CN106712035B - Economic dispatching method for power system - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses an economic dispatching method for an electric power system, relates to the technical field of optimized operation of the electric power system, and improves the reliability of economic dispatching for the electric power system. The method comprises the following steps: decoupling a power system into a coordination center and a plurality of zones; establishing an economic dispatching model of the power system according to the coordination center and the plurality of areas, wherein the economic dispatching model of the power system aims at the sum of the total power generation cost and the new energy power generation shedding penalty cost of a conventional unit in the power system within the dispatching duration; decomposing an economic dispatching model of the power system into an inter-area coordination model and a plurality of area dispatching models, wherein the plurality of area dispatching models correspond to the plurality of areas one by one, the inter-area coordination model corresponds to a coordination center, and the inter-area coordination model performs distributed optimization on boundary nodes of each area; and calculating an economic dispatching result of the power system according to the inter-area coordination model and the plurality of area dispatching models.
Description
Technical Field
The invention relates to the technical field of optimized operation of an electric power system, in particular to an economic dispatching method of the electric power system.
Background
At present, the rapid development of new energy electric fields, such as wind energy electric fields, solar energy electric fields, tidal electric fields and the like, is promoted by the proposal of energy environmental problems. However, due to the large randomness and volatility of new energy farms, power systems incorporating new energy farms face a more severe form, and in particular, economic dispatch of power systems faces a greater challenge.
The economic dispatching of the power system refers to dispatching the unit output of each unit in the power system on the premise of ensuring the safe and reliable operation of the power system and meeting the requirements of electric energy quality and power consumption, so that the energy consumption and the operation cost of the whole power system are minimized, and the maximum economic benefit is obtained. The existing economic dispatching of the power system is generally made by a dispatching center in a unified manner, that is, an economic dispatching result of the power system is obtained by adopting a centralized optimization manner, for example, the sum of the total power generation cost and the power generation load shedding penalty cost of new energy (such as wind shedding, solar energy shedding and tide shedding) of conventional units (such as thermal power units and hydroelectric power units) in the power system in a dispatching cycle can be used as a target of the economic dispatching of the power system, that is, the economic dispatching result with the minimum sum of the total power generation cost and the power generation load shedding penalty cost of new energy (such as wind shedding, solar energy shedding and tide shedding) of the conventional units (such as thermal power units and hydroelectric power units) in the dispatching cycle in the power system is solved to achieve economic dispatching of the power system, and at this time, the dispatching center generally needs to obtain the whole network data of the power system. However, with the gradual incorporation of a new energy electric field, the scale of an electric power system is continuously enlarged, and when the dispatching center performs economic dispatching on the electric power system in a unified manner, the whole network data acquired by the dispatching center is huge and complicated, which easily causes communication blockage and data loss, and further causes poor reliability of economic dispatching of the electric power system.
Disclosure of Invention
The invention aims to provide an economic dispatching method of an electric power system, which is used for solving the problem of poor reliability of economic dispatching of the existing electric power system.
In order to achieve the above purpose, the invention provides the following technical scheme:
an economic dispatching method for an electric power system is characterized by comprising the following steps:
step S100, decoupling a power system into a coordination center and a plurality of areas;
step S200, establishing an economic dispatching model of the power system according to the coordination center and the plurality of areas, wherein the economic dispatching model of the power system aims at the sum of the total power generation cost and the power generation load shedding penalty cost of abandoned new energy in a dispatching duration of a conventional unit in the power system;
step S300, decomposing the economic dispatching model of the power system into an inter-area coordination model and a plurality of area dispatching models, wherein the plurality of area dispatching models correspond to the plurality of areas one by one, the inter-area coordination model corresponds to the coordination center, and the inter-area coordination model performs distributed optimization on boundary nodes of the areas;
and S400, calculating an economic dispatching result of the power system according to the inter-area coordination model and the plurality of area dispatching models.
According to the economic dispatching method of the power system, the power system is decoupled into the coordination center and the multiple regions, then the economic dispatching model of the power system is established according to the coordination center and the multiple regions, then the economic dispatching model of the power system is decomposed into the inter-region coordination model and the multiple region dispatching models, and then the economic dispatching result of the power system is calculated according to the inter-region coordination model and the multiple region dispatching models. Therefore, in the invention, when the economic dispatching result of the power system is calculated, a plurality of regional dispatching models are respectively calculated, each regional dispatching model is optimized in a distributed mode by using the inter-regional coordination model, namely, the economic dispatching result of each area is calculated by the area dispatching model corresponding to the area, the boundary nodes of the area are optimized in a distributed mode by utilizing the inter-area coordination model, therefore, when the coordination center performs distributed optimization on the boundary nodes of each area by using the inter-area coordination model, the coordination center only needs to acquire the variables of the boundary nodes of each area, and does not need to acquire other variables in each area, the coordination center does not need to acquire the whole network data of the power system, so that communication blockage and data loss caused by the whole network data which needs to be acquired are avoided, and the reliability of economic dispatching of the power system can be improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a first flowchart of a power system economic dispatch method according to an embodiment of the present invention;
FIG. 2 is a second flowchart of an economic dispatching method of an electric power system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of power system decoupling according to an embodiment of the present invention.
Detailed Description
In order to further explain the economic dispatching method of the power system provided by the embodiment of the invention, the following detailed description is made in conjunction with the attached drawings of the specification.
Referring to fig. 1, an economic dispatching method for an electric power system according to an embodiment of the present invention includes:
and S100, decoupling the power system into a coordination center and a plurality of areas.
