CN116388153A - Optimal configuration method for flexible interconnection equipment in active power distribution network - Google Patents

Optimal configuration method for flexible interconnection equipment in active power distribution network Download PDF

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CN116388153A
CN116388153A CN202310224683.8A CN202310224683A CN116388153A CN 116388153 A CN116388153 A CN 116388153A CN 202310224683 A CN202310224683 A CN 202310224683A CN 116388153 A CN116388153 A CN 116388153A
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赵莹莹
司文荣
刘召杰
江安烽
许唐云
范钰波
施刚
周剑桥
张建文
陈洁
田越
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State Grid Shanghai Electric Power Co Ltd
East China Power Test and Research Institute Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention relates to an optimal configuration method of flexible interconnection equipment in an active power distribution network, which comprises the steps of constructing a two-stage SOP planning model; the method comprises the steps of constructing a power distribution network operation scene containing renewable energy power generation in a first stage, calculating system node marginal electricity price SOP distribution through a primary-dual internal point generation algorithm, and selecting nodes with large node electricity price distribution difference and high system active and reactive network loss sensitivity as a basis for configuring SOP positions through an LMP analysis method by improving social benefit balance angles; and in the second stage, a mixed integer second order cone planning model is constructed by using a mathematical programming method to solve, so that the flexible interconnection equipment optimal configuration scheme with the minimum cost and highest economical efficiency is obtained. Compared with the prior art, the invention has the advantages of effectively reducing the running cost of the power system and the like.

Description

Optimal configuration method for flexible interconnection equipment in active power distribution network
Technical Field
The invention relates to the technical field of flexible interconnection equipment, in particular to an optimal configuration method of flexible interconnection equipment in an active power distribution network.
Background
The traditional medium-low alternating current distribution network is a radiation type structure with unidirectional flowing power, and all feeder lines can not be interconnected to run due to the problem of electromagnetic ring network. In recent years, the permeability of renewable distributed energy sources (distributed energy resources, debs) in an active power distribution network is increasingly improved, and the access of diversified source network charge storage equipment brings great challenges to the operation of a traditional power distribution network. The wide access of distributed power sources (distributed generator, DG) helps to peak cut and valley fill, improve power reliability, and reduce user electricity costs. But at the same time, the voltage limit caused by DG access is out of limit, and the problems of network blocking and bidirectional tide are increasingly prominent. The traditional distribution network has limited regulation modes, and the problem of distribution network operation caused by high-permeability DG access is widely focused on how to effectively solve.
Node marginal electricity prices (locational marginal price, LMP) are defined as the marginal cost of the power system increase when new load demands are met. This pricing is commonly employed in centralized power markets. After the new electricity is changed in China, the LMP is selected as a spot market pricing mechanism in Guangdong, shandong, zhejiang and other places. The LMP provides not only price signals to producers, consumers and managers, but also investment signals to investors. The problems of network blockage and the like caused by high DG permeability in the system can lead to unbalanced LMP distribution, the LMP of a partial area is increased, and the electricity cost of a user is increased.
In the above background, flexible interconnection devices mainly comprising flexible interconnection equipment (SOP) are derived to replace conventional Tie Switches (TSs) so as to construct flexible power distribution. The distribution network realizes flexible closed-loop operation by virtue of the flexible interconnection device (flexible interconnection device, FID), and the problems of unbalanced feeder load, out-of-limit voltage and the like caused by the fact that a wide-range distributed power supply is connected into a traditional distribution network can be effectively solved. The SOP can independently regulate and control active power and reactive power at two ends of the feeder line in real time, dynamically and continuously, further balances line load and optimizes voltage distribution, and is beneficial to improving active regulation of the power distribution network. Meanwhile, due to isolation and power regulation of the direct-current side of the flexible interconnection equipment, the flexible power distribution network with fault protection can obviously reduce fault time and power supply recovery time. Due to the changing policies of the power and energy departments, a large number of DG's are incorporated into the distribution network, resulting in increasingly restrictive aspects of traditional distribution networks. Therefore, intelligent distribution networks and active distribution networks using SOPs to replace TS are favored, and the prior art has little discussion on the scheme of locating and sizing flexible interconnection equipment, or only the operation characteristics of DGs are considered when planning is carried out, or the location of SOPs is determined by using an opportunity constraint method, or LMPs are calculated by using an original-dual interior point method, but few researches simultaneously relate to the locating and sizing problems of flexible interconnection equipment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an optimal configuration method for flexible interconnection equipment in an active power distribution network, which considers uncertainty of DG operation, establishes an optimal installation position of SOP in the active power distribution network from the angles of improving system voltage distribution and social benefit balance, and effectively improves pertinence of SOP to blocking management and voltage price adjustment. And then, establishing an SOP constant volume optimization model based on the node electricity price, and converting the original problem into an MISOCP model by adopting a linearization and cone relaxation method. Finally, on the improved IEEE33 node power distribution system, the proposed SOP optimal configuration method is calculated and analyzed.
