CN104200296A - Wind-solar-stored energy-firewood autonomous micro-grid group cross-domain collaboration energy dispatching and fitting method - Google Patents

Abstract

The invention discloses a wind-solar-stored energy-firewood autonomous micro-grid group cross-domain collaboration energy dispatching and fitting method. The wind-solar-stored energy-firewood autonomous micro-grid group cross-domain collaboration energy dispatching and fitting method includes that constructing a micro-grid topology matrix through reasonable matrix generating weights; confirming the optimal DG-CL and S-CL power supply correspondence through an improved MST algorithm so as to obtain a trunk; using an LMI algorithm to select NL, and adding the NL to the trunk in a leaf form so as to jointly generate a new autonomous micro-grid structure. The wind-solar-stored energy-firewood autonomous micro-grid group cross-domain collaboration energy dispatching and fitting method uses an IEEE33-bus network to supply a detailed algorithm description for a test system, and a series of experiments proves the effectiveness of the strategy for improving the overall electric power use ratio of the system and continuously and safely providing power for important load.

Description

The cross-domain collaborative energy scheduling of the autonomous microgrid group of a kind of wind-light storage bavin and adaptation method

Technical field

The present invention relates to distributed power generation and the energy storage device field of micro-grid system, relate in particular to the cross-domain coordination energy scheduling of a kind of autonomous microgrid group containing wind, light, storage, bavin power supply and the adaptive cooperation operation method of optimizing.

Background technology

Be accompanied by the day by day ripe of autonomous micro-grid system, this electric power networks system that can independent operating or be incorporated into the power networks has more adopted renewable energy technologies.Although but have the comparatively reliable and stable electric power systems such as internal combustion engine generator group, gas turbine generator at present conventional distributed power source, but need to consume traditional energy while power supply due to it, therefore its scale and delivery can be subject to certain limitation, cannot meet in autonomous microgrid all electricity consumption requirements of power loads completely, and sun power, wind power generation system because having the feature such as clean, renewable, it are also more penetrated into autonomous micro-grid system.But the latter is owing to being subject to the impact of the factor such as weather, environment, and its power supply has the feature such as intermittence, undulatory property, and therefore continual and steady electric power supply cannot be provided.This has just caused and will reduce the operational efficiency of autonomous micro-grid system from two aspects: on the one hand, in the time that distributed power source (DG) power supply is sufficient, a large amount of electric energy cannot obtain effective utilization.Although now energy storage device can receive the unnecessary electric weight of a part, its effect is limited and need a large amount of energy-storage units, thereby has greatly increased input and the maintenance cost of autonomous micro-grid system; On the other hand, in the time of DG electricity shortage, load cannot obtain sufficient electric power supply and be restricted, and especially, in the time of important load in system (CL) electricity shortage, its loss causing will be more serious.

" the three grades of layers (tertiary level) " that the present invention is directed in autonomous micro-grid system are considered by the monitoring equipment at electric system connecting valve place, as multiple agent etc., obtain system status information and utilize it to control interconnection switch, and then from logic level reconstructed network topology, the electricity supplying and using system of multiple autonomous micro-grid systems is carried out to optimal combination again, realize the Optimized Operation of electric energy by cooperation.Should " cauline leaf generation strategy " mainly comprise two parts algorithm, minimum spanning tree (MST) is searched for optimum DG-CL power supply relation, determines network primary structure; LMI (LMI) is determined interpolation or the deletion of non-important load (NL) node, ensures the maximum utilization of DG electric energy.Its novelty and technical contribution are mainly reflected in several aspects: (1) considers multifactor structure network topology matrix weights, and metric lower these correlativitys is organically united, lay a good foundation thereby greatly reduced the simplification that the number of constraint condition is algorithm; (2) MST searching algorithm and LMI optimized algorithm are combined to use and construct respectively " trunk " and " leaf " under new autonomous microgrid topological structure, not only utilize advantage separately of algorithm but also facilitated (can repartitioning or increase and decrease " leaf " node only according to system state) at any time in conjunction with using with splitting of algorithm, thereby all reruning this algorithm are avoided after each sampling, greatly simplify calculated amount, improved efficiency of algorithm; (3) charging and discharging state of reasonable arrangement energy-storage units, makes this strategy be able to, from two dimensions of room and time, electric energy is optimized to arrangement, thereby the utilization factor of electric energy is improved greatly; Meanwhile, reduce the number of times that discharges and recharges of energy storage device as far as possible and also avoided the discharge and recharge action of energy storage device (S) to energy storage device (S), thereby reduced the use cost of energy-storage units, extended its serviceable life.Under international IEEE (IEEE) 33 node standard testing platforms, a series of simulation architecture shows, this strategy has effectively ensured the electricity consumption of CL, and the service efficiency that has improved DG electric energy has also ensured the electricity consumption requirement of NL greatly.

Summary of the invention

For the deficiencies in the prior art, the object of the present invention is to provide the cross-domain collaborative energy scheduling of the autonomous microgrid group of a kind of wind-light storage bavin and adaptation method.

