Method for suppressing three-phase imbalance of voltage in active power distribution network based on flexible soft switch

Abstract

The application discloses a kind of active distribution network voltage three-phase imbalance suppression methods based on flexible soft switch, after establishing multi-port flexible soft switch dynamic optimization model and active distribution network three-phase asymmetric optimal power flow model respectively, fusion obtains the active distribution network voltage three-phase imbalance suppression model based on multi-port flexible soft switch, based on the model, each item weight of objective function is changed, the decision of three-phase imbalance, line loss, carbon discharge is considered comprehensively, while, three-phase voltage imbalance degree and regulation cost are comprehensively considered, further multi-objective optimization model is established, and the decision of regulation cost is obtained by changing each item weight of objective function, three-phase imbalance suppression is realized.The application considers three-phase voltage imbalance degree, and regulation cost is considered, and the corresponding regulation scheme is obtained when regulation cost is different.

Description

Technical Field

[0001] This invention relates to a technology in the field of power grid control, specifically a method for suppressing three-phase voltage imbalance in active distribution networks based on flexible soft switching. Background Technology

[0002] Active distribution networks contain a large number of unbalanced loads. The single-phase connection of electric vehicles and photovoltaic systems exacerbates the three-phase imbalance problem. Flexible switching can effectively compensate for the frequent output fluctuations of unbalanced renewable energy sources and loads. For three-phase imbalance, flexible switching can improve system imbalance, enabling the power grid to operate more safely and reliably. Summary of the Invention

[0003] To address the aforementioned shortcomings of existing technologies, this invention proposes a method for suppressing three-phase voltage imbalance in active distribution networks based on flexible soft switches. After establishing an optimal power flow model for three-phase asymmetry in the active distribution network based on the phase-to-phase coupling relationship of the lines, a multi-port flexible soft switch is added to the model. By adjusting the real-time power of each port of the flexible soft switch, power flow transfer between different lines and power flow between phases of the same feeder are achieved. Electricity prices are adjusted to guide electric vehicles to charge in phase-specific and time-based manner, thereby improving the three-phase imbalance. The model considers a series of indicators such as line loss, energy consumption, carbon emissions, and mains grid power purchases to achieve efficient and energy-saving operation of the active distribution network. The adjustment costs of the flexible soft switch and electric vehicles are introduced into the objective function, taking into account both the three-phase voltage imbalance and adjustment costs, resulting in corresponding adjustment schemes for different adjustment costs.

[0004] This invention is achieved through the following technical solution:

[0005] This invention relates to a method for suppressing three-phase voltage imbalance in an active distribution network based on flexible soft switching. By establishing a dynamic optimization model of a multi-port flexible soft switch and an optimal power flow model of three-phase asymmetry in an active distribution network, a three-phase voltage imbalance suppression model based on a multi-port flexible soft switch is obtained. Based on this model, the weights of the objective function are changed to obtain a decision that comprehensively considers three-phase imbalance, line loss, and carbon emissions, while also comprehensively considering three-phase voltage imbalance and regulation costs. A multi-objective optimization model is further established, and by changing the weights of the objective function, decisions are obtained when regulation costs are different, thus achieving three-phase imbalance suppression. Attached Figure Description

[0006] Figure 1 This is a flowchart of the present invention;

[0007] Figure 2 Network topology diagram for an example;

[0008] Figure 3 Output diagrams for load demand and wind turbine / solar power forecasts;

[0009] Figure 4 A comparison diagram of three-phase imbalance;

[0010] Figure 5 This is a comparison chart of line loss.

[0011] Figure 6 This is a schematic diagram of the three-phase voltage for scenario #1.

[0012] Figure 7 This is a schematic diagram of the three-phase voltage for scenario #2.