Step S200, establishing an economic dispatching model of the power system according to the coordination center and the plurality of areas, wherein the economic dispatching model of the power system aims at the sum of the total power generation cost and the load shedding penalty cost of the abandoned new energy power generation of the conventional units in the power system within the dispatching duration.
Step S300, decomposing the economic dispatching model of the power system into an inter-area coordination model and a plurality of area dispatching models, wherein the plurality of area dispatching models correspond to the plurality of areas one by one, the inter-area coordination model corresponds to a coordination center, and the inter-area coordination model performs distributed optimization on boundary nodes of each area.
And S400, calculating an economic dispatching result of the power system according to the inter-region coordination model and the plurality of region dispatching models.
Specifically, when the power system is decoupled into the coordination center and the plurality of zones, a method of constructing a virtual zone and copying boundary node variables may be employed, for example, see fig. 3, taking the decoupling of the power system into the coordination center and the two areas as an example for explanation, the power system is firstly constructed into the two areas, namely the area a and the area b, by utilizing the method for constructing the virtual area, the two areas are connected by an inter-area communication line, one end of the inter-area communication line is connected to a boundary node m of an area a, the other end of the inter-area communication line is connected to a boundary node n of an area b, the boundary node may be understood as a node connecting a certain region and other regions, and then the phase angle variable of the boundary node m and the phase angle variable of the boundary node n are copied once by using a method of copying the boundary node variable, which is respectively.Andwherein,andbelongs to the area a of the field, and the area b,andbelonging to the area b. When a coordination center is formed, the phase angle of a boundary node m is determined by a method of copying a boundary node variableThe phase angle variables of the variable and the boundary node n are copied once more, respectivelyAndthus, for the same boundary node, each relevant region and the coordination center have a set of corresponding variables representing the phase angle of the boundary node.
After the power system is decoupled into the coordination center and the plurality of regions, an economic dispatching model of the power system can be established according to the coordination center and the plurality of regions formed by decoupling, wherein the economic dispatching model of the power system can be set as the sum of the total power generation cost and the penalty cost of abandoning new energy power generation load shedding of a conventional unit in the power system within the dispatching duration, namely the economic dispatching result of the power system is obtained by solving, the sum of the total power generation cost and the penalty cost of abandoning new energy power generation load shedding of the conventional unit in the power system within the dispatching duration is required to be minimum, and the economic dispatching model of the power system is the economic dispatching model of the whole power system and comprises all parameters in each region and parameters between regions, namely whole network data of the power system.
After the power system economic dispatching model is completed, the power system economic dispatching model is decomposed into inter-area coordination models corresponding to a coordination center and a plurality of area dispatching models corresponding to a plurality of areas one by utilizing a target cascade analysis method and a multi-cutting decomposition algorithm containing partial aggregation, the coordination center receives variables (such as phase angle variables) of boundary nodes uploaded by the areas, calculates the variables corresponding to the boundary nodes in the coordination center according to the inter-area coordination models, and then sends the variables corresponding to the boundary nodes in the coordination center to the corresponding areas so as to perform distributed optimization on the boundary nodes of the areas.
After the economic dispatching model of the power system is decomposed into an inter-region coordination model and a plurality of region dispatching models, the economic dispatching result of the power system is obtained through calculation through multiple distributed optimization between the inter-region coordination model and each region dispatching model according to the inter-region coordination model and the plurality of region dispatching models, and the economic dispatching result of the power system is composed of the region dispatching results of each region.
As can be seen from the above, in the power system economic scheduling method provided in the embodiment of the present invention, the power system is decoupled into the coordination center and the plurality of regions, the power system economic scheduling model is then established according to the coordination center and the plurality of regions, the power system economic scheduling model is then decomposed into the inter-region coordination model and the plurality of region scheduling models, and the economic scheduling result of the power system is then calculated according to the inter-region coordination model and the plurality of region scheduling models. Therefore, in the embodiment of the invention, when the economic dispatching result of the power system is calculated, the plurality of regional dispatching models are respectively calculated, the regional dispatching models are optimized in a distributed mode by utilizing the inter-regional coordination model, namely, the economic dispatching result of each area is calculated by the area dispatching model corresponding to the area, the boundary nodes of the area are optimized in a distributed mode by utilizing the inter-area coordination model, therefore, when the coordination center performs distributed optimization on the boundary nodes of each area by using the inter-area coordination model, the coordination center only needs to acquire the variables of the boundary nodes of each area, and does not need to acquire other variables in each area, the coordination center does not need to acquire the whole network data of the power system, so that communication blockage and data loss caused by the whole network data which needs to be acquired are avoided, and the reliability of economic dispatching of the power system can be improved.
In addition, in the embodiment of the invention, the economic scheduling result of each region is calculated by the region scheduling model corresponding to the region, and the boundary nodes of the region are optimized in a distributed manner by using the inter-region coordination model, so that when the coordination center performs distributed optimization on the boundary nodes of each region by using the inter-region coordination model, the coordination center only needs to acquire the variables of the boundary nodes of each region, and does not need to acquire other variables in each region, that is, the coordination center does not need to acquire the whole network data of the power system. Therefore, independent scheduling of each zone can be realized, and protection of data privacy of certain zones can be realized.
Furthermore, in the embodiment of the present invention, the economic dispatching model of the power system is decomposed into the inter-region coordination model and the plurality of region dispatching models, that is, a larger problem is decomposed into a plurality of small problems, and then the plurality of small problems are calculated respectively, so that the process of calculating the economic dispatching result of the power system can be simplified, the efficiency of calculating the economic dispatching result of the power system can be improved, and meanwhile, the reliability of economic dispatching of the power system can be further improved because the number of parameters related to each small problem is small.