The object of the invention is to realize by following technological means, concrete implementation step is as follows:

Step (1), taking time interval Δ t as the sampling time, periodically the exerting oneself in real time and electricity consumption situation of DG and load in supervisory system, obtain the work state information of current time system:

I) in the time that the output power of DG in system is greater than the need for electricity of whole loads, time, keep current autonomous microgrid structure, and execution step (4); Wherein N _dGfor total number of DG, N _cLfor total number of CL, N _nLfor total number of NL;

Described whole loads comprise important load (CL), non-important load (NL);

II) be greater than the need for electricity of whole important loads when the output power of DG in system, but cannot meet whole burden requirement time, $(\underset{i\_\mathrm{CL}=1}{\overset{N_{\mathrm{CL}}}{\mathrm{\Σ}}}{\mathrm{CL}}_{i\_\mathrm{CL}}+\underset{i\_\mathrm{NL}=1}{\overset{N_{\mathrm{NL}}}{\mathrm{\Σ}}}{\mathrm{NL}}_{i\_\mathrm{nl}})>\underset{i\_\mathrm{DG}=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{\mathrm{DG}}_{i\_\mathrm{DG}}>\underset{i=1}{\overset{N_{\mathrm{CL}}}{\mathrm{\Σ}}}{\mathrm{CL}}_{i\_\mathrm{CL}},$ Execution step (2);

III) in the time that the output power of DG in system cannot meet whole important load need for electricity, time, part important load is done to delete and process, ensure that as far as possible many important load CL obtain power supply and meet, ensure that the output load of DG is fully utilized, then execution step (6) simultaneously;

Step (2), when judgement system is in state I I) time, in each autonomous microgrid i_AMG (DG-CL) _{i_AMG}variation than the division trigger gate limit value θ that whether exceedes setting, i_AMG=1 ..., N _aMG(t-Δ t), if repartition autonomous microgrid structure, performs step (3); Add if not or the non-important load NL of deletion, i.e. execution step (17); Wherein N _aMG(t-Δ be t) (t-Δ is the number of autonomous microgrid that exists of moment t), with be respectively that (t-Δ is the power of DG and CL poor in autonomous microgrid i_AMG of moment and t moment t);

Real time power loss value and the risk factor of each branch road circuit under step (3), calculating microgrid current state, and the two normalized value of weighting, the branch road weights of formation system, and then taking these weights as matrix element, build the network topology matrix A (t) in current t moment:

Suppose a certain power load j in autonomous microgrid, its active power and reactive power are respectively P _jand Q _j, its upstream supply node is i, the all-in resistance the supply line from node i to node j and total reactance are respectively R _ijand X _ij; The voltage of supposing node j remains U _j, the real time power loss value that is transferred to the circuit the supply line of node j from node i can be expressed as:

$P_{\mathrm{loss}}^{\mathrm{ij}}=\frac{{P_j}^2+{Q_j}^2}{{U_j}^2}\·R_{\mathrm{ij}}---\left(1\right)$

Utilize expression formula (1) can obtain the real time power loss value of the circuit between any two nodes in network.

In Model in Reliability Evaluation of Power Systems process, the degree of unavailability K of circuit and device is a conventional measurement index, it be by year circuit failure-frequency f and r repair time of circuit determine,

$K=\frac{f\·r}{8760}---\left(2\right)$

Except the statistical value of repair time of using failure-frequency to circuit and circuit calculates its degree of unavailability, the expert assessment and evaluation value of circuit is also an important factor.Therefore, in conjunction with the risk factor of the two each circuit of trade-off evaluation system

$K_{\mathrm{risk}}^{\mathrm{ij}}=\mathrm{\η}\·K_{\mathrm{ij}}+(1-\mathrm{\η})\·E_{\mathrm{ij}}---\left(3\right)$

Wherein, K _ijfor the degree of unavailability of circuit between node i and node j, tried to achieve by expression formula (2); E _ijfor the expert assessment and evaluation value of circuit between node i and node j; η is regulatory factor, can adjust the two proportion in risk assessment process of the degree of unavailability of circuit and the expert assessment and evaluation value of circuit.

For the two is better unified under a Measure Indexes, first we need the two normalization.

If the circuit between node i and node j is L _ij, the real time power loss value of its normalized circuit and the risk factor of circuit are respectively:

$P_{\mathrm{norm}\_\mathrm{loss}}^{\mathrm{ij}}=\frac{P_{\mathrm{loss}}^{\mathrm{ij}}}{\underset{i,j=1;i\≠j}{\overset{N}{\mathrm{\Σ}}}{P}_{\mathrm{loss}}^{\mathrm{ij}}}---\left(4\right)$

$K_{\mathrm{norm}\_\mathrm{risk}}^{\mathrm{ij}}=\frac{K_{\mathrm{risk}}^{\mathrm{ij}}}{\underset{i,j=1;i\≠j}{\overset{N}{\mathrm{\Σ}}}{K}_{\mathrm{risk}}^{\mathrm{ij}}}---\left(5\right)$

Wherein, N is the nodes in whole network system;

Utilize circuit real time power loss value and circuit risk factor after normalization, obtain circuit L _ijfinal branch road weights be:

$a_{\mathrm{ij}}=\mathrm{\β}\·P_{\mathrm{norm}\_\mathrm{loss}}^{\mathrm{ij}}+(1-\mathrm{\β})\·K_{\mathrm{norm}\_\mathrm{risk}}^{\mathrm{ij}}---\left(6\right)$

β is regulatory factor, can be according to actual conditions adjustment.