[0013] Figure 8 A diagram showing the recommended charging time for electric vehicles at node 5. Detailed Implementation

[0014] like Figure 1 As shown in this embodiment, a method for suppressing three-phase voltage imbalance in an active distribution network based on flexible soft switching is proposed. The method includes: establishing a three-phase three-wire medium-voltage power grid voltage imbalance suppression model based on multi-port flexible soft switching, specifically: the objective function is min f1, where f1 = W u f v +W l f l +W c f c Three-phase voltage imbalance Line loss carbon emissions W u W l and W c These are the weighting coefficients. For node i Phase voltage value, N is the number of nodes in the network, and b is the number of branches. For branch m Phase current value, For branch m Phase resistance value, The total power injected into the main network can be proportionally converted into carbon emissions.

[0015] The constraints of the objective function include: U j =U i +Z ij I ij S j =P j +jQ j S ij =p ij +jQ ij , Wherein: each node and branch is a three-phase node and branch, that is: Wherein: S i S is the power injected into node i. ij U represents the power injected from node i into node j. i Z is the voltage at node i; ij I is the impedance between branches ij; ij Let be the current flowing through branch ij, with the positive direction being from node i to node j.

[0016] The power flow model described does not satisfy the conditions of the second-order cone model, therefore it needs to be transformed first. Specifically, this involves transforming the non-convex model using a second-order cone relaxation method, specifically: U j =U i +Z ij I ij Multiplying both sides by their conjugates, we get: when Right now This positive semidefinite constraint is equivalent to: According to the Sylvester criterion, the inequality constraints can be relaxed as follows:

[0017]

[0018] The aforementioned change in the weights of the objective function refers to the following: a large number of electric vehicles connected to a node can be considered as a collection of individual electric vehicles, which is approximately equivalent to a distributed energy storage device (ESS). Its constraints include: charge / discharge state constraints.

[0019] Power constraints:

[0020] Capacity constraints: Among them: B ESS For the set of all nodes that contain electric vehicle charging devices; The phase for electric vehicle access; In charging state;

[0021] It is in a discharge state; These are the maximum and minimum charging power, respectively. These are the maximum and minimum discharge powers, respectively. The electricity consumption during time period t. These represent the upper and lower limits of the optimal charging range, respectively. Represents the charge and discharge efficiency coefficient, under normal circumstances.

[0022] Through specific practical experiments, in such Figure 2 The Rizhao Port power grid shown considers various actively managed elements, such as electric vehicles and distributed power sources. A multi-terminal flexible soft switch is added to the network. The corresponding program was written using MATLAB software, and the commercial algorithm packages YALMIP and CPLEX were installed. The development environment was MATLAB R2016a.

[0023] The network topology used in this embodiment is shown in the figure. Nodes 18, 22, and 33 are interconnected through multi-terminal flexible soft switches. b =1MVA, U b =12.66kV.

[0024] The node voltage limits are 0.94 pu and 1.06 pu. Five single-phase photovoltaic power generation systems are connected to network nodes 19, 22, 25, 27, and 29 respectively to simulate three-phase unbalanced distributed renewable energy. The specific connection phases are shown in Table 1. Electric vehicle charging piles are connected to all three phases at node 5.

[0025] Table 1 Photovoltaic Access Phase

[0026]

[0027]

[0028] For the aforementioned model, to verify the optimized control effect of the multi-terminal flexible soft switch under different source load power, simulations were conducted with a time scale of 6 hours and a time unit of 15 minutes. The load and renewable energy output were as follows: Figure 3 As shown.

[0029] To compare the effect of flexible soft switching on regulating three-phase voltage imbalance, the following two scenarios are designed:

[0030] Scenario #1: Multi-terminal flexible soft-opening and closing lock.

[0031] Scenario #2: Multi-terminal flexible soft switches participate in scheduling to optimize the three-phase voltage imbalance of the system.

[0032] The comparison of the three-phase voltage imbalance of the network at different time periods under the two scenarios is as follows: Figure 4 and Figure 5 As shown.

[0033] Table 2 Comparison of line loss and three-phase voltage imbalance

[0034] Line loss / pu Three-phase voltage imbalance / pu Scene #1 29.3546 5.5813 Scene #2 13.3890 0.8495

[0035] Depend on Figure 4 , Figure 5 It can be seen that the line loss and voltage imbalance in scenario #2 are significantly lower than those in scenario #1 at all times. This is because the multi-terminal flexible soft switch can adjust its power in real time, thereby adjusting the transmission power between different feeders and between different phases of the same feeder. Therefore, it can effectively cope with the fluctuations in load and renewable energy output, as well as the problem of single-phase access of photovoltaic and load. Thus, it has a significant improvement effect on the above two indicators of the system.

