…,k1+ M short-term predicted power of each sub-microgrid fan, photovoltaic and load, and calculating rollingOptimizing the scheduling plan of each sub-microgrid in the multi-microgrid system in the period;
at the mth iteration, the sub-microgrid system i receives the reference interaction power from the IEP
Figure FDA0002879239340000075
And lagrange multiplier
Figure FDA0002879239340000076
Solving a scheduling plan in the rolling scheduling duration of each sub-microgrid in the mth iteration;
Figure FDA0002879239340000081
at the interaction layer of the multi-microgrid system, the IEP receives the power interaction value of the mth iteration of each sub-microgrid
Figure FDA0002879239340000082
Solving the reference interactive power and the Lagrange multiplier of the (m + 1) th iteration;
Figure FDA0002879239340000083
the constraint conditions are as follows:
Figure FDA0002879239340000084
lagrange multiplier update:
Figure FDA0002879239340000085
when the condition is satisfied
Figure FDA0002879239340000086
Receiving in time and iterationConverging and solving to obtain k in the rolling period of each sub-microgrid system1,k1+1,…,k1+ M scheduling plan, and k1The time of day schedule is implemented to the control system.
Finally, at k1At +1 moment, the real-time augmented dynamic link matrix Y (k) is updated1) Variable u with flag dynamic link occurrenceg(k1) And repeating the rolling optimization steps in the day, carrying out a new round of optimization, and finishing iteration to obtain the optimized scheduling result of the multiple micro-grid systems in the day.
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