Supposing has circuit to be directly connected in network between any two node i and node j, trying to achieve the two weights in network topology matrix A (t) by expression formula (6) is a _ij; If otherwise these two node i be not directly connected with j, the two weights in network topology matrix A (t) is a _ij=0; In addition, the diagonal entry in network topology matrix A (t) is defined as a _ii=0.

With a _ijfor the matrix weights between node i and node j, can obtain the weights between system any two points; Set it as matrix element, can obtain the topological relation matrix A of any time system ^{Δ t}, then execution step (5);

Step (4), when judgement system is in state I) time, be when in system, the output power of DG is greater than the need for electricity of whole loads, using whole energy storage devices in system as power load, then according to himself further judgement of state-of-charge (SOC) do, execution step (7);

Step (5), judge that energy storage device is regarded as power load (load) or power supply (generator), need to do further judgement according to himself state-of-charge (SOC), simultaneously execution step (7) and step (8);

Step (6), when judgement system is in state I II) time, it is the power load that total supply load of system DG is less than all consumers in system, in system, whole energy storage devices is regarded power supply as, need to make further judgement, execution step (8) according to himself state-of-charge (SOC);

Step (7), judge whether the SOC state of each energy storage device is greater than max cap. threshold value (SOC _max), if perform step (9); Perform step if not (10);

Step (8), judge whether the SOC state of each energy storage device is less than minimum capacity threshold value (SOC _min), if perform step (9); Perform step if not (11);

Step (9) is if the state-of-charge of certain energy storage device meets SOC > SOC _maxor SOC _min> SOC, this energy storage device is failure to actuate, neither as Power supply, also not as consumer electricity consumption, execution step (12);

Step (10) is not if certain energy storage device is greater than its max cap. threshold value (SOC _max), and the output power that now system meets DG is less than the need for electricity of whole loads, and this energy storage device is used as power load, and execution step (12);

Step (11) is not if certain energy storage device is less than its minimum capacity threshold value (SOC _min), and the output power that now system meets DG is greater than the need for electricity of whole loads, and this energy storage device is used as power supply, and execution step (12);

Step (12), taking the node at each power supply in autonomous microgrid (comprising the definite energy storage device of DG and step (11)) place as root node, search minimum spanning tree; Calculate in every minimum spanning tree, the minimum weights from root node to each important load (CL) node and;

Incorporate each important load into each power supply taking minimum weights with as target decision, be responsible for its power supply;

Step (13), select the corresponding power supply of minimum value in whole " minimum weights and " of certain important load (CL) supply node as this CL;

Step (14), determine all " power supply (G)-important load (CL) " corresponding power supply set of relationship according to step (13);

Step (15), judge whether the supply load of power supply in each " power supply (G)-important load (CL) " corresponding power supply set of relationship is greater than the electricity consumption requirement of important load, if execution step (17), performs step (16) if not;

Step (16), select the corresponding power supply of sub-minimum in " the minimum weights and " of this CL supply node the execution step (17) as this CL;

Step (17), made full use of to greatest extent as principle taking the electric energy of the power generation of powering, adopted LMI algorithm, determined the number of all types of non-important load NL between power supply (G) and important load (CL);

Step (18), all types of non-important load (NL) between power supply (G) and important load (CL), select non-important load by the definite number of step (17), and form a new autonomous microgrid together with power supply (G) and important load (CL);

Step (19), by above step by system all nodes be assigned in different new autonomous microgrids, thereby form new autonomous microgrid electric power system.

Beneficial effect of the present invention is: the present invention is directed to distributed power source power supply and have undulatory property, randomness and intermittent feature, (1) consider multifactor structure network topology matrix weights, and metric lower these correlativitys is organically united, lay a good foundation thereby greatly reduced the simplification that the number of constraint condition is algorithm; (2) MST searching algorithm and LMI optimized algorithm are combined to use and construct respectively " trunk " and " leaf " under new autonomous microgrid topological structure, not only utilize advantage separately of algorithm but also facilitated (can repartitioning or increase and decrease " leaf " node only according to system state) at any time in conjunction with using with splitting of algorithm, thereby all reruning this algorithm are avoided after each sampling, greatly simplify calculated amount, improved efficiency of algorithm; (3) charging and discharging state of reasonable arrangement energy-storage units, makes this strategy be able to, from two dimensions of room and time, electric energy is optimized to arrangement, thereby the utilization factor of electric energy is improved greatly; Meanwhile, reduce the number of times that discharges and recharges of energy storage device as far as possible and also avoided the discharge and recharge action of S to S, thereby reduced the use cost of energy-storage units, extended its serviceable life.It should be noted that, (1), in order to make energy storage device have longer serviceable life, it discharges and recharges and leaves certain remaining conventionally, chooses it herein and discharges and recharges the 20%-80% that scope is maximum capacitance of storage.(2) this strategy is considered as " electricity consumption " or " electric discharge " equipment according to overall electric power supply situation in system by all energy storage device unifications, this has certain realistic meaning: this strategy can effectively be avoided the action from an energy storage device to another energy storage device charging, thereby has avoided the repeated charge " concussion " between battery.(3) this strategy first ensures that all CL and NL have obtained sufficient electric power supply in charging to energy storage device, carry out according to the power supply priority of CL>NL>S, this can reduce the action that discharges and recharges to energy storage device as far as possible, thereby extend the serviceable life of battery, reduce operating cost.This scheduling strategy can effectively be realized the input-output power coupling under the not enough condition of output power, realizes the harmony that how autonomous microgrid is unified electricity consumption.Ensureing on the basis that important load is fully powered, also improving to a certain extent the security of whole system important load electricity consumption, can also well realize effective utilization of electric energy between how autonomous microgrid simultaneously.

