CN116054179A - Event-triggering-based reactive power preference control system and method for power system - Google Patents
Event-triggering-based reactive power preference control system and method for power system 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
Abstract
The invention provides an event-triggering-based reactive power preference control system and method for a power system. Selecting a plurality of nodes in the power transmission network model to incorporate new energy; calculating the fluctuation amount of active power at each moment of the power transmission network model, and executing global optimization if the fluctuation amount is larger than an active fluctuation triggering threshold; establishing a multi-time reactive power optimization model, and solving to obtain optimized reactive power of each node in the power transmission network model at a plurality of future moments by an interior point method; uniformly dividing the time intervals of two adjacent moments into a plurality of small moment intervals, calculating the active power fluctuation quantity of each small moment interval of each node, and executing local optimization if the active power fluctuation quantity of each small moment interval of each node is larger than a node active fluctuation trigger threshold; and calculating reactive power adjustment quantity of the fluctuation time interval of the fluctuation node, and performing reactive power control of the fluctuation time interval by the on-site controller according to the reactive power adjustment quantity. The invention can realize the rapid reactive power optimization of the power system with significant power fluctuation, reduce the network loss of the system and prevent the system voltage from exceeding the limit.
Description
Technical Field
The invention belongs to the field of power system optimization control, and particularly relates to an event-triggered power system reactive power preference control system and method.
Background
Renewable energy sources are rapidly developed due to environmental friendliness, and as power users are diversified, a plurality of random loads are connected into a power system, and the continuous increase of the renewable energy sources and the random load permeability brings great challenges to the safety and economy of the operation of the power system.
Reactive power optimization is an important means for maintaining normal operation of a power system, and multiple objective functions such as generator cost, grid loss, voltage deviation, reactive power regulation cost and the like are minimized by scheduling various reactive power equipment. For the traditional centralized control algorithm, the central server needs to monitor the states of all the devices and solve the optimal power flow to realize the optimal control of the system, and along with the continuous expansion of the scale of the power system, huge calculation burden is faced. In order to overcome the shortcomings of the centralized algorithm, some decentralized algorithms divide a large power system into a plurality of subareas for optimization calculation, such as a multiplier alternating direction method. However, in the iterative process of the decentralized optimization algorithm, the coordination of the centralized controller cannot be completely eliminated. Some distributed optimization methods are proposed to balance the calculation load of the whole network in the multi-agent system, a central server is not needed in the distributed algorithm, the optimal running state is obtained through coordination among the multi-agents, and although the calculation amount and the communication cost are reduced, the distributed algorithm has the problems of higher local communication requirement and low convergence speed. In view of the limited computing power and communication bandwidth of the system, the conventional reactive power optimization method generally performs optimal control on the system once at intervals, but as the penetration degree of new energy and random load increases, significant short-term power fluctuation in a period of time reduces the accuracy of reactive power optimization control of the power grid. To accommodate power fluctuations, many algorithms require frequent execution of optimizers, which greatly increases the amount of computation and communication data.
The event triggering algorithm is widely applied to a system with limited computing capacity and bandwidth, for example, in a multi-agent system, the event triggering algorithm is utilized to reduce the communication resource requirement among the multi-agents and reduce the iteration times of the algorithm, thereby reducing the computing burden of the agents. In the field of power systems, event-triggered algorithms are commonly applied to frequency control, economic dispatch, and the like. With the continuous expansion of the scale of a modern power system, the uncertainty of accessing to a power grid is larger and larger, a large amount of data needs to be collected and processed in real time, and an event triggering algorithm becomes one of the feasible solutions for improving the operation efficiency of the power system.
Disclosure of Invention
In order to solve the technical problems, the invention provides an event-triggered power system reactive power optimization control system and an event-triggered power system reactive power optimization control method, which are used for realizing the rapid reactive power optimization of a power system with significant fluctuation and reducing the calculation burden of a central server and the communication burden of a system network.
The technical scheme of the system of the invention is an event-triggered power system reactive power preference control system, which comprises:
a central server, a plurality of in-situ controllers;
the central server is sequentially connected with the plurality of in-situ controllers;
deploying an in-situ controller at each node in the grid model;
the central server selects a plurality of nodes in the power transmission network model to be integrated with new energy; the central server calculates the active power fluctuation quantity of the power transmission network model at each moment, and if the active power fluctuation quantity is larger than an active power fluctuation triggering threshold value, global optimization is executed; establishing a multi-time reactive power optimization model, and solving to obtain optimized reactive power of each node in the power transmission network model at a plurality of future moments by an interior point method; uniformly dividing the time intervals of two adjacent moments into a plurality of small moment intervals, calculating the active power fluctuation quantity of each small moment interval of each node, and executing local optimization if the active power fluctuation quantity of each small moment interval of each node is larger than a node active fluctuation trigger threshold; and calculating reactive power adjustment quantity of fluctuation time intervals of the fluctuation nodes, and performing reactive power control of the fluctuation time intervals according to the reactive power adjustment quantity by an in-situ controller of each node in the power transmission network model.