The aim of the invention can be achieved by the following technical scheme:
according to one aspect of the invention, an optimal configuration method of flexible interconnection equipment in an active power distribution network is provided, and a two-stage SOP planning model is built;
the method comprises the steps of constructing a power distribution network operation scene containing renewable energy power generation in a first stage, calculating system node marginal electricity price SOP distribution through a primary-dual internal point generation algorithm, and selecting nodes with large node electricity price distribution difference and high system active and reactive network loss sensitivity as a basis for configuring SOP positions through an LMP analysis method by improving social benefit balance angles;
and in the second stage, a mixed integer second order cone planning model is constructed by using a mathematical programming method to solve, so that the flexible interconnection equipment optimal configuration scheme with the minimum cost and highest economical efficiency is obtained.
As an optimal technical scheme, the two-stage SOP planning model comprises the step of solving the optimal running state of the power distribution system in a classical scene, including the step of solving the optimal power flow and the step of solving the SOP capacity at the current position.
As an preferable technical scheme, the SOP planning model adopts a direct current power flow model.
As a preferred technical solution, the original-dual internal point algorithm is specifically as follows:
min p T P G
s.t.e T (P G -P D )＝0
T(P G -P D )≤F max
P Gmin ≤P G ≤P Gmax
wherein p is the generator bid vector; p (P) G For generating a force vector, the load node force is 0; p (P) D The node load is; t is a node power and branch power flow sensitivity matrix; f (F) max For line tide capacity constraint, P Gmin Minimum generator output, P Gmax The maximum output value of the generator is set;
introducing relaxation variables s, s 1 ,s 2 Converting the inequality constraint into an equality constraint;
T(P G -P D )+s-F max ＝0
P G -s 1 -P Gmin ＝0
P G +s 2 -P Gmax ＝0
introducing a logarithmic barrier function into the objective function, eliminating the non-negative constraint of the relaxation variable; introduces a Lagrangian multiplier variable lambda 1 、λ 2 And multiplier vector r 1 、r 2 They represent the relaxation constraint price signals corresponding to the constraints, respectively, resulting in the following Lagrangian function;
Figure SMS_1
wherein L (p, μ) is Lagrangian function equation, s i 、s 1i 、s 2i The relaxation variable introduced for constructing the Lagrangian function, μ is the Lagrangian multiplier;
is available according to the Kuhn-Tucker optimality condition
Figure SMS_2
p i Quoting T for the ith node generator ki For the node power matrix of the kth unit on the node i, the correlation r 1i 、r 2i Solving the introduced ith node multiplier vector for the KKT condition;
based on the definition of the marginal electricity price of the node, P is calculated for the above method D The derivative gives the electricity price of the node:
Figure SMS_3
wherein ρ is i The electricity price of the node i is the node number of the power distribution system accessed by the SOP, lambda 1 Lambda is the Lagrangian multiplier variable 2i Is a Lagrangian multiplier vector;
at the optimal solution of the original-dual interior point algorithm, the barrier parameter mu should approach zero, so that a correction strategy of a dual gap method is adopted to iteratively reduce the value of mu;
to maintain the original feasibility and dual feasibility, the solving process needs to select iteration step length, and the original step length t is adopted p Dual step t D The method of (2) reduces the iteration times;
Figure SMS_4
Figure SMS_5
Δs、Δs 1 、Δs 2 for introduced relaxation variable variation Δr 1 、Δr 2 Lambda is the variation of Lagrangian multiplier vector 2 Is a lagrangian multiplier variable.
As an preferable technical solution, in the SOP planning model, a constraint equation expression of the SOP is specifically as follows:
1) SOP capacity constraint:
Figure SMS_6
Figure SMS_7
2) SOP active power balance constraint:
P SOP,i ＝|P SOP,j +P SOP,loss,ij |
Figure SMS_8
3) SOP reactive power constraint:
|Q SOP,i |≤ηS SOP,ij
|Q SOP,j |≤ηS SOP,ij
in the above formulae, S SOP,ij The capacity of the SOP connected between nodes i and j; p (P) SOP,i ，P SOP,j ，Q SOP,i ，Q SOP,j Active power and reactive power output by the SOP are respectively input to the power grid to be positive; p (P) SOP,loss,ij A device loss for the SOP; c SOP,loss A loss factor for the SOP; η is the reactive power limit coefficient of the SOP.