Referring to fig. 1 and fig. 2, before step S100, the method for economic dispatch of an electric power system according to an embodiment of the present invention further includes:
step S10, determining the scheduling time length for carrying out economic scheduling on the power system, and averagely dividing the scheduling time length into nTA period of time in which nT≥2。
For example, the scheduling period for economic scheduling of the power system may be set to one day, i.e., 24 hours, with the scheduling period being divided into n on averageTA period of time in which nTIn the periods, the time lengths of the respective periods are the same, and for example, 24 hours may be divided into 24 periods, one period per hour, or 24 hours may be divided into 96 periods, one period per 15 minutes.
Referring to fig. 2, after step S100 and before step S200, the method for economic dispatch of an electric power system according to an embodiment of the present invention further includes:
in step S100', a prediction scene is set for each region, and a plurality of error scenes are extracted for the region having the new energy electric field.
Specifically, a scene method may be adopted to set a prediction scene for each region, and extract an error scene for a region having a new energy electric field, for example, 100 error scenes may be extracted for a region having a new energy electric field, and when the power system economic dispatch model is established after setting of the prediction scene and extraction of the error scenes are completed, the power system economic dispatch model includes relevant parameters of the prediction scene and relevant parameters of the error scenes, the power system economic dispatch model takes randomness and waviness of the new energy electric field into consideration, and the power system economic dispatch model established in this way can cope with randomness and waviness of the new energy electric field in the power system.
Referring to fig. 2, after step S300 and before step S400, the method for economically scheduling an electric power system according to the embodiment of the present invention further includes:
step S300', the region scheduling model is decomposed into a region prediction scene model and a region error scene model.
In step S300', for a region with a new energy electric field, the region scheduling model of the region is decomposed into a region prediction scene model and a region error scene model, and when an economic scheduling result of the region is calculated, the region prediction scene model is calculated first, and then a result obtained after the region prediction scene model is calculated is randomly optimized for multiple times by using the region error scene model. Therefore, in the embodiment of the present invention, when the economic scheduling result of the region is calculated, one large problem of the region is also decomposed into two small problems respectively corresponding to the prediction scenario and the error scenario, so that the process of calculating the economic scheduling result of the region can be simplified, the efficiency of calculating the economic scheduling result of the region can be improved, and meanwhile, since the number of parameters involved in each small problem is small, the reliability of economic scheduling of the region can be improved, and further the reliability of economic scheduling of the power system can be improved.
In the above embodiment, the economic dispatching model of the power system may be:
an objective function:
constraint conditions are as follows:
predicted scene constraints for regions:
BaPa+Daθa≤Ea；1≤a≤N (2)
error scenario constraint for region:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa+Ha,sθa；1≤a≤N,1≤s≤Sa (3)
constraint conditions of the coordination center:
coordination of the coupling constraints between the center and the zones:
in the above formula, faPredicting a total cost of the scene for area a; f. ofa,sAbandoning new energy power generation cost for the error scene of the area a; n is the number of the regions;the number of the conventional units in the area a is shown;the number of new energy machine sets in the area a;the number of the load nodes in the region a in the time period t is shown; saThe number of error scenes in the region a;in a region a of a time interval t, the active power output of a conventional unit i is predicted under a scene;andrespectively are the power generation cost coefficients of the conventional unit i in the area a;in the time period t, the power generation power of the abandoned new energy of the new energy unit w in the area a under the prediction scene; q. q.sWA penalty cost coefficient for generating new energy for the area a;load shedding power of a load node d in a prediction scene of the area a in a time period t; q. q.sDPenalizing a cost coefficient for load shedding of the area a; p is a radical ofsProbability of error scene s, p, for region as＝1/Sa；In a time period t, the power generation power of the abandoned new energy of the new energy unit w in the area a under the error scene s;for time period t, region a loads the load shedding power of node d under error scenario s.
PaThe output matrix P of each conventional unit in each time interval in the prediction scene of the area a is shown as the output matrixaThe element of (a) is the output of the conventional unit i in the time period t in the prediction scene of the area a, and the output matrix PaIs composed ofOf a matrix orA matrix of (a); thetaaFor the phase angle matrix of each node in each time interval in the prediction scene of the area a, the nodes comprise: nodes (loaded nodes, unloaded nodes, etc.) within region a, boundary nodes of region a, and boundary nodes connected to region a in other regions, phase angle matrix θaThe element of (a) is the phase angle of a certain node in a time period t in a prediction scene of the area a; b isa、DaAnd EaAll the parameter matrixes are parameter matrixes of the area a in a prediction scene; pa,sThe output matrix P of each conventional unit in each time interval under the error scene s in the area a is shown as the output matrixa,sThe element of (a) is the output of the conventional unit i in the time t under the error scene s in the area a, and the output matrix Pa,sIs composed ofOf a matrix orA matrix of (a); thetaa,sA phase angle matrix theta of each node in each time interval under the error scene s for the region aa,sThe element of (a) is the phase angle of a certain node in a time period t under an error scene s; b isa,s、Da,s、Ea,s、Ga,sAnd Ha,sAll the parameter matrixes are parameter matrixes of the area a under the error scene s; TLab,aA boundary node set connected with the area b in the area a is obtained; TLab,bA boundary node set connected with the area a in the area b is provided, and m and n are two boundary nodes corresponding to connecting lines connecting the area a and the area b;a phase angle matrix corresponding to the boundary node m in the region a at each time interval for the coordination centerThe element of (a) is a coordination center corresponding to a phase angle of a boundary node m in the area a in a time period t;a phase angle matrix corresponding to the boundary node n in the region a at each time interval for the coordination centerThe element of (a) is a coordination center corresponding to a phase angle of a boundary node n in the area a in a time period t;a phase angle matrix corresponding to the boundary node m in the region b at each time interval for the coordination centerThe element of (b) is a coordination center corresponding to a phase angle of a boundary node m in the region b in a time period t;a phase angle matrix corresponding to the boundary node n in the region b at each time interval for the coordination centerThe element of (a) is a coordination center corresponding to a phase angle of a boundary node n in the region b in a time period t;is a phase angle matrix of the boundary node m in the region a in each time periodIs the phase angle of the boundary node m in the area a in the time period t;is a phase angle matrix of the boundary node n in the region a in each time periodIs the phase angle of the boundary node n in the region a at the time period t.