The technical scheme of the method is an event-triggered power system reactive power preference control system, which comprises the following steps:
step 1: constructing a power transmission network model, and selecting a plurality of nodes in the power transmission network model to incorporate new energy sources;
step 2: the central server acquires the active power of each node in the power transmission network model in real time, calculates the active power fluctuation amount of each node in the power transmission network model, jumps to step 3 to execute global optimization if the active power fluctuation amount of each node in the power transmission network model is larger than the power transmission network active fluctuation triggering threshold value, the power transmission network model state is a fluctuation state, otherwise, continues to execute step 2;
step 3: the central server predicts the active power at a plurality of historical moments of each node in the power transmission network model through a linear regression prediction method to obtain the active power at a plurality of future moments of each node in the power transmission network model; the method comprises the steps of taking the minimization of the network loss of a power transmission network model as an optimization target, taking a power balance constraint equation, a state variable constraint equation and a control variable constraint equation as constraint conditions, taking reactive power at a plurality of future moments of each node in the power transmission network model as decision variables, taking voltages and phase angles at a plurality of future moments of each node in the power transmission network model as state variables, establishing a multi-moment reactive power optimization model, and solving by an interior point method to obtain optimized reactive power, optimized voltage and optimized phase angles at a plurality of future moments of each node in the power transmission network model; the central server sends the optimized reactive power, the optimized voltage and the optimized phase angle of each node in the power transmission network model to an on-site controller of each node for reactive power control;
step 4: the on-site controller of each node in the power transmission network model uniformly divides the time intervals of two adjacent moments in the step 2 into a plurality of small moment intervals, the active power of each node in the power transmission network model is collected according to each small moment interval, the active power fluctuation amount of each small moment interval of each node in the power transmission network model is calculated, if the active power fluctuation amount of each small moment interval of each node in the power transmission network model is larger than a node active fluctuation trigger threshold value, the node in the corresponding power transmission network model is defined as a fluctuation node in the power transmission network model, the corresponding small moment interval is defined as a fluctuation moment interval, and the step 5 is skipped to execute local optimization, otherwise, the step 4 is continuously executed;
step 5: the central server obtains a sensitivity matrix of the power transmission network model according to the optimized voltage and the optimized phase angle of each node in the power transmission network model at a plurality of future moments through the Jacobian matrix inversion calculation; and calculating the voltage of each fluctuation time interval of each node in the power transmission network model corresponding to the fluctuation node in the power transmission network model by combining the sensitivity matrix of the power transmission network model, further calculating the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model, and performing reactive power control of the fluctuation time interval according to the reactive power adjustment quantity by an in-situ controller of the fluctuation node in the power transmission network model.
Preferably, each time in the step 2 is defined as a kth time, K is [1, K ], and K represents the number of times;
and step 2, calculating the fluctuation amount of the active power of the power transmission network model, which is specifically as follows:
wherein ,Pi,k Representing the active power, W, at the kth time of the ith node in the grid model i Representing the active power of the ith node in the last global optimized grid model, ΔP k Active power fluctuation quantity at the kth moment of the power transmission network model is represented, and N represents the number of nodes in the power transmission network model;
preferably, the plurality of history times in step 3 are specifically defined as follows:
taking the kth moment as the current moment;
taking the k-L moment, the k-L+1 moment, the first time and the k-1 moment as L historical moments;
the future times in step 3 are specifically defined as follows:
time k+1, time k+2,.. 1 Time of day K 1 A future time;
preferably, the active power fluctuation amount of each small time interval of each node in the power transmission network model is calculated in the step 4, and is specifically defined as follows:
the absolute value of the difference between the active power of each small time interval of each node in the power transmission network model and the active power of each small time interval of each node in the power transmission network model in the last local optimization is calculated;
preferably, in step 5, the voltage of the fluctuation time interval of each node in the power transmission network model corresponding to the fluctuation node in the power transmission network model is calculated as follows:
i＝1,2…N,j＝1,2…M
t b ∈[1,T]
b∈[1,B]
wherein N represents the number of nodes in the power transmission network model, M represents the number of fluctuation nodes in the power transmission network model, T represents the number of small time intervals, B represents the number of fluctuation time intervals, and node j Representing the node in the grid model j The number of each node, j, represents the number of the jth fluctuation node in the power transmission network model,
representing the voltage of the b-th fluctuation time interval of the i-th node in the power transmission network model corresponding to the j-th fluctuation node in the power transmission network model, namely the node in the power transmission network model j The t of the i-th node in the power transmission network model corresponding to each node b Voltage at small time intervals V 0,i Reference voltage representing the ith node in the grid model,/->
