CN110266057A - A kind of cross-domain collaboration interaction of wind-light storage bavin autonomy microgrid group and consumption method - Google Patents
A kind of cross-domain collaboration interaction of wind-light storage bavin autonomy microgrid group and consumption method Download PDFInfo
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Abstract
The invention discloses a kind of cross-domain collaboration interactions of wind-light storage bavin autonomy microgrid group and consumption method, consider multifactor building network topology matrix weight, and the lower metric of these correlations is organically united, it lays a good foundation to greatly reduce the simplification that the number of constraint condition is algorithm, MST searching algorithm and LMI optimization algorithm are used in combination with to " trunk " and " leaf " constructed under new microgrid topological structure respectively, not only the respective advantage of algorithm had been utilized but also has facilitated combining and split at any time and using for algorithm, so as to avoid all being reruned to this algorithm after each sampling, enormously simplify calculation amount, improve efficiency of algorithm, the charging and discharging state of reasonable arrangement energy-storage units, the strategy is set to optimize arrangement to electric energy from two dimensions of room and time, to make the utilization rate of electric energy obtain It is a greater degree of to improve.
Description
Technical Field
The invention relates to the field of distributed power generation and energy storage equipment of a microgrid system, in particular to a microgrid cluster cross-domain coordinated energy scheduling and adaptive optimization cooperative operation method with wind, light, storage and diesel power sources.
Background
With the gradual maturity of microgrid systems, renewable energy technologies are more adopted in such power network systems that can operate independently or in a grid-connected manner. However, although there are stable and reliable power supply systems such as an internal combustion power generator set and a gas turbine generator in a commonly used distributed power supply at present, because the conventional energy needs to be consumed during power supply, the scale and the power supply amount of the distributed power supply are limited to a certain extent, the power consumption requirements of all power loads in the microgrid cannot be completely met, and the solar and wind power generation systems have the characteristics of cleanness, reproducibility and the like and are more penetrated into the microgrid system. However, the latter is influenced by weather, environment and the like, and the power supply thereof has the characteristics of intermittency, fluctuation and the like, so that the power supply cannot provide continuous and stable power supply. This results in reducing the operating efficiency of the autonomous microgrid system from two aspects: on the one hand, when the DG is sufficiently powered, a large amount of electrical energy will not be efficiently utilized. Although the energy storage device can absorb a part of redundant electric quantity at the moment, the effect is limited and a large number of energy storage units are needed, so that the investment and maintenance cost of the microgrid system are greatly increased; on the other hand, when the DG is not sufficiently supplied, the load is limited because sufficient power cannot be supplied, and particularly when the CL is not sufficiently supplied in the system, the loss caused by the insufficient power is more serious.
Disclosure of Invention
The invention aims to solve the technical problem of providing a cross-domain cooperative interaction and consumption method for a wind-solar-diesel-storage autonomous micro-grid group, which is to optimize and combine power supply and utilization systems of a plurality of autonomous micro-grid systems again and realize optimized scheduling of electric energy through cooperation.