The above power system economic dispatch model is compact, and actually, in the above power system economic dispatch model,
the predicted scene constraints for a region include:
in the time period t, the output matrix of each conventional unit in the area a under the prediction scene is shown as the output matrixIn the form of a row or column matrix, a force matrixIs that in time period t, the region a is predictedThe output of a conventional unit i under a scene;the output matrix of each new energy source unit in the prediction scene of the region a in the time period t is obtainedIn the form of a row or column matrix, a force matrixThe element of (a) is the output of a new energy unit w in the area a in a prediction scene at the time t;for the load matrix of each load node in the prediction scene of the region a in the time period t, the load matrixIn the form of a row or column matrix, a load matrixIs the load of the load node d in the prediction scene of the area a in the time period t;in the time period t, the new energy power generation power matrix is abandoned for each new energy unit in the area a under the prediction scene, and the new energy power generation power matrix is abandonedAbandoning the new energy power generation power matrix for a row matrix or a column matrixIn the time period t, the power generation power of the abandoned new energy of the new energy unit w in the area a under the prediction scene;for a period t, the region a is in advanceLoad shedding power matrix of each load node under test scene, and load shedding power matrixAs a row matrix or a column matrix, load-shedding power matrixIs the load of the load node d in the prediction scene of the area a in the time period t; b isaA node admittance matrix established for the neglected branch resistance and the earthed branch of the area a;for the phase angle matrix of each node of the region a in the prediction scene in the time period t, the phase angle matrixBeing a row matrix or a column matrix, a phase angle matrixThe element of (a) is the phase angle of a certain node in the prediction scene of the region a in the time period t;the active output lower limit of the conventional unit i in the area a is set;the active output upper limit of the conventional unit i in the area a is set;in the time period t, the active output of the new energy unit w in the area a under the prediction scene is obtained;the maximum active output of the new energy unit w in the time interval t area a;as a conventional machine in area aActive power output ramp limiting of group i;limiting the active output landslide of the conventional unit i in the area a;in a time period t-1, the active output of a conventional unit i in an area a under a prediction scene is determined; n is a radical ofJThe number of lines related to the area a in the power system is shown, wherein the lines comprise internal lines of the area a and inter-area communication lines for connecting the area a with other areas;the maximum transmission power value for line j associated with region a;is the reactance value of line j associated with region a;is the phase angle at node j1 of line j in the time period t, the prediction scenario;is the phase angle at node j2 of line j in the time period t, the prediction scenario; sBIs a reference value, SB＝100MW；The output increment of the conventional unit i in the area a can be adjusted within 10 minutes;in the time period t, the active output of the conventional unit i in the error scene s in the area a is obtained.
The error scenario constraints for the regions include:
in time t, the output matrix of each conventional unit in the area a under the error scene s, and the output matrixIn the form of a row or column matrix, a force matrixThe element of (a) is the output of a conventional unit i in a time interval t and an area a under an error scene s;in a time period t, the output matrix of each new energy source unit in the area a under the error scene s, and the output matrixIn the form of a row or column matrix, a force matrixThe element of (a) is the output of a new energy unit w in the error scene s in the time interval t and the area a;for the load matrix of each load node in the error scene s in the region a in the time period t, the load matrixIn the form of a row or column matrix, a load matrixIs the load of the load node d in the region a under the error scene s at the time period t;in the time period t, the area a abandons a new energy power generation matrix of each new energy unit under the error scene s, abandons the new energy power generation matrixAbandoning the new energy power generation power matrix for a row matrix or a column matrixIn the time period t, the power generation power of the abandoned new energy of the new energy unit w in the area a under the error scene s;load shedding power matrix of each load node in the error scene s in the region a in the time period tAs a row matrix or a column matrix, load-shedding power matrixIs the load of the load node d in the region a under the error scene s at the time period t;for the phase angle matrix of each node in the region a under the error scene s in the time period t, the phase angle matrixBeing a row matrix or a column matrix, a phase angle matrixThe element of (a) is the phase angle of a certain node in the error scene s in the time period t in the area a;in a time period t, the active output of the new energy unit w in the region a under an error scene s;in a time period t, the maximum active output of the new energy unit w in the region a under an error scene s;in a time period t-1, the active power output of a conventional unit i in an area a under an error scene s;is the phase angle at node j1 of line j at time period t, error scenario s;is the phase angle at node j2 of line j at time period t, error scenario s;the phase angle of a boundary node m of the region a under the prediction scene in the time period t;the phase angle of a boundary node m of the region a under the error scene s in the time period t;the phase angle of a boundary node n of the region a under the prediction scene in the time period t;for time period t, region a bounds the phase angle of node n under error scenario s.
