CN118213994A - Distributed energy global coordination and collaborative autonomous regulation recovery strategy method and device - Google Patents
Distributed energy global coordination and collaborative autonomous regulation recovery strategy method and device Download PDFInfo
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Abstract
The invention relates to a distributed energy global coordination and collaborative autonomous regulation recovery strategy method, which comprises the steps of dividing load nodes with electric distances meeting initial partition threshold values into partitions where corresponding main power supplies are located, partitioning the load nodes to be partitioned in a node set to be partitioned by utilizing a low-level model, and deleting the partitioned load nodes from the node set to be partitioned; partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model; establishing a partition recovery model of each partition; establishing an extremely short-term control strategy model of each partition; and solving a low-level model, a partition optimization model, a partition recovery model and an extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of a load node and a main power supply in the target power distribution network. The invention obviously accelerates the recovery speed of the power distribution network and effectively improves the elasticity level of the power distribution network. The invention also relates to a device and a storage medium.
Description
Technical Field
The invention relates to the technical field of power partitioning, in particular to a distributed energy global coordination and collaborative autonomous regulation recovery strategy method and device.
Background
In the context of new power system construction, a large number of heterogeneous distributed energy sources (DER) represented by energy storage, demand response, distributed generation are gradually accessing into a power distribution network and actively participating in the operation and control of the power distribution network. The distribution network is forward actively supplying power, flexible and highly random. Meanwhile, the power grid faces more and more complex internal and external threats, such as cascading failures, broadband oscillation, network attacks, human errors, extreme natural disasters and the like. When the power transmission system is interrupted and power cannot be timely supplied to the power distribution system, a large-scale power failure can be caused, and serious economic and social losses are caused. Aiming at the possible occurrence of a major power failure, how to establish a rapid emergency recovery strategy is a technical problem which needs to be solved at present.
Disclosure of Invention
Aiming at solving the problem that a rapid emergency recovery strategy is established aiming at the possible occurrence of a major outage, the invention provides a distributed energy global coordination and collaborative autonomous regulation recovery strategy method and device.
In a first aspect, the present invention provides a distributed energy global coordination and coordinated autonomous regulation recovery strategy method, the method comprising:
Dividing load nodes with the electric distance meeting an initial partition threshold into partitions where the corresponding main power supplies are located according to the electric distances between the main power supplies and the load nodes in the target power distribution network, after the unassigned load nodes are placed into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned;
partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and solving to obtain an optimal partition;
establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of load nodes in each partition;
Establishing an extremely short-term control strategy model of each partition according to operation prediction data and actual operation data of load nodes in each partition;
And solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network.
In a second aspect, the present invention also provides a distributed energy global coordination and coordinated autonomous regulation recovery policy apparatus, the apparatus comprising:
the first construction module is used for dividing the load nodes with the electric distance which accords with an initial partition threshold value into partitions where the corresponding main power supplies are located according to the electric distances between the main power supplies and the load nodes in the target power distribution network, after the unassigned load nodes are placed into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned;
the second construction module is used for partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and obtaining an optimal partition by means of solution;
The third construction module is used for building a partition recovery model of each partition based on the partition result of the target power distribution network and the actual operation data of the load nodes in each partition;
The fourth construction module is used for building an extremely short-term control strategy model of each partition according to the operation prediction data and the actual operation data of the load nodes in each partition;
And the fifth construction module is used for solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network.
In a third aspect, the present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and where the processor implements a distributed energy global coordination and coordinated autonomous adjustment restoration policy method according to any of the first aspects when the computer program is executed.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a distributed energy global coordination and collaborative autonomous regulatory recovery policy method according to any of the first aspects.
The invention provides a distributed energy global coordination and collaborative autonomous regulation recovery strategy method, which comprises the steps of dividing load nodes with the electric distance which accords with an initial partition threshold into partitions where corresponding main power supplies are located according to the electric distances between main power supplies and load nodes in a target power distribution network, putting the unassigned load nodes into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned; partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and solving to obtain an optimal partition; establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of load nodes in each partition; establishing an extremely short-term control strategy model of each partition according to operation prediction data and actual operation data of load nodes in each partition; and solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network. The invention obviously accelerates the recovery speed of the power distribution network and effectively improves the elasticity level of the power distribution network.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a schematic flow chart of a distributed energy global coordination and coordinated autonomous regulation recovery strategy method provided by an embodiment of the invention;
FIG. 2 is a schematic flow chart of a method for global coordination and coordinated autonomous regulation of distributed energy recovery strategy according to another embodiment of the present invention;
fig. 3 is a schematic block diagram of a distributed energy global coordination and coordinated autonomous regulation recovery strategy device according to another embodiment of the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
The following will be a description of a method for global coordination and coordinated autonomous adjustment and recovery strategy of distributed energy provided by the embodiment of the invention with reference to fig. 1, where the method includes:
100. Dividing load nodes with the electric distance meeting an initial partition threshold into partitions where the corresponding main power supplies are located according to the electric distances between the main power supplies and the load nodes in the target power distribution network, after the unassigned load nodes are placed into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned;
200. Partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and solving to obtain an optimal partition;
300. Establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of load nodes in each partition;
400. Establishing an extremely short-term control strategy model of each partition according to operation prediction data and actual operation data of load nodes in each partition;
500. And solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network.
Based on the above embodiment, further, in step 100, according to the electrical distance between the main power source and the load node in the target power distribution network, the load node whose electrical distance meets the initial partition threshold is divided into the partitions where the corresponding main power source is located, which specifically includes:
Determining the partition number of the target power distribution network according to the position of the main power supply in the target power distribution network;
Calculating the shortest electrical distance Q between a load node in the target power distribution network and each main power supply by using a shortest path algorithm, wherein the weight value is the reactance value of each line;
and dividing the load nodes with the shortest electrical distance Q smaller than the initial partition threshold value into the partitions where the corresponding main power supplies are located.