In order to solve the technical problems, the invention adopts the following technical scheme: a wind-solar-diesel-storage autonomous micro-grid group cross-domain cooperative interaction and absorption method comprises the following steps:
(1) sampling according to a time interval delta t to obtain state information of a system distributed generator DGs, an important load CLs, a common load NLs and energy storage devices Ss at the current moment;
(2) judging whether to usei＝1,…,NAMG(t- Δ t), if present, performing (3), otherwise performing (18), wherein,andthe difference between the DG power and the CL power in the autonomous microgrid i _ AMG at the time (t-delta t) and the time t, NAMG(t-delta t) is the number of autonomous microgrids existing at the moment (t-delta t);
(3) constructing a network topology matrix A (t) at the current time t;
(4) judging whether to useIf yes, executing (5), otherwise executing (6), wherein NDGIs the total number of DGs in the microgrid group, NCLIs the total number of CL, NNLIs the total number of NL;
(5)Si∈{x|x is load},i＝1,…,Nsand performing (9) wherein SiIs the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group is set;
(6) judging whether to useIf yes, executing (7), otherwise, executing (8);
(7)and performing (9) and (10);
(8)Si∈{y|y is generater},i＝1,…,Nsand performing (10);
(9) judging whether each SiSOC (1)i≥80％,i＝1,…,NsIf yes, then execute (11), otherwise execute (12), where SOCiThe state of charge of the ith energy storage device;
(10) judging whether each SiSOC (1)i≤20％,i＝1,…,NsIf yes, executing (11), otherwise, executing (13);
(11)wherein S isiIs the ith energy storage device, GiFor the i-th power supply, LiThe ith electric equipment; n is a radical ofGFor the total number of power generation sources in the microgrid group, NLThe total number of the electric loads in the microgrid group;
(12)performing (14), wherein SiIs the ith energy storage device, GiFor the ith power supply source of the power,regarding the energy storage devices as the total number of power generation sources in the microgrid group;
(13)performing (14), wherein SiIs the ith energy storage device, LiFor the ith electricity utilization device, the power supply is connected with the power supply,regarding the energy storage devices as the total number of power loads in the microgrid group;
(14) is calculated fromToThe minimum weight sum of: min [ sum (weights)]ijObtaining NG×NCLA minimal tree, wherein GiFor the ith power supply, NGThe total number of the power generation power supplies in the microgrid group, CLjFor the j important load, NCLSum (-) is a summation function, and weights are network topology matrix weights, wherein sum (-) is the total number of important loads in the microgrid group;
(15) selecting Min [ sum (weights) with minimum weight sum]ijCorresponding GiCL as jth important loadjThe power supply node of (1);
(16) determining all NG×NCLSet of (G)i,CLj},i∈(1,NG),j∈(1,NCL) Wherein G isiFor the ith power supply, CLjIs the jth important load;
(17) determine if each set Gi,CLj},i∈(1,NG),j∈(1,NCL) Middle Gi-CLjIf the value is more than 0, executing (19), otherwise executing (18);
(18) selecting the next smallest weight sum Submin (weights)]ijCorresponding GiCL as jth important loadjAnd (19) is executed, wherein, Submin (cndot) represents the secondary minimum value, sum (cndot) is a summation function, and weights are network topology matrix weights;
(19) determination of GiAnd CLjThe number of Municipal electricity Munical, third industry electricity Tertiary and Light industry electricity Light in the ordinary load NLs is stored in the variable nM，nTAnd nLPerforming the following steps;
(20) constructing a linear matrix inequality LMI: ε ═ min { x · M (t) + y · T (t) + z · L (t) - (G)i-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
Wherein, M (t), T (t) and L (t) are three load power values of NLs at time t, x, y and z are power GiAnd the important load CLjThe number of three NLs that are desired to be connected between;
(21) determining values of x, y and z by solving the LMI optimization problem;
(22) selection Gi，CLjAnd between the twoy and z Munical, Tertiary, Light form a set { k | kth AutonomousMG, k ∈ (1, N)G×NCL) And f, i.e. the kth autonomous piconet.
Optionally, the method for determining the branch weights of the network topology matrix a (t) includes:
suppose that the active power and the reactive power of a certain electric load j in the microgrid are respectively PjAnd QjWhen the upstream power supply node is i, the total resistance and total reactance on the power supply line from the node i to the node j are respectively RijAnd XijAssume that the voltage at node j remains UjThen the line loss on the power supply line from node i to node j can be expressed as:
the real-time network loss value between any two nodes in the network can be obtained by using the expression (1);
in the reliability evaluation process of the power system, the unavailability K of lines and devices is a common measure and is determined by annual fault frequency and repair time, namely
Wherein f is the annual fault frequency number; r is the fault repair time;
besides calculating the unavailability of the line by using the statistical values of the fault frequency and the repair time of the line, the risk coefficient of each line of the system is comprehensively evaluated by combining the actual engineering experience of an expert,