A voltage sensitivity coefficient representing an active power fluctuation amount of a b-th fluctuation time interval of a j-th fluctuation node in the power transmission network model to the i-th node in the power transmission network model,/a>
Obtained by means of a sensitivity matrix->
Active power fluctuation amount representing the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model, namely representing the node in the power transmission network model j T of the individual node b The active power fluctuation quantity of each small time interval is obtained by calculating the absolute value of the active power difference between the active power of the b fluctuation time interval of the j fluctuation node in the power transmission network model and the active power of the corresponding fluctuation time interval of the j fluctuation node in the power transmission network model in the last local optimization;
and 5, calculating reactive power adjustment quantity of fluctuation time intervals of fluctuation nodes in the power transmission network model, wherein the reactive power adjustment quantity is specifically as follows:
if the voltage of each node in the power transmission network model at the fluctuation time interval is in the normal range of the power transmission network model voltage, the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model is calculated as follows:
i∈[1,N],j∈[1,M],b∈[1,B]
wherein ,
the target reactive power preference adjustment quantity of the system is reduced by representing the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model, namely the node in the power transmission network model j T of the individual node b Reactive power preference adjustment quantity k at intervals of small time 1 Represents a first voltage safety factor and k 1 <1，V i,H Representing an upper voltage limit representing an ith node in the grid model, ±>
The voltage sensitivity coefficient of the reactive power fluctuation quantity of the b fluctuation time interval of the j fluctuation node in the power transmission network model to the i node in the power transmission network model, < >>
Obtaining through a sensitivity matrix, wherein min represents taking the minimum value;
will be
Reactive power regulation quantity of the b-th fluctuation time interval serving as the j-th fluctuation node in the power transmission network model;
if the voltage of the fluctuation time interval of any node in the power transmission network model exceeds the normal voltage range of the power transmission network model, the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model is calculated as follows:
i∈[1,N],j∈[1,M],b∈[1,B]
wherein ,
the elimination voltage out-of-limit reactive power optimization trend adjustment quantity of the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model is represented, namely the node in the power transmission network model j T of the individual node b Eliminating voltage out-of-limit reactive power optimization regulating quantity k at small time intervals 2 Represents a second voltage safety factor and k 2 >1, will->
As the reactive power regulation quantity of the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model.
The beneficial effects of the invention are as follows:
because the local reactive power optimization trend control is completed by using sensitivity information, global coordination and centralized optimization are not needed, and the real-time reactive power optimization of the whole power system with significant power fluctuation can be rapidly realized. The reactive power optimization time scale is obviously shortened by controlling the controllers in situ in real time under the condition of not increasing the calculation burden of the overall controllers and the communication burden of the whole network.
By optimizing the system on a shorter time scale and tracking the optimal running point of the system, reactive power of the system can be regulated and controlled more accurately, voltage out-of-limit and network loss increase phenomena which can occur by considering the significant fluctuation of new energy and random load power are prevented, and the running safety and economy of the power system are improved.
The multi-stage global optimization is performed in a rolling mode, but only the optimization result of the first period is utilized, so that the system operation robustness is improved, and the built global optimization model improves the voltage safety by narrowing the voltage feasible region in the voltage constraint condition.
By adopting the event triggering algorithm, unnecessary optimization operation during system optimization is reduced. When the power fluctuation of the power system is smaller, global optimization and local reactive power optimization trend control are not needed to be executed, and the optimization efficiency of the system is improved.
Drawings
Fig. 1: the method of the embodiment of the invention is a flow chart;
fig. 2: the new energy and random load power curve of the embodiment of the invention;
fig. 3: the time cost of the first method and the second method of the embodiment of the invention;
fig. 4: the global optimization execution condition based on time triggering of the embodiment of the invention;
fig. 5: the reactive power in-situ optimization control schematic diagram of the embodiment of the invention;
fig. 6: the third method of the embodiment of the invention is based on the time-triggered optimal control execution condition;
fig. 7: the method III of the embodiment of the invention is reactive in-situ optimization control time cost;
fig. 8: the fan reactive curves of different optimization methods of the embodiment of the invention;
fig. 9: the different optimization methods of the embodiment of the invention are the network loss curves;
fig. 10: the method of the embodiment of the invention comprises a system voltage curved surface;
fig. 11: the node voltage curves of the different optimization methods of the embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In particular, the method according to the technical solution of the present invention may be implemented by those skilled in the art using computer software technology to implement an automatic operation flow, and a system apparatus for implementing the method, such as a computer readable storage medium storing a corresponding computer program according to the technical solution of the present invention, and a computer device including the operation of the corresponding computer program, should also fall within the protection scope of the present invention.