Based on the above embodiment, further, in step 100, partitioning the load node to be partitioned in the node set to be partitioned by using a low-level model specifically includes:
Taking a main power supply in each partition as a starting node, traversing load nodes which accord with preset priorities in the power supply radius of the starting node, wherein the power supply radius is an available output range of the main power supply when a fault occurs;
judging whether the total power of the load nodes conforming to the preset priority exceeds a preset limit value or not;
If yes, placing the load nodes with the priority lower than the first preset priority in the load nodes conforming to the preset priority into the node set to be partitioned;
Judging whether the power of the load nodes in each partition after the load nodes lower than the first preset priority are removed exceeds a preset limit value or not;
if yes, after the initial partition threshold is modified, partitioning is conducted on the load nodes in the target power distribution network again;
And acquiring other available distributed power supplies which are in accordance with the power supply radius in each partition as a main power supply, and merging the partitions of the main power supplies with the overlapped power supply radius when the power supply radius of the main power supplies is overlapped.
Based on the above embodiment, further, step 200 specifically includes:
According to the recovery time of each partition, the partition evaluation recovery time, the reactive power charging times in the partition recovery path and the switching times in the partition recovery path, an objective function with the lowest total emergency recovery cost is established as an objective function in the partition optimization model:
wherein F is the total emergency recovery cost, D is the total number of partitions, T n is the recovery time cost for partition n, T is the partition average recovery time cost, where G 1,n represents the reactive charge times in the recovery path of the partition n, g 2,n represents the switching times in the recovery path of the partition n, and g 1,n、g2,n and T n jointly form the recovery cost of the partition n;
Establishing constraints of the partition optimization model by using the running states of the distributed power supplies and the running states of the load nodes in each partition, wherein the constraints comprise:
Power adequacy constraints:
Wherein Ω G,d is the number of distributed power supplies in the nth partition, β G,n,i represents the start-up state of the ith distributed power supply in the nth partition, wherein 1 represents that power is started and is being generated, 0 represents that power is not started, P G,n,i is the active output power of the ith load node in the nth partition, Ω L,d is the number of the ith load node in the nth partition, β L,n,i is the recovery state value of the ith load node in the nth partition, wherein 1 represents that power is recovered, 0 represents that power is not recovered, and P L,n,i is the active power of the load node j in the partition n;
Distributed power output constraints:
wherein P Gm is the active output of the started distributed power supply, Q Gm is the reactive output of the started distributed power supply, and Ω G is the set of all distributed power supplies in the partition;
node voltage constraint:
Where U i is the voltage magnitude of the recovered load node i, Ω N is the set of recovered load nodes in the partition;
Node power balancing constraints:
Wherein P i and Q i are respectively active power and reactive power injected by a load node i, U i and U j are respectively voltage amplitudes of the load node i and a load node j, and G ij、Bij and theta ij are conductance, susceptance and voltage phase angle differences between the load node i and the load node j;
transmission line capacity limiting constraint
Wherein P Ll represents the active power transmitted in line l;
radiation operation constraints:
H∈ΩH
Wherein Ω H represents a set of radiating topologies in the target distribution network;
And obtaining an optimal partitioning result of the load nodes to be partitioned in the node set to be partitioned by utilizing the objective function and the constraint in the partition optimization model.
Based on the above embodiment, further, step 300 specifically includes:
The establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of load nodes in each partition specifically includes:
for each partition, the following objective function is established:
Where Ω L,n is the number of all load nodes in the nth partition, Ω l,n is the number of all lines in the nth partition, t r,i represents the time to power up load node i in partition d at recovery, Indicating when partition failure has been resolved and partition n resumes normal operation, β L,d,i indicating the recovery state of load node i in partition n, 1 indicating recovery, 0 indicating no recovery, ρ L,n,i (t) indicating the priority weight of load node i in partition n at time t, P L,n,i (t) indicating the active power of load node i in partition n at time t, Z l indicating the weight of line l;
Establishing the weight of the production line operation time, the cost weight of line recovery and the weight of the line of each partition in the target power distribution network;
the weight of the production line operation time comprises the operation time of a line;
The expected value E (t l) and variance σ l of the run time of the line are:
The variance σ l of the run time of the line is:
Where l ε. Phi. L and ψ L represent the set of all lines in the partition;
The weight of the production line operation time also comprises the mean value and variance of the recovery path operation time;
assuming that L lines form the power transmission path at a specific node i, the mean and variance of the recovery path running time are:
the mean value of the recovery path running time
Variance of the recovery path run time
Wherein A is the operation optimistic estimated time of each line, B is the operation pessimistic estimated time of each line and M is the operation most probable estimated time of each line, t l is the actual recovery time of each line;
Cost weight of the line reclamation:
Wl＝Cl+Sl，
wherein the total charge capacitance on the load node i recovery path is
Wherein C L represents the total charge capacitance, C l represents the charge capacitance of the line l, and S l is the running cost of the on-off switch of the line l;
The weight of the line: z l＝μ1Wl+μ2tl, where W l is the recovery cost weight, t l is the run time weight, and μ 1 and μ 2 are parameters that adjust the balance between run time and recovery cost.