As an optimal technical scheme, the two-stage SOP planning model comprises an upper layer site selection model and a bottom layer capacity optimization model, wherein the upper layer optimization model is used for determining the installation position of the SOP, and the bottom layer optimization model is used for determining the optimal operation condition of the whole power distribution system in a typical scene so as to determine the capacity.
As an optimal technical scheme, the bottom capacity optimization model enables the planning model to minimize the sum of network loss and SOP loss of each condition under the condition that various constraint conditions of a power distribution network are met, and the mathematical expression is as follows:
Figure SMS_9
wherein: c is electricity price; t is the power supply time; s is the number of scenes; n is the number of system nodes; p (P) i (s) is the active injected at the s-th scene node iThe sum of the powers; p(s) is probability corresponding to the s-th scene, P SOP,loss,ij Is SOP transmission power loss, where P i (s) can be expressed by an active power flow constraint equation.
As a preferable technical scheme, the various constraint conditions satisfied by the bottom optimization model are specifically as follows:
Figure SMS_10
Figure SMS_11
Figure SMS_12
Figure SMS_13
wherein Ω (i) represents a set of nodes connected to node i; v (V) i.t 、V j,t And theta ij,t The voltage amplitude and phase angle difference of the t scene nodes i and j are respectively; g ii 、B ii 、G ij 、B ij Self-conductance, self-susceptance, mutual-conductance and mutual-susceptance in the node admittance matrix respectively; p (P) L,i,t 、Q L,i,t Active power and reactive power injected by loads on the node i are respectively; p (P) DG,i,t 、Q DG,i,t Active power and reactive power injected by the distributed power supply on the s-th scene node i are respectively;
Figure SMS_14
and->
Figure SMS_15
Respectively the upper limit and the lower limit of the voltage amplitude of the node i; i ij,t The current amplitude of the t scene of the branch j; />
Figure SMS_16
Is the electricity of branch ijAn upper flow magnitude limit; p (P) SOP,i,t Active power, Q, for SOP transmission of the t-th scene on node i SOP,i,t Reactive power for SOP transmission of the t-th scenario on node i.
As an optimal technical scheme, the upper layer site selection model transmits the position of the SOP to the bottom layer capacity optimization model, the bottom layer capacity optimization model optimizes the running state of the power distribution system based on the structure and operation constraint of each scene, then the optimization result is returned to the upper layer site selection model, and the upper layer site selection model uses the result transmitted by the bottom layer capacity optimization model to calculate the objective function value of the current SOP planning scheme.
As an optimal technical scheme, the SOP position optimal configuration further comprises a management benefit method, namely the system active and reactive power network loss sensitivity is calculated, and the model is as follows:
after the power balance equation of the system node is expanded according to Taylor first order, the reactive variable delta Q and the active variable delta P are made to be 0, and the relation between the voltage variable delta V and the voltage variable delta Q and the voltage variable delta P can be obtained
Figure SMS_17
Figure SMS_18
Wherein: v, θ are the voltage amplitude and phase angle, respectively.
In the two-stage SOP planning model, from the perspective of social benefit balance, the SOP is configured among nodes with larger LMP difference by considering the network loss sensitivity of the nodes. After the SOP is accessed into the system, the network loss sensitivity is calculated to optimize the subsequent SOP installation position. The solving process is divided into two layers, and the MISOOCP model aims at optimizing SOP installation capacity and aims at the lowest annual comprehensive cost of the power distribution system. The LMP running layer is used for judging whether the SOP configuration can meet social welfare balance or not and optimizing LMP distribution. And computing the system LMP, and configuring the SOP according to the electricity price difference. In summary, the invention aims at the problem of SOP site selection and volume determination, comprehensively considers the investment and operation cost of SOP and the uncertainty of a distributed power supply, and aims at the minimum cost and the highest economical efficiency of the whole scheme to provide a flexible interconnection equipment site selection and volume determination method based on data driving and node electricity prices. The method provides an active power distribution network SOP optimal configuration method based on node marginal electricity price analysis. Firstly, considering uncertainty of DG output, adopting a fuzzy clustering method to perform scene reduction, and generating a typical DG output scene. And (3) carrying out site selection on the SOP by combining active and reactive network loss sensitivity distribution of the system nodes through marginal electricity price analysis of the system nodes. And then, establishing a mathematical optimization model which takes the annual comprehensive operation cost of the power distribution network as a minimum target and takes system power flow constraint into consideration and SOP access capacity as a decision variable, carrying out model conversion through second-order cone planning, and solving the SOP optimization configuration model after LMP calculation.