wherein, KijThe unavailability of the line between the node i and the node j is obtained by the expression (2); eijExpert estimates for the line between node i and node j, η is an adjustment factor,
in order to better unify the two under one measurement index, the two need to be normalized first,
let L be the line between node i and node jijThen, the normalized line loss and the normalized unavailability are respectively:
wherein N is the number of nodes in the whole microgrid;
the line L can be obtained by using the normalized line network loss and the line unavailabilityijThe comprehensive weight evaluation indexes are as follows:
if the nodes i and j are directly connected by a wire, the weight is aij(ii) a Otherwise, if the node i and the node j are not directly connected, aij0; diagonal element aii＝0。
By adopting the technical scheme, the invention has the following beneficial effects:
(1) the network topology matrix weight is constructed by considering multiple factors, and the metric values with lower correlation are organically unified, so that the number of constraint conditions is greatly reduced, and a foundation is laid for the simplification of an algorithm;
(2) the MST search algorithm and the LMI optimization algorithm are combined to be used for respectively constructing a tree trunk and tree leaves under a new micro-grid topological structure, the advantages of the algorithms are utilized, and the algorithms are conveniently combined and disassembled at any time (the nodes of the tree leaves can be re-divided or only increased and decreased according to the system state), so that the algorithm is prevented from being re-operated after each sampling, the calculated amount is greatly simplified, and the algorithm efficiency is improved;
(3) the charge and discharge states of the energy storage unit are reasonably arranged, so that the strategy can optimize the electric energy from two dimensions of space and time, and the utilization rate of the electric energy is improved to a greater extent; meanwhile, the charging and discharging times of the energy storage device are reduced to the greatest extent, and the charging and discharging actions from the energy storage (S) to the energy storage (S) are also avoided, so that the use cost of the energy storage unit is reduced, and the service life of the energy storage unit is prolonged. A series of simulation structures under an IEEE 33-bus standard test platform show that the strategy effectively ensures the power consumption of CL, improves the use efficiency of DG electric energy and also greatly ensures the power consumption requirement of NL.
The following detailed description of the present invention will be provided with reference to the accompanying drawings.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a network topology matrix approach;
FIG. 2 is an energy storage device operating area;
FIG. 3 is a scheduling policy flow diagram;
FIG. 4 is an IEEE 33-bus system test topology and initial network partitioning;
FIG. 5 is a DG and load characteristics curve;
FIG. 6(a) shows DG at time 00:001MSTs generated for the root node;
FIG. 6(b) shows DG at time 00:002MSTs generated for the root node;
FIG. 6(c) shows DG at time 00:003MSTs generated for the root node;
FIG. 7(a) shows DG at time 05:001MSTs generated for the root node;
FIG. 7(b) shows DG at time 05:002MSTs generated for the root node;
FIG. 7(c) shows DG at time 05:003MSTs generated for the root node;
FIG. 7(d) shows time S at time 05:001MSTs generated for the root node;
FIG. 7(e) shows time S at time 05:002MSTs generated for the root node;
FIG. 7(f) shows time S at time 05:003MSTs generated for the root node;
FIG. 8(a) shows the result of cooperative scheduling at time 00: 00;
FIG. 8(b) shows the result of cooperative scheduling at time 05: 00;
FIG. 9 is a comparison of CLs 24h power supply under an initial structure versus scheduling structure;
FIG. 10 is a DG utilization versus curve;
FIG. 11(a) is a graph of a load satisfaction status without use of a policy;
FIG. 11(b) is a graph of a condition using this strategy but with no energy storage device load in the system being satisfied;
FIG. 11(c) is a graph of load satisfaction status using the policy;
fig. 12 shows the 24-hour cooperation scheduling result.
Detailed Description
The invention aims at the 'tertiary level' in the microgrid system, and takes the monitoring equipment such as a multi-agent and the like at the position of a power system connecting switch into consideration to obtain the system state information and utilize the system state information to control the contact switch, so as to reconstruct the network topology from the logic level, optimize and combine the power supply and utilization systems of a plurality of autonomous microgrid systems again, and realize the optimized scheduling of electric energy through cooperation. The stem leaf generation strategy mainly comprises two algorithms, namely MST searches the optimal DG-CL power supply relation and determines a network basic system; and the LMI determines the addition or deletion of the NL node, and ensures the maximum utilization of DG electric energy.
