CN108599270A - A kind of electrical power system wide-area coordination consumption method considering wind-powered electricity generation randomness - Google Patents
A kind of electrical power system wide-area coordination consumption method considering wind-powered electricity generation randomness Download PDFInfo
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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
A kind of electrical power system wide-area coordination consumption method considering wind-powered electricity generation randomness, the described method comprises the following steps：A. it is several relatively independent regions by the decoupling of multi area interconnection power grid；B. the multizone dynamic economic dispatch model under the prediction scene for not considering wind-powered electricity generation randomness is established；C. the stochastic and dynamic economic load dispatching model for introducing wind-powered electricity generation error scene is established；D. the whole network dispersion optimization problem and region stochastic optimization problems are alternately solved, the angle values of each boundary node are obtained.Present invention employs multi-agent technologies, can not only ensure data-privacy and dispatch the extensive random wind-powered electricity generation of independent consumption, but also can realize the mutual supplement with each other's advantages of different zones wind power resources so that power grid copes with the randomness of wind-powered electricity generation there are more nargin.This method solves the whole network dispersion dynamic economic dispatch model and each region stochastic and dynamic Economic Dispatch Problem using target cascade analytic approach, and calculating speed is very fast, is suitable for solving large scale electric network dynamic economic dispatch problem.
Description
Technical Field
The invention relates to a method for absorbing large-scale random wind power under the condition of ensuring data privacy and independent scheduling, and belongs to the technical field of power transmission and distribution.
Background
With the rapid and continuous increase of wind power grid-connected capacity in China, a power supply pattern with intensive power generation, centralized grid connection and reverse energy and load distribution is gradually formed in the wind power development, and the randomness and the intermittency of wind power generation make the traditional power grid dispatching control mode difficult to effectively solve the problem of large-scale wind power output fluctuation and the problem of large-scale wind power coordination and absorption capacity become the bottleneck of the wind power sustainable development. Aiming at the problem of coordination and consumption of large-scale wind power, the method mainly comprises the following steps.
The random dynamic economic dispatching model based on the scene method contains a large number of scenes in which the wind power output is likely to appear, and the scenes are used for representing the fluctuation of the wind power output. When the number of scenes is small, the method cannot effectively reflect the average value of the system operation cost; when the number of scenes is large, the calculation amount of the method is too large. Therefore, the multi-scenario power system scheduling model needs to select typical scenarios according to the historical operating conditions of the actual system, and calculate the probability corresponding to each scenario.
The basic idea of the economic dispatching model based on opportunity constraint is to ensure that the system constraint condition can be established under a certain confidence level under the condition of wind power random fluctuation. Although the new energy power generation output has certain randomness, the prediction error distribution has certain regularity. Random variables in the constraint conditions are considered, strictly-established inequality constraints in the constraint conditions are converted into established opportunity constraints on a certain confidence level, an opportunity-constrained power system economic dispatching model is constructed, and uncertainty caused by the random variables can be better described.
Robust economic dispatching aims at finding out the worst scene with the maximum influence of wind power output on the safety and the economy of the system, and a reasonable uncertain set of the wind power output is established to ensure that the power system can safely operate in any scene with the power generation output of new energy such as wind power and the like in a prediction error range; the system economic operation is realized by controlling the system operation cost under the worst scene, and the system operation cost under any other scene is ensured not to be higher than the system operation cost under the worst scene.
Based on the characteristics of hierarchical partition interconnection operation of a modern power system, the multi-stage power dispatching center implements decentralized coordination dispatching on the interconnection system, and the method is an effective dispatching method. A multi-stage coordination scheduling mode (an interconnected power grid active scheduling and control scheme considering wind power space-time complementation characteristics, power system protection and control, 2014, 42 (21): P140-144) is proposed, and the wind power space-time complementation characteristic is fully utilized to uniformly coordinate standby and peak regulation arrangement of a plurality of regions. An interconnected power grid active scheduling scheme (an interconnected power grid active scheduling and control scheme suitable for large-scale wind power access, power system automation, 2010, 34 (17): 37-41) of hierarchical coordinated control is also provided, local balance of hierarchical control and a whole network unified balance mode are realized, and the problems of resource waste and coordination difficulty of distributed control are solved. A target cascade analysis-based distributed coordination risk scheduling method (a distributed coordination risk scheduling method of a multi-region interconnected power system. the journal of the electro-mechanical engineering, 2015, 35 (14): 3724-3733) is also provided, the upper-level scheduling realizes coordination processing of the power of the interconnection lines among interconnected regions, and the lower-level scheduling realizes a risk constraint scheduling scheme of each sub-grid. The lower-level scheduling of each subsystem operates independently, so that the autonomous regulation and control of each subsystem are ensured; and the superior scheduling optimizes the power of the junctor so as to realize the economic operation of the whole system.
The conventional economic dispatching problem of the power system is carried out in a centralized optimization framework, and the dispatching center dispatches the output of the whole grid unit, so that the dispatching center needs to process a large amount of data, communication blockage is easily caused, and the dispatching independence and the data privacy of each regional power grid in a multi-region power system are not facilitated.