The constraint conditions of the coordination center are specifically as follows:
the coupling constraint conditions between the coordination center and the region are specifically as follows:
coordinating the phase angle of the center corresponding to the boundary node m in the area a for a period t;coordinating the phase angle of the center corresponding to the boundary node m in the region b for the time period t;coordinating the phase angle of the center corresponding to the boundary node n in the region a for a period t;the phase angle corresponding to the boundary node n in the region b at the center is coordinated for the period t.
The regional prediction scene model is as follows:
an objective function:
constraint conditions are as follows:
BaPa+Daθa≤Ea；1≤a≤N (21)
issuing a phase angle matrix of a boundary node m of the area a in each time period for the kth distributed optimization iterative coordination center;the phase angle matrix of the boundary node n of the area a issued by the kth distributed optimization iterative coordination center in each time period;all of which correspond to lagrangian multipliers over time periods for coupling constraints between the coordination center and the region,each time interval is a second penalty function multiplier of a coupling constraint condition corresponding to the coordination center and the region in the kth distributed optimization iteration;for the intermediate variables corresponding to the region a and the error scene aggregation group X, total XaA plurality of; e is a column matrix, the elements of the column matrix are allIs 1; faThe optimal cutting coefficient matrix is obtained; maAnd NaAre all optimal cutting coefficient matrixes; pa TThe method comprises the following steps of (1) taking a transpose matrix of an output matrix of each conventional unit in each time period in a prediction scene of an area a;and (3) a transposed matrix of the phase angle matrix of each node in each time interval under the prediction scene for the area a.
The regional error scene model is:
an objective function:
constraint conditions are as follows:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa,l+Ha,sθa,l；1≤a≤N,1≤s≤Sa (24)
Pa,lfor the first random optimization iteration, calculating an output matrix of each conventional unit in each time period in a prediction scene of the region a according to the region prediction scene model; thetaa,lAnd for the ith random optimization iteration, calculating a phase angle matrix of each node of the region a in each time period in the prediction scene according to the region prediction scene model.
The inter-region coordination model is as follows:
the objective function is:
the constraint conditions are as follows:
for the kth distributed optimization iteration, calculating a phase angle matrix of a boundary node m of the region a at each time period according to the region prediction scene model, and uploading the phase angle matrix to the coordination center;and for the kth distributed optimization iteration, calculating a phase angle matrix of the boundary node n of the region a in each time period according to the region prediction scene model, and uploading the phase angle matrix to the coordination center.
Referring to fig. 2, in the embodiment of the present invention, step S400 may include:
step S410, setting initial values of parameters in the power system, where the initial values include initial distributed optimization results corresponding to each region in the coordination center. Specifically, the distributed optimization iteration number k may be set to 1, and a parameter may be setThat is, lagrangian multipliers of the 1 st distributed optimization iteration corresponding to coupling constraint conditions between the coordination center and the region in each time interval are all 100, quadratic penalty function multipliers of the 1 st distributed optimization iteration corresponding to coupling constraint conditions between the coordination center and the region in each time interval are also all 100, the phase angle of the boundary node m of the 1 st distributed optimization iteration in the time interval t region a under the prediction scene in the time interval t is 0, and the phase angle of the boundary node n of the 1 st distributed optimization iteration in the time interval t region a under the prediction scene is 0.
Step S420, calculating an initial economic scheduling result of each region according to the region prediction scene model of each region, and performing distributed optimization on the boundary nodes of each region by using the inter-region coordination model, so that the initial economic scheduling result of each region all meets a first convergence criterion, where the first convergence criterion is:
ε is convergence accuracy, ε is 10-3；For the kth distributed optimization iteration, coordinating the phase angle of the center corresponding to the boundary node m in the area a in a time period t;for the kth distributed optimization iteration, the phase angle of a boundary node m in a region a in a time period t under a prediction scene;for the kth distributed optimization iteration, coordinating the phase angle of the center corresponding to the boundary node m in the region b in a time period t;for the kth distributed optimization iteration, region a is at the phase angle of boundary node m in region b under the prediction scenario for time period t.
According to the region prediction scene model of each region, the initial economic dispatching result of each region is calculated, and the boundary nodes of each region are optimized in a distributed mode by using the inter-region coordination model, so that a better Lagrange multiplier initial value can be provided for subsequent calculation, the subsequent calculation is facilitated, and the calculation time is reduced.
And step S430, calculating the prediction economic dispatching result of each region according to the region prediction scene model of each region. Namely, the prediction economic scheduling result of each region is calculated according to the Lagrange multiplier initial value and the region prediction scene model obtained in the step S420.
And step S440, calculating a random optimization result of each region according to the region error scene model of each region. That is, the stochastic optimization result of each region is calculated using the predicted economic scheduling result and the region error scene model calculated in step S430.
Step S450, judging whether the predicted economic dispatching result of each area and the random optimization result of each area both meet a second convergence criterion; if so, uploading the parameters of the boundary nodes in the prediction economic dispatching result of each region to a coordination center, and executing the step S460; when the cutting parameters are not satisfied, establishing an optimal cutting model,
and calculating the optimal cutting value of each region by using the optimal cutting model and the random optimization result of each region, correspondingly incorporating the optimal cutting value of each region into the constraint condition of the region prediction scene model, and executing the step S430.
Wherein the second convergence criterion is:
wherein,
fa,lpredicting the total scene cost of the area a for the first random optimization iteration;for the first random optimization iteration, a phase angle matrix of the boundary node m in the region a in each time period;for the first random optimization iteration, boundary section in region aThe phase angle matrix of point n at each time interval.