Compared with the prior art, the invention aims at the problem of optimal configuration of the flexible interconnection device in the flexible active power distribution network, one stage aims at social benefit balance, two stages aim at network loss minimization, a two-stage optimal configuration method based on node electricity price-network loss sensitivity analysis is provided, a typical scene is generated by a fuzzy clustering method, and an electricity price distribution data set is constructed; through the example simulation test, compared with the traditional scheme, the invention effectively reduces the running cost of the power system, and the design of the interconnection scheme obtained by using the invention has better flexibility and economy.
Drawings
FIG. 1 is a solution flow chart of an active power distribution network flexible interconnection equipment configuration method comprehensively considering economic and management benefits according to an embodiment of the present invention;
fig. 2 is a schematic diagram of SOP access locations of an active power distribution network flexible interconnection equipment configuration method that comprehensively considers economic and management benefits according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a power distribution network with DG according to a method for configuring flexible interconnection equipment of an active power distribution network, which comprehensively considers economic and management benefits according to an embodiment of the present invention;
FIG. 4 is a graph of typical wind power output for an active power distribution network flexible interconnect equipment configuration method that takes into account both economic and management benefits in accordance with one embodiment of the present invention;
FIG. 5 is a graph of typical photovoltaic power generation output of an active distribution grid flexible interconnect equipment configuration method that takes into account both economic and management benefits in accordance with one embodiment of the present invention
FIG. 6 is a schematic view showing LMP improvement brought by a configuration scheme of an active power distribution network flexible interconnection equipment configuration method comprehensively considering economic and management benefits according to an embodiment of the present invention
Fig. 7 is a schematic diagram of node loss sensitivity of an active power distribution network flexible interconnection equipment configuration method comprehensively considering economic and management benefits according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The following describes in detail an embodiment of the flexible interconnection equipment site selection and volume determination method based on data driving and node electricity prices.
A typical scene of renewable energy power generation is constructed by combining a fuzzy clustering method based on density peaks; a fuzzy c-means algorithm (FCM) based on soft partitioning is adopted, and the category to which the sample belongs is judged in a fuzzy way by introducing the concept of membership. The specific steps are as follows.
Step 1: and determining the number of categories, an initial cluster center, a membership matrix and iteration termination conditions.
Step 2: a distance matrix of the data samples to the respective cluster centers is calculated.
Step 3: the cluster center is known and the membership is updated. And (5) recalculating the objective function, and ending the iteration when the objective function value is smaller than the iteration error.
Based on the data of the measured annual wind speed and the annual illumination intensity in a certain place, wind power generation and photovoltaic power generation data of a unit capacity unit are respectively calculated, and then the four-season typical wind-light output scene based on a fuzzy mean clustering algorithm is calculated.
A two-stage SOP planning model is constructed, and the installation position of the SOP is determined by comprehensively considering the DG operation characteristics. The method considers uncertainty of DG operation, establishes the optimal installation position of SOP in the active power distribution network from the angles of improving system voltage distribution and social benefit balance, and effectively improves the pertinence of SOP to blocking management and voltage price adjustment. And then, establishing an SOP constant volume optimization model based on the node electricity price, and converting the original problem into an MISOCP model by adopting a linearization and cone relaxation method.
Further, the optimization objective of the upper planning model is to minimize the annual average total cost, the optimization variables are the capacities of the SOPs of each group, and the mathematical expression is:
Figure SMS_19
calculating SOP fixed investment cost C per year SOP
Figure SMS_20
Wherein N is the number of system nodes; Ω (i) is the set of all neighboring nodes of node i; c m The cost of the unit capacity of SOP;
Figure SMS_21
is the installed capacity of the SOP; CRF (Cryptographic CRF) SOP Is the capital recovery coefficient of SOP, d is the discount rate, and y is the age.
Figure SMS_22
SOP annual operation maintenance cost
Figure SMS_23
Figure SMS_24
η is the annual SOP operation maintenance cost coefficient.
Annual power supply loss cost C of power distribution network loss
Figure SMS_25
Wherein: c is electricity price; t is the power supply time; s is the number of scenes; n is the number of system nodes; p (P) i (s) is the sum of the active powers injected at the s-th scene node i; p(s) is probability corresponding to the s-th scene, P SOP,loss,ij Is SOP transmission power loss.
The SOP is mainly installed at a conventional tie switch, as shown in fig. 2, and can flexibly control active power transmitted between two feeder lines and provide a certain reactive power support. The realization is mainly based on a full-control type power electronic device, and the specific realization modes mainly comprise 3 types: back-to-back voltage source converters (back to back voltage source converter, BTB-VSC), static synchronous series compensators (static synchronous series compensator, SSSC) and unified power flow controllers (unified power flow controller, UPFC).