The problem can be solved by adopting a hierarchical optimization scheduling model to solve the multi-region dynamic economic scheduling problem, tasks of all levels of the hierarchical optimization scheduling model are clear, a subordinate scheduling center only exchanges data with a superior scheduling center, data do not need to be exchanged among the regional scheduling centers, and data confidentiality in a region is achieved. However, the upper and lower optimization targets may be different, which is not favorable for solving the global optimal solution, and an upper scheduling center needs to be arranged, and the scheduling structure is relatively complex, so that further improvement is necessary.
Disclosure of Invention
The invention aims to provide a wide-area coordinated digestion method of an electric power system considering wind power randomness aiming at the defects of the prior art so as to realize the wide-area coordinated digestion of wind power output.
The problems of the invention are solved by the following technical scheme:
a power system wide area coordinated digestion method considering wind power randomness, the method comprising the steps of:
a. decoupling a multi-region interconnected power grid into a plurality of relatively independent regions, and interconnecting different regions through connecting line boundary node variables;
b. establishing a multi-region dynamic economic dispatching model under a prediction scene without considering wind power randomness:
establishing a centralized multi-region dynamic economic dispatching model:
wherein, Ba、DaAnd EaA coefficient matrix of an internal constraint equation of the a region; paFiring power of zone aA unit output vector; thetaaIs a voltage phase angle vector of an internal node of the area a; in the tie boundary node of zone a, ZaaAs a set of nodes belonging to zone a, ZabIs a node set not belonging to the a area; m and n are boundary nodes; the phase angle values of m and n nodes in the area a at the time t respectively,respectively is the phase angle value of m and n nodes in the b area at the moment t; f. ofaThe sum of the power generation cost and the wind abandon penalty cost of the area a is as follows:
wherein,respectively are the power generation cost coefficients of the conventional unit i in the area a; q is a wind curtailment penalty coefficient;the active power output of the conventional unit i in the area a in the time period t,the predicted active power output for the wind farm w in region a during time period t,scheduling active power output for the wind power plant in a time period t; n is the total number of regions, NTIn order to schedule the total number of time segments,the total number of conventional units in the area a,the total wind power field number of the area a;
② solving the multi-region dynamic economic dispatch model by using an objective cascade analysis method (ATC) based on a multi-agent technology:
constructing each regional Agent (Agent) in each decomposed sub-region, then constructing a virtual total coordination Agent, and dividing the centralized dynamic economic dispatching model into a regional optimization sub-problem and a total coordination main problem:
the sub-problem model for optimizing the power grid in each area is as follows:
wherein,to couple the lagrangian multipliers in the constraint,is a quadratic penalty function multiplier;issuing the boundary node phase angle value of the sub-problem to the main problem of the k-th iteration total coordination;respectively representing boundary node phase angle values in each regional power grid;
the overall coordination master problem model is as follows:
wherein,respectively obtaining boundary node phase angle values of the total coordination agents;uploading boundary node phase angle values of the total coordination Agent to each regional power grid subproblem for the kth iteration;
③ solving the main and sub problems iteratively;
c. a random dynamic economic dispatching model introducing a wind power error scene is established, and the specific method comprises the following steps:
sub-problem of error scene of power grid in each region
An objective function:
wherein S is the number of error scenes; p is a radical ofsIs the probability of occurrence of the s-th scene; Δ Ww,t,sIs the wind curtailment power delta D of the w wind power plant at the moment t under the s scenet,sThe virtual load shedding power at the moment t under the s-th scene; q is a wind curtailment penalty coefficient; c. CdPenalizing costs for virtual load shedding; n is a radical ofTIs a scheduled total time period; n is a radical ofWThe total number of the wind power plants;
constraint conditions are as follows:
d, a direct current power flow equation of the nodes inside the region:
wherein,is the output vector of the thermal power generating unit in the area a in the time period t under the s-th scene,the output vector is scheduled for the wind farm,is a node load vector; sBIs a power flow reference value; b isaNeglecting the branch resistance and the node admittance matrix of the ground branch for the area a;a node phase angle vector of a region a in a time period t under the s-th scene;
and (3) constraining the upper and lower output limits of the thermal power generating unit:
wherein,andrespectively setting the lower limit and the upper limit of active output of the thermal power generating unit i;the active power output of a conventional unit i in an area a in the s-th scene in a time period t;
and (3) restraining the upper and lower output limits of the wind turbine generator:
wherein,the predicted active power output of the wind power plant w in the area a in the time period t;
unit climbing and landslide restraint:
wherein,andrespectively limiting active output climbing and landslide of the unit i; n is a radical ofTIs the total scheduling period;
constraint of line transmission power:
wherein,the transmission power value of the line kl of the area a in the s-th scene in a time period t;phase angle values of the nodes k and l at the moment t in the s-th scene respectively;the maximum transmission power value of the line;is the reactance value of the line kl;
output regulation rate constraint under the same time period prediction scene and the error scene:
-Δi≤Pi,t-Pi,t,s≤Δi
wherein, DeltaiThe output increment of the thermal power generating unit i which can be rapidly adjusted within 10 minutes is achieved; pi,tThe active power output of a conventional unit i in the region in a time period t under a prediction scene is obtained; pi,t,sThe active power output of a conventional unit i in the region in the s-th scene in a time period t;
boundary node phase angle value constraint:
wherein,respectively obtaining node phase angle values of the node m in the region a in the s-th scene and the predicted scene in the time period t;the node phase angle values of the node n in the region a in the s-th scene and the prediction scene in the time period t are respectively;
② predicting main problems of scene
An objective function:
wherein,d is the number of intermediate variables in the region a; faThe optimal cutting coefficient vector is obtained; ma、NaThe optimal cutting coefficient matrix is obtained; e is a unit vector;transposing the output vector of the thermal power generating unit in the area a;transpose of voltage phase angle vector of internal node of a area a;
d. and alternately solving the whole network dispersion optimization problem and the region random optimization problem to obtain the phase angle value of each boundary node.