It should be noted that (1) in order to make the energy storage device have a longer service life, there is usually a certain margin for charging and discharging, and the charging and discharging range is selected to be 20% -80% of the maximum storage capacity, as shown in fig. 2 as an operating region B. (2) the strategy uniformly considers all energy storage devices as 'power utilization' or 'discharge' devices according to the overall power supply condition in the system, which has certain practical significance: the strategy can effectively avoid the action of charging from one energy storage device to another energy storage device, thereby avoiding the repeated charge and discharge 'oscillation' between the batteries. (3) When the strategy is used for charging the energy storage equipment, all CL and NL are ensured to be fully supplied with power, namely, the power supply is carried out according to the power supply priority of CL > NL > S, so that the charging and discharging actions of the energy storage equipment can be reduced as much as possible, the service life of a battery is prolonged, and the operation cost is reduced. The scheduling strategy can effectively realize input and output power matching under the condition of insufficient output power, and realize the coordination of multi-microgrid unified power utilization. On the basis of ensuring the sufficient power supply of the important loads, the safety of the electricity consumption of the important loads of the whole system is improved to a certain extent, and meanwhile, the effective utilization of the electric energy among multiple micro networks can be well realized.
Fig. 3 shows a process flow diagram of the invention. Specific implementations thereof will be described below with reference to specific examples. The detailed steps of the IEEE 33 node topology will be described as a partition network, and the topology structure thereof is shown in fig. 4.
As can be seen from fig. 4, there are 3 DGs in the entire network, wherein it is assumed that a DG is1Is photovoltaic (0-624.205MW), DG2And DG3Is wind energy (82.01-419.50MW), and the power characteristic curve comes from Belgian electric conductivity transmitter electric (May 13)th2014) as shown in fig. 5 (a). Assuming each energy storage unit has a maximum capacity of 900MWh, the maximum total capacity of the energy storage system in the system shown in fig. 4 is 2700 MWh. In addition, the system shown in fig. 4 includes 6 important loads and 21 non-important loads, and a typical 24h operation characteristic curve thereof is shown in fig. 5 (b). Each sectionThe point connection type is as in table I.
TABLE I
33 nodes network nodes connecting distributed power, important load and non-important load in test network (NODESCONNECTED WITH DGS CLS AND NLS IN 33-BUS TEST SYSTEM)
Referring to fig. 3, a method for cross-domain collaborative interaction and consumption of a wind, photovoltaic, diesel and autonomous micro-grid group includes the following steps:
(1) and sampling according to a time interval delta t to obtain the state information of the distributed power DGs, the important loads CLs, the common loads NLs and the energy storage devices Ss of the system at the current moment.
(2) Judging whether to usei＝1,…,NAMG(t- Δ t), if present (3), otherwise (18). The significance is as follows: in each autonomous microgrid i (DG-CL) under the architecture determined by the last sampling periodiIf the change ratio of (3) does not exceed the set division trigger threshold value θ, the division is performed again if the change ratio is present, and if the change ratio is not present, only NLs is increased or decreased. Wherein,andthe difference between the DG power and the CL power in the autonomous microgrid i _ AMG at the time (t-delta t) and the time t, NAMGAnd (t-delta t) is the number of autonomous piconets existing at the moment (t-delta t).
(3) And constructing a network topology matrix A (t) at the current time t. The branch weights are determined by expressions (1) - (6).
Suppose that the active power and the reactive power of a certain electric load j in the microgrid are respectively PjAnd QjWhen the upstream power supply node is i, the total resistance and total reactance on the power supply line from the node i to the node j are respectively RijAnd Xij. Assume that the voltage at node j remains UjThen the line loss on the power supply line from node i to node j can be expressed as:
the real-time network loss value between any two nodes in the network can be obtained by using the expression (1).
In the reliability evaluation process of the power system, the unavailability K of lines and devices is a common measure and is determined by annual fault frequency and repair time, namely
Wherein f is the annual fault frequency number; and r is the fault repair time.
It is noted that using a single method or statistical data alone may be difficult to effectively assess the actual risk status or operating conditions of the system. In fact, besides using statistics on the failure frequency and repair time of the line to calculate its unavailability, the actual engineering experience of the expert is also an important factor. Thus, the risk factor of each line of the system is evaluated in combination [15 ].
Wherein, KijThe unavailability of the line between the node i and the node j is obtained by the expression (2); eijExpert evaluation for the line between node i and node j η ∈ [0,1 ]]To adjust the factor (set to 0.6 here), the weights of both actual statistics and expert experience assessment in the risk assessment process can be adjusted to obtain a more reasonable assessment value.
In order to better unify the two under one metric, we need to normalize the two first.