The following technical scheme of the system of the embodiment of the invention is an event-triggered power system reactive power preference control system, which comprises:
a central server, a plurality of in-situ controllers;
the central server is sequentially connected with the plurality of in-situ controllers;
deploying an in-situ controller at each node in the grid model;
the central server selects a plurality of nodes in the power transmission network model to be integrated with new energy; the central server calculates the active power fluctuation quantity of the power transmission network model at each moment, and if the active power fluctuation quantity is larger than an active power fluctuation triggering threshold value, global optimization is executed; establishing a multi-time reactive power optimization model, and solving to obtain optimized reactive power of each node in the power transmission network model at a plurality of future moments by an interior point method; uniformly dividing the time intervals of two adjacent moments into a plurality of small moment intervals, calculating the active power fluctuation quantity of each small moment interval of each node, and executing local optimization if the active power fluctuation quantity of each small moment interval of each node is larger than a node active fluctuation trigger threshold; and calculating reactive power adjustment quantity of fluctuation time intervals of the fluctuation nodes, and performing reactive power control of the fluctuation time intervals according to the reactive power adjustment quantity by an in-situ controller of each node in the power transmission network model.
The model of the central server is an IBM server;
the model of the in-situ controller is an RTU controller;
the following describes a technical scheme of the method of the embodiment of the invention with reference to fig. 1 to 11, which is an event-triggered power system reactive power preference control method, comprising:
according to the power system reactive power optimization control method based on event triggering, global optimization and reactive power local optimization control are combined, unnecessary operation of a system can be reduced, calculation and communication burden of the system can be reduced, network loss of the system can be reduced, and voltage out-of-limit can be prevented.
As shown in fig. 1, a flow chart of the method of the present invention is shown.
Step 1: constructing a power transmission network model, and selecting a plurality of nodes in the power transmission network model to incorporate new energy sources;
step 2: the central server acquires the active power of each node in the power transmission network model in real time, calculates the active power fluctuation amount of each node in the power transmission network model, jumps to step 3 to execute global optimization if the active power fluctuation amount of each node in the power transmission network model is larger than the power transmission network active fluctuation triggering threshold value, the power transmission network model state is a fluctuation state, otherwise, continues to execute step 2;
and step 2, calculating the fluctuation amount of the active power of the power transmission network model, which is specifically as follows:
wherein ,Pi,k Representing the active power, W, at the kth time of the ith node in the grid model i Representing the active power of the ith node in the last global optimized grid model, ΔP k Active power fluctuation amount at the kth moment of the power transmission network model is represented, and n=39 represents the number of nodes in the power transmission network model;
in the examples, 30 minutes of simulation experiments were performed on an IEEE-39 power system. The photovoltaic station such as node 33, the random load access node 34, the wind power plant access nodes 36 and 38 are arranged, the power fluctuation is subjected to normal distribution in a multi-period oral taking mode, the standard difference of the first period is 25MW, the correlation coefficient of the two wind power plants is 0.5, the confidence level beta=0.95, and the minimum voltage value and the maximum voltage value of the system voltage are respectively 0.9pu and 1.1pu. The multi-period global optimization judgment based on event triggering is executed every 5 minutes, the time length of each period is 1 minute, and the reactive power on-site optimization control based on event triggering is executed every 5 seconds. Since the length of the time interval for the multi-period optimization in this embodiment is relatively small, it is assumed that the discrete variable remains unchanged for the multi-period. Event trigger thresholds ζ and γ are 5MW. Safety factor k 1 0.95, k 2 1.05, let node {30, 34, 36, 38} install a sufficient capacity of SVG installations. To verify the feasibility of the proposed algorithm, the different optimization methods are compared, and the different methods introduce that as shown in table 1, the method performs global optimization every minute, simultaneously contracts the voltage feasible region in the multi-period optimization to improve the system safety, the method performs global optimization every 5s, simultaneously does not contract the voltage feasible region in the multi-period optimization, because new energy or random load uncertainty in 5s can be ignored, the method performs global optimization once every minute based on event triggering, simultaneously contracts the voltage feasible region in the multi-period optimization to improve the system safety, and then performs in-situ reactive power optimization control once every 5s based on event triggering.
Table 1: different optimization methods of the embodiments of the present invention introduce tables
The actual active power of the nodes 33, 34, 36, 38 is shown as a solid line in fig. 2, which measures the active fluctuations around the time period.