Brief description of the drawings

Fig. 1 is network topology matrix method;

Fig. 2 is energy storage device perform region;

Fig. 3 is the process flow diagram of scheduling strategy method of the present invention;

Fig. 4 is that IEEE33 node system test topology and initial network are divided;

Fig. 5 is DG and load characteristic curve; Wherein (a) is DG and load characteristic curve, is (b) load characteristic curve

Fig. 6 (a) is that the 00:00 moment is with DG ₁for the MST of root node generation;

Fig. 6 (b) is that the 00:00 moment is with DG ₂for the MST of root node generation;

Fig. 6 (c) is that the 00:00 moment is with DG ₃for the MST of root node generation;

Fig. 7 (a) is that the 05:00 moment is with DG ₁for the MST of root node generation;

Fig. 7 (b) is that the 05:00 moment is with DG ₂for the MST of root node generation;

Fig. 7 (c) is that the 05:00 moment is with DG ₃for the MST of root node generation;

Fig. 7 (d) is that the 05:00 moment is with S ₁for the MST of root node generation;

Fig. 7 (e) is that the 05:00 moment is with S ₂for the MST of root node generation;

Fig. 7 (f) is that the 05:00 moment is with S ₃for the MST of root node generation;

Fig. 8 (a) is 00:00 moment cooperative scheduling result;

Fig. 8 (b) is 05:00 moment cooperative scheduling result;

Fig. 9 is CLs24h power supply contrast under initial configuration contrast scheduling structure;

Figure 10 is DG utilization factor correlation curve;

Figure 11 (a) is not for having usage policy load to meet condition diagram;

Figure 11 (b) uses this strategy but in system, does not have energy storage device load to meet condition diagram;

Figure 11 (c) is for using this strategy load to meet condition diagram;

Figure 12 is 24 hours cooperative scheduling results.

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described:

Shown in Fig. 3 is the processing flow chart of this invention.Its concrete enforcement is described below in connection with instantiation.Below employing IEEE33 node topology is described its concrete steps for dividing network, its topological structure as shown in Figure 4.

Can be seen in whole network, there are 3 DG by Fig. 4, wherein suppose DG ₁for photovoltaic (0-624.205MW), DG ₂and DG ₃for wind energy (82.01-419.50MW), its power characteristic is from the Belgium transmission of electricity Elijah of operator (Belgian electricity transmission operator Elias) (May13 ^th, 2014), as shown in Fig. 5 (a).Each energy-storage units hypothesis has the max cap. of 900MWh, and in system shown in Figure 4, the maximum total volume of accumulator system is 2700MWh.In addition, include 6 important loads and 21 non-important loads in system shown in Figure 4, its typical 24h characteristic working curve is as shown in Fig. 5 (b).Each node connection type is as Table I.

Table I

IEEE33 node property list

Step (1), taking time interval Δ t as the sampling time, periodically the exerting oneself in real time and electricity consumption situation of DG and load in supervisory system, obtain the work state information of current time system, and determine its state classification according to its duty:

I) in the time that the output power of DG in system is greater than the need for electricity of whole loads, time, keep current autonomous microgrid structure, and execution step (4); Wherein N _dGfor total number of DG, N _cLfor total number of CL, N _nLfor total number of NL;

Described whole loads comprise important load (CL), non-important load (NL);

II) be greater than the need for electricity of whole important loads when the output power of DG in system, but cannot meet whole burden requirement time, $(\underset{i\_\mathrm{CL}=1}{\overset{N_{\mathrm{CL}}}{\mathrm{\Σ}}}{\mathrm{CL}}_{i\_\mathrm{CL}}+\underset{i\_\mathrm{NL}=1}{\overset{N_{\mathrm{NL}}}{\mathrm{\Σ}}}{\mathrm{NL}}_{i\_\mathrm{nl}})>\underset{i\_\mathrm{DG}=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{\mathrm{DG}}_{i\_\mathrm{DG}}>\underset{i=1}{\overset{N_{\mathrm{CL}}}{\mathrm{\Σ}}}{\mathrm{CL}}_{i\_\mathrm{CL}},$ Execution step (2);

III) in the time that the output power of DG in system cannot meet whole important load need for electricity, time, part important load is done to delete and process, ensure that as far as possible many CL obtain power supply and meet, ensure that the output load of DG is fully utilized, execution step (6) simultaneously;

Step (2), when judgement system is in state I I) time, in each autonomous microgrid i_AMG (DG-CL) _{i_AMG}variation than the division trigger gate limit value θ that whether exceedes setting, i_AMG=1 ..., N _aMG(t-Δ t), if repartition autonomous microgrid structure, performs step (3); Add if not or the non-important load NL of deletion, i.e. execution step (17); Wherein, N _aMG(t-Δ is t) that (t-Δ is the number of the autonomous microgrid of moment existence t); with be respectively that (t-Δ is the power of DG and CL poor in autonomous microgrid i_AMG of moment and t moment t);

Network loss and the risk factor of each branch road under step (3), calculating microgrid current state, and the two normalized value of weighting, form the branch road weights of system, and then taking these weights as matrix element, the network topology matrix A (t) that builds the current t moment, its concrete steps are as follows:

Suppose a certain power load j in autonomous microgrid, its active power and reactive power are respectively P _jand Q _j, its upstream supply node is i, the all-in resistance the supply line from node i to node j and total reactance are respectively R _ijand X _ij; The voltage of supposing node j remains U _j, the real time power loss value that is transferred to the circuit the supply line of node j from node i can be expressed as:

$P_{\mathrm{loss}}^{\mathrm{ij}}=\frac{{P_j}^2+{Q_j}^2}{{U_j}^2}\·R_{\mathrm{ij}}---\left(1\right)$

Utilize expression formula (1) can obtain the real time power loss value of the circuit between any two nodes in network.