Based on the above embodiment, further, step 400 specifically includes:
A first objective function y 1 of the extremely short-term control strategy model is built with the aim of minimizing the fault loss by controlling the cut-off load or the recovery amount:
where P L,n,i (τ) represents the load power actually measured by load node i of partition n at time τ, Δp L,n,i (τ) represents the load power variation predicted by load node i of partition n at time τ;
Establishing a second objective function y 2 with zoned network loss minimization:
where ΔP loss,n (τ) represents the network loss value of partition n at time τ;
Establishing constraints of the extremely short-term control strategy model of the partition:
Constraints of the extremely short-term control strategy model include energy storage charge-discharge power constraints:
-PES,N≤PES(t)≤PES,N
The constraints of the extremely short-term control strategy model further comprise energy storage state constraints under charging constraints:
SSOC,ES,min≤SSOC,ES(t)≤SSOC,ES,max
wherein P ES,N is the energy storage rated power, and S soc,ES,min,Ssoc,ES,max and S soc,ES (t) are the upper and lower limits of the energy storage state of charge and the state of charge in the recovery process respectively;
Wherein,
Wherein delta is the self-discharge rate of energy storage, eta c and eta d are the charge-discharge efficiency respectively,And/>Is the charge-discharge power at time t, and S s is the energy storage capacity.
Based on the above embodiment, further, step 500 specifically includes:
510. determining the running state of each partition according to the state x (tau) of the load node of each partition at the current time tau, the short-term active output of wind power and photoelectricity and the power consumption of each load node;
Calculating power deviation between the distributed power supply and the load in the partition, and determining whether load nodes need to be reduced at a preset future time according to the power deviation;
if not, go to step 530, otherwise, go to step 520;
520. optimizing a preset future time according to the first objective function y 1 to reduce the number of load nodes, if the load nodes are reduced, reducing the network loss value of the partition to the preset loss value, executing step S3, otherwise, performing global feedback correction on the target power distribution network;
530. After optimizing the power output by using the objective function y 2, updating the partition state x (τ+1) when τ+1, then executing the steps S1 to S3 until the partition state of the preset future time is updated, and executing the step S4;
540. Calculating power deviation between the distributed power sources and the load nodes in each partition, and after the load nodes in each partition are redistributed according to the priority restoration weight rho i (t), executing step 560 if the power deviation between the distributed power sources and the load nodes in each partition is in a preset power deviation area;
Otherwise, go to step 550;
550. and reducing the preliminary partition limit, expanding the equal node set to be partitioned, and re-executing step 510.
560. And solving the partition optimization model, the partition recovery model and the extremely short-term control strategy model by a method combining a genetic algorithm and a Floyd algorithm to obtain a global optimal solution of the partition and partition recovery of the target power distribution network.
It is understood that the invention discloses a rapid recovery optimization strategy for short-term global coordination and collaborative autonomous regulation of distributed energy based on a model predictive control framework, which belongs to the technical field of operation control of an electric power system and comprises the following steps: by predicting the network state under the abnormal condition, a short-term rapid recovery global coordination optimization model is established, and the model comprises energy management of distributed energy and a recovery plan of key load; in the aspect of distributed energy source main regulation, an ultra-short-term rolling control strategy is formulated based on a global coordination optimization result, with minimum load shedding loss and grid loss as targets and with output power and load switching as control variables. The model and the method provided by the invention obviously accelerate the recovery speed of the power distribution network and effectively improve the elasticity level of the power distribution network.
The invention aims at the condition of large-area power failure of the power distribution network caused by power transmission network faults. The use of rich Distributed Energy Sources (DERs) to assist in rapid self-healing of critical loads in a power distribution network has created new demands. The invention fully considers the time-varying characteristic of the source load and the influence of the source load on the system recovery process. Based on a Model Predictive Control (MPC) framework, a power distribution network rapid emergency recovery strategy combining short-term global coordination optimization and ultra-short-term subsystem autonomous control is constructed. The safe operation of the system is ensured by gradually refining the prediction information and feeding back, correcting and rolling optimization in the subsystem recovery process.
The invention mainly aims to establish a short-term rapid recovery global coordination optimization model through predicting the network state under the abnormal condition; in the aspect of distributed energy source main regulation, an ultra-short-term rolling control strategy is formulated based on a global coordination optimization result. The method is shown in fig. 2, and comprises the following steps:
step 1: and carrying out preliminary partition in space according to the short-term predicted value of the source load at the power failure moment of the power distribution network.
Step 2: and (3) for the node which is not divided in the step (1) and the node which has no significant difference in the space distance from the power supply, further considering the time level, and establishing a partition optimization model.
Step3: and establishing a subsystem recovery model, and reconstructing the power-losing network to realize continuous and reliable power supply to the internal important load.
Step 4: and an extremely short-term control strategy model is established, so that possible fault risks in the recovery process of the power distribution network are reduced.
Step 5: and (3) providing subsystem rolling optimization and feedback correction control strategies to solve the models provided in the steps 1 to 4.
