Power distribution network reactive power optimization configuration method considering three-terminal intelligent soft switch containing energy storage

Abstract

The invention relates to a power distribution network reactive power optimization configuration method considering a three-terminal intelligent soft switch containing energy storage, and the method comprises the following steps: 1, taking the positions and capacities of the three-terminal intelligent soft switch containing ESS, a CB and an SVC as decision variables, and taking the minimum investment maintenance operation cost of each device as a target to construct an economic model; 2, constructing a technical model by taking a CB gear, a change moment, SVC reactive power output and energy storage charging and discharging power as decision variables and taking minimum voltage deviation as a target; 3, adopting an improved non-dominated sorting dung beetle algorithm; 4, on the basis of the model, considering the operation economy of the power distribution network, and taking the minimum comprehensive cost and voltage deviation as targets, constructing the reactive power optimization configuration of the power distribution network considering the three-terminal intelligent soft switch containing the ESS; 5, finally, simulation verification is carried out through an IEEE33 node system; the method has the advantages that the three-terminal SOP containing the ESS is considered, the network loss and the voltage deviation are reduced, and the economical efficiency and the reliability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization, and in particular relates to a distribution network reactive power optimization configuration method considering a three-terminal intelligent soft switch with energy storage. Background Art

[0002] With the implementation of the "dual carbon" policy, the penetration rate of renewable energy such as photovoltaic and wind power in distribution networks has been continuously increasing. However, the large-scale access of distributed power sources (DG) will bring about problems such as bidirectional power flow, voltage over-limit and uneven power distribution, which will lead to increased network losses, serious wind and solar power curtailment and reduced operational reliability of the distribution network. Installing reactive compensation equipment such as capacitor banks (CBs) or static VAR compensators (SVCs) at appropriate locations in the distribution network and controlling and scheduling them can improve voltage distribution and reduce power losses. Therefore, research on reactive power configuration issues in distribution networks has attracted much attention in recent years. Although reactive compensation devices can increase the voltage at feeder nodes, improve the distribution of reactive power flow in the distribution network, and reduce line losses, their effect in solving the voltage offset problem caused by active power fluctuations is limited. Moreover, existing research only configures reactive compensation equipment and energy storage in the distribution network, which may not achieve the optimal safety, stability and economy of the distribution network at the same time. As the accuracy of real-time operation of the distribution network continues to improve, traditional optimization methods are difficult to meet the requirements. Smart soft switching (SOP) provides a new optimization measure for the distribution network.

[0003] Compared with the conventional two-terminal SOP, the multi-terminal structure enhances the regulation and control capability of the SOP, further improving the flexibility of the distribution network operation control. Under normal operating conditions, the multi-terminal SOP can flexibly connect multiple nodes, balance the power between multiple nodes, and act as a reactive compensation device to reduce voltage fluctuations. The multi-port structure also gives full play to the potential of fully controlled power electronic devices, greatly improving the utilization rate of equipment, reducing the initial investment cost of the active distribution network, and improving the economy of its operation. However, the conventional multi-terminal SOP can only transfer power spatially in the distribution network to adjust the flow, but cannot store electric energy and does not have the function of coordinating the distribution of electric energy over a period of time, which greatly limits its ability to optimize the distribution network. The integration of multi-terminal SOP and energy storage system can not only realize real-time power regulation and fluctuation smoothing between feeders in a larger range, but also further improve energy conversion efficiency. When conducting optimal configuration research on distribution network, the model contains many conditions, has complex coupling relationships, and the solution environment is relatively complex. With the rapid development of artificial intelligence algorithms, heuristic search algorithms represented by genetic algorithms and particle swarm optimization (PSO) have provided new ideas for solving the reactive power optimization problem of distribution network. However, the algorithms of existing schemes do not adequately consider the optimization of key nodes, and the optimization solution method is prone to fall into local optimality. Therefore, it is very necessary to provide a distribution network reactive power optimization configuration method that considers three-terminal SOP containing ESS, reduces network loss and voltage deviation, and improves economy and reliability, taking into account three-terminal intelligent soft switches containing energy storage. Summary of the Invention

[0004] (1) Technical issues

[0005] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0006] 1. Although reactive power compensation devices can increase the voltage at feeder nodes, improve the distribution of reactive power flow in the distribution network, and reduce line losses, they are limited in their effectiveness in resolving voltage offsets caused by active power fluctuations.