Let L be the line between node i and node jijThen, the normalized line loss and the normalized unavailability are respectively:
wherein N is the number of nodes in the whole microgrid;
the line L can be obtained by using the normalized line network loss and the line unavailabilityijThe comprehensive weight evaluation indexes are as follows:
if the nodes i and j are directly connected by a wire, the weight is aijWhich isMiddle β E [0,1 ]](ii) a Otherwise, if the node i and the node j are not directly connected, aij0; diagonal element aii0. The network topology matrix construction method is as shown in fig. 1.
(4) Judgment ofIf yes, executing (5), otherwise executing (6). The significance is as follows: whether the total power generation of the DGs in the system is greater than the total demand of all the power loads. Wherein N isDGIs the total number of DGs in the microgrid group, NCLIs the total number of CL, NNLIs the total number of NL.
(5)Si∈{x|x is load},i＝1,…,NsAnd (9) is executed. The significance is as follows: all energy storage devices are considered as electrical loads. Wherein S isiIs the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group is obtained.
(6) Judgment ofIf yes, executing (7), otherwise executing (8). The significance is as follows: whether the total power production of the DGs in the system is greater than the total demand of all CLs. Wherein N isDGIs the total number of DGs in the microgrid group, NCLIs the total number of CL.
(7)Si∈{x|x is load}∪{y|y is generater},i＝1,…,NsAnd (9) and (10) are performed. The significance is as follows: the energy storage device is considered as an electrical load or a power supply (generator). Wherein S isiIs the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group is obtained.
(8)Si∈{y|y is generater},i＝1,…,NsAnd (10) is executed. The significance is as follows: all energy storage devices are considered to be DG. Wherein S isiIs the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group is obtained.
(9) Each S is judgediSOC (1)i≥80％,i＝1,…,NsIf yes (as in the area C in fig. 2), then (11) is executed, otherwise (12) is executed. Wherein S isiFor the ith energy storage device, SOCiState of charge of the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group.
(10) Each S is judgediSOC (1)i≤20％,i＝1,…,NsIf yes (as area a in fig. 2), then (11) is executed, otherwise (13) is executed. Wherein S isiIs as followsiAn energy storage device, SOCiState of charge of the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group.
(11)(meaning: SOC of energy storage deviceiNot less than 80% or SOCiLess than or equal to 20%, the energy storage device does not act, neither as a power source nor as a power-consuming device). Wherein S isiIs the ith energy storage device, GiFor the ith power supply, LiThe ith electric equipment; n is a radical ofGFor the total number of power generation sources in the microgrid group, NLThe total number of the electric loads in the microgrid group.
(12)Carrying out (14) (meaning: the energy storage device is considered as a power source) wherein SiIs the ith energy storage device, GiFor the ith power supply source of the power,and the total number of the energy storage devices in the microgrid group is regarded as the total number of the power generation power supplies.
(13)Execution (14) (meaning: the energy storage device is considered as a load) wherein SiIs the ith energy storage device, LiFor the ith power utilization equipmentPreparing;the total number of the energy storage devices in the microgrid group is regarded as the total number of the used electric loads.
(14) Is calculated fromToThe minimum weight sum of: min [ sum (weights)]ijObtaining NG×NCLA minimal tree is created. Wherein G isiFor the ith power supply, NGThe total number of the power generation power supplies in the microgrid group, CLjFor the j important load, NCLSum (-) is a summation function for the total number of important loads in the microgrid group, and weights are network topology matrix weights.
(15) Selecting Min [ sum (weights) with minimum weight sum]ijCorresponding GiCL as jth important loadjThe power supply node of (1).
(16) Determining all NG×NCLSet of (G)i,CLj},i∈(1,NG),j∈(1,NCL). Wherein G isiFor the ith power supply, CLjIs the j-th important load.
(17) Determine each set { Gi,CLj},i∈(1,NG),j∈(1,NCL) Middle Gi-CLjIs > 0? If so, execution is performed (19), otherwise, execution is performed (18).
(18) Selecting the next smallest weight sum Submin (weights)]ijCorresponding GiCL as jth important loadjAnd performing (19). Wherein, Submin (-) represents the secondary minimum value, sum (-) is a summation function, and weights are network topology matrix weights.
(19) Determination of GiAnd CLjThe number of Municipal electricity Munical, third industry electricity Tertiary and Light industry electricity Light in the common load NLs is stored in the variable nM，nTAnd nLIn (1).