In the above power system wide area coordination digestion method considering wind power randomness, the constraint conditions of the multi-region dynamic economic dispatching model are as follows:
(ii) regional internal constraint conditions
The direct current flow equation of the node in the area a is as follows:
wherein,for a conventional unit output vector for time period t,scheduling a contribution vector for the wind farm for time period t,a node load vector for time period t; sBIs a power flow reference value; b isaA node admittance matrix for neglecting branch resistance and a ground branch;is the nodal phase angle vector of time period t;
secondly, restraining the upper and lower output limits of the thermal power generating unit:
wherein,andrespectively setting the lower limit and the upper limit of active output of the thermal power generating unit i;
thirdly, restraining the upper and lower limits of the output of the wind turbine generator:
and fourthly, restraining the unit in climbing and landslide:
wherein,andrespectively limiting active output climbing and landslide of the unit i;
the constraint of line transmission power is as follows:
wherein,for the transmission power value of the line kl at the time t,the maximum transmission power value of the line;andphase angles of the nodes k and l at the moment t are respectively;is the reactance value of the line kl;
coupled constraint between regions
The boundary node power balance constraint between two adjacent areas a and b with the inter-area links is as follows:
wherein m and n are boundary nodes, and Z is an inter-area boundary node set of the whole multi-area power grid;
the boundary node phase angle values of the regions a and b at time t, respectively.
According to the wide-area coordination consumption method considering the wind power randomness of the power system, when the main problem and the sub problem are solved in an iterative mode, the penalty function multiplier needs to be updatedThe phase angle values of the boundary nodes solved by the main problem and the sub problem tend to be equal, and the penalty function multiplier updating formula is as follows:
wherein, alpha is a parameter for adjusting the step length, and the value range of the step length is [1,3] in general;
the formula for judging algorithm convergence is as follows:
where ε is the convergence accuracy.
The invention adopts a multi-agent technology, can consume large-scale random wind power under the condition of ensuring data privacy and independent scheduling, can realize advantage complementation of wind power resources in different areas, and realizes wide-area coordinated consumption of wind power output, so that a power grid has more margin to deal with the randomness of the wind power. The method solves the problems of the whole network decentralized dynamic economic dispatching model and the random dynamic economic dispatching of each region by adopting a target cascade analysis method, has high calculation speed, and is suitable for solving the problems of the large-scale power grid dynamic economic dispatching.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of a power system wide area coordinated digestion method;
FIG. 2 is a diagram of an IEEE-39 standard test system.
The symbols in the text are respectively expressed as: taking the area a as an example,andrespectively setting the lower limit and the upper limit of active output of the thermal power generating unit i;is the maximum transmission power value of the line kl;the active power output of the conventional unit i at the moment t;for the transmission power value of the line kl at the time t,the transmission power value of the line kl of the area a in the s-th scene in a time period t;the active output of the conventional unit i at the moment t in the s-th scene is obtained; p is a radical ofsIs the probability of occurrence of the s-th scene;for a conventional unit output vector for time period t,is the output vector of the thermal power generating unit in the area a in the time period t under the s-th scene,the predicted output of the wind power plant w in the time period t is obtained;scheduling a contribution vector for the wind farm for time period t,the output vector is scheduled for the wind farm,a node load vector for time period t;is a node load vector; paThe output vector of the thermal power generating unit in the area a is obtained; faThe optimal cutting coefficient vector is obtained; f. ofaThe sum of the power generation cost and the wind abandon punishment cost of the area a; sBis a power flow reference value, Z is an inter-area boundary node set of the whole multi-area power grid, α is a parameter for adjusting step length(ii) a ε is the convergence accuracy; b isa、DaAnd EaA coefficient matrix of an internal constraint equation of the a region; zaaAs a set of nodes belonging to zone a, ZabIs a node set not belonging to the a area; n is the total area number; m and n are boundary nodes;to couple the lagrangian multipliers in the constraint,is a quadratic penalty function multiplier; s is the number of error scenes; Δ Ww,t,sIs the wind curtailment power delta D of the w wind power plant at the moment t under the s scenet,sThe virtual load shedding power at the moment t under the s-th scene; c. CdPenalizing costs for virtual load shedding; n is a radical ofTIs the total scheduling period; n is a radical ofWThe total number of the wind power plants; b isaNeglecting the branch resistance and the node admittance matrix of the ground branch for the area a;andrespectively limiting active output climbing and landslide of the unit i;is the reactance value of the line kl; deltaiThe output increment of the thermal power generating unit i which can be rapidly adjusted within 10 minutes is achieved;is the intermediate variable of the a area; d is the number of intermediate variables; ma、NaThe optimal cutting coefficient matrix is obtained; e is a unit vector;transposing the output vector of the thermal power generating unit in the area a;transpose of voltage phase angle vector of internal node of a area a;phase angle values of the nodes k and l at the moment t in the s-th scene respectively; thetaaIs a voltage phase angle vector of an internal node of the area a;a node phase angle vector of a region a in a time period t under the s-th scene;andphase angles of the nodes k and l at the moment t are respectively;respectively obtaining node phase angle values of the node m in the region a in the s-th scene and the predicted scene in the time period t;the node phase angle values of the node n in the region a in the s-th scene and the prediction scene in the time period t are respectively;uploading boundary node phase angle values of the total coordination Agent to each regional power grid subproblem for the kth iteration;issuing the boundary node phase angle value of the sub-problem to the main problem of the k-th iteration total coordination;respectively representing boundary node phase angle values in each regional power grid;respectively edges of the Total coordination AgentsA boundary node phase angle value;is the nodal phase angle vector for time period t.