In Model in Reliability Evaluation of Power Systems process, the degree of unavailability K of circuit and device is a conventional measurement index, it be by year circuit failure-frequency f and r repair time of circuit determine,

$K=\frac{f\·r}{8760}---\left(2\right)$

Except the statistical value of repair time of using failure-frequency to circuit and circuit calculates its degree of unavailability, the expert assessment and evaluation value of circuit is also an important factor.Therefore, in conjunction with the risk factor of the two each circuit of trade-off evaluation system

$K_{\mathrm{risk}}^{\mathrm{ij}}=\mathrm{\η}\·K_{\mathrm{ij}}+(1-\mathrm{\η})\·E_{\mathrm{ij}}---\left(3\right)$

Wherein, K _ijfor the degree of unavailability of circuit between node i and node j, tried to achieve by expression formula (2); E _ijfor the expert assessment and evaluation value of circuit between node i and node j; η is regulatory factor, can adjust the two proportion in risk assessment process of the degree of unavailability of circuit and the expert assessment and evaluation value of circuit.

For the two is better unified under a Measure Indexes, first we need the two normalization.

If the circuit between node i and node j is L _ij, the real time power loss value of its normalized circuit and the risk factor of circuit are respectively:

$P_{\mathrm{norm}\_\mathrm{loss}}^{\mathrm{ij}}=\frac{P_{\mathrm{loss}}^{\mathrm{ij}}}{\underset{i,j=1;i\≠j}{\overset{N}{\mathrm{\Σ}}}{P}_{\mathrm{loss}}^{\mathrm{ij}}}---\left(4\right)$

$K_{\mathrm{norm}\_\mathrm{risk}}^{\mathrm{ij}}=\frac{K_{\mathrm{risk}}^{\mathrm{ij}}}{\underset{i,j=1;i\≠j}{\overset{N}{\mathrm{\Σ}}}{K}_{\mathrm{risk}}^{\mathrm{ij}}}---\left(5\right)$

Wherein, N is the nodes in whole network system;

Utilize circuit real time power loss value and circuit risk factor after normalization, obtain circuit L _ijfinal weights be:

$a_{\mathrm{ij}}=\mathrm{\β}\·P_{\mathrm{norm}\_\mathrm{loss}}^{\mathrm{ij}}+(1-\mathrm{\β})\·K_{\mathrm{norm}\_\mathrm{risk}}^{\mathrm{ij}}---\left(6\right)$

β is regulatory factor, can be according to actual conditions adjustment.

Supposing has circuit to be directly connected in network between any two node i and node j, trying to achieve the two weights in network topology matrix A (t) by expression formula (6) is a _ij; If otherwise these two node i be not directly connected with j, the two weights in network topology matrix A (t) is a _ij=0; In addition, the diagonal entry in network topology matrix A (t) is defined as a _ii=0.Accordingly, build network topology matrix example as Fig. 1:

In Fig. 1, node 1 is connected with 2, and its weights are tried to achieve by (1)-(6), is a ₁₂(or a ₂₁, a ₁₂=a ₂₁); And node 3 is not connected with 4, its weights a ₁₃=a ₃₁=0; In addition, diagonal entry a in Fig. 1 ₁₁=a ₂₂=a ₃₃=a ₄₄=a ₅₅=a ₆₆=0.

With a _ijfor the matrix weights between node i and node j, can obtain the weights between system any two points; Set it as matrix element, can obtain the topological relation matrix A of any time system ^{Δ t}, then execution step (5);

Step (4), when judgement system is in state I) time, be when in system, the output power of DG is greater than the need for electricity of whole loads, using whole energy storage devices in system as power load, then according to himself further judgement of state-of-charge (SOC) do, execution step (7);

Step (5), judge that energy storage device is regarded as power load (load) or power supply (generator), need to do further judgement according to himself state-of-charge (SOC), simultaneously execution step (7), step (8);

Step (6), when judgement system is in state I II) time, it is the power load that total supply load of system DG is less than all consumers in system, in system, whole energy storage devices is regarded power supply as, need to make further judgement, execution step (8) according to himself state-of-charge (SOC);

Step (7), judge whether the SOC state of each energy storage device is greater than max cap. threshold value (SOC _max=80%, as shown in Figure 2), if perform step (9); Perform step if not (10);

Step (8), judge whether the SOC state of each energy storage device is less than minimum capacity threshold value (SOC _min=20%, as shown in Figure 2), if perform step (9); Perform step if not (11);

Step (9) is if the state-of-charge of certain energy storage device meets SOC > SOC _max(SOC herein _max=80%, as shown in Figure 2) or SOC _min> SOC (SOC herein _min=20%, as shown in Figure 2), this energy storage device is failure to actuate, neither as Power supply, and also not as consumer electricity consumption, execution step (12);