(20) Constructing a linear matrix inequality LMI:
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
the optimization target is that under the constraint of the maximum node number of the three common loads NLs, leaf non-important loads are added into each autonomous microgrid, so that redundant electric energy of a power supply is utilized to the maximum extent to achieve full utilization of the electric energy. Wherein, M (t), T (t) and L (t) are three load power values of NLs at time t, x, y and z are power GiAnd the important load CLjThree NLs numbers of connections are desired.
(21) The values of x, y and z are determined by solving the LMI optimization problem described above.
(22) Selection Gi，CLjAnd between themy and z Munical, Tertiary, Light constitute the set { k | kth Autonomous MG, k ∈ (1, N)G×NCL) And f, i.e. the kth autonomous piconet.
Therefore, as can be seen from the DG load curve shown in FIG. 4, at time 00:00, the system isThat is, the total output is greater than the total demand, so that all energy storage devices in the system are treated as electric loads and determine whether to charge the energy storage devices according to the SOC conditions of the energy storage devices (step 9), and the system only uses the DGs as root nodes to search the MSTs so as to search the MSTsThe power supply for the CL is determined. The MSTs from DG to CLs obtained from the network topology matrix a at time 00:00 (00:00) are shown in fig. 6 (a-c). The sum of the weights from each DG to each CL is calculated from the MSTs shown in fig. 6(a-c), as shown in table II. The bold font represents the smallest sum of weights of certain CL to 3 root nodes, namely the power supply root node of the CL. However, it should be noted that the time DG at 00:001Is 0, so that according to the proposed strategy, the 7, 8 and 21CLs, which are responsible for powering, are arranged to be powered by the other two DGs, respectively, according to the second smallest weight and principle (per step 18), as indicated by the underlined values in table II. Accordingly, the 00:00 microgrid system is reconstructed into two microgrid subsystems, and DGs are generated according to the two microgrid subsystems2And DG3Power supply capability and other load requirements NLs, to fully utilize the DGs remaining capacity, non-essential load nodes added to each sub-microgrid are determined according to the LMI algorithm (per step 20). The result of the cooperative scheduling at time 00:00 is shown in fig. 8 (a).
TABLE II
Weighted sum of power supply paths FROM distributed power sources TO important loads (WEIGHT SUMS FROM DGS TO CLS)
Unlike time 00, the system at time 05:00At this time, all energy storage devices (S) in the system are used as power sources according to the proposed strategy, and whether to discharge or not is determined according to the self SOC state (Step 10). MSTs with DGs and Ss as root nodes obtained from network topology matrix a (05:00) at time 05:00 are shown in fig. 7 (a-f). At this time, the 05:00 microgrid system is reconstructed into five subsystems according to the weight sum from each root node to each CL, and the DGs are determined according to the five subsystems at this time2、DG3、S1-S3Power supply capability of other NLsAccording to the load requirement, according to an LMI algorithm (Step 20), adding 'leaf' non-important loads to each autonomous microgrid, and therefore the redundant electric energy of the power supply is utilized to the maximum extent to achieve full utilization of the electric energy. The result of the coordinated scheduling at time 05:00 is shown in fig. 8 (b).
It is assumed that the system is composed of 3 autonomous microgrid systems in the initial state, as shown in fig. 4. Each autonomous microgrid has a DG, an energy storage unit and two CLs. A comparison of the initial configuration with the scheduling proposed herein, in which the system CLs is wholly powered, is shown in fig. 9. As can be seen from the results shown in fig. 9, the satisfaction rate of the CLs in the system for obtaining power supply under the unified scheduling of the cooperation strategy is significantly higher than that of the CLs in the original autonomous microgrid system structure. This demonstrates, in one aspect, that this strategy has significant economic value, since the CLs load has a greater significance and value to the system than NLs.
Fig. 10 shows a graph of the power generation utilization ratio of the system DG. It is apparent from fig. 10 that the utilization rate of the generation of electricity by the DG under the autonomous microgrid coordinated scheduling policy proposed herein is higher than that without the coordinated scheduling policy (the percentage of the generation utilization efficiency of the DG is shown in the small window of fig. 10). By reconstructing the architecture of the microgrid system and reasonably scheduling the charging and discharging actions of the energy storage equipment, the electric energy generated by the DG is utilized by the electric load or stored by the energy storage equipment and is released when the system is short of power supply, so that the electric energy can be effectively optimized and used from two dimensions of space and time in the actual system, the utilization efficiency of the DG electric energy is improved, and the method has important significance in the actual power grid.