The invention takes BTB-VSC as an example to study SOP planning problem in a power distribution system. The controllable variables of SOP include 4: the active power and the reactive power output by the two converters. Although BTB-VSC efficiency is very high, when distributed power supply and load maldistribution occur, and active power flow needs to be transferred in a large range, certain power loss can be generated. The SOP is assumed to inject power into the grid in the positive direction. The reactive power output by the two converters is not affected by the isolation of the direct current links, and only the capacity constraint of the respective converters is required to be met. The PQ-VdcQ control is selected as the control mode of SOP, and the following SOP constraint equation is obtained:
1) Active power limiting of SOP
P i,SOP (s)+P j,SOP (s)+P ij,SOP (s)＝0
P ij,SOP (s)＝A i,SOP |P i,SOP (s)|+A j,SOP |P j,SOP (s)|
2) Reactive power limiting of SOP
-μS ij,SOP ≤Q i,SOP (s)≤μS ij,SOP
-μS ij,SOP ≤Q j,SOP (s)≤μS ij,SOP
3) Capability restriction of SOP
Figure SMS_26
Figure SMS_27
In the formula, s is an operation optimization scene; i. j is the node number of the power distribution system accessed by the SOP; p (P) i,SOP (s)、P j,SOP (s)、Q i,SOP (s)、Q j,SOP (s) respectively injecting active power and reactive power into the two converters of the s-th scene SOP; a is that i,SOP And A j,SOP Is the loss coefficient of the converter; p (P) ij,SOP (s) SOP transmission loss; μ is the absolute value of the power factor angular sine; s is S ij,SOP For SOP capacity between nodes i, j.
And calculating line current in the SOP planning model by adopting a direct current flow method.
The SOP site selection model is constructed based on the node electricity price, and the mathematical model of the LMP calculated by adopting the original-dual interior point method is specifically as follows:
introducing a logarithmic barrier function into the objective function, eliminating the non-negative constraint of the relaxation variable; introduces a Lagrangian multiplier variable lambda 1 ，λ 2 And multiplier vector r 1 ，r 2 These represent the relaxation constraint price signals corresponding to the constraints, respectively, resulting in the following Lagrangian function.
Figure SMS_28
Is available according to the Kuhn-Tucker optimality condition
Figure SMS_29
Based on the definition of the marginal electricity price of the node, P is calculated for the above method D The derivative gives the electricity price of the node:
Figure SMS_30
wherein ρ is i The electricity price of the node i is the node number of the power distribution system accessed by the SOP, lambda 1 Lambda is the Lagrangian multiplier variable 2i Is a lagrangian multiplier vector.
At the optimal solution of the original-dual interior point algorithm, the barrier parameter μ should approach zero, so a "dual gap" method correction strategy is employed to iteratively reduce the value of μ.
To maintain the original feasibility and dual feasibility, the solving process needs to select iteration step length, and the original step length t is adopted p Dual step t D The method of (2) reduces the number of iterations.
Figure SMS_31
Figure SMS_32
Further, the two-stage SOP planning model comprises an upper layer optimization model and a bottom layer optimization model, wherein the upper layer optimization model is used for determining the installation position of SOP, and the bottom layer optimization model is used for determining the optimal operation of the whole power distribution system and the installation capacity of SOP in a typical scene.
In the invention, from the perspective of social benefit balance, SOP is configured among nodes with larger LMP difference by considering the network loss sensitivity of the nodes. After the SOP is accessed into the system, the network loss sensitivity is calculated to optimize the subsequent SOP installation position. The solving process is divided into two layers, and the MISOOCP model aims at optimizing SOP installation capacity and aims at the lowest annual comprehensive cost of the power distribution system. The LMP running layer is used for judging whether the SOP configuration can meet social welfare balance or not and optimizing LMP distribution. And computing the system LMP, and configuring the SOP according to the electricity price difference.
The underlying problem is to determine the location of the SOP from the previously calculated LMPs. The planning model is to minimize the sum of network loss and SOP loss for each scenario while satisfying various constraints of the distribution network. The objectives of this model are as follows.
Figure SMS_33
Wherein S is the number of scenes, N is the number of system nodes, and P i (s) is the sum of the active powers injected at the s-th scene node i, A i,SOP As the loss coefficient of the converter, P i,SOP (s) active power injected for the s-th scene SOP converter
The objective of the underlying optimization model is to minimize the sum of network losses and SOP losses for each case for the planning model under various constraints of the distribution network, the mathematical expression is as follows:
Figure SMS_34
wherein S is the number of scenes, N is the number of system nodes, and P i (s) is the sum of the active powers injected at the s-th scene node i, A i,SOP As the loss coefficient of the converter, P i,SOP And(s) is the active power injected by the s-th scene SOP converter.