Step 3: the central server predicts the active power at a plurality of historical moments of each node in the power transmission network model through a linear regression prediction method to obtain the active power at a plurality of future moments of each node in the power transmission network model; the method comprises the steps of taking the minimization of the network loss of a power transmission network model as an optimization target, taking a power balance constraint equation, a state variable constraint equation and a control variable constraint equation as constraint conditions, taking reactive power at a plurality of future moments of each node in the power transmission network model as decision variables, taking voltages and phase angles at a plurality of future moments of each node in the power transmission network model as state variables, establishing a multi-moment reactive power optimization model, and solving by an interior point method to obtain optimized reactive power, optimized voltage and optimized phase angles at a plurality of future moments of each node in the power transmission network model; the central server sends the optimized reactive power, the optimized voltage and the optimized phase angle of each node in the power transmission network model to an on-site controller of each node for reactive power control;
the plurality of history moments in step 3 are specifically defined as follows:
taking the kth moment as the current moment;
taking the k-L moment, the k-L+ 1 moment, the first time and the k-1 moment as L historical moments;
the future times in step 3 are specifically defined as follows:
the time cost of global optimization is as shown in fig. 3 (a), the power system only needs to be optimized once per minute, the calculation burden and the network communication burden of the central server are low, but the influence of short-term power fluctuation (namely second-level fluctuation) on the power system cannot be solved by the method. The time cost of optimizing the power system by the second method is as shown in fig. 3 (b), and the system control precision is high because the power system is optimized by the second method on a shorter time scale, however, frequent global optimization brings a great amount of calculation and communication burden to the system. The average time cost of executing the optimization program every time is close to 5s, and the time for collecting the system state and issuing the instruction is added, so that in actual operation, the system optimization with 5s as the time scale is difficult to realize by the second method. Method three performs global optimization every time period (i.e., 1 minute), and thus the computational burden of the central server and the communication cost of the network are relatively low. As shown in fig. 4, global optimization during periods 11, 13, 16, 19, 21, 25 is avoided because their systematic power deviation is less than the threshold ζ, i.e. 5MW, whose last global optimization result (i.e. period 10, 12, 15, 18, 20, 24) will continue to be used during periods 11, 13, 16, 19, 21, 25. Statistically, 20% of the global optimization was eliminated in the 30-minute simulation experiment.
Step 4: the on-site controller of each node in the power transmission network model uniformly divides the time intervals of two adjacent moments in the step 2 into a plurality of small moment intervals, the active power of each node in the power transmission network model is collected according to each small moment interval, the active power fluctuation amount of each small moment interval of each node in the power transmission network model is calculated, if the active power fluctuation amount of each small moment interval of each node in the power transmission network model is larger than a node active fluctuation trigger threshold value, the node in the corresponding power transmission network model is defined as a fluctuation node in the power transmission network model, the corresponding small moment interval is defined as a fluctuation moment interval, and the step 5 is skipped to execute local optimization, otherwise, the step 4 is continuously executed;
a schematic diagram of reactive on-site optimization control is shown in fig. 5, where the black solid arrow indicates the active fluctuation of time, and the black empty arrow indicates real-time reactive regulation. Taking the node {33, 34} as an example, the real-time power deviation and the in-situ reactive power preference control execution are performed only if the real-time power deviation is greater than a threshold, as shown in fig. 6. As can be seen from table 2, 67.99% reactive on-site optimization control is avoided, thus introducing an event-triggered algorithm reduces the computational and control burden of the on-site controller. The time cost of the reactive on-site trending control of the third method in this embodiment is shown in fig. 7, and the time cost of each trending control is in ms level, and as shown in the statistics of table 3, the trending control time scale of the third method is far smaller than that of the second method.
Table 2: the method of the embodiment of the invention comprises the following steps of
Table 3: time cost statistics table for optimizing control of second and third methods of the embodiment of the invention
Step 5: the central server obtains a sensitivity matrix of the power transmission network model according to the optimized voltage and the optimized phase angle of each node in the power transmission network model at a plurality of future moments through the Jacobian matrix inversion calculation; and calculating the voltage of each fluctuation time interval of each node in the power transmission network model corresponding to the fluctuation node in the power transmission network model by combining the sensitivity matrix of the power transmission network model, further calculating the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model, and performing reactive power control of the fluctuation time interval according to the reactive power adjustment quantity by an in-situ controller of the fluctuation node in the power transmission network model.