Step 1: and carrying out preliminary division from two dimensions of space and time according to a short-term predicted value of 'source load' at power failure moment of the power distribution network.
1) Determining the number of partitions:
And analyzing the time-varying characteristics of the distributed resource output, and determining the priority weight of key load recovery at the moment of occurrence of the power distribution network fault. Energy storage with self-starting capability and good voltage and frequency regulation capability is then selected as the primary power source for each subsystem. And determining the number of the partitions required by the power distribution network according to the positions of the main power supplies in each subsystem.
2) Making an initial partition plan:
The reactance value X is chosen as the weight of the line. And selecting the node where the black start power supply is located as the root node. The shortest electrical distance Q from all load nodes to the root node is calculated using Dijkstra's algorithm. A preliminary partition threshold a lim is set and the shortest electrical distances of the load nodes to the different black start power supply nodes are compared. And carrying out preliminary division on the load nodes in the space dimension according to the distance. For load nodes whose distance is less than the initial partition threshold, they are assigned to respective partitions. Nodes that cannot be assigned to a partition due to distances less than the preliminary partition threshold are temporarily placed in the set of nodes to be further partitioned ψ net awaiting further optimization of the low-level model.
3) Correcting an initial partition target according to the power supply radius:
And taking the main power supply in each partition as a starting node, and defining the power radius as the available output of the main power supply at the moment of power failure. A certain margin is reserved in the supply radius taking into account the time-varying nature of the "source load". Primary and secondary loads are searched within a defined power supply radius using breadth-first traversal. And considering the available output of the main power supply, determining the power supply range of the power supply, checking whether the power in the partition exceeds the limit, and verifying the rationality of the partition. If the power exceeds the limit, the load node with lower importance of recovery is prioritized and assigned to the set of nodes requiring further partitioning. If not, the initial partition threshold A lim in step 2 is modified and the load is redistributed. For other available distributed power sources within the power supply range, the outputs thereof are summarized and the power supply radius and the power supply range are updated accordingly. When the mains supply radii overlap, the two partitions are merged into one larger subsystem.
Step 2: for the node that is not divided in step 1, and for the node that has no significant difference in spatial distance from the power source, further consideration is required on a time level. All load nodes in the set of nodes to be further divided are regarded as decision variables. Using a genetic algorithm, a plurality of partitioning schemes are randomly generated to form a set of partitioned targets S, the targets including minimizing the total cost of emergency recovery. And for each partition scheme in the target set S, calling a subsystem recovery optimization model, and finally solving the optimal partition and the optimal subsystem recovery scheme. The detailed description of the partition optimization model is as follows:
1) Target object
The goal of the partition optimization model is to minimize the total cost of emergency recovery. The recovery costs include time costs, line running costs, and capacitor charging costs in the overall system recovery process. The objective function is established as follows:
Where f 1 represents the total emergency recovery cost, consisting of the recovery costs of all subsystems. D represents the total number of partitions. T n represents the recovery time penalty for subsystem n. Representing the average recovery time cost of all subsystems a, whereG 1,n、g2,n represents the reactive charge and switching times in the recovery path of subsystem n, g 1,n、g2,n and T n together constitute the recovery cost of subsystem n.
2) Constraint
(1) Power adequacy
Wherein Ω G,d is the number of distributed power supplies in the nth subsystem. Beta G,n,i represents the start-up state of the ith distributed power supply in the nth subsystem, where 1 represents started and generating power, and 0 represents not started. P G,n,i is the active output power of the ith load node in the nth subsystem. Omega L,d is the number of i-th load nodes in the nth subsystem. Beta L,n,i is the recovery state of the nth subsystem's ith load node. Where 1 indicates recovered and 0 indicates unrecoverable. P L,n,i is the active power of load j in subsystem n.
(2) Distributed power supply output
Where P Gm is the active output of the active distributed power supply. Q Gm is the reactive output of the started distributed power supply. Omega G is the set of all distributed power sources in the system.
(3) Node voltage
Where U i is the voltage magnitude at restored node i. Omega N is the set of restored nodes in the system.
(4) Node power balancing
Where P i and Q i are the active and reactive power injected by node i, respectively. U i and U j are the voltage magnitudes of node i and node j, respectively. G ij、Bij and θ ij are conductance, susceptance, and voltage phase angle differences between node i and node j.
(5) Transmission line capacity limitation
Where P Ll represents the active power transmitted in line l.
(6) Radiation operation
H∈ΩH (0.7)
Where Ω H represents the set of radiating topologies in the distribution network.
Step 3: and establishing a subsystem recovery model. The subsystem recovery model is used for reconstructing the power-losing network, jointly optimizing the load recovery sequence and the charging path to obtain a target network to be recovered, and finally realizing continuous and reliable power supply to the internal important load. The invention provides an elastic operation strategy based on model predictive control so as to realize real-time optimization and adjustment of output power and load switching quantity.
1) Target object
For each subsystem, the following objective functions are established:
Where Ω L,n is the number of all load nodes in the nth subsystem. Omega l,n is the number of all lines in the nth subsystem. t r,i represents the time at which the load node i in subsystem d is powered up at recovery. Indicating the time when the system failure has been resolved and subsystem n resumes normal operation. β L,d,i represents the recovery state of load node i in subsystem n (1 represents recovery, 0 represents no recovery ρ L,n,i (t) represents the priority weight of load node i in subsystem n at time t.P L,n,i (t) represents the active power of load node i in subsystem n at time t.Z l represents the weight of line l.