Detailed Description
The invention provides a wide area coordination consumption method of a power system considering wind power randomness, which comprises the following specific steps:
1. decomposing and collaboratively optimizing a multi-region power grid, and introducing tie line variables to establish coupling constraints among regions, wherein the specific method comprises the following steps:
decoupling a multi-region interconnected power grid into a plurality of relatively independent regions for coordinated scheduling, and interconnecting the partitioned power subsystems through interconnection line boundary node variables. Taking a tie line between the two areas a and b as an example, copying the tie line boundary nodes m and n between the two areas a and b once, so that the variables of the same node in different areas should be equal to satisfy the following coupling constraint:
wherein,phase angles of nodes m and n are respectively;line reactance from node m, n to the break point respectively;for the active power flow of the node m in the area a to the open point,active tide for node n in b region to flow to break pointAnd (4) streaming. Similarly, the boundary node variables on the connecting lines between other areas in the power grid can be copied as above.
2. The method comprises the following steps of establishing a multi-region dynamic economic dispatching model under a prediction scene without considering wind power randomness, wherein the specific method comprises the following steps:
according to the above regional decomposition principle, a centralized multi-region dynamic economic dispatching model can be established, and for simplifying the model, it is assumed that: 1) adopting a direct current optimal power flow model without considering loss, and setting the node voltage amplitude as 1; 2) the power generation cost of the thermal power generating unit is expressed by using a quadratic convex function.
(1) Objective function
The method aims to minimize the sum of the total power generation cost and the wind abandon penalty cost of all conventional generating units (thermal power generating units) in each region of the whole network in a dispatching cycle, namely:
wherein,respectively are the power generation cost coefficients of the conventional unit i in the area a; q is a wind curtailment penalty coefficient;the useful output of the conventional unit i in the area a in the time period t,the predicted active power output for the wind farm w in region a during time period t,scheduling active power output for the wind power plant in a time period t; n is the total number of regions, NTIn order to schedule the total number of time segments,the total number of conventional units in the area a,the total wind power field number of the area a is shown.
(2) Constraint conditions inside region
Taking the area a as an example:
the node direct current power flow equation is as follows:
wherein,for a conventional unit output vector for time period t,scheduling a contribution vector for the wind farm for time period t,a node load vector for time period t; sBSetting the standard value of the power flow as 100 MVA; b isaA node admittance matrix for neglecting branch resistance and a ground branch;is the nodal phase angle vector for time period t.
And (3) constraining the upper and lower output limits of the thermal power generating unit:
wherein,andrespectively representing the lower limit and the upper limit of the active power output of the thermal power generating unit i.
And (3) restraining the upper and lower output limits of the wind turbine generator:
unit climbing and landslide restraint:
wherein,andrespectively the active output climbing and landslide limits of the unit i.
The line transmission power constraint is:
wherein,for the transmission power value of the line kl at the time t,the maximum transmission power value of the line;andphase angles of the nodes k and l at the moment t are respectively;the reactance value of the line kl.
(3) Coupling constraints between regions
Taking two adjacent areas a and b as an example, there are interconnections between the areas, and the boundary nodes are i and j, then the power balance constraint of the boundary node between the areas a and b is:
and Z is an inter-area boundary node set of the whole multi-area power grid.
(4) Centralized multi-region dynamic economic dispatching model
Then, in summary, the centralized multi-region dynamic economic scheduling model can be written in the form of:
wherein f isaThe sum of the power generation cost and the wind abandon punishment cost of the area a; b isa、DaAnd EaA coefficient matrix of an internal constraint equation of the a region; paThe output vector of the thermal power generating unit in the area a is obtained; thetaaIs a voltage phase angle vector of an internal node of the a regionaIs the phase angle vector of the boundary node of the area a; in the tie boundary node of zone a, ZaaAs a set of nodes belonging to zone a, ZabIs a set of nodes not belonging to the a-zone.