And 5, calculating the voltage of each fluctuation time interval of each node in the power transmission network model corresponding to the fluctuation node in the power transmission network model, wherein the voltage is specifically calculated as follows:
i＝1,2…N,j＝1,2…M
t b ∈[1,T]
b∈[1,B]
where N represents the number of nodes in the grid model and M represents the fluctuations in the grid modelThe number of nodes, T represents the number of small time intervals, B represents the number of fluctuation time intervals, node j Representing the node in the grid model j The number of each node, j, represents the number of the jth fluctuation node in the power transmission network model,
representing the voltage of the b-th fluctuation time interval of the i-th node in the power transmission network model corresponding to the j-th fluctuation node in the power transmission network model, namely the node in the power transmission network model j The t of the i-th node in the power transmission network model corresponding to each node b Voltage at small time intervals V 0,i Reference voltage representing the ith node in the grid model,/->
A voltage sensitivity coefficient representing an active power fluctuation amount of a b-th fluctuation time interval of a j-th fluctuation node in the power transmission network model to the i-th node in the power transmission network model,/a>
Obtained by means of a sensitivity matrix->
Active power fluctuation amount representing the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model, namely representing the node in the power transmission network model j T of the individual node b The active power fluctuation quantity of each small time interval is obtained by calculating the absolute value of the active power difference between the active power of the b fluctuation time interval of the j fluctuation node in the power transmission network model and the active power of the corresponding fluctuation time interval of the j fluctuation node in the power transmission network model in the last local optimization;
and 5, calculating reactive power adjustment quantity of fluctuation time intervals of fluctuation nodes in the power transmission network model, wherein the reactive power adjustment quantity is specifically as follows:
if the voltage of each node in the power transmission network model at the fluctuation time interval is in the normal range of the power transmission network model voltage, the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model is calculated as follows:
i∈[1,N],j∈[1,M],b∈[1,B]
wherein ,
the target reactive power preference adjustment quantity of the system is reduced by representing the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model, namely the node in the power transmission network model j T of the individual node b Reactive power preference adjustment quantity k at intervals of small time 1 Represents a first voltage safety factor and k 1 <1，V i,H Representing an upper voltage limit representing an ith node in the grid model, ±>
The voltage sensitivity coefficient of the reactive power fluctuation quantity of the b fluctuation time interval of the j fluctuation node in the power transmission network model to the i node in the power transmission network model, < >>
Obtaining through a sensitivity matrix, wherein min represents taking the minimum value;
will be
Reactive power regulation quantity of the b-th fluctuation time interval serving as the j-th fluctuation node in the power transmission network model;
if the voltage of the fluctuation time interval of any node in the power transmission network model exceeds the normal voltage range of the power transmission network model, the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model is calculated as follows:
i∈[1,N],j∈[1,M],b∈[1,B]
wherein ,
the elimination voltage out-of-limit reactive power optimization trend adjustment quantity of the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model is represented, namely the node in the power transmission network model j T of the individual node b Eliminating voltage out-of-limit reactive power optimization regulating quantity k at small time intervals 2 Represents a second voltage safety factor and k 2 >1, will->
As the reactive power regulation quantity of the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model.
It is assumed here that the in-situ controllers are able to quickly adjust their output reactive by controlling the converter, each in-situ controller performing in-situ reactive optimization at each moment in the time period, assuming that no fluctuation of the power of the other nodes occurs, i.e. the in-situ controllers only respond to the fluctuation of their own active by adjusting their own reactive. According to the linear superposition principle, all nodes containing new energy and random loads in the system synchronously execute the on-site reactive power optimization trend control, and the reactive power optimization trend control of the whole system is realized.
Under different optimization methods, the reactive power curves of the nodes {33, 34, 36, 38} are shown in fig. 8. For method one, since the reactive power remains unchanged for the period of time, the voltage feasible region is contracted, its reactive power deviates from the optimal reactive power, especially for nodes 36 and 38. The optimal reactive power is obtained by a second method, but for the second method, global execution is carried out every 5 seconds, so that a great calculation and communication burden is brought to the power system. The reactive power of the third method is closer to the optimal reactive power, which indicates that the proposed algorithm can effectively track the optimal reactive power in consideration of the influence of short-term power fluctuation. For method three, the global optimization based on event triggering is performed once every minute, the reactive in-situ optimization control based on event triggering is performed once every 5s, and the calculation and communication burden is not great.
As shown in fig. 9, the system loss of the third method is closer to the system optimum loss obtained by the second method than the first method. Through the statistics of table 4, the net loss of the algorithm presented herein was reduced by 0.17% compared to method one, but only by 0.085% above the optimal net loss. Therefore, compared with the first method, the method can consider short-term power fluctuation and effectively reduce system network loss.
Table 4: network loss statistical table for different optimization methods of the embodiment of the invention
The system voltage curve obtained by the method one is shown in fig. 10, and since a large short-term power fluctuation occurs at the node 38, a voltage out-of-limit phenomenon occurs at the node {25, 26} which is electrically close to the node 38 at the timings 307 to 312. The voltage curves of the nodes {25, 26, 36, 38} under different methods are shown in fig. 11, and the voltage curve of the third method is always kept within the allowable range in the whole simulation experiment process of 30min, so that the proposed algorithm can effectively prevent the voltage out-of-limit possibly caused by the significant fluctuation of short-term power.
The embodiment demonstrates the feasibility of the proposed reactive power optimization control algorithm of the electric power system based on event triggering, reduces unnecessary optimization operation of the system, can give consideration to the system calculation cost and communication burden, reduces the system network loss, and prevents the system voltage from exceeding the limit.
It should be noted that each step/component described in the present application may be split into more steps/components, or two or more steps/components or part of the operations of the steps/components may be combined into new steps/components, as needed for implementation, to achieve the object of the present invention.