Further, the various constraint conditions satisfied by the underlying optimization model are specifically as follows:
Figure SMS_35
Figure SMS_36
Figure SMS_37
Figure SMS_38
wherein Ω (i) represents a set of nodes connected to node i; v (V) i (s)、V j (s) and θ ij (s) voltage amplitude and phase angle difference of the s-th scene nodes i and j respectively; g ii 、B ii 、G ij 、B ij Self-conductance, self-susceptance, mutual-conductance and mutual-susceptance in the node admittance matrix respectively; p (P) L,i,t 、Q L,i,t Active power and reactive power injected by loads on the node i are respectively; p (P) DG,i,t 、Q DG,i,t Active power and reactive power injected by the distributed power supply on the s-th scene node i are respectively;
Figure SMS_39
and->
Figure SMS_40
Respectively the upper limit and the lower limit of the voltage amplitude of the node i; i ij (s) is the current magnitude of the s-th scenario of branch j; />
Figure SMS_41
Is the upper current amplitude limit for branch ij.
Variables that need to be solved include: the installation location and capacity of the SOP, the voltage amplitude and phase angle of each scene node, and the active power and reactive power delivered by the SOP.
Aiming at the problem of SOP site selection and volume determination, the invention comprehensively considers the investment and operation cost of SOP and the uncertainty of a distributed power supply, and aims at the minimum cost and the highest economical efficiency of the whole scheme, and provides a flexible interconnection equipment site selection and volume determination method based on data driving and node electricity price. The method provides an active power distribution network SOP optimal configuration method based on node marginal electricity price analysis. Firstly, considering uncertainty of DG output, adopting a fuzzy clustering method to perform scene reduction, and generating a typical DG output scene. And (3) carrying out site selection on the SOP by combining active and reactive network loss sensitivity distribution of the system nodes through marginal electricity price analysis of the system nodes. And then, establishing a mathematical optimization model which takes the annual comprehensive operation cost of the power distribution network as a minimum target and takes system power flow constraint into consideration and SOP access capacity as a decision variable, carrying out model conversion through second-order cone planning, and solving the SOP optimization configuration model after LMP calculation. The application of the above structure and method is further described below in conjunction with specific simulation examples.
The invention takes IEEE33 node as an example, and analyzes and verifies the proposed planning method. An example of an IEEE33 node is shown in FIG. 5, with a voltage level of 12.66kV. The model is solved based on the matpower toolbox in Matlab and the simulated annealing algorithm.
The method is used for verifying the voltage out-of-limit and network blocking improvement effect of SOP on high DG permeability in the power distribution network. The three-table WT and the two-table PV power are connected into a power distribution system, the installation positions of the PV are 13 nodes and 22 nodes, and the installation capacities are 700 kW and 500kW respectively. The WT was installed at 7, 27 and 32 nodes with installation capacities of 800, 600 and 300kW, respectively. .
According to the distribution data of the wind speed and the illumination intensity in a certain year, a typical landscape output scene based on a clustering algorithm is calculated by adopting the method, and the homeotropic distribution of new energy output (standard value) of unit capacity is shown in figure 4.
The original-dual interior point method is adopted to analyze and calculate the whole-day LMP of the system, the initial LMP and the LMP distribution considering the network loss cost are shown in figure 5, and the active and reactive network loss sensitivity distribution of the system is shown in figure 7. The contrast analysis judges that the LMP difference of the node 8-21 system is the largest, and the sensitivity of the network loss at the node is higher. The primary access node of the SOP should be called. And continuously calculating LMP and system loss sensitivity according to the SOP optimal configuration flow, estimating the access position of the next SOP to be selected, and taking the node 12-22 as the node according to the calculation result. And so on, the access location to the third candidate SOP is node 25-29. Since the SOP configuration cost is high, the SOP maximum access point is set to 3.
SOP configuration schemes are divided into 4 types through an LMP-based SOP optimization configuration scheme: scheme 1, not accessing SOP; scheme 2, nodes 8-21 access an SOP; scheme 3, nodes 8-21, 12-22 access 2 SOPs; scheme 4, access 3 SOPs. The different SOP access scheme cost pairs are shown in table 1.
TABLE 1
Figure SMS_42
Figure SMS_43
According to the invention, the randomness and the fluctuation of wind power generation and photovoltaic power generation are fully considered, and an SOP planning model based on LMP and considering the running characteristic of a distributed power supply is established. And solving the problem by adopting an intelligent optimization algorithm and a mathematical programming method.
SOPs have flexible and versatile control modes that can provide voltage support in the event of faults, reactive compensation in the event of voltage overrun, and increase distributed capacity. In view of the above economic and environmental benefits, the practical value of standard operating procedures will be further enhanced. The practical value of SOP will be further improved.