2) Evaluation index of circuit importance
When the framework network is established, the influence of line running time, charging capacitance and switching operation on the line weight is considered, so that the efficiency is considered, and the risk is considered. The importance of the line is evaluated by setting the line weight, and the result of the optimization is a restoration path composed of the high importance line.
(1) Weight distribution of production line operation time
The evaluation criteria for the run time of each line during the emergency restoration of the distribution network is defined as the time running weight t, which is the time taken by the line from the start of charging to the completion of charging. In a practical system, the running time of each line is generally classified into an optimistic estimated time a, a pessimistic estimated time B and a most probable estimated time M according to the experience of an operator. The time required for the actual recovery of each line is noted as t l, obeying the Beta distribution between A and B. For the run time of a line l, its expected value E (t l) and variance σl are:
where l ε. Phi. L and ψ L represent the set of all lines in the system. Assuming that L lines form the power transmission path at a specific node i, the mean and variance of the recovery path running time are:
(2) Cost weighting for line reclamation
In the process of power grid reconstruction, the total charging capacitance is a critical consideration, because the excessive capacitance can affect the system safety, resulting in line overvoltage, especially in no-load or light-load conditions. Managing and optimizing such capacitances is critical to ensure reliability and safety of the reconfigured power distribution network. The total charge capacitance on the load node i recovery path can be expressed as:
where C L represents the total charge capacitance and C l represents the charge capacitance of line l.
If the recovery cost weight W l of the line l is used as an index of the difficulty level of the recovery process, setting the recovery cost weight simply according to the influence of the charging capacitance of the line may result in that a plurality of recovery paths are formed by the line with smaller charging capacitance, instead of the recovery of a single line. This in turn can lead to frequent switching operations on the line. To solve this problem, the recovery cost weight of the line l may be set to:
Wl＝Cl+Sl (0.14)
wherein S l is the operation cost of the on-off switch of the line l. In general, circuit breakers are installed at both ends of the line, and it is necessary to close the circuit breakers during the restoration process. Assuming that the running cost S l for each line is the same, adjusting the value of S l can adjust the effect of line charge capacitance and switching frequency on restoration path optimization.
3) Weights of each line
The weight Z l of a line is determined by two evaluation indexes: the recovery cost weight W l and the runtime weight t l. The weight of the line is as follows:
Zl＝μ1Wl+μ2tl (0.15)
Where μ 1 and μ 2 are parameters that adjust the balance between run time and recovery cost, and μ 1+μ2 =1. When the time running weight and the recovery cost weight of a line are smaller, the overall weight of the line is also smaller.
Step 4: and establishing an extremely short-term control strategy model. Deviations in the "source load" power predictions can result in power imbalances, increasing grid losses, and thus increasing the risk of recovery failures in the distribution network. To address this problem, a self-regulating control strategy is proposed in the ultra-short term subsystem. The strategy takes a short-term global coordination optimization scheme as a reference, and the wind speed, solar irradiance and load ultra-short-term prediction information are updated in real time. The method adopts a rolling optimization method, and an optimal control sequence is constructed in a given time interval tau+delta t at the tau moment. This real-time rolling optimization is intended to determine the best adjustments to the distributed power output and the projected load. The primary objectives are to ensure power balance within each subsystem, to enhance operational stability, and to improve power quality. The roll control horizon is defined as T c, the sampling time interval is Δt, and the optimization objectives include cut load loss and network loss minimization.
The objective function of the ultra-short-term self-regulating control of the subsystem is to minimize the power failure loss by controlling the load shedding or recovery amount according to the load recovery condition obtained by the short-term global coordination optimization scheme. The aim is to achieve optimal power matching between "source charges" in the distribution network. The objective function y 1 is described as:
where P L,n,i (τ) represents the load power actually measured by node i of subsystem n at time τ. Δp L,n,i (τ) represents the load power variation predicted by node i of subsystem n at time τ.
Further, the load recovery state obtained by optimizing the y 1 is used as a reference, and the subsystem amount target is realized based on the short-term forecast information of the wind speed, the solar irradiance and the load. Autonomous control is to reduce the "source load" power imbalance by adjusting the output power of the controllable distributed power source, and minimize the network loss in the controlled subsystem. The subsystem autonomous control objective function y 2 is represented as:
Where ΔP loss,n (τ) represents the net loss of subsystem n at time τ.
Power balancing, distributed power supply output, node voltage constraints, etc., have been described above, i.e., constraints (4) - (6).
The energy storage system has a rapid start-up capability and the state of charge (SOC) remains relatively constant. It can provide stable voltage and frequency support for other power sources during recovery. Considering the energy storage state under the energy storage charging and discharging power constraint and the charging constraint, the energy storage states are respectively expressed as:
-PES,N≤PES(t)≤PES,N (0.18)
SSOC,ES,min≤SSOC,ES(t)≤SSOC,ES,max (0.19)
Wherein P ES,N is the energy storage rated power. S soc,ES,min,Ssoc,ES,max and S soc,ES (t) are the upper and lower limits of the stored state of charge and the state of charge during recovery. S soc,ES (t) can be described as:
Where δ is the self-discharge rate of the stored energy. η c and η d are charge-discharge efficiencies, respectively. And/>Is the charge-discharge power at time t. S s is the energy storage capacity size.