Step (10) is not if certain energy storage device is greater than its max cap. threshold value (SOC _max=80%, as shown in Figure 2), and the output power that now system meets DG is less than the need for electricity of whole loads, and this energy storage device is used as power load, and execution step (12);

Step (11) is not if certain energy storage device is less than its minimum capacity threshold value (SOC _min=20%, as shown in Figure 2), and the output power that now system meets DG is greater than the need for electricity of whole loads, and this energy storage device is used as power supply, and execution step (12);

Step (12), taking the node at each power supply in autonomous microgrid (comprising the definite energy storage device of DG and step (11)) place as root node, search minimum spanning tree; Calculate in every minimum spanning tree, the minimum weights from root node to each important load (CL) node and;

Incorporate each important load into each power supply taking minimum weights with as target decision, be responsible for its power supply.

Step (13), select the corresponding power supply of minimum value in whole " minimum weights and " of certain important load (CL) supply node as this CL;

Step (14), determine all " power supply (G)-important load (CL) " corresponding power supply set of relationship according to step (13);

Step (15), judge whether the supply load of power supply in each " power supply (G)-important load (CL) " corresponding power supply set of relationship is greater than the electricity consumption requirement of important load, if execution step (17), performs step (16) if not;

Step (16), select the corresponding power supply of sub-minimum in " the minimum weights and " of this CL supply node the execution step (17) as this CL;

Step (17), made full use of to greatest extent as principle taking the electric energy of the power generation of powering, adopted LMI algorithm, determined the number of all types of non-important load NL between power supply (G) and important load (CL);

Step (18), all types of non-important load (NL) between power supply (G) and important load (CL), select non-important load by the definite number of step (17), and form a new autonomous microgrid together with power supply (G) and important load (CL);

Step (19), by above step by system all nodes be assigned in different new autonomous microgrids, thereby form new autonomous microgrid electric power system.

Therefore, DG load curve is as shown in Figure 4 known, in 00:00 moment, system i.e. total output is greater than aggregate demand, therefore, now in system, all energy storage devices are treated as power load and are determined whether it is carried out to charging operations (by step 9) according to himself SOC situation, and system is only searched for MST to determine the power supply of CL using DG as root node.The MST from DG to CL obtaining according to the network topology matrix A (00:00) in 00:00 moment as shown in Figure 6.According to the MST shown in Fig. 6, calculate weights from each DG to each CL and, as shown in Table II.Wherein add boldface type represented be weights and minimum in certain CL to 3 root node, i.e. the power supply root node of this CL.But, it is pointed out that 00:00 moment DG ₁for electric output power be 0, therefore, according to carried strategy, its be responsible for power supply node 7,8 and 21CL according to inferior little weights and principle (by step 16), arrange to be respectively responsible for power supply by two other DGs, as shown in underscore numerical value in Table II.Accordingly, 00:00 network system is reconfigured as two autonomous microgrid subsystems, and according to DG now ₂and DG ₃power supply capacity and the burden requirement of other NLs, to make full use of DG dump energy as target, determine the non-important load node adding in the autonomous microgrid of every height according to LMI algorithm (by step 17).00:00 moment cooperative scheduling result is as shown in Fig. 8 (a).

Table II

Weights from DGS to CLS and

Different from the 00:00 moment, etching system in the time of 05:00 now, all determine whether discharge as power supply and according to self SOC state (step 10) according to energy storage device (S) in carried policy system.The MST taking DGs and Ss as root node obtaining according to the network topology matrix A (05:00) in 05:00 moment as shown in Figure 7.Now, the weights from each root node to each CL and, as shown in Table III.According to the result of Table III, 05:00 network system is reconfigured as five subsystems, and according to DG now ₂, DG ₃, S ₁-S ₃power supply capacity and the burden requirement of other NL, according to LMI algorithm (step 17) to adding " leaf " non-important load in each autonomous microgrid, thereby utilize to greatest extent the unnecessary electric energy of power supply to realize making full use of of electric energy.05:00 moment cooperative scheduling result is as shown in Fig. 8 (b).

Table III

Weights from DGS to CLS and

Under supposing the system original state, formed by 3 autonomous micro-grid systems altogether, as shown in Figure 4.In each autonomous microgrid, respectively there is a DG, an energy-storage units and two CL.Under the scheduling proposing with this paper under this initial configuration, the contrast situation of system CL entirety acquisition power supply as shown in Figure 9.Result can be found out as shown in Figure 9, and what in system, CL obtained power supply under the United Dispatching of coordination strategy meets the meet rate of rate apparently higher than CL under original autonomous micro-grid system structure.Because CL load has the meaning and value larger than NL for system, therefore this proves that from an aspect this strategy has effective economic worth.

It shown in Figure 10, is system DG capacity factor correlation curve.The utilization factor that can obviously find out the generating of DG under the autonomous microgrid cooperative scheduling strategy effect of proposing herein from Figure 10 will be higher than the utilization factor (being the generating utilization ratio number percent of DG shown in Figure 10 wicket) not having in cooperative scheduling strategy situation.By the architecture of the autonomous micro-grid system of reconstruct, simultaneously rational management energy storage device discharge and recharge action, the electric energy that DG sends is utilized by power load or is stored by energy storage device, and in the time that lacking electric power supply, system emits, this can effectively be optimized use from two dimensions of room and time to electric power in real system, thereby improving DG efficiency, this has great importance in actual electric network.