[0007] 2. Existing research only configures reactive power compensation equipment and energy storage in distribution networks, which may not simultaneously optimize the security, stability, and economy of distribution networks.

[0008] 3. Conventional multi-terminal SOPs can only transfer power spatially in the distribution network to adjust the power flow, but cannot store electrical energy or coordinate the distribution of electrical energy over a period of time, which greatly limits their ability to optimize the distribution network.

[0009] 4. The algorithms of existing solutions do not give sufficient consideration to the optimization of key nodes, and the optimization solution method is prone to fall into local optimality.

[0010] (2) Technical solution

[0011] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a distribution network reactive power optimization configuration method considering a three-terminal intelligent soft switch including energy storage, which takes into account the three-terminal SOP including ESS, reduces network loss and voltage deviation, and improves economy and reliability.

[0012] The object of the present invention is achieved by: considering a distribution network reactive power optimization configuration method of a three-terminal intelligent soft switch including energy storage, the method comprising the following steps:

[0013] Step 1: Using the three-terminal intelligent soft switch including the ESS and the location and capacity of the CB and SVC as decision variables, an economic model is constructed with the goal of minimizing the investment, maintenance and operation costs of each device;

[0014] Step 2: Using the CB gear position and change time, SVC reactive output, and energy storage charge and discharge power as decision variables, and aiming to minimize voltage deviation, a technical model is constructed.

[0015] Step 3: Adopt the improved non-dominated sorting dung beetle algorithm and use tent mapping to initialize the population to improve the search ability and avoid falling into the global optimal solution;

[0016] Step 4: Based on the above model, considering the economic operation of the distribution network and taking the comprehensive cost and voltage deviation as the minimum, the reactive power optimization configuration of the distribution network considering the three-terminal intelligent soft switch including ESS is constructed;

[0017] Step 5: Finally, simulation verification is carried out through the IEEE33-node system, and the improved non-dominated sorting dung beetle algorithm is used to solve the reactive compensation optimization model of the distribution network.

[0018] Furthermore, the economic model in step 1 is specifically as follows: the configuration model takes the lowest comprehensive system cost as the goal, considers the investment and maintenance costs of SOP, ESS, CB and SVC, and converts them into annual units. In addition, the network loss during the operation of the distribution network belongs to the system operation technical indicators, which are converted into economic measures to construct the distribution network planning model. The economic cost objective function is: minf1 = C SOP +C ESS +C CB +C SVC +C op (7), where C SOP Invest maintenance costs for SOP; C ESS Invest in maintenance costs for ESS; C CB CB investment maintenance cost; C SVC Invest in maintenance costs for SVC; C op For running costs.

[0019] Furthermore, the investment and maintenance costs of the SOP and ESS are:

[0020]

[0021] Where N VSC is the number of SOP installations; r is the discount rate; c main is the equipment maintenance cost coefficient; y vsc 、y ESS The service life of SOP and ESS; They are the unit capacity investment cost of VSC, the unit capacity investment cost of ESS, and the unit capacity investment cost of DC-DC converter;

[0022] They are the rated capacity of SOP, the unit capacity of DC-DC converter, and the rated capacity of energy storage.

[0023] Furthermore, the CB investment and maintenance cost is: Where N CB is the number of CB installations; y CB is the service life of the CB; is the unit power investment cost of CB; is the rated power of the i-th CB.

[0024] Furthermore, the SVC investment and maintenance cost is: Where N SVC is the number of SVC installations; y SVC is the useful life of the SVC; is the unit power investment cost of SVC; is the rated power of the i-th SVC.

[0025] Furthermore, the operating cost is: C op =C loss +C buy (5), Where C loss 、C buy are network loss cost and main grid power purchase cost respectively; C Ploss 、C Price are the network loss electricity price and the electricity purchase price respectively; T is the control period; N l is the number of distribution network branches; R l is the resistance of line 1; I l,t The resistance of branch l at time t; P t g is the power purchased by the main grid at time t.

[0026] Furthermore, the objective function of the technical model in step 2 is: Where V i,t is the voltage level of node i at time t; V i rated is the standard voltage of node i at time t; V i,max 、V i,min are the maximum and minimum voltage values ​​of node i respectively.