Step 5: solving the models in the steps 1 to 4. In order to reduce load shedding loss and network loss in the recovery process and realize optimal matching between different types of distributed power supplies and loads, subsystem rolling optimization and feedback correction control strategies are provided. It is assumed that the distribution network is divided into A, B subsystems.
1) Implementing stage scrolling autonomous control strategy
Step a: and obtaining short-term active output of wind power and photoelectricity and electricity consumption of each node by using a prediction model according to the current time k and the actually measured source charge state x (tau) in the subsystem. The operating state of the subsystem is further deduced and the power deviation between the distributed power supply and the load is calculated. It is determined whether future times τ+1, τ+2,..τ+t c require load shedding. If no load shedding is needed, directly performing the step c, otherwise, continuously performing the step b.
Step b: the future number of deloading of τ+1, τ+2, τ+t c is optimized according to the objective function y 1 if deloading is required. The first optimal solution in the control sequence is utilized to minimize load shedding losses. The expected load shedding losses are compared. If the subsystem A and the subsystem B have positive or negative power deviation, rolling optimization and autonomous control are performed, so that the distributed power supply and the load are matched until the load shedding loss is reduced to a reasonable level. And continuing to execute the step c. If the partial load needs to be cut off after the optimization of the subsystem A due to the power load difference, the load shedding loss obviously exceeds the expectation, and the subsystem B still has larger positive deviation after the important load is recovered, which indicates that the partition scheme in the short-term global coordination optimization of the power distribution network needs to be fed back and adjusted. Global feedback correction is performed.
Step c: and taking the optimized load recovery state as a reference, and further optimizing the power output power by using an objective function y 2. An optimal match between the distributed power supply and the load is achieved. The subsystem state x (τ+1) at τ+1 is updated, and then the above steps are repeated.
2) Feedback correction control strategy
The time-varying nature of the "source load" and preliminary partition limitations can affect recovery of the subsystem. It is necessary to adjust the partitioning scheme based on subsystem rolling optimization.
Step a: according to the preliminary partitioning scheme, the load is redistributed among the set of nodes waiting for partitioning. And (3) according to the power deviation between the distributed power supply and the load, which is obtained through the rolling optimization control, reallocating the load allocated to the subsystem A by the source according to the priority restoration weight rho i (t), and allocating the priority to the subsystem B. After reassignment, if the power deviations of A, B subsystems are within a reasonable range, the process is ended. Otherwise, continuing to execute the step b.
Step b: reducing the preliminary partition limit, expanding the node set waiting for partition, and then re-executing step a. If there is still a biased subsystem, step b of the real-time scrolling autonomous control strategy is directly performed, and if the load needs to be reduced to eliminate the excessive load, the global feedback correction is ended.
3) Model solving
The invention adopts a method of combining a genetic algorithm and a Floyd algorithm to solve the partition and subsystem recovery optimization model. The load node variables in the partition node set are coded by real integers, and the load node variables in each subsystem are coded by substitution. For each of the partitioned scheme subsystems, load recovery nodes and sequences are randomly generated. The Floyd algorithm is then invoked to search for a restoration path for each sequence. This process yields a globally optimal solution for overall system partition and subsystem recovery.
In conclusion, the model and the method provided by the invention obviously accelerate the recovery speed of the power distribution network and effectively improve the elasticity level of the power distribution network.
The following will be a device for global coordination and coordinated autonomous adjustment and recovery strategy of distributed energy provided by an embodiment of the present invention with reference to fig. 3, where the device includes:
the first construction module is used for dividing the load nodes with the electric distance which accords with an initial partition threshold value into partitions where the corresponding main power supplies are located according to the electric distances between the main power supplies and the load nodes in the target power distribution network, after the unassigned load nodes are placed into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned;
the second construction module is used for partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and obtaining an optimal partition by means of solution;
The third construction module is used for building a partition recovery model of each partition based on the partition result of the target power distribution network and the actual operation data of the load nodes in each partition;
The fourth construction module is used for building an extremely short-term control strategy model of each partition according to the operation prediction data and the actual operation data of the load nodes in each partition;
And the fifth construction module is used for solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network.
The invention provides a distributed energy global coordination and collaborative autonomous regulation recovery strategy device, which comprises the steps of dividing load nodes with the electric distance which accords with an initial partition threshold into partitions where corresponding main power supplies are located according to the electric distances between main power supplies and load nodes in a target power distribution network, putting the unassigned load nodes into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned; partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and solving to obtain an optimal partition; establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of load nodes in each partition; establishing an extremely short-term control strategy model of each partition according to operation prediction data and actual operation data of load nodes in each partition; and solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network. The invention obviously accelerates the recovery speed of the power distribution network and effectively improves the elasticity level of the power distribution network
In addition, the embodiment of the invention comprises a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the distributed energy global coordination and collaborative autonomous regulation recovery strategy method according to any one of the technical schemes when executing the computer program.
The embodiment of the invention also comprises a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the distributed energy global coordination and collaborative autonomous regulation recovery strategy method according to any one of the technical schemes when being executed by a processor.
The above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A distributed energy global coordination and coordinated autonomous regulation restoration strategy method, the method comprising:
Dividing load nodes with the electric distance meeting an initial partition threshold into partitions where the corresponding main power supplies are located according to the electric distances between the main power supplies and the load nodes in the target power distribution network, after the unassigned load nodes are placed into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned;
partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and solving to obtain an optimal partition;
establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of load nodes in each partition;
Establishing an extremely short-term control strategy model of each partition according to operation prediction data and actual operation data of load nodes in each partition;
And solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network.