The optimal cutting model is as follows:
πa,s,lfor the first random optimization iteration, a dual variable matrix of the constraint conditions of the regional error scene model in each time period; xaThe number S of error scenes in the region aaAveraging the number of error scene aggregation groups formed after aggregation, each error scene aggregation group comprising Sa/XaAn error scenario.
Step S430 to step S450 are actually to perform random optimization on the predicted economic scheduling result of the region calculated by the region prediction scene model of the region by using the region error scene model of the region, and when the predicted economic scheduling result of each region and the random optimization result of each region both satisfy the second convergence criterion, it indicates that the random optimization of each region is converged, and then the random optimization is completed; when the predicted economic scheduling result of each region and the random optimization result of each region do not satisfy the second convergence criterion, the random optimization convergence of the region is indicated, and at this time, the region error scene model of the region needs to be continuously utilized to perform random optimization on the predicted economic scheduling result of the region calculated by the region predicted scene model of the region, that is, the next random optimization is performed. Therefore, the optimal prediction economic dispatching result of each area is obtained through multiple times of random optimization.
In the above embodiment, the optimal cutting model employs SaAverage formed X after aggregation of error scenesaThe error scene aggregation group structure can also directly adopt S to the optimal cutting model in practical applicationaThe error scene is directly constructed, and specifically, the optimal cutting model may be:
wherein,for the intermediate variable corresponding to the error scene S in the region a, SaAnd (4) respectively.
In this case, the area prediction scene model may be:
an objective function:
constraint conditions are as follows:
BaPa+Daθa≤Ea；1≤a≤N (21)
and step S460, calculating distributed optimization results respectively corresponding to the areas according to the inter-area coordination model. After the area error scene model of the area is used for randomly optimizing the prediction economic dispatching result of the area, which is obtained by calculating the area prediction scene model of the area, the coordination center obtains the distributed optimization result by calculation according to the parameters of the boundary nodes in the prediction economic dispatching result uploaded by each area and the inter-area coordination model.
Step S470, judging whether the predicted economic dispatching result of each area and the distributed optimization result respectively corresponding to each area both meet a first convergence criterion; if so, taking the predicted economic dispatching result of each region as the economic dispatching result of the power system, and executing the step S480; if not, a parameter update model is established, the updated parameters are calculated by using the parameter update model, and step S430 is executed. Wherein, the parameter updating model is as follows:
lagrangian multipliers corresponding to coupling constraint conditions between the coordination center and the region in each period in the (k-1) th distributed optimization iteration;each corresponding to a quadratic penalty function multiplier for the coupling constraint between the coordination center and the region at each time interval, α being an adjustment step parameter, 1 ≦ α ≦ 3, e.g., α ≦ 1.05.
Step S460 and step S470 are actually that the coordination center performs distributed optimization on the boundary nodes of each region by using the inter-region coordination model to calculate an optimal region prediction economic scheduling result; when the predicted economic dispatching result of each region and the distributed optimization results respectively corresponding to each region meet the first convergence criterion, at the moment, the coordination center performs distributed optimization convergence on the boundary nodes of each region, and the predicted economic dispatching results of each region jointly form the economic dispatching result of the power system; when the predicted economic scheduling result of each region and the distributed optimization result respectively corresponding to each region do not satisfy the first convergence criterion, the distributed optimization of the coordination center on the boundary node of each region is not converged, the next distributed optimization is required, and when the next distributed optimization is performed, the region error scene model of the region is required to be reused to perform random optimization on the predicted economic scheduling result of the region calculated by the region predicted scene model of the region because the parameters are updated according to the parameter update model.
And S480, outputting an economic dispatching result of the power system.
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 any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An economic dispatching method for an electric power system is characterized by comprising the following steps:
step S100, decoupling a power system into a coordination center and a plurality of areas;
step S200, establishing an economic dispatching model of the power system according to the coordination center and the plurality of areas, wherein the economic dispatching model of the power system aims at the sum of the total power generation cost and the power generation load shedding penalty cost of abandoned new energy in a dispatching duration of a conventional unit in the power system;
step S300, decomposing the economic dispatching model of the power system into an inter-area coordination model and a plurality of area dispatching models, wherein the area dispatching models correspond to the areas one by one, and for the areas with new energy electric fields, the area dispatching models can be decomposed into area prediction scene models and area error scene models, the area prediction scene models are used for calculating the prediction economic dispatching results of the areas, the area error scene models are used for calculating the random optimization results of the areas, the inter-area coordination model corresponds to the coordination center, and the inter-area coordination model is used for performing distributed optimization on the boundary nodes of the areas;
and S400, calculating an economic dispatching result of the power system according to the inter-area coordination model and the plurality of area dispatching models.
2. The power system economic dispatch method of claim 1, wherein prior to the step S100, the power system economic dispatch method further comprises:
step S10, determining the scheduling duration for economic scheduling of the power system, and averagely dividing the scheduling duration into nTA period of time in which nT≥2。
3. The power system economic dispatch method of claim 1, wherein after the step S100 and before the step S200, the power system economic dispatch method further comprises:
step S100', a prediction scene is set for each of the regions, and a plurality of error scenes are extracted for the region having the new energy electric field.
4. The power system economic dispatch method of claim 3, wherein after the step S300 and before the step S400, the power system economic dispatch method further comprises:
step S300', the region scheduling model is decomposed into a region prediction scene model and a region error scene model.