[0027] Furthermore, the constraints of the economic model objective function in step 1 are: Where Q ESS,min , Q ESS,max The upper and lower limits of ESS rated capacity; Q CB,min , Q CB,max They are the upper and lower limits of CB rated power; Q SVC,min , Q SVC,max are the upper and lower limits of the SVC rated power respectively; the three-terminal SOP operation constraints including energy storage are: Where, P SOP,i,t , Q SOP,i,t is the active power and reactive power output from the i-th port of the three-terminal SOP during period t; S SOP,i,max is the maximum capacity of the VSC of the i-th port of the three-terminal SOP; P loss,i,t is the active power loss of the ith port of the three-terminal SOP in period t; P ESS,t is the charge and discharge power of ESS during period t; Q SOP,i,min , Q SOP,i,max A is the upper and lower limits of reactive power injected by SOP at node i; SOP,i is the converter loss coefficient in the SOP at node i; is the power loss in the SOP at node i at time t.

[0028] Furthermore, the constraints of the economic model objective function in step 1 also include: power flow constraints, energy storage charge constraints, and SVC and CB constraints.

[0029] Furthermore, the reactive power optimization configuration of the distribution network including the three-terminal intelligent soft switch of the ESS in step 4 is specifically as follows: a multi-objective optimization capacity configuration model, wherein the economic objective constitutes the objective function f1; and the technical objective constitutes the objective function f2:

[0030] (3) Beneficial effects

[0031] 1. This invention considers the reactive power optimization configuration of the distribution network based on the three-terminal intelligent soft switch including ESS when a high proportion of renewable energy is connected, which can improve the economy and reliability of the distribution network operation;

[0032] 2. This invention considers the economic operation of the distribution network and aims to enhance its reliability. With the goal of minimizing comprehensive cost and voltage deviation, it designs a reactive power optimization configuration for the distribution network using a three-terminal intelligent soft switch including an ESS. The improved non-dominated sorting dung beetle algorithm is used to solve the reactive power compensation optimization model for the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a structural diagram of the three-terminal SOP with energy storage of the present invention.

[0034] Figure 2 This is a flow chart for solving the reactive power optimization model of the distribution network of the present invention.

[0035] Figure 3 This is a topological diagram of the 33-node distribution network system of the present invention.

[0036] Figure 4 This is a comparison chart of different algorithm test functions of the present invention.

[0037] Figure 5 This is a comparison chart of average rankings of different algorithms in the present invention.

[0038] Figure 6 This is a comparison diagram of voltage deviations of different solutions of the present invention.

[0039] Figure 7 This is a comparison chart of network losses of different solutions of the present invention.

[0040] Figure 8 This is the time-of-use electricity price diagram of the present invention.

[0041] Figure 9 Schematic diagram of the prior art of the present invention Figure 1 .

[0042] Figure 10 Schematic diagram of the prior art of the present invention Figure 2 . DETAILED DESCRIPTION

[0043] Currently, reactive power optimization is still primarily modeled based on the candidate installation nodes and capacities of ESS, SVC, and CB. However, these optimization results are subject to significant human factors and are not optimal for distribution network operation. ESS and reactive power compensation devices are not configured. Therefore, in the case of a high proportion of renewable energy access, this paper considers reactive power optimization configuration for distribution networks based on three-terminal intelligent soft switches including ESS, which can improve the economic and reliability of distribution network operation.

[0044] The present invention will be further described below with reference to the embodiments and / or drawings.

[0045] Example 1

[0046] like Figure 1-8 As shown, a method for optimizing reactive power configuration of a distribution network considering a three-terminal intelligent soft switch with energy storage is provided, and the method comprises the following steps:

[0047] Step 1: Using the three-terminal intelligent soft switch including the ESS and the location and capacity of the CB and SVC as decision variables, an economic model is constructed with the goal of minimizing the investment, maintenance and operation costs of each device;

[0048] In this invention, the configuration model takes the lowest comprehensive system cost as the goal, considers the investment and maintenance costs of SOP, ESS, CB and SVC, and converts them into annual units. In addition, the network loss during the operation of the distribution network belongs to the system operation technical indicators, which are converted into economic measures to construct the distribution network planning model; the economic cost objective function is: minf1 = C SOP +C ESS +C CB +C SVC +C op (7).