(5) Model solving method based on multi-agent technology and by utilizing target cascade analysis method
The objective cascade Analysis (ATC) is mainly used for solving the problem of coordination and optimization of a multi-level structure, allows an upper-level structure to make an autonomous decision on the upper-level optimization problem, performs coordination and optimization on the optimization problem of a lower-level structure to obtain a global optimal solution, and has the characteristic of high convergence speed. The multi-Agent technology divides a large-scale problem into small subtasks to be distributed to each Agent by adopting a core idea of 'divide and conquer', the agents are independent of each other, and meanwhile, information is exchanged with other agents through upper-layer agents, so that the multi-Agent technology is very suitable for processing the wide-area coordination problem of a power system.
Solving the multi-region dynamic economic dispatching model by utilizing an ATC (automatic traffic control) and a multi-Agent technology, and constructing each region Agent in each decomposed sub-region, wherein the region agents comprise local information such as local region tie line power, power generation cost and the like; then a virtual total coordination Agent is constructed, and boundary node phase angle vectors of all areas are copiedBy usingRepresents the boundary node phase angle vector in the total coordination Agent and is to be satisfiedThe coupling constraint is effectively equivalent to:
in order to solve the optimization problem of a multi-region power system by adopting ATC (automatic transfer control), realize the alternative iterative solution of a total coordination Agent and a lower-layer region Agent, the centralized dynamic economic dispatching model is divided into a region optimization sub-problem and a total coordination main problem:
the objective function of each regional power grid optimization subproblem is based on the total cost of the region, the coupling constraint (10) is relaxed into the function by using an augmented Lagrange function, Lagrange quadratic terms are introduced to reduce oscillation and reduce the iteration times of convergence, and meanwhile, a penalty function is constructed to enable the boundary vector of the subproblem to be continuously close to the boundary vector of the main problem in the iterative solution process. The subproblem model is as follows:
wherein,to couple the lagrangian multipliers in the constraint,is a quadratic penalty function multiplier;issuing the boundary node phase angle value of the sub-problem to the main problem of the k-th iteration total coordination;respectively representing boundary node phase angle values in each regional power grid;
the main general coordination problem has the function of coordinating the phase angle value of the boundary node solved by the general coordination Agent and the phase angle value solved in each regional subproblem, and the model is as follows:
wherein,respectively obtaining boundary node phase angle values of the total coordination agents;uploading boundary node phase angle values of the total coordination Agent to each regional power grid subproblem for the kth iteration;
when the main and sub problems are solved in an iterative way, the multiplier of the penalty function needs to be updatedThe phase angle values of the boundary nodes solved by the main problem and the sub problem tend to be equal, and the penalty function multiplier updating formula is as follows:
wherein, α is a parameter for adjusting the step length, and the value range of the step length is [1,3] in general.
The formula for judging algorithm convergence is as follows:
3. a random dynamic economic dispatching model introducing a wind power error scene is established, and the specific method comprises the following steps:
the centralized dynamic economic dispatching model in the step 2 is established without considering the randomness of wind power, and is the optimization of problems in a prediction scene. In order to ensure that enough rotation reserve is reserved in the system to deal with wind power randomness, a scene method is adopted to introduce wind power error scenes of each region in step 3, a random dynamic economic dispatching model of each region is established, and the model is divided into a prediction scene main problem and an error scene sub-problem by using ATC to carry out alternate iterative solution.
(1) Sub-problem of error scenario of each regional power grid
An objective function: minimizing the air curtailment rate and the virtual load shedding punishment cost under each error scene, wherein the formula is as follows:
wherein S is the number of error scenes; p is a radical ofsIs the probability of occurrence of the s-th scene; Δ Ww,t,sFor the w wind power plant under the s scene at the momentt waste wind power, Δ Dt,sThe virtual load shedding power at the moment t under the s-th scene; q is wind abandon punishment cost; c. CdPenalizing costs for virtual load shedding; n is a radical ofTIs a scheduled total time period; n is a radical ofWThe total number of the wind power plants;
constraint conditions are as follows:
d, a direct current power flow equation of the nodes inside the region:
wherein,is the output vector of the thermal power generating unit in the area a in the time period t under the s-th scene,the output vector is scheduled for the wind farm,is a node load vector; sBSetting the power flow reference value as 100 MVA; b isaNeglecting the branch resistance and the node admittance matrix of the ground branch for the area a;is the node phase angle vector of the a area in the s scene in the time period t.
And (3) constraining the upper and lower output limits of the thermal power generating unit:
and (3) restraining the upper and lower output limits of the wind turbine generator:
unit climbing and landslide restraint:
constraint of line transmission power:
wherein,the transmission power value of the line kl of the area a in the s-th scene in a time period t;the phase angle values of the nodes k and l at the time t under the s-th scene are respectively.