[0036] Analyzing the trend of the curve, when the output of new energy sources increases or the load increases, the three-phase voltage imbalance increases because photovoltaic and some loads are connected to the network in a single phase. When the output of new energy sources decreases or the load increases, the active power transmitted by the main grid increases, and the line loss increases accordingly.

[0037] As shown in Table 2, compared with scenario #1, the network loss and three-phase voltage imbalance in scenario #2 are significantly improved. The comparison shows that the adjustment effect of flexible soft switching is very significant in reducing network loss and voltage imbalance.

[0038] Depend on Figure 6 and Figure 7 It can be seen that the voltage deviation and three-phase voltage imbalance in Scenario #2 are significantly reduced compared to Scenario #1. This is because the flexible soft switch in Scenario #1, with its locking mechanism, cannot effectively regulate single-phase loads such as photovoltaic systems and electric vehicles, resulting in a large network imbalance. In contrast, the multi-terminal flexible soft switch in Scenario #2 participates in the optimization process, avoiding the problems present in Scenario #1.

[0039] The mainnet injection power for scenarios #1 and #2 is shown in Tables 3 and 4.

[0040] Table 3 Injection Power for Scenario #1

[0041]

[0042]

[0043] Table 4 Injection Power for Scenario #2

[0044] phase Active power injection / MW Reactive power injection / MVar A 7.7838 1.9286 B 2.9878 1.5931 C 4.3030 1.8021 total 15.0746 53.238

[0045] As shown in Tables 3 and 4, in scenario #2, due to the function of the multi-terminal flexible soft switch, power interaction can be performed between feeders, reducing the long-distance transmission of active power, reducing network loss and active power injection; in addition, each port of the flexible soft switch can independently generate reactive power, realizing local compensation and reducing reactive power injection.

[0046] Table 5 Comparison of line loss, carbon emissions, and grid purchase costs

[0047] Line loss / MWh <![CDATA[Carbon emissions / 10 3 kg]]> <![CDATA[Main online electricity purchase cost / 10 3 $]]> Scene #1 2.9355 19.3623 15.8059 Scene #2 1.3389 14.7731 12.0597

[0048] Table 5 shows a comparison of line losses, carbon emissions, and grid purchase costs. Carbon emissions equal energy consumption multiplied by the carbon emission coefficient. Grid purchase costs equal the purchased electricity volume multiplied by the unit purchase cost. All indicators in Scenario #2 are significantly improved compared to those in Scenario #1. It can be seen that flexible soft switching can significantly reduce energy losses and carbon emissions and improve the economics of active distribution networks.

[0049] Table 6 Algorithm Performance Comparison

[0050] algorithm Calculation time / s Three-phase imbalance / pu PSO 823.57 3.0626 SOCR 178.65 0.8315

[0051] To analyze the efficiency and convergence of different algorithms, Particle Swarm Optimization (PSO) was used to address the aforementioned problem. Table 6 shows the solution time and three-phase voltage imbalance for different optimization models. The second-order cone relaxation (SOCR) used in this embodiment ensures that the convex optimization model has a unique optimal solution, demonstrating significant advantages in both computational results and solution time.

[0052] The new port power grid includes not only traditional loads but also a large number of active loads, including a significant number of electric vehicles (EVs). The power demand of EVs fluctuates with electricity prices and can therefore be adjusted. In the above model, EVs are considered as one of the constraints for network optimization. Taking scenario #2 as an example, with three-phase imbalance as the objective, the reasonable charging phase time for EVs is derived as follows: Figure 8 As shown.