[0049] ① The investment and maintenance costs of SOP and ESS are: Where N VSC is the number of SOP installations; r is the discount rate; c main is the equipment maintenance cost coefficient; y vsc 、y ESS The service life of SOP and ESS; They are the unit capacity investment cost of VSC, the unit capacity investment cost of ESS, and the unit capacity investment cost of DC-DC converter; They are the rated capacity of SOP, the unit capacity of DC-DC converter, and the rated capacity of energy storage.

[0050] ②CB investment and maintenance costs are: Where N CB is the number of CB installations; y CB is the service life of the CB; is the unit power investment cost of CB; is the rated power of the i-th CB.

[0051] ③SVC investment and maintenance costs are: Where N SVC is the number of SVC installations; y SVC is the useful life of the SVC; is the unit power investment cost of SVC; is the rated power of the i-th SVC.

[0052] ④Operating cost: C op =Closs +C buy (5), Where C loss 、C buy are network loss cost and main grid power purchase cost respectively; C Ploss 、C Price are the network loss electricity price and the electricity purchase price respectively; T is the control period; N l is the number of distribution network branches; R l is the resistance of line 1; I l,t The resistance of branch l at time t; P t g is the power purchased by the main grid at time t.

[0053] Step 2: Using the CB gear position and change time, SVC reactive output, and energy storage charge and discharge power as decision variables, and aiming to minimize voltage deviation, a technical model is constructed.

[0054] In the present invention, the technical objective function is: Where V i,t is the voltage level of node i at time t; V i rated is the standard voltage of node i at time t; V i,max 、V i,min are the maximum and minimum voltage values ​​of node i respectively.

[0055] Step 3: Improve the non-dominated sorting dung beetle algorithm. First, use Tent mapping to initialize the population to improve the search ability of the non-dominated sorting dung beetle algorithm and avoid falling into the global optimal solution.

[0056] Step 4: Based on the above model, considering the economics of distribution network operation and to enhance the reliability of distribution network operation, with the goal of minimizing comprehensive cost and voltage deviation, the reactive power optimization configuration of the distribution network is designed, which takes into account the three-terminal intelligent soft switch including ESS.

[0057] In the present invention, the multi-objective optimization capacity allocation model is composed of economic objectives to form the objective function f1; and technical objectives to form the objective function f2: The constraints of the economic target model are: Where Q ESS,min , Q ESS,max The upper and lower limits of ESS rated capacity; Q CB,min , Q CB,max They are the upper and lower limits of CB rated power; Q SVC,min , Q SVC,max are the upper and lower limits of the SVC rated power respectively; the three-terminal SOP operation constraints including energy storage are: Where, PSOP,i,t , Q SOP,i,t is the active power and reactive power output by the i-th port of the three-terminal SOP during period t; S SOP,i,max is the maximum capacity of the VSC of the i-th port of the three-terminal SOP; P loss,i,t is the active power loss of the ith port of the three-terminal SOP in period t; P ESS,t is the charge and discharge power of ESS during period t; Q SOP,i,min , Q SOP,i,max A is the upper and lower limits of reactive power injected by SOP at node i; SOP,i is the converter loss coefficient in the SOP at node i; is the power loss in the SOP at node i at time t.

[0058] Other constraints: ① Power flow constraint: To ensure the power quality and safe operation of the distribution network, the variables need to meet certain constraints. The equality constraint of the model is the power flow constraint, and the specific calculation model is as follows: Where, P i,t , Q i,t are the active power and reactive power of node i in period t respectively; δ ij,t is the phase difference between nodes i and j during period t; G ii 、B ii and G ij 、B ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix respectively; U i,t 、U j,t are the voltages at node i and node j at time t respectively.

[0059] Where: I ij,t , I ij,max are the current amplitude and upper limit of branch ij in period t respectively.

[0060] ② Energy storage charge constraints: Where, is the power of the i-th ESS at time t; P ESS,i is the rated power of the i-th ESS; S SOC,i (t) is the state of charge of the i-th ESS at time t; S SOC,i,min 、S SOC,i,max are the upper and lower bounds of the state of charge of the i-th ESS; η is the charge and discharge efficiency.