The invention establishes a SOP (system on demand) site selection and volume determination planning model, takes the annual average total cost in a planning period as an optimization target, establishes a typical scene by using a fuzzy clustering combination method aiming at the operating characteristics of DGs, and considers the annual average total cost in the planning period, the investment cost and the annual operating cost of the SOP in the planning period and the network loss cost of a power distribution network in detail. Comprehensively considering economic and management benefits; in order to solve the problem of site selection and volume setting planning, a mathematical planning method is adopted for solving. Finally, the effectiveness and economy of the scheme of the invention are verified on an IEEE33 node experimental feeder line.
In an alternative embodiment, a fuzzy clustering method based on softening points or other clustering algorithms may also be used to find the typical scene.
It should be further noted that the invention is exemplified by back-to-back voltage source converters, and may be modified by other typical topologies such as a unified power flow controller, or otherwise, within the spirit of the invention. The flow chart of the method can be optimized in detail, the flow of algorithm steps is increased or simplified, or other modifications are possible within the essence of the invention.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (10)

1. The method is characterized by constructing a two-stage SOP planning model;
the method comprises the steps of constructing a power distribution network operation scene containing renewable energy power generation in a first stage, calculating system node marginal electricity price SOP distribution through a primary-dual internal point generation algorithm, and selecting nodes with large node electricity price distribution difference and high system active and reactive network loss sensitivity as a basis for configuring SOP positions through an LMP analysis method by improving social benefit balance angles;
and in the second stage, a mixed integer second order cone planning model is constructed by using a mathematical programming method to solve, so that the flexible interconnection equipment optimal configuration scheme with the minimum cost and highest economical efficiency is obtained.
2. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 1, wherein the two-stage SOP planning model comprises solving an optimal running state of a power distribution system in a classical scene, and comprises solving an optimal power flow and solving SOP capacity at a current position.
3. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 1, wherein a direct current power flow model is adopted in the SOP planning model.
4. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 1, wherein the original-dual internal point algorithm is specifically as follows:
min p T P G
s.t.e T (P G -P D )＝0
T(P G -P D )≤F max
P Gmin ≤P G ≤P Gmax
wherein p is the generator bid vector; p (P) G For generating a force vector, the load node force is 0; p (P) D The node load is; t is a node power and branch power flow sensitivity matrix; f (F) max For line tide capacity constraint, P Gmin Minimum generator output, P Gmax The maximum output value of the generator is set;
introducing relaxation variables s, s 1 ,s 2 Converting the inequality constraint into an equality constraint;
T(P G -P D )+s-F max ＝0
P G -s 1 -P Gmin ＝0
P G +s 2 -P Gmax ＝0
introducing a logarithmic barrier function into the objective function, eliminating the non-negative constraint of the relaxation variable; introduces a Lagrangian multiplier variable lambda 1 、λ 2 And multiplier vector r 1 、r 2 They respectively represent the relaxation constraint values corresponding to the constraintsA lattice signal, obtaining the following Lagrangian function;
Figure FDA0004118119540000021
wherein L (p, μ) is Lagrangian function equation, s i 、s 1i 、s 2i The relaxation variable introduced for constructing the Lagrangian function, μ is the Lagrangian multiplier;
is available according to the Kuhn-Tucker optimality condition
Figure FDA0004118119540000022
p i Quoting T for the ith node generator ki For the node power matrix of the kth unit on the node i, the correlation r 1i 、r 2i Solving the introduced ith node multiplier vector for the KKT condition;
based on the definition of the marginal electricity price of the node, P is calculated for the above method D The derivative gives the electricity price of the node:
Figure FDA0004118119540000023
wherein ρ is i The electricity price of the node i is the node number of the power distribution system accessed by the SOP, lambda 1 Lambda is the Lagrangian multiplier variable 2i Is a Lagrangian multiplier vector;
at the optimal solution of the original-dual interior point algorithm, the barrier parameter mu should approach zero, so that a correction strategy of a dual gap method is adopted to iteratively reduce the value of mu;
to maintain the original feasibility and dual feasibility, the solving process needs to select iteration step length, and the original step length t is adopted p Dual step t D The method of (2) reduces the iteration times;
Figure FDA0004118119540000024
Figure FDA0004118119540000025
Δs、Δs 1 、Δs 2 for introduced relaxation variable variation Δr 1 、Δr 2 Lambda is the variation of Lagrangian multiplier vector 2 Is a lagrangian multiplier variable.
5. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 1, wherein in the SOP planning model, a constraint equation expression of the SOP is specifically as follows:
1) SOP capacity constraint:
Figure FDA0004118119540000026
Figure FDA0004118119540000031
2) SOP active power balance constraint:
P SOP,i ＝|P SOP,j +P SOP,loss,ij |
Figure FDA0004118119540000032
3) SOP reactive power constraint:
|Q SOP,i |≤ηS SOP,ij
|Q SOP,j |≤ηS SOP,ij
in the above formulae, S SOP,ij The capacity of the SOP connected between nodes i and j; p (P) SOP,i ，P SOP,j ，Q SOP,i ，Q SOP,j Respectively the active power and the reactive power of the SOP output toThe injection power grid is positive; p (P) SOP,loss,ij A device loss for the SOP; c SOP,loss A loss factor for the SOP; η is the reactive power limit coefficient of the SOP.
6. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 1, wherein the two-stage SOP planning model comprises an upper layer site selection model and a bottom layer capacity optimization model, the upper layer optimization model is used for determining an installation position of the SOP, and the bottom layer optimization model is used for determining an optimal operation condition of the whole power distribution system under a typical scene to determine capacity.
7. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 6, wherein the bottom capacity optimization model minimizes the sum of network loss and SOP loss of each case under the condition that the planning model satisfies various constraint conditions of the power distribution network, and the mathematical expression is as follows:
Figure FDA0004118119540000033
wherein: c is electricity price; t is the power supply time; s is the number of scenes; n is the number of system nodes; p (P) i (s) is the sum of the active powers injected at the s-th scene node i; p(s) is probability corresponding to the s-th scene, P SOP,loss,ij Is SOP transmission power loss, where P i (s) can be expressed by an active power flow constraint equation.
8. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 7, wherein various constraint conditions satisfied by the bottom layer optimization model are specifically as follows:
Figure FDA0004118119540000034
Figure FDA0004118119540000035
Figure FDA0004118119540000036
Figure FDA0004118119540000037
Figure FDA0004118119540000041
Figure FDA0004118119540000042
wherein Ω (i) represents a set of nodes connected to node i; v (V) i.t 、V j,t And theta ij,t The voltage amplitude and phase angle difference of the t scene nodes i and j are respectively; g ii 、B ii 、G ij 、B ij Self-conductance, self-susceptance, mutual-conductance and mutual-susceptance in the node admittance matrix respectively; p (P) L,i,t 、Q L,i,t Active power and reactive power injected by loads on the node i are respectively; p (P) DG,i,t 、Q DG,i,t Active power and reactive power injected by the distributed power supply on the s-th scene node i are respectively;
Figure FDA0004118119540000043
and->
Figure FDA0004118119540000044
Respectively the upper limit and the lower limit of the voltage amplitude of the node i; i ij,t The current amplitude of the t scene of the branch j; />
Figure FDA0004118119540000045
Is the upper current amplitude limit of branch ij; p (P) SOP,i,t Active power, Q, for SOP transmission of the t-th scene on node i SOP,i,t Reactive power for SOP transmission of the t-th scenario on node i.
9. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 6, wherein the upper layer site selection model transmits the SOP position to the bottom layer capacity optimization model, the bottom layer capacity optimization model optimizes the running state of the power distribution system based on the structure and operation constraint of each scene, then returns the optimization result to the upper layer site selection model, and the upper layer site selection model uses the result transmitted by the bottom layer capacity optimization model to calculate the objective function value of the current SOP planning scheme.
10. The method for optimizing configuration of flexible interconnection equipment in an active power distribution network according to claim 4, wherein the SOP position optimizing configuration further comprises a management benefit method, namely, the system active and reactive power loss sensitivity is calculated, and the model is as follows:
after the power balance equation of the system node is expanded according to Taylor first order, the reactive variable delta Q and the active variable delta P are made to be 0, and the relation between the voltage variable delta V and the voltage variable delta Q and the voltage variable delta P can be obtained
Figure FDA0004118119540000046
Figure FDA0004118119540000047
Wherein: v, θ are the voltage amplitude and phase angle, respectively.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117039892A (en) * 2023-10-08 2023-11-10 江苏省电力试验研究院有限公司 Distribution area flexible interconnection system optimization scheduling method, system, equipment and storage medium
CN117728448A (en) * 2024-02-08 2024-03-19 北京智芯微电子科技有限公司 Dynamic regulation and control method, device, equipment and medium for active power distribution network

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117039892A (en) * 2023-10-08 2023-11-10 江苏省电力试验研究院有限公司 Distribution area flexible interconnection system optimization scheduling method, system, equipment and storage medium
CN117039892B (en) * 2023-10-08 2024-01-19 江苏省电力试验研究院有限公司 Distribution area flexible interconnection system optimization scheduling method, system, equipment and storage medium
CN117728448A (en) * 2024-02-08 2024-03-19 北京智芯微电子科技有限公司 Dynamic regulation and control method, device, equipment and medium for active power distribution network
CN117728448B (en) * 2024-02-08 2024-04-23 北京智芯微电子科技有限公司 Dynamic regulation and control method, device, equipment and medium for active power distribution network

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