FIG. 11(a-c) is a graph showing a comparison of the total load satisfaction ratios. As can be seen from fig. 11, when the system includes an energy storage unit, the algorithm can guarantee the power demand of the entire system load for most of the time, as shown in fig. 11 (a). And if the energy storage equipment in the system has enough large capacity, the reasonable autonomous microgrid cooperative scheduling strategy is combined, so that the whole load power consumption in the whole time period can be ensured. Secondly, even in the case of no energy storage device in the system, due to the coordinated scheduling action of the strategy, the power demand of the whole load can be satisfied in most of the time period, and only when the total power output of the system is smaller than the total power demand in part of the time period, the part NLs cannot be supplied with power, as shown in fig. 11 (b). In sharp contrast, even if there is an energy storage device in the system, the system cannot meet the power utilization requirement of the entire load most of the time without proper scheduling, as shown in fig. 11 (c). Fig. 11 fully illustrates the significance and value of the reasonable scheduling of the autonomous microgrid, and simultaneously illustrates that the reasonable cooperative scheduling strategy has more significance for the optimal use of power resources than merely adding energy storage devices in the system.
Fig. 12 shows the result of the scheduling of the cooperative scheduling strategy proposed herein for all power and load usage of the system during a day. From the results shown in fig. 12, it can be seen that, by reasonable charge and discharge scheduling of the energy storage device, the surplus power generated by the DGs in the time periods 00:00-04:30, 07:30-08:30 and 11:00-13:00 is fully absorbed, so that the power consumption of the energy storage device is reduced to 05:00-07:00, 09: the insufficient power requirements of 00-10:30 and 18:30-21:00 are effectively supplemented; meanwhile, in the time period from 00:00 to 22:30, all important load electricity is fully utilized, and the important load electricity demand only in the time period from 22:30 to 24:00 has no electricity output because DG and stored energy do not exist, namely the total electricity supply in the system is less than the total demand of CLs, so that the dispatching cannot be met in any way, and part of CL electricity supply has to be abandoned. In practice, however, this power gap can be compensated for by buying power from the utility grid. In addition, for non-important loads, the power utilization requirement of NLs in most time periods in the system can be fully met through cooperative scheduling, the power can not be met through scheduling only when the total power demand of the system is larger than the total supply, and the power can still be obtained through power purchase.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and those skilled in the art should understand that the present invention includes but is not limited to the contents described in the above specific embodiments. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (2)
1. A wind-solar-diesel-storage autonomous micro-grid group cross-domain cooperative interaction and absorption method is characterized by comprising the following steps:
(1) sampling according to a time interval delta t to obtain state information of a system distributed generator DGs, an important load CLs, a common load NLs and energy storage devices Ss at the current moment;
(2) judging whether to useIf so, execute(3) Otherwise, performing (18), wherein,andthe difference between the DG power and the CL power in the autonomous microgrid i _ AMG at the time (t-delta t) and the time t, NAMG(t-delta t) is the number of autonomous microgrids existing at the moment (t-delta t);
(3) constructing a network topology matrix A (t) at the current time t;
(4) judging whether to useIf yes, executing (5), otherwise executing (6), wherein NDGIs the total number of DGs in the microgrid group, NCLIs the total number of CL, NNLIs the total number of NL;
(5)Si∈{x|x is load},i＝1,…,Nsand performing (9) wherein SiIs the ith energy storage device, NsThe total number of the energy storage devices in the microgrid group is set;
(6) judging whether to useIf yes, executing (7), otherwise, executing (8);
(7)Si∈{x|x|is|load}∪{y|y is generater},i＝1,…,Nsand performing (9) and (10);
(8)Si∈{y|y is generater},i＝1,…,Nsand performing (10);
(9) judging whether each SiSOC (1)i≥80％,i＝1,…,NsIf yes, then execute (11), otherwise execute (12), where SOCiThe state of charge of the ith energy storage device;
(10) judging whether each SiSOC (1)i≤20％,i＝1,…,NsIf yes, executing (11), otherwise, executing (13);