[0061] ③SVC and CB constraints: The gear changes of CB cannot be too frequent, so the number of gear changes of CB is constrained: Where Q i t , CBis the reactive power at node i at time t; is the number of CB groups deployed at node i at time t; is the capacity of a single group of CBs; is the total number of CB groups installed at node i; The maximum number of CB switching times; is the reactive power output of the SVC at node i at time t; is the upper limit of the SVC reactive power at node i.

[0062] Step 5: Finally, simulation verification is carried out through the IEEE33-node system, and the improved non-dominated sorting dung beetle algorithm is used to solve the reactive compensation optimization model of the distribution network.

[0063] The present invention provides a method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch containing energy storage, namely, a reactive power configuration optimization strategy taking into account a three-terminal intelligent soft switch containing energy storage in the context of large-scale access of a high proportion of distributed power sources to the distribution network. In use, the present invention considers the reactive power optimization configuration of the distribution network based on a three-terminal intelligent soft switch containing an ESS under the condition of a high proportion of new energy access, thereby improving the economy and reliability of the distribution network operation. The present invention considers the economy of distribution network operation and, in order to enhance the reliability of distribution network operation, designs a reactive power optimization configuration of the distribution network taking into account a three-terminal intelligent soft switch containing an ESS with the goal of minimizing comprehensive cost and voltage deviation, and solves the reactive power compensation optimization model of the distribution network using an improved non-dominated sorting dung beetle algorithm. The present invention has the advantages of considering the three-terminal SOP containing the ESS, reducing network loss and voltage deviation, and improving economy and reliability.

[0064] Example 2

[0065] like Figure 1-8 As shown, a method for optimizing reactive power configuration of a distribution network considering a three-terminal intelligent soft switch with energy storage is provided, and the method comprises the following steps:

[0066] Step 1: Using the three-terminal intelligent soft switch including the ESS and the location and capacity of the CB and SVC as decision variables, an economic model is constructed with the goal of minimizing the investment, maintenance and operation costs of each device;

[0067] Step 2: Using the CB gear position and change time, SVC reactive output, and energy storage charge and discharge power as decision variables, and aiming to minimize voltage deviation, a technical model is constructed.

[0068] Step 3: Improve the non-dominated sorting dung beetle algorithm. First, use Tent mapping to initialize the population to improve the search ability of the non-dominated sorting dung beetle algorithm and avoid falling into the global optimal solution.

[0069] In the present invention, the solution method is as follows: the present invention further considers the planning of the SOP reactive compensation device containing ESS. The model contains many conditions and the solution environment is relatively complex. Therefore, the present invention adopts the improved non-dominated sorting dung beetle algorithm for solution.

[0070] The non-dominated sorting dung beetle algorithm combines the non-dominated sorting method in the NSGA-Ⅱ algorithm to perform non-dominated sorting on the dung beetle population; replaces the traditional external archiving strategy with dynamic external archiving, and proposes a new crowding distance formula to enhance the algorithm's optimization ability while maintaining the diversity of the population.

[0071] The rolling dung beetle uses the sun as a navigation tool to ensure that the dung ball rolls on a straight path. Natural factors such as light intensity and wind can affect the dung beetle's path. The dung beetle's position is updated as follows:

[0072] Where t represents the current number of iterations; x i (t) represents the position information of the i-th dung beetle at the t-th iteration; α is a natural coefficient, indicating whether it deviates from the original direction, and is assigned to -1 or 1 according to the probability method; k represents the deflection coefficient; b represents a constant; X w represents the global worst position; Δx is used to simulate the change of light intensity.

[0073] When a dung beetle encounters an obstacle and cannot move forward, it needs to adjust its direction by dancing. The formula for the dung beetle to update its position by dancing is defined as follows: i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)|(20), where θ represents the deflection angle. When θ is equal to 0, π / 2, or π, the position of the dung beetle will not be updated.

[0074] The non-dominated sorting dung beetle algorithm has high search accuracy, fast convergence speed, good stability, and strong robustness, but it is prone to falling into local optimal problems. To this end, the Tent mapping is used to initialize the population. The Tent chaotic sequence has the characteristics of randomness, ergodicity, and regularity. Using these characteristics for optimization search can effectively maintain population diversity, prevent the algorithm from falling into local optimality, and improve the global search capability. The expression of the Tent chaotic sequence is as follows: Where i is the particle number, i = 1, 2, ..., N.