2. The method according to claim 1, wherein the dividing the load node whose electrical distance meets the initial partition threshold into the partition where the corresponding main power source is located according to the electrical distance between the main power source and the load node in the target power distribution network specifically includes:
Determining the partition number of the target power distribution network according to the position of the main power supply in the target power distribution network;
calculating the shortest electrical distance from a load node to each main power supply in the target power distribution network by using a shortest path algorithm, wherein the weight value is the reactance value of each line;
And dividing the load node with the shortest electrical distance smaller than the initial partition threshold value into the partition where the corresponding main power supply is located.
3. The method according to claim 2, wherein the partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model specifically comprises:
Taking a main power supply in each partition as a starting node, traversing load nodes which accord with preset priorities in the power supply radius of the starting node, wherein the power supply radius is an available output range of the main power supply when a fault occurs;
judging whether the total power of the load nodes conforming to the preset priority exceeds a preset limit value or not;
If yes, placing the load nodes with the priority lower than the first preset priority in the load nodes conforming to the preset priority into the node set to be partitioned;
Judging whether the power of the load nodes in each partition after the load nodes lower than the first preset priority are removed exceeds a preset limit value or not;
if yes, after the initial partition threshold is modified, partitioning is conducted on the load nodes in the target power distribution network again;
And acquiring other available distributed power supplies which are in accordance with the power supply radius in each partition as a main power supply, and merging the partitions of the main power supplies with the overlapped power supply radius when the power supply radius of the main power supplies is overlapped.
4. The method according to claim 3, wherein the partitioning the load nodes to be partitioned in the node set to be partitioned by using a partition optimization model, and solving to obtain an optimal partition specifically includes:
According to the recovery time of each partition, the partition evaluation recovery time, the reactive power charging times in the partition recovery path and the switching times in the partition recovery path, an objective function with the lowest total emergency recovery cost is established as an objective function in the partition optimization model:
Where F is the total emergency recovery cost, D is the total number of partitions, T n is the recovery time cost for partition n, Is partition average recovery time cost, where/>G 1,n represents the reactive charge times in the recovery path of the partition n, g 2,n represents the switching times in the recovery path of the partition n, and g 1,n、g2,n and T n jointly form the recovery cost of the partition n;
Establishing constraints of the partition optimization model by using the running states of the distributed power supplies and the running states of the load nodes in each partition, wherein the constraints comprise:
Power adequacy constraints:
Wherein Ω G,d is the number of distributed power supplies in the nth partition, β G,n,i represents the start-up state of the ith distributed power supply in the nth partition, wherein 1 represents that power is started and is being generated, 0 represents that power is not started, P G,n,i is the active output power of the ith load node in the nth partition, Ω L,d is the number of the ith load node in the nth partition, β L,n,i is the recovery state value of the ith load node in the nth partition, wherein 1 represents that power is recovered, 0 represents that power is not recovered, and P L,n,i is the active power of the load node j in the partition n;
Distributed power output constraints:
Where P Gm is the active output of the active distributed power supply, Q Gm is the reactive output of the active distributed power supply, Ω G is the set of all distributed power supplies in the partition, Is the minimum active output of the started distributed power supply,/>Is the maximum active output of the started distributed power supply,/>Is the minimum reactive output of the started distributed power supply,/>The maximum reactive output of the started distributed power supply;
node voltage constraint:
where U i is the voltage magnitude of the restored load node i, Ω N is the set of restored load nodes in the partition, Is the maximum voltage magnitude of the recovered load node i,/>Is the minimum voltage amplitude of the recovered load node i;
Node power balancing constraints:
Wherein P i and Q i are respectively active power and reactive power injected by a load node i, U i and U j are respectively voltage amplitudes of the load node i and a load node j, and G ij、Bij and theta ij are conductance, susceptance and voltage phase angle differences between the load node i and the load node j;
transmission line capacity limiting constraint
Where P Ll denotes the active power transmitted in line i,Is the maximum active power transmitted in line l;
radiation operation constraints:
H∈ΩH
Wherein Ω H represents a set of radiating topologies in the target distribution network;
And obtaining an optimal partitioning result of the load nodes to be partitioned in the node set to be partitioned by utilizing the objective function and the constraint in the partition optimization model.