5. The economic dispatch method for power system as claimed in claim 4, wherein the step S400 comprises:
step S410, setting initial values of parameters in the power system, wherein the initial values comprise initial distributed optimization results corresponding to the regions in the coordination center respectively;
step S420, calculating an initial economic dispatching result of each region according to the region prediction scene model of each region, and performing distributed optimization on boundary nodes of each region by using the inter-region coordination model to enable the initial economic dispatching result of each region to meet a first convergence criterion;
step S430, calculating a prediction economic dispatching result of each region according to the region prediction scene model of each region;
step S440, calculating a random optimization result of each region according to the region error scene model of each region;
step S450, judging whether the predicted economic dispatching result of each area and the random optimization result of each area both meet a second convergence criterion;
if so, uploading the parameters of the boundary nodes in the predicted economic dispatching result of each region to the coordination center, and executing the step S460;
if not, establishing an optimal cutting model, calculating an optimal cutting value of each region by using the optimal cutting model and a random optimization result of each region, correspondingly incorporating the optimal cutting value of each region into a constraint condition of the region prediction scene model, and executing the step S430;
step S460, calculating iterative distributed optimization results respectively corresponding to the regions according to the inter-region coordination model;
step S470, judging whether the predicted economic dispatching result of each region and the iterative distributed optimization result respectively corresponding to each region both meet the first convergence criterion;
if yes, taking the predicted economic dispatching result of each area as the economic dispatching result of the power system, and executing step S480;
if not, establishing a parameter updating model, calculating an updated parameter by using the parameter updating model, and executing the step S430;
and S480, outputting an economic dispatching result of the power system.
6. The power system economic dispatch method of claim 5, wherein the power system economic dispatch model is:
an objective function:
constraint conditions are as follows:
the constraint condition of the region prediction scene model of the region is as follows:
BaPa+Daθa≤Ea；1≤a≤N；
constraint conditions of a regional error scene model of the region:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa+Ha,sθa；1≤a≤N,1≤s≤Sa；
constraints of the coordination center:
coupling constraints between the coordination center and the region:
wherein the constraint conditions of the region prediction scene model of the region comprise:
the constraint conditions of the regional error scene model of the region comprise:
the constraint conditions of the coordination center are specifically as follows:
the coupling constraint conditions between the coordination center and the region are specifically as follows:
fapredicting a total cost of the scene for area a; f. ofa,sAbandoning new energy power generation cost for the error scene of the area a; n is the number of the regions;the number of the conventional units in the area a is shown;the number of new energy machine sets in the area a;the number of the load nodes in the region a in the time period t is shown; saThe number of error scenes in the region a;in a region a of a time interval t, the active power output of a conventional unit i is predicted under a scene;βi aandrespectively are the power generation cost coefficients of the conventional unit i in the area a;in the time period t, the power generation power of the abandoned new energy of the new energy unit w in the area a under the prediction scene; q. q.sWA penalty cost coefficient for generating new energy for the area a;load shedding power of a load node d in a prediction scene of the area a in a time period t; q. q.sDPenalizing a cost coefficient for load shedding of the area a; p is a radical ofsProbability of error scene s, p, for region as＝1/Sa；In a time period t, the power generation power of the abandoned new energy of the new energy unit w in the area a under the error scene s;load shedding power of a load node d in an error scene s in a region a at a time period t;
Pathe output matrix of each conventional unit in each time period in the prediction scene of the area a is obtained; thetaaA phase angle matrix of each node in each time period in a prediction scene for the region a; b isa、DaAnd EaAll the parameter matrixes are parameter matrixes of the area a in a prediction scene; pa,sThe output matrix of each conventional unit in each time interval under the error scene s in the area a is obtained; thetaa,sA phase angle matrix of each node in each time interval under an error scene s for the area a; b isa,s、Da,s、Ea,s、Ga,sAnd Ha,sAll the parameter matrixes are parameter matrixes of the area a under the error scene s; TLab,aA boundary node set connected with the area b in the area a is obtained; TLab,bA boundary node set connected with the area a in the area b is provided, and m and n are two boundary nodes corresponding to connecting lines connecting the area a and the area b;a phase angle matrix corresponding to the boundary node m in the area a in each time period for the coordination center;a phase angle matrix corresponding to the boundary node n in the area a in each period for the coordination center;a phase angle matrix corresponding to the boundary node m in the area b in each time period for the coordination center;a phase angle matrix corresponding to the boundary node n in the region b at each time interval for the coordination center;a phase angle matrix of the boundary node m in the area a in each time period;a phase angle matrix of the boundary node n in the region a in each time period;
the output matrix of each conventional unit in the area a under the prediction scene at the time t is shown;the output matrix of each new energy source unit in the area a under the prediction scene at the time t;a load matrix of each load node in the region a under a prediction scene in a time period t;in a time period t, a new energy power generation power matrix of each new energy unit in the area a under a prediction scene is abandoned;a load shedding power matrix of each load node in the area a under a prediction scene in a time period t; b isaA node admittance matrix established for the neglected branch resistance and the earthed branch of the area a;a phase angle matrix of each node of the region a in a prediction scene at a time interval t;the active output lower limit of the conventional unit i in the area a is set;the active output upper limit of the conventional unit i in the area a is set;in the time period t, the active output of the new energy unit w in the area a under the prediction scene is obtained;the maximum active output of the new energy unit w in the time interval t area a;limiting active output climbing of a conventional unit i in the area a;limiting the active output landslide of the conventional unit i in the area a;in a time period t-1, the active output of a conventional unit i in an area a under a prediction scene is determined; n is a radical ofJThe number of lines related to the area a in the power system is the number of the lines, wherein the lines comprise internal lines of the area a and inter-area communication lines for connecting the area a with other areas;the maximum transmission power value for line j associated with region a;is the reactance value of line j associated with region a;is the phase angle at node j1 of line j in the time period t, the prediction scenario;is the phase angle at node j2 of line j in the time period t, the prediction scenario; sBIs a reference value, SB＝100MW；The output increment of the conventional unit i in the area a can be adjusted within 10 minutes;the active output of the conventional unit i in the region a under the error scene s in the time period t is shown;