[0075] The Tent map is expressed as follows after the Bernoulli shift transformation: i+1 =(2z i )mod1(22), where mod1 means modulo 1.

[0076] Analysis shows that there are small periods and unstable periodic points in the Tent chaotic sequence. To prevent it from falling into small periodic points or unstable periodic points while not destroying the three major characteristics of chaotic variables, random variables are introduced into the original Tent mapping expression. The improved expression is as follows: Where N is the number of particles in the sequence; rand(0,1) is a random number in the range [0,1].

[0077] Step 4: Based on the above model, considering the economics of distribution network operation and to enhance the reliability of distribution network operation, with the goal of minimizing comprehensive cost and voltage deviation, the reactive power optimization configuration of the distribution network is designed, which takes into account the three-terminal intelligent soft switch including ESS.

[0078] Step 5: Finally, simulation verification is carried out through the IEEE33-node system, and the improved non-dominated sorting dung beetle algorithm is used to solve the reactive compensation optimization model of the distribution network.

[0079] In this invention, the example analysis: ① Example description: The example analysis is carried out using the IEEE 33-node distribution network system. The network structure is as follows: Figure 3 As shown in the figure, the base voltage of the distribution network is 12.66 kV, and the base capacity is 100 MV·A. In the simulation, one SOP with energy storage is connected; two CBs are connected, each with a unit power of 100 kvar, and a maximum of six groups are connected at each location; one SVC is connected, with a unit power of 50 kvar; and the maximum ramp rate of the main network is 500 kW / h.

[0080] In order to verify the advantages of the proposed optimization strategy in improving the voltage quality of the distribution network, reducing network losses, and minimizing the peak-valley difference of load, the following four schemes were used for comparative analysis:

[0081] Option 1: Consider only DG;

[0082] Solution 2: Based on Solution 1, consider the collaborative optimization of CB and SVC;

[0083] Option 3: Consider collaborative optimization with SOP based on Option 2;

[0084] Option 4: Based on Option 3, consider collaborative optimization with SOP containing energy storage.

[0085] Solution 1 takes DG into consideration, providing a certain amount of reactive power support for the distribution network, thereby improving the network loss and voltage deviation of the distribution network to a certain extent.

[0086] Option 2 takes CB and SVC into consideration, which effectively improves the network loss and voltage level of the distribution network, but still does not solve the problem of matching renewable energy output with load demand.

[0087] Solution 3 adds SOP to Solution 2, achieving power flow optimization and improving voltage distribution, thereby further enhancing the stability and flexibility of the system.

[0088] Scheme 4, based on Scheme 3, incorporates a SOP with ESS, improving the grid's power balancing and voltage regulation capabilities. The rapid response of the energy storage unit effectively addresses load fluctuations and the uncertainty of renewable energy access, smoothing power output and reducing wind and solar curtailment. The SOP improves the system's voltage stability and operational flexibility through flexible power flow adjustment and dynamic reactive power compensation. The SOP with ESS not only enhances the grid's adaptability to multiple operating conditions, but also reduces equipment operating pressure, minimizes system losses, and improves renewable energy absorption.

[0089] Compared with Scheme 1, Scheme 2 reduces network loss and voltage deviation by 50.68% and 25.77% respectively;

[0090] Scheme 3 adds SOP to Scheme 2. Compared with Scheme 2, Scheme 3 reduces network loss by 28.86% and voltage deviation by 20.95%.

[0091] The addition of ESS further improves the load curve. Compared with Scheme 3, Scheme 4 reduces network loss by 17.17% and voltage deviation by 35.17%.

[0092] It can be seen that the proposed optimization strategy reduces network losses and voltage deviations, and improves the economy and safety of distribution network operation.