Although the present invention uses more terms such as event triggering, new energy, power fluctuations, global optimization, reactive local optimization control, loss of network, voltage out-of-limit, central server, local controller, etc., the possibility of using other terms is not precluded. These terms are used merely for convenience in describing and explaining the nature of the invention; they are to be interpreted as any additional limitation that is not inconsistent with the spirit of the present invention.
Although the terms of visible light positioning base station, positioning terminal, etc. are used more herein, the use of such terms is not precluded
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.
Claims (8)
1. An event-triggered power system reactive power preference control system, comprising:
a central server, a plurality of in-situ controllers;
the central server is sequentially connected with the plurality of in-situ controllers;
deploying an in-situ controller at each node in the grid model;
the central server selects a plurality of nodes in the power transmission network model to be integrated with new energy; the central server calculates the active power fluctuation quantity of the power transmission network model at each moment, and if the active power fluctuation quantity is larger than an active power fluctuation triggering threshold value, global optimization is executed; establishing a multi-time reactive power optimization model, and solving to obtain optimized reactive power of each node in the power transmission network model at a plurality of future moments by an interior point method; uniformly dividing the time intervals of two adjacent moments into a plurality of small moment intervals, calculating the active power fluctuation quantity of each small moment interval of each node, and executing local optimization if the active power fluctuation quantity of each small moment interval of each node is larger than a node active fluctuation trigger threshold; and calculating reactive power adjustment quantity of fluctuation time intervals of the fluctuation nodes, and performing reactive power control of the fluctuation time intervals according to the reactive power adjustment quantity by an in-situ controller of each node in the power transmission network model.
2. A method for event-triggered based reactive power system optimization control by using the event-triggered based reactive power system optimization control system of claim 1, comprising the steps of:
step 1: constructing a power transmission network model, and selecting a plurality of nodes in the power transmission network model to incorporate new energy sources;
step 2: the central server acquires the active power of each node in the power transmission network model in real time, calculates the active power fluctuation amount of each node in the power transmission network model, jumps to step 3 if the active power fluctuation amount of each node in the power transmission network model is larger than the power transmission network active fluctuation triggering threshold value, the power transmission network model state is a fluctuation state, otherwise, continues to execute step 2;
step 3: the central server predicts the active power at a plurality of historical moments of each node in the power transmission network model through a linear regression prediction method to obtain the active power at a plurality of future moments of each node in the power transmission network model, establishes a multi-moment reactive power optimization model, solves the reactive power through an interior point method to obtain the optimized reactive power, the optimized voltage and the optimized phase angle at a plurality of future moments of each node in the power transmission network model, and further realizes global optimization;
step 4: acquiring the active power of each node in the power transmission network model according to each small time interval, calculating the active power fluctuation quantity of each small time interval of each node in the power transmission network model, if the active power fluctuation quantity of each small time interval of the node in the power transmission network model is larger than the node active fluctuation triggering threshold, defining the node in the corresponding power transmission network model as a fluctuation node in the power transmission network model, defining the fluctuation time interval of the corresponding small time interval and jumping to the step 5, otherwise, continuously executing the step 4;
step 5: the central server obtains a sensitivity matrix of the power transmission network model according to the optimized voltage and the optimized phase angle of each node in the power transmission network model at a plurality of future moments through the Jacobian matrix inversion calculation; and calculating the voltage of each fluctuation time interval of each node in the power transmission network model corresponding to the fluctuation node in the power transmission network model by combining the sensitivity matrix of the power transmission network model, further calculating the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model, and further realizing local optimization.
3. The event-triggered based reactive power system optimization control method as claimed in claim 2, wherein:
step 2, defining each moment as a kth moment, wherein K is [1, K ], and K represents the number of moments;
and step 2, calculating the fluctuation amount of the active power of the power transmission network model, which is specifically as follows:
wherein ,Pi,k Representing the active power, W, at the kth time of the ith node in the grid model i Representing the active power of the ith node in the last global optimized grid model, ΔP k The active power fluctuation amount at the kth moment of the power transmission network model is represented, and N represents the number of nodes in the power transmission network model.
4. The event-triggered based reactive power system optimization control method as claimed in claim 3, wherein:
the plurality of history moments in step 3 are specifically defined as follows:
taking the kth moment as the current moment;
taking the k-L moment, the k-L+1 moment, the first time and the k-1 moment as L historical moments;
the future times in step 3 are specifically defined as follows:
time k+1, time k+2,.. 1 Time of day K 1 A future time;
and 3, establishing a multi-time reactive power optimization model, which is specifically as follows:
the method comprises the steps of taking the network loss minimization of a power transmission network model as an optimization target, taking a power balance constraint equation, a state variable constraint equation and a control variable constraint equation as constraint conditions, taking reactive power of each node in the power transmission network model at a plurality of future moments as decision variables, and taking voltage and phase angle of each node in the power transmission network model at a plurality of future moments as state variables.