the output matrix of each conventional unit in the area a under the error scene s in the time interval t is shown;in a time period t, the output matrix of each new energy source unit in the area a under the error scene s;the load matrix of each load node in the region a under the error scene s in the time period t;in a time period t, a power generation power matrix of abandoned new energy of each new energy unit in the area a under an error scene s;a load shedding power matrix of each load node in the region a under an error scene s in a time period t;a phase angle matrix of each node in the region a under an error scene s in a time period t;in a time period t, the active output of the new energy unit w in the region a under an error scene s;in a time period t, the maximum active output of the new energy unit w in the region a under an error scene s;in a time period t-1, the active power output of a conventional unit i in an area a under an error scene s;is the phase angle at node j1 of line j at time period t, error scenario s;is the phase angle at node j2 of line j at time period t, error scenario s;the phase angle of a boundary node m of the region a under the prediction scene in the time period t;the phase angle of a boundary node m of the region a under the error scene s in the time period t;the phase angle of a boundary node n of the region a under the prediction scene in the time period t;the phase angle of a boundary node n of the region a under the error scene s in the time period t;
coordinating the phase angle of the center corresponding to the boundary node m in the area a for a period t;coordinating the phase angle of the center corresponding to the boundary node m in the region b for the time period t;coordinating the phase angle of the center corresponding to the boundary node n in the region a for a period t;the phase angle corresponding to the boundary node n in the region b at the center is coordinated for the period t.
7. The power system economic dispatch method of claim 6,
the region prediction scene model is as follows:
an objective function:
constraint conditions are as follows:
BaPa+Daθa≤Ea；1≤a≤N；
issuing a phase angle matrix of a boundary node m of the area a in each time period for the kth distributed optimization iterative coordination center;the phase angle matrix of the boundary node n of the area a issued by the kth distributed optimization iterative coordination center in each time period;each kth distributed optimization iteration corresponding to a lagrangian multiplier over time periods for a coupling constraint between the coordination center and the region,each of the k distributed optimization iterations corresponds to a second penalty function multiplier of a coupling constraint condition between the coordination center and the region at each time interval;for the intermediate variables corresponding to the region a and the error scene aggregation group X, total XaA plurality of; e is a column matrix, and the elements of the column matrix are all 1; faTo be optimalCutting the coefficient matrix; maAnd NaAre all optimal cutting coefficient matrixes; pa TThe method comprises the following steps of (1) taking a transpose matrix of an output matrix of each conventional unit in each time period in a prediction scene of an area a;a transposed matrix of a phase angle matrix of each node in each time period in a prediction scene of the area a;
the regional error scene model is as follows:
an objective function:
constraint conditions are as follows:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa,l+Ha,sθa,l；1≤a≤N,1≤s≤Sa；
Pa,lfor the first random optimization iteration, calculating an output matrix of each conventional unit in each time period in a prediction scene of the region a according to the region prediction scene model; thetaa,lFor the first random optimization iteration, calculating a phase angle matrix of each node of the region a in each time period in a prediction scene according to the region prediction scene model;
the inter-region coordination model is as follows:
the objective function is:
the constraint conditions are as follows:
for the kth distributed optimization iteration, calculating a phase angle matrix of a boundary node m of the region a, which is obtained according to the region prediction scene model and uploaded to the coordination center, in each time period;and for the kth distributed optimization iteration, calculating a phase angle matrix of the boundary node n of the region a to the coordination center in each time period according to the region prediction scene model, and uploading the phase angle matrix.
8. The power system economic dispatch method of claim 7,
the first convergence criterion is:
ε is convergence accuracy, ε is 10-3；For the kth distributed optimization iteration, coordinating the phase angle of the center corresponding to the boundary node m in the area a in a time period t;for the kth distributed optimization iteration, the phase angle of a boundary node m in a region a in a time period t under a prediction scene;for the kth distributed optimization iteration, coordinating the phase angle of the center corresponding to the boundary node m in the region b in a time period t;for the kth distributed optimization iteration, the phase angle of a boundary node m in a region b under a prediction scene in a time period t is determined;
the second convergence criterion is:
wherein,
fa,lpredicting the total scene cost of the area a for the first random optimization iteration;for the first random optimization iteration, a phase angle matrix of the boundary node m in the region a in each time period;for the ith random optimization iteration, the phase angle matrix of the boundary node n in the region a at each time interval.
9. The power system economic dispatch method of claim 7,
the optimal cutting model is as follows:
πa,s,lfor the first random optimization iteration, a dual variable matrix of the constraint conditions of the regional error scene model in each time period; xaThe number S of error scenes in the region aaAveraging the number of error scene aggregation groups formed after aggregation, wherein each error scene aggregation group comprises Sa/XaError scene。
10. The power system economic dispatch method of claim 7,
the parameter updating model is as follows:
lagrangian multipliers corresponding to coupling constraint conditions between the coordination center and the region in each period in the (k-1) th distributed optimization iteration;the k-1 distributed optimization iteration corresponds to a quadratic penalty function multiplier of the coupling constraint condition between the coordination center and the region in each time period, and α is an adjusting step parameter, wherein the adjusting step parameter is 1- α -3.
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