(11)wherein S isiIs the ith energy storage device, GiFor the ith power supply, LiThe ith electric equipment; n is a radical ofGFor the total number of power generation sources in the microgrid group, NLThe total number of the electric loads in the microgrid group;
(12)performing (14), wherein SiIs the ith energy storage device, GiFor the ith power supply source of the power,regarding the energy storage devices as the total number of power generation sources in the microgrid group;
(13)performing (14), wherein SiIs the ith energy storage device, LiIs the ith electric equipment which is used as the electric equipment,regarding the energy storage devices as the total number of power loads in the microgrid group;
(14) is calculated fromToThe minimum weight sum of: min [ sum (weights)]ijObtaining NG×NCLA minimal tree, wherein GiFor the ith power supply, NGThe total number of the power generation power supplies in the microgrid group, CLjFor the j important load, NCLSum (-) is a summation function and weights are the total number of important loads in the microgrid groupNetwork topology matrix weight;
(15) selecting Min [ sum (weights) with minimum weight sum]ijCorresponding GiCL as jth important loadjA power supply node of (1);
(16) determining all NG×NCLSet of (G)i,CLj},i∈(1,NG),j∈(1,NCL) Wherein G isiFor the ith power supply, CLjIs the jth important load;
(17) determine if each set Gi,CLj},i∈(1,NG),j∈(1,NCL) Middle Gi-CLjIf > 0, executing (19), otherwise executing (18);
(18) selecting the next smallest weight sum Submin (weights)]ijCorresponding GiCL as jth important loadjAnd (19) is executed, wherein, Submin (cndot) represents the secondary minimum value, sum (cndot) is a summation function, and weights are network topology matrix weights;
(19) determination of GiAnd CLjThe number of Municipal electricity Munical, third industry electricity Tertiary and Light industry electricity Light in the common load NLs is stored in the variable nM，nTAnd nLPerforming the following steps;
(20) constructing a linear matrix inequality LMI: ε ═ min { x · M (t) + y · T (t) + z · L (t) - (G)i-CLj)}
s.t.:0≤ε
x≤nM；y≤nT；z≤nL
Wherein, M (t), T (t) and L (t) are three load power values of NLs at time t, x, y and z are power GiAnd the important load CLjThe number of three NLs that are desired to be connected between;
(21) determining values of x, y and z by solving the LMI optimization problem;
(22) selection Gi，CLjAnd between the twoy and z Munical, TertiaryLight constitutes the set { k | kth | Autonomous MG, k ∈ (1, N)G×NCL) And f, i.e. the kth autonomous piconet.
2. The method for cross-domain collaborative interaction and consumption of the wind, light, storage and diesel autonomous micro-grid group according to claim 1, is characterized in that: the method for determining the branch weight of the network topology matrix A (t) comprises the following steps:
suppose that the active power and the reactive power of a certain electric load j in the microgrid are respectively PjAnd QjWhen the upstream power supply node is i, the total resistance and total reactance on the power supply line from the node i to the node j are respectively RijAnd XijAssume that the voltage at node j remains UjThen the line loss on the power supply line from node i to node j can be expressed as:
the real-time network loss value between any two nodes in the network can be obtained by using the expression (1);
in the reliability evaluation process of the power system, the unavailability K of lines and devices is a common measure and is determined by annual fault frequency and repair time, namely
Wherein f is the annual fault frequency number; r is the fault repair time;
besides calculating the unavailability of the line by using the statistical values of the fault frequency and the repair time of the line, the risk coefficient of each line of the system is comprehensively evaluated by combining the actual engineering experience of an expert,
wherein,Kijthe unavailability of the line between the node i and the node j is obtained by the expression (2); eijIs node i and nodejExpert estimates of the lines between η is the adjustment factor,
in order to better unify the two under one measurement index, the two need to be normalized first,
let L be the line between node i and node jijThen, the normalized line loss and the normalized unavailability are respectively:
wherein N is the number of nodes in the whole microgrid;
the line L can be obtained by using the normalized line network loss and the line unavailabilityijThe comprehensive weight evaluation indexes are as follows:
if the nodes i and j are directly connected by a wire, the weight is aij(ii) a Otherwise, if the node i and the node j are not directly connected, aij0; diagonal element aii＝0。
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