[0053] Therefore, the electricity price at node 5 should be set as follows: low price for phase A and high price for phases B and C during the first two hours and the last hour, guiding electric vehicles to charge on phases B and C. The opposite should be applied during the remaining hours, thus balancing the three-phase voltage. After electric vehicles charged according to the above schedule, the three-phase voltage imbalance of the network decreased from 0.8495 to 0.8286, demonstrating that electric vehicle charging management has a certain effect on reducing the three-phase voltage imbalance.

[0054] Both flexible soft-switching and electric vehicle charging management play a role in reducing three-phase voltage imbalance, but their regulation costs differ. This section comprehensively considers three-phase voltage imbalance and regulation costs, establishes a multi-objective optimization model, and obtains the lowest-cost regulation scheme by implementing a charging system for the regulation power of flexible soft-switching and electric vehicles.

[0055] The objective function in this embodiment is min ρ v ·f v +ρ P ·f P , where: ρ P ·f P =ρ SOP ·fSOP +ρ EV ·f EV , ρ v ρ P ρ SOP and ρ EV These are the weighting coefficients, ρ SOP and ρ EV The size of f is directly proportional to its adjustment cost. SOP For the total power regulated by flexible soft switching, f EV This refers to the total power regulated by the electric vehicle.

[0056] Simultaneously, attention is paid to imbalance and adjustment costs, and ρ is set. v / ρ P =0.5 / 0.5. Assuming the cost of flexible soft-switching to regulate voltage imbalance is lower than the cost of electric vehicle charging management, set ρ... SOP / ρ EV =0.2 / 0.8. The optimization results are shown in Table 7.

[0057] Table 7. Flexible soft switching and total regulation power of electric vehicles 1

[0058]

[0059] Assuming the cost of flexible soft-switching to regulate voltage imbalance is higher than the cost of electric vehicle charging management, set ρ SOP / ρ EV =0.95 / 0.05. The optimization results are shown in Table 8.

[0060] Table 8. Flexible soft switching and total regulating power of electric vehicles 2

[0061]

[0062] The comparison shows that when the cost of flexible soft switching regulation is low, the system tends to use flexible soft switching for regulation, while reducing the use of electricity price regulation for charging phase and time management of electric vehicles. Conversely, the conclusion is the opposite.

[0063] Compared with existing technologies, this invention targets three-phase voltage imbalance, optimizes the power of each port of the flexible soft switch, and ultimately reduces three-phase voltage imbalance to achieve economical grid operation. Economic considerations are also taken into account. Considering that the charging demand of electric vehicles is affected by electricity prices, suggested charging times and electricity price adjustment schemes for electric vehicles are proposed, leveraging the charging flexibility of electric vehicles to improve the optimization range of the flexible soft switch. A multi-objective model based on three-phase voltage imbalance and adjustment costs is established to obtain grid-preferred adjustment schemes when adjustment costs vary.

[0064] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A method for suppressing three-phase voltage imbalance in an active distribution network based on flexible soft switching, characterized in that, After establishing a dynamic optimization model for multi-port flexible soft switches and an optimal power flow model for three-phase asymmetry in active distribution networks, a voltage three-phase imbalance suppression model for active distribution networks based on multi-port flexible soft switches is obtained. Based on this model, the weights of the objective function are changed to obtain a decision that comprehensively considers the three-phase imbalance, line loss, and carbon emissions, while also comprehensively considering the voltage three-phase imbalance and regulation cost. A multi-objective optimization model is further established, and by changing the weights of the objective function, a decision is obtained when the regulation cost is different, thus achieving three-phase imbalance suppression. The aforementioned active distribution network voltage three-phase imbalance suppression model based on multi-port flexible soft switching is specifically as follows: Objective function ,in: Three-phase voltage imbalance Line loss carbon emissions , , and Weighting coefficients At node i Phase voltage value, N is the number of nodes in the network, and b is the number of branches. For branch m Phase current value, For branch m Phase resistance value, The total power injected into the main network is proportionally converted into carbon emissions. The constraints of the objective function include: , , , , , Where: each node and branch is a three-phase node and branch, i.e. , , , , , , , , , ,in: To inject power into node i, The power injected from node i into node j, The voltage at node i; The impedance between branches ij; Let be the current flowing through branch ij, with the positive direction being from node i to node j.

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