It shown in Figure 11, is the curve that all loads meet rate contrast.As seen from Figure 11, in the time including energy-storage units in system, this algorithm can ensure the need for electricity of all loads of system in the most of the time, as shown in Figure 11 (a).And if energy storage device has enough large capacity in system, can ensure all load electricity consumptions in all the period of time in conjunction with rational autonomous microgrid cooperative scheduling strategy.Secondly, both made is the in the situation that of there is no energy storage device in system, due to this tactful coordinated scheduling effect, in most of the time section, the need for electricity of all loads also can be met, just part-time is interior in the time that the total electric energy output of system is less than electric energy aggregate demand, just have part NL and can not get power supply, as shown in Figure 11 (b).Form with it sharp contrast, even if there is energy storage device in system, in the situation that there is no rational management, system still cannot meet the electricity consumption requirement of all loads in most of time, as shown in Figure 11 (c).Figure 11 has absolutely proved the meaning and value of autonomous microgrid rational management, rational cooperative scheduling strategy is described simultaneously and uses and have prior meaning for the optimization of electric power resource than only increase energy storage device in system.

Figure 12 shows that the cooperative scheduling strategy that proposes the herein arrangement result to all power supplies of system in a day and load electricity consumption.Result as shown in Figure 12 can be found out, rationally discharge and recharge scheduling by energy storage device, 00:00-04:30, the unnecessary electric weight that in 07:30-08:30 and 11:00-13:00 time period, DG produces is fully absorbed, and make 05:00-07:00, the power requirement of 09:00-10:30 and 18:30-21:00 deficiency obtain effectively supplementing; Simultaneously, within the 00:00-22:30 time period, whole important load electricity consumptions are all fully used, only the important load electric weight demand within the 22:30-24:00 time is because DG and energy storage all do not have electric power output, be that in system, total electricity supply is less than CL aggregate demand, in any case therefore scheduling all cannot meet and has to abandon the power supply of part CL.But in practical operation, this part electric power breach can be compensated by buying electricity to public electric wire net.In addition, for non-important load, by cooperative scheduling herein, make the NL electricity consumption in most of time section in system require to attain full and complete satisfaction, only, in the time that systematic electricity aggregate demand is greater than aggregate supply, just cannot meet by scheduling, and this part electric power still can obtain by buying electricity.

Claims (2)

1. the cross-domain collaborative energy scheduling of the autonomous microgrid group of wind-light storage bavin and an adaptation method, is characterized in that the method comprises the following steps:

Step (1), taking time interval Δ t as the sampling time, periodically the exerting oneself in real time and electricity consumption situation of DG and load in supervisory system, obtain the work state information of current time system:

I) in the time that the output power of DG in system is greater than the need for electricity of whole loads, time, keep current autonomous microgrid structure, and execution step (4); Wherein N _dGfor total number of DG, N _cLfor total number of important load, N _nLfor total number of non-important load;

Described whole loads comprise important load, non-important load;

II) be greater than the need for electricity of whole important loads when the output power of DG in system, but cannot meet whole burden requirement time, $(\underset{i\_\mathrm{CL}=1}{\overset{N_{\mathrm{CL}}}{\mathrm{\Σ}}}{\mathrm{CL}}_{i\_\mathrm{CL}}+\underset{i\_\mathrm{NL}=1}{\overset{N_{\mathrm{NL}}}{\mathrm{\Σ}}}{\mathrm{NL}}_{i\_\mathrm{nl}})>\underset{i\_\mathrm{DG}=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{\mathrm{DG}}_{i\_\mathrm{DG}}>\underset{i=1}{\overset{N_{\mathrm{CL}}}{\mathrm{\Σ}}}{\mathrm{CL}}_{i\_\mathrm{CL}},$ Execution step (2);

III) in the time that the output power of DG in system cannot meet whole important load need for electricity, time, part important load is done to delete and process, ensure that as far as possible many important loads obtain power supply and meet, ensure that the output load of DG is fully utilized, i.e. execution step (6) simultaneously;

Step (2), when judgement system is in state I I) time, in each autonomous microgrid i_AMG (DG-CL) _{i_AMG}variation than the division trigger gate limit value θ that whether exceedes setting, i_AMG=1 ..., N _aMG(t-Δ t), if repartition autonomous microgrid structure, performs step (3); Add if not or the non-important load of the non-important load of deletion, i.e. execution step (17); Wherein N _aMG(t-Δ be t) (t-Δ is the number of autonomous microgrid that exists of moment t), with be respectively that (t-Δ is the power of DG and important load poor in autonomous microgrid i_AMG of moment and t moment t);

Real time power loss value and the risk factor of each branch road circuit under step (3), calculating microgrid current state, and the two normalized value of weighting, the branch road weights of formation system, and then taking these weights as matrix element, build the network topology matrix A (t) in current t moment, then execution step (5);

Step (4), when judgement system is in state I) time, be when in system, the output power of DG is greater than the need for electricity of whole loads, using whole energy storage devices in system as power load, then according to himself further judgement of state-of-charge do, execution step (7);

Step (5), judge that energy storage device is regarded as power load or power supply, need to do further judgement according to himself state-of-charge, simultaneously execution step (7) and step (8);