5. The method according to claim 4, wherein the establishing a partition recovery model of each partition based on the partition result of the target power distribution network and actual operation data of the load nodes in each partition specifically includes:
for each partition, the following objective function is established:
Where Ω L,n is the number of all load nodes in the nth partition, Ω l,n is the number of all lines in the nth partition, t r,i represents the time to power up load node i in partition d at recovery, Indicating when partition failure has been resolved and partition n resumes normal operation, β L,d,i indicating the recovery state of load node i in partition n, 1 indicating recovery, 0 indicating no recovery, ρ L,n,i (t) indicating the priority weight of load node i in partition n at time t, P L,n,i (t) indicating the active power of load node i in partition n at time t, Z l indicating the weight of line l;
Establishing the weight of the production line operation time, the cost weight of line recovery and the weight of the line of each partition in the target power distribution network;
the weight of the production line operation time comprises the operation time of a line;
the expected value E (t l) and variance σl of the run time of the line are:
The variance σ l of the run time of the line is:
Where l ε. Phi. L and ψ L represent the set of all lines in the partition;
The weight of the production line operation time also comprises the mean value and variance of the recovery path operation time;
assuming that L lines form the power transmission path at a specific node i, the mean and variance of the recovery path running time are:
the mean value of the recovery path running time
Variance of the recovery path run time
Wherein A is the operation optimistic estimated time of each line, B is the operation pessimistic estimated time of each line and M is the operation most probable estimated time of each line, t l is the actual recovery time of each line;
Cost weight of the line reclamation:
Wl＝Cl+Sl，
wherein the total charge capacitance on the load node i recovery path is
Wherein C L represents the total charge capacitance, C l represents the charge capacitance of the line l, and S l is the running cost of the on-off switch of the line l;
The weight of the line: z l＝μ1Wl+μ2tl, where W l is the recovery cost weight, t l is the run time weight, and μ 1 and μ 2 are parameters that adjust the balance between run time and recovery cost.
6. The method according to claim 5, wherein the establishing the extremely short-term control strategy model of each partition according to the operation prediction data and the actual operation data of the load node in each partition specifically comprises:
A first objective function y 1 of the extremely short-term control strategy model is built with the aim of minimizing the fault loss by controlling the cut-off load or the recovery amount:
where P L,n,i (τ) represents the load power actually measured by load node i of partition n at time τ, Δp L,n,i (τ) represents the load power variation predicted by load node i of partition n at time τ;
Establishing a second objective function y 2 with zoned network loss minimization:
where ΔP loss,n (τ) represents the network loss value of partition n at time τ;
Establishing constraints of the extremely short-term control strategy model of the partition:
Constraints of the extremely short-term control strategy model include energy storage charge-discharge power constraints:
-PES,N≤PES(t)≤PES,N
The constraints of the extremely short-term control strategy model further comprise energy storage state constraints under charging constraints:
SSOC,ES,min≤SSOC,ES(t)≤SSOC,ES,max
Wherein P ES,N is the energy storage rated power, P ES (t) is the energy storage power, and S soc,ES,min,Ssoc,ES,max and S soc,ES (t) are the upper and lower limits of the energy storage state of charge and the state of charge in the recovery process respectively;
Wherein,
Where δ is the self-discharge rate of the stored energy, η c and η d are the charge efficiency and discharge efficiency respectively,And/>The charge power and the discharge power at time t, respectively, and S s is the energy storage capacity.
7. The method according to claim 6, wherein the solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network specifically comprises:
s1, determining the running state of each partition according to the state x (tau) of the load node of each partition at the current time tau, short-term active output of wind power and photoelectricity and the power consumption of each load node;
Calculating power deviation between the distributed power supply and the load in the partition, and determining whether load nodes need to be reduced at a preset future time according to the power deviation;
if not, executing the step S3, otherwise, executing the step S2;
S2, optimizing a preset future time according to the first objective function y 1 to reduce the number of load nodes, if the load nodes are reduced, reducing the network loss value of the partition to the preset loss value, executing a step S3, otherwise, performing global feedback correction on the target power distribution network;
s3, after optimizing the power output by using an objective function y 2, updating the partition state x (tau+1) of tau+1, and then executing the steps S1 to S3 until the partition state of the preset future time is updated, and executing the step S4;
S4, calculating power deviation between the distributed power sources and the load nodes in the partitions, and after the load nodes in the partitions are redistributed according to the priority recovery weight rho i (t), executing a step S6 if the power deviation between the distributed power sources and the load nodes in the partitions is in a preset power deviation area;
otherwise, executing the step S5;
S5, reducing preliminary partition limitation, expanding the node sets to be partitioned and the like, and re-executing the step S1;
And S6, solving the partition optimization model, the partition recovery model and the extremely short-term control strategy model by a method combining a genetic algorithm and a Floyd algorithm to obtain a global optimal solution of the partition and partition recovery of the target power distribution network.
8. A distributed energy global coordination and coordinated autonomous regulation restoration strategy device, the device comprising:
the first construction module is used for dividing the load nodes with the electric distance which accords with an initial partition threshold value into partitions where the corresponding main power supplies are located according to the electric distances between the main power supplies and the load nodes in the target power distribution network, after the unassigned load nodes are placed into a node set to be partitioned, partitioning the load nodes to be partitioned in the node set to be partitioned by using a low-level model, and deleting the partitioned load nodes from the node set to be partitioned;
the second construction module is used for partitioning the load nodes to be partitioned in the node set to be partitioned by utilizing a partition optimization model, and obtaining an optimal partition by means of solution;
The third construction module is used for building a partition recovery model of each partition based on the partition result of the target power distribution network and the actual operation data of the load nodes in each partition;
The fourth construction module is used for building an extremely short-term control strategy model of each partition according to the operation prediction data and the actual operation data of the load nodes in each partition;
And the fifth construction module is used for solving the low-level model, the partition optimization model, the partition recovery model and the extremely short-term control strategy model by implementing a stage rolling autonomous control strategy and a feedback correction control strategy to obtain an optimal matching result of the load node and the main power supply in the target power distribution network.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of restoration optimization strategy for distributed energy global coordination and coordinated autonomous regulation of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the distributed energy global coordination and coordinated autonomous tuning restoration policy method of any of claims 1 to 7.
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