Output regulation rate constraint under the same time period prediction scene and the error scene:
-Δi≤Pi,t-Pi,t,s≤Δi(21)
wherein, DeltaiThe output increment of the thermal power generating unit i can be rapidly adjusted within 10 minutes.
Boundary node phase angle value constraint:
(2) predicting scene major problems
An objective function: on the regional power grid subproblem, the intermediate variable and the optimal cutting in the error scene subproblem are added, and the formula is as follows:
wherein,is the intermediate variable of the a area; faThe optimal cutting coefficient vector is obtained; ma、NaAnd (4) obtaining an optimal cutting coefficient matrix. The first constraint condition is the constraint condition inside each sub-region, and the second constraint condition is the optimal cutting formed by the error scene inside each region.
4. Alternately solving a whole-network dispersion optimization problem and a region random optimization problem, wherein the specific method comprises the following steps:
(1) solving a full-network decentralized optimization problem
When ATC is used for carrying out whole-network decentralized optimization on the centralized multi-region dynamic economic dispatching model, a total coordination Agent is introduced to equivalently construct a virtual region on the boundary of each adjacent region, and the virtual region comprises all region connecting lines, so that all different regions are connected with the virtual region instead of being directly connected with the adjacent regions. And establishing a region Agent for each decomposed region, so that local information such as power flow, power demand, production cost and the like on the contact line can be obtained. And the total coordination Agent monitors and manages the lower-level regional agents in real time, and each regional Agent exchanges information through the total coordination Agent.
When each area Agent solves the area optimization subproblem of the area, the boundary node phase angle value is obtained through solvingUploading the phase angle value to a total coordination Agent; the total coordination Agent solves the total coordination main problem according to the phase angle value uploaded by the lower sub-problem, and calculates to obtain the phase angle value of the upper boundary node And issuing a calculation result to each lower layer region Agent, thereby realizing the alternate iterative solution of the upper layer and the lower layer. Alternate iterative solution is carried out by continuously updating the penalty function multiplier, so that the boundary variable of each area Agent is continuously close to the boundary variable of the total coordination Agent, and the total coordination Agent has constraint conditions a. The phase angle value obtained by solving the area b is continuously approximated.
(2) Solving a regional stochastic optimization problem
When a wind power error scene is introduced to correct the phase angle value of the prediction scene, the main problem of the prediction scene is solved to obtain a boundary variable value, the boundary variable value is issued to the sub problem of the error scene, the boundary variable value is obtained by solving the model and then uploaded to the main problem, and the penalty function multiplier is continuously updated to correct the boundary variable value of the main problem of the prediction scene, so that random optimization is realized.
And simultaneously correcting the boundary nodes and the unit output of the prediction scene in the error scene, wherein the phase angle values of the boundary nodes are possibly changed, so that the whole network decentralized optimization may be performed again after random optimization, and the total coordination Agent is used for coordinating the phase angles of the boundary nodes of each area to ensure that the phase angles meet the coupling constraint.
Examples
By taking an IEEE-39 standard test system as an embodiment, each regional power grid of the IEEE-39 system comprises 10 thermal power generating units and 39 nodes, wherein a region a comprises a wind power plant, and a specific topological diagram of the wind power plant is shown in FIG. 2. The system wide-area coordination digestion process is as follows (see fig. 1):
the first step is as follows: initialization parameters
0) setting a step length adjusting parameter α to be 1.05, and setting an initial value of a phase angle value of each region boundary node to be 0;
the second step is that: performing decentralized optimization in a predictive scenario
1) Solving each regional power grid optimization sub-problem for each region to obtain boundary node phase angle values Uploading to a total coordination Agent;
2) solving the upper layer total coordination main problem, and calculating to obtain the phase angle value of the boundary nodeIssuing the data to each regional power grid dispatching center;
3) and (3) judging convergence: if the convergence condition is met, entering a third step; otherwise, updating the multiplier of the penalty function to perform the next iteration, performing the second step again, and returning to step 2);
the third step: performing a random optimization of each region
4) Solving a main problem of a prediction scene of the region;
5) solving an error scene subproblem of the region;
6) and (3) judging convergence: if the convergence condition is met, the error correction optimization of the region is considered to be converged, and the phase angle value of the boundary node is obtainedUploading to a total coordination Agent, and entering 7); otherwise, the third step is carried out again to carry out the next iteration, and the step returns to 4);
7) judging whether the error correction optimization of all the areas is convergence: if all convergence is achieved, entering the fourth step; otherwise, the third step is carried out again, and the step 4) is returned;
the fourth step: re-decentralized co-ordination optimization
8) Solving the upper layer total coordination main problem, and calculating to obtain the phase angle value of the boundary nodeIssuing the data to each regional power grid dispatching center;
9) and (3) judging convergence: if the convergence condition is met, the whole multi-scene distributed scheduling model is considered to be completely converged, the algorithm is terminated, and a whole network power generation scheduling scheme is output; otherwise, updating the multiplier of the penalty function, and repeating the second step, and returning to the step 2).
The predicted wind power is shown in table 1.