Step (6), when judgement system is in state I II) time, it is the power load that total supply load of system DG is less than all consumers in system, in system, whole energy storage devices is regarded power supply as, need to make further judgement, execution step (8) according to himself state-of-charge;

Step (7), judge whether the SOC state of each energy storage device is greater than max cap. threshold value SOC _maxif, execution step (9); Perform step if not (10);

Step (8), judge whether the SOC state of each energy storage device is less than minimum capacity threshold value SOC _minif, execution step (9); Perform step if not (11);

Step (9) is if the state-of-charge of certain energy storage device meets SOC > SOC _maxor SOC _min> SOC, this energy storage device is failure to actuate, neither as Power supply, also not as consumer electricity consumption, execution step (12);

Step (10) is not if certain energy storage device is greater than its max cap. threshold value (SOC _max), and the output power that now system meets DG is less than the need for electricity of whole loads, and this energy storage device is used as power load, and execution step (12);

Step (11) is not if certain energy storage device is less than its minimum capacity threshold value (SOC _min), and the output power that now system meets DG is greater than the need for electricity of whole loads, and this energy storage device is used as power supply, and execution step (12);

Step (12), taking the node at each power supply place in autonomous microgrid as root node, search minimum spanning tree; Calculate in every minimum spanning tree, the minimum weights from root node to each important load node and; Incorporate each important load into each power supply taking minimum weights with as target decision, be responsible for its power supply;

Described power supply comprises DG and the definite energy storage device of step (11);

Step (13), select the corresponding power supply of minimum value in whole " the minimum weights and " of certain important load supply node as this important load;

Step (14), determine all " power supply-important load " corresponding power supply set of relationship according to step (13);

Step (15), judge whether the supply load of power supply in each " power supply-important load " corresponding power supply set of relationship is greater than the electricity consumption requirement of important load, if execution step (17), performs step (16) if not;

Step (16), select the corresponding power supply of sub-minimum in " the minimum weights and " of this important load supply node the execution step (17) as this important load;

Step (17), made full use of to greatest extent as principle taking the electric energy of the power generation of powering, adopted LMI algorithm, determined the number of all types of non-important loads between power supply and important load;

Step (18), all types of non-important load between power supply and important load, select non-important load by the definite number of step (17), and and power supply and important load form a new autonomous microgrid;

Step (19), by above step by system all nodes be assigned in different new autonomous microgrids, thereby form new autonomous microgrid electric power system.

2. the cross-domain collaborative energy scheduling of the autonomous microgrid group of a kind of wind-light storage bavin as claimed in claim 1 and adaptation method, it is characterized in that real time power loss value and the risk factor of the each branch road circuit of step (3), and the two normalized value of weighting, the branch road weights of formation system, and then taking these weights as matrix element, build the network topology matrix A (t) in current t moment;

Real time power loss value calculate by expression formula (1):

$P_{\mathrm{loss}}^{\mathrm{ij}}=\frac{{P_j}^2+{Q_j}^2}{{U_j}^2}\·R_{\mathrm{ij}}---\left(1\right)$

Wherein P _jfor the active power of a certain power load j in autonomous microgrid, Q _jfor the reactive power of a certain power load j in autonomous microgrid, R _ijfor the all-in resistance the power supply branch road circuit from node i to node j, X _ijfor the total reactance the power supply branch road circuit from node i to node j, U _jfor the voltage of node j;

Degree of unavailability K by year circuit failure-frequency f and r repair time of circuit determine,

$K=\frac{f\·r}{8760}---\left(2\right)$

The risk factor of each circuit

$K_{\mathrm{risk}}^{\mathrm{ij}}=\mathrm{\η}\·K_{\mathrm{ij}}+(1-\mathrm{\η})\·E_{\mathrm{ij}}---\left(3\right)$

Wherein K _ijfor the degree of unavailability of circuit between node i and node j, tried to achieve by expression formula (2); E _ijfor the expert assessment and evaluation value of circuit between node i and node j, η is regulatory factor;

After normalization, the real time power loss value of circuit and the risk factor of circuit are respectively:

$P_{\mathrm{norm}\_\mathrm{loss}}^{\mathrm{ij}}=\frac{P_{\mathrm{loss}}^{\mathrm{ij}}}{\underset{i,j=1;i\≠j}{\overset{N}{\mathrm{\Σ}}}{P}_{\mathrm{loss}}^{\mathrm{ij}}}---\left(4\right)$

$K_{\mathrm{norm}\_\mathrm{risk}}^{\mathrm{ij}}=\frac{K_{\mathrm{risk}}^{\mathrm{ij}}}{\underset{i,j=1;i\≠j}{\overset{N}{\mathrm{\Σ}}}{K}_{\mathrm{risk}}^{\mathrm{ij}}}---\left(5\right)$

Wherein N is the nodes in whole network system,

Utilize circuit real time power loss value and circuit risk factor after normalization, obtain circuit L _ijfinal branch road weights be:

$a_{\mathrm{ij}}=\mathrm{\β}\·P_{\mathrm{norm}\_\mathrm{loss}}^{\mathrm{ij}}+(1-\mathrm{\β})\·K_{\mathrm{norm}\_\mathrm{risk}}^{\mathrm{ij}}---\left(6\right)$

β is regulatory factor;

With a _ijfor the matrix weights between node i and node j, obtain the weights between system any two points; Set it as matrix element, can obtain the topological relation matrix A of any time system ^{Δ t}.