5. The event-triggered based reactive power system optimization control method of claim 4, wherein:
step 3, realizing global optimization, which is specifically as follows:
and the central server sends the optimized reactive power, the optimized voltage and the optimized phase angle at the next moment of each node in the power transmission network model to an on-site controller of each node for reactive power control, so that global optimization is realized.
6. The event-triggered based reactive power system optimization control method of claim 5, wherein:
the specific processing procedure of each hour interval in the step 4 is as follows:
the in-situ controller of each node in the power transmission network model uniformly divides the time interval between two adjacent moments into a plurality of small time intervals;
and step 4, calculating the active power fluctuation quantity of each small time interval of each node in the power transmission network model, wherein the active power fluctuation quantity is specifically defined as follows:
the method is obtained by calculating the absolute value of the difference between the active power of each small time interval of each node in the power transmission network model and the active power of each small time interval of each node in the power transmission network model in the last local optimization.
7. The event-triggered based reactive power system optimization control method of claim 6, wherein:
and 5, calculating the voltage of each fluctuation time interval of each node in the power transmission network model corresponding to the fluctuation node in the power transmission network model, wherein the voltage is specifically calculated as follows:
i＝1,2…N,j＝1,2…M
t b ∈[1,T]
b∈[1,B]
wherein N represents the number of nodes in the power transmission network model, M represents the number of fluctuation nodes in the power transmission network model, T represents the number of small time intervals, B represents the number of fluctuation time intervals, and node j Representing the node in the grid model j The number of each node, j, represents the number of the jth fluctuation node in the power transmission network model,
representing the voltage of the b-th fluctuation time interval of the i-th node in the power transmission network model corresponding to the j-th fluctuation node in the power transmission network model, namely the node in the power transmission network model j The t of the i-th node in the power transmission network model corresponding to each node b Voltage at small time intervals V 0,i Reference voltage representing the ith node in the grid model,/->
A voltage sensitivity coefficient representing an active power fluctuation amount of a b-th fluctuation time interval of a j-th fluctuation node in the power transmission network model to the i-th node in the power transmission network model,/a>
By means of the sensitivity matrix it is obtained,
active power fluctuation amount representing the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model, namely representing the node in the power transmission network model j T of the individual node b The fluctuation amount of the active power at small time intervals passes through the absolute difference of the active power at the b fluctuation time interval of the j fluctuation node in the power transmission network model and the active power at the corresponding fluctuation time interval of the j fluctuation node in the power transmission network model in the last local optimizationAnd calculating the value.
8. The event-triggered based reactive power system optimization control method of claim 7, wherein:
and 5, calculating reactive power adjustment quantity of fluctuation time intervals of fluctuation nodes in the power transmission network model, wherein the reactive power adjustment quantity is specifically as follows:
if the voltage of each node in the power transmission network model at the fluctuation time interval is in the normal range of the power transmission network model voltage, the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model is calculated as follows:
i∈[1,N],j∈[1,M],b∈[1,B]
wherein ,
the target reactive power preference adjustment quantity of the system is reduced by representing the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model, namely the node in the power transmission network model j T of the individual node b Reactive power preference adjustment quantity k at intervals of small time 1 Represents a first voltage safety factor and k 1 <1，V i,H Representing an upper voltage limit representing an ith node in the grid model,
the voltage sensitivity coefficient of the reactive power fluctuation quantity of the b fluctuation time interval of the j fluctuation node in the power transmission network model to the i node in the power transmission network model, < >>
Obtaining through a sensitivity matrix, wherein min represents taking the minimum value;
will be
Reactive power regulation quantity of the b-th fluctuation time interval serving as the j-th fluctuation node in the power transmission network model;
if the voltage of the fluctuation time interval of any node in the power transmission network model exceeds the normal voltage range of the power transmission network model, the reactive power adjustment quantity of the fluctuation time interval of the fluctuation node in the power transmission network model is calculated as follows:
i∈[1,N],j∈[1,M],b∈[1,B]
wherein ,
the elimination voltage out-of-limit reactive power optimization trend adjustment quantity of the b-th fluctuation time interval of the j-th fluctuation node in the power transmission network model is represented, namely the node in the power transmission network model j T of the individual node b Eliminating voltage out-of-limit reactive power optimization regulating quantity k at small time intervals 2 Represents a second voltage safety factor and k 2 >1, will->
Reactive power regulation quantity of the b-th fluctuation time interval serving as the j-th fluctuation node in the power transmission network model;
the local optimization is realized in the step 5, which is concretely as follows
And the in-situ controller of the fluctuation node in the power transmission network model performs reactive power control of fluctuation time intervals according to the reactive power adjustment quantity.
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