TABLE 1 IEEE-39 system a area wind power prediction power meter
1. And (4) coordinating the coordination condition of the total coordination Agent in the multi-region dynamic economic dispatching model.
Table 2 shows the node phase angle values calculated from the total coordinating Agent, the a-area Agent, and the b-area Agent for the same boundary node when t is 1. It can be seen that the node phase angle values calculated by the a and b area agents are continuously close along with the iteration, and the node phase angle value of the total coordination Agent is always between the two area values, which indicates that the total coordination Agent has a coordination function, so that the phase angle values calculated by different areas are gradually close.
TABLE 2 same-time same-boundary-node phase angle values
2. Tie line power conditions.
Under the condition of considering the randomness of wind power, a centralized dynamic economic dispatching model (model 1) and a distributed coordination dispatching model (model 2) in the invention are respectively adopted for calculation, the power values flowing through the same contact line at different moments are given in a table 3, and the following can be seen:
(1) at most moments, the power of the model 2 tie line is smaller than that of the model 1, because each regional unit in the model 2 needs to correspond to the wind power randomness of the region and more rotation standby units are reserved;
(2) and the power of the model 2 connecting line is lifted to some extent during the load trough period, so that the surplus wind power during the trough period of the area a is favorably sent to the area b, and the cross-region consumption of large-scale wind power is realized.
TABLE 3 Connection line power values calculated by different models
According to the wide area coordinated consumption method considering the wind power randomness of the power system, the multi-agent technology is adopted, large-scale random wind power can be consumed under the condition that data privacy and scheduling are independent, the requirement of decentralized optimization is met, the spatial advantage complementation of wind power resources in different areas can be realized, the wind power output wide area coordinated consumption is realized, more margin is left for a power grid to correspond to the wind power randomness, a target cascade analysis method is adopted to solve a whole-network decentralized dynamic economic scheduling model and the random dynamic economic scheduling problem of each area, the calculation speed is high, and the method is suitable for solving the large-scale power grid dynamic economic scheduling problem.
Interpretation of terms in the present invention
Target cascade Assay (ATC): the method is mainly used for solving the coordination optimization problem of the multi-level structure, allows the upper-layer structure to make an autonomous decision on the upper-layer optimization problem, performs coordination optimization on the optimization problem of the lower-layer structure to obtain a global optimal solution, and has the characteristic of high convergence speed.
Multi-agent system (multi-agent system): agent's chinese expression is "Agent" or "Agent", and the goal that artificial intelligence wants to achieve is to develop agents that can think and handle problems like humans. The effect of calculating and processing problems of a single Agent is very limited, but a plurality of agents can form an integral system to jointly deal with a huge system and solve a very complicated problem, namely a multi-Agent system. The multi-Agent system is composed of a plurality of agents with independent functions and has distributed control and distributed computing capabilities so as to enhance the control capability and the computing capability of an upper-level scheduling center in the multi-region power system. The multi-Agent technology divides a large-scale problem into small subtasks to be distributed to each Agent by adopting a core idea of 'divide and conquer', the agents are independent of each other, and meanwhile, information is exchanged with other agents through upper-layer agents, so that the multi-Agent technology is very suitable for processing the wide-area coordination problem of a power system.
Claims (3)
1. A power system wide area coordination digestion method considering wind power randomness is characterized by comprising the following steps:
a. decoupling a multi-region interconnected power grid into a plurality of relatively independent regions, and interconnecting different regions through connecting line boundary node variables;
b. establishing a multi-region dynamic economic dispatching model under a prediction scene without considering wind power randomness:
establishing a centralized multi-region dynamic economic dispatching model:
wherein, Ba、DaAnd EaA coefficient matrix of an internal constraint equation of the a region; paThe output vector of the thermal power generating unit in the area a is obtained; thetaaIs a voltage phase angle vector of an internal node of the area a; in the tie boundary node of zone a, ZaaAs a set of nodes belonging to zone a, ZabIs a node set not belonging to the a area; m and n are boundary nodes; the phase angle values of m and n nodes in the area a at the time t respectively,respectively is the phase angle value of m and n nodes in the b area at the moment t; f. ofaThe sum of the power generation cost and the wind abandon penalty cost of the area a is as follows:
wherein,respectively are the power generation cost coefficients of the conventional unit i in the area a; q is a wind curtailment penalty coefficient;the active power output of the conventional unit i in the area a in the time period t,the predicted active power output for the wind farm w in region a during time period t,scheduling active power output for the wind power plant in a time period t; n is the total number of regions, NTIn order to schedule the total number of time segments,the total number of conventional units in the area a,the total wind power field number of the area a;
② solving the multi-region dynamic economic dispatch model by using an objective cascade analysis method (ATC) based on a multi-agent technology:
constructing each regional Agent (Agent) in each decomposed sub-region, then constructing a virtual total coordination Agent, and dividing the centralized dynamic economic dispatching model into a regional optimization sub-problem and a total coordination main problem:
the sub-problem model for optimizing the power grid in each area is as follows:
wherein,to couple the lagrangian multipliers in the constraint,is a quadratic penalty function multiplier;issuing the boundary node phase angle value of the sub-problem to the main problem of the k-th iteration total coordination;respectively representing boundary node phase angle values in each regional power grid;
the overall coordination master problem model is as follows:
wherein,the boundary node phase angle value is the total coordination Agent;uploading boundary node phase angle values of the total coordination Agent to each regional power grid subproblem for the kth iteration;
③ solving the main and sub problems iteratively;
c. a random dynamic economic dispatching model introducing a wind power error scene is established, and the specific method comprises the following steps:
sub-problem of error scene of power grid in each region
An objective function:
wherein S is the number of error scenes; p is a radical ofsIs the probability of occurrence of the s-th scene; Δ Ww,t,sIs the wind curtailment power delta D of the w wind power plant at the moment t under the s scenet,sThe virtual load shedding power at the moment t under the s-th scene; c. CdPenalizing costs for virtual load shedding; n is a radical ofTIs a scheduled total time period; n is a radical ofWThe total number of the wind power plants;
constraint conditions are as follows:
d, a direct current power flow equation of the nodes inside the region:
wherein,is the output vector of the thermal power generating unit in the area a in the time period t under the s-th scene,the output vector is scheduled for the wind farm,is a node load vector; sBIs a power flow reference value; b isaNeglecting the branch resistance and the node admittance matrix of the ground branch for the area a;a node phase angle vector of a region a in a time period t under the s-th scene;
and (3) constraining the upper and lower output limits of the thermal power generating unit:
wherein,andrespectively setting the lower limit and the upper limit of active output of the thermal power generating unit i;the active power output of a conventional unit i in an area a in the s-th scene in a time period t;
and (3) restraining the upper and lower output limits of the wind turbine generator:
wherein,the predicted active power output of the wind power plant w in the area a in the time period t;
unit climbing and landslide restraint:
wherein,andrespectively limiting active output climbing and landslide of the unit i; n is a radical ofTIs the total scheduling period;
constraint of line transmission power:
wherein,the transmission power value of the line kl of the area a in the s-th scene in a time period t;phase angle values of the nodes k and l at the moment t in the s-th scene respectively;the maximum transmission power value of the line;is the reactance value of the line kl;
output regulation rate constraint under the same time period prediction scene and the error scene:
-Δi≤Pi,t-Pi,t,s≤Δi
wherein, DeltaiThe output increment of the thermal power generating unit i which can be rapidly adjusted within 10 minutes is achieved; pi,tThe active power output of a conventional unit i in the region in a time period t under a prediction scene is obtained; pi,t,sConventional unit i in the area under the s-th sceneActive power output at time t;
boundary node phase angle value constraint:
wherein,respectively obtaining node phase angle values of the node m in the region a in the s-th scene and the predicted scene in the time period t;the node phase angle values of the node n in the region a in the s-th scene and the prediction scene in the time period t are respectively;
② predicting main problems of scene
An objective function:
wherein,d is the number of intermediate variables in the region a; faThe optimal cutting coefficient vector is obtained; ma、NaThe optimal cutting coefficient matrix is obtained; e is a unit vector;transposing the output vector of the thermal power generating unit in the area a;transpose of voltage phase angle vector of internal node of a area a;
d. and alternately solving the whole network dispersion optimization problem and the region random optimization problem to obtain the phase angle value of each boundary node.
2. The method of claim 1, wherein the constraint conditions of the multi-region dynamic economic dispatching model are as follows:
(ii) regional internal constraint conditions
The direct current flow equation of the node in the area a is as follows:
wherein,for a conventional unit output vector for time period t,scheduling a contribution vector for the wind farm for time period t,a node load vector for time period t; sBIs a power flow reference value; b isaA node admittance matrix for neglecting branch resistance and a ground branch;is the nodal phase angle vector of time period t;
secondly, restraining the upper and lower output limits of the thermal power generating unit:
wherein,andrespectively setting the lower limit and the upper limit of active output of the thermal power generating unit i;
thirdly, restraining the upper and lower limits of the output of the wind turbine generator:
and fourthly, restraining the unit in climbing and landslide:
wherein,andrespectively limiting active output climbing and landslide of the unit i;
the constraint of line transmission power is as follows:
wherein,for the transmission power value of the line kl at the time t,the maximum transmission power value of the line;andphase angles of the nodes k and l at the moment t are respectively;is the reactance value of the line kl;
coupled constraint between regions
The boundary node power balance constraint between two adjacent areas a and b with the inter-area links is as follows:
wherein m and n are boundary nodes, and Z is an inter-area boundary node set of the whole multi-area power grid; the boundary node phase angle values of the regions a and b at time t, respectively.
3. The method for wide-area coordinated digestion of electric power system considering wind power randomness according to claim 1 or 2, characterized in that when the main and sub-problems are solved iteratively, the penalty function multiplier needs to be updatedThe phase angle values of the boundary nodes solved by the main problem and the sub problem tend to be equal, and the penalty function multiplier updating formula is as follows:
wherein, alpha is a parameter for adjusting the step length, and the value range of the step length is [1,3] in general;
the formula for judging algorithm convergence is as follows:
where ε is the convergence accuracy.
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