[0093] Table 1 Optimization configuration results

[0094]

[0095] Table 2 Simulation parameter settings

[0096]

[0097] The present invention provides a method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch containing energy storage, namely, a reactive power configuration optimization strategy taking into account a three-terminal intelligent soft switch containing energy storage in the context of large-scale access of a high proportion of distributed power sources to the distribution network. In use, the present invention considers the reactive power optimization configuration of the distribution network based on a three-terminal intelligent soft switch containing an ESS under the condition of a high proportion of new energy access, thereby improving the economy and reliability of the distribution network operation. The present invention considers the economy of distribution network operation and, in order to enhance the reliability of distribution network operation, designs a reactive power optimization configuration of the distribution network taking into account a three-terminal intelligent soft switch containing an ESS with the goal of minimizing comprehensive cost and voltage deviation, and solves the reactive power compensation optimization model of the distribution network using an improved non-dominated sorting dung beetle algorithm. The present invention has the advantages of considering the three-terminal SOP containing the ESS, reducing network loss and voltage deviation, and improving economy and reliability.

Claims

1. A method for optimizing reactive power configuration in a distribution network using a three-terminal intelligent soft switch with energy storage is characterized by: The method comprises the following steps: Step 1: Using the three-terminal intelligent soft switch including the ESS and the location and capacity of the CB and SVC as decision variables, an economic model is constructed with the goal of minimizing the investment, maintenance and operation costs of each device; Step 2: Using the CB gear position and change time, SVC reactive output, and energy storage charge and discharge power as decision variables, and aiming to minimize voltage deviation, a technical model is constructed. Step 3: Adopt the improved non-dominated sorting dung beetle algorithm and use tent mapping to initialize the population to improve the search ability and avoid falling into the global optimal solution; Step 4: Based on the above model, considering the economic operation of the distribution network and taking the comprehensive cost and voltage deviation as the minimum, the reactive power optimization configuration of the distribution network considering the three-terminal intelligent soft switch including ESS is constructed; Step 5: Finally, simulation verification is carried out through the IEEE33-node system, and the improved non-dominated sorting dung beetle algorithm is used to solve the reactive compensation optimization model of the distribution network.

2. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 1, characterized in that: The economic model in step 1 is specifically as follows: the configuration model takes the lowest comprehensive system cost as the goal, considers the investment and maintenance costs of SOP, ESS, CB and SVC, and converts them into annual units. In addition, the network loss during the operation of the distribution network belongs to the system operation technical indicators, which are converted into economic measures to construct the distribution network planning model. The economic cost objective function is: minf1 = C SOP +C ESS +C CB +C SVC +C op (7), where C SOP Invest maintenance costs for SOP; C ESS Invest in maintenance costs for ESS; C CB CB investment maintenance cost; C SVC Invest in maintenance costs for SVC; C op For operating costs.

3. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 2, characterized in that: The SOP and ESS investment and maintenance costs are: Where N VSC is the number of SOP installations; r is the discount rate; c main is the equipment maintenance cost coefficient; y vsc 、y ESS The service life of SOP and ESS; They are the unit capacity investment cost of VSC, the unit capacity investment cost of ESS, and the unit capacity investment cost of DC-DC converter; They are the rated capacity of SOP, the unit capacity of DC-DC converter, and the rated capacity of energy storage.

4. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 2, characterized in that: The CB investment and maintenance costs are: Where N CB is the number of CB installations; y CB is the service life of the CB; is the unit power investment cost of CB; is the rated power of the i-th CB.

5. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 2, characterized in that: The SVC investment and maintenance cost is: Where N SVC is the number of SVC installations; y SVC is the useful life of the SVC; is the unit power investment cost of SVC; is the rated power of the i-th SVC.

6. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 2, characterized in that: The operating cost is: C op =C loss +C buy (5), Where C loss 、C buy are network loss cost and main grid power purchase cost respectively; C Ploss 、C Price are the network loss electricity price and the electricity purchase price respectively; T is the control period; N l is the number of distribution network branches; R l is the resistance of line 1; I l,t The resistance of branch l at time t; is the power purchased by the main grid at time t.

7. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 1, characterized in that: The objective function of the technical model in step 2 is: Where V i,t is the voltage level of node i at time t; V i rated is the standard voltage of node i at time t; V i,max 、V i,min are the maximum and minimum voltage values ​​of node i respectively.

8. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 2, characterized in that: The constraints of the economic model objective function in step 1 are: Where Q ESS,min , Q ESS,max The upper and lower limits of ESS rated capacity; Q CB,min , Q CB,max They are the upper and lower limits of CB rated power; Q SVC,min , Q SVC,max are the upper and lower limits of the SVC rated power respectively; the three-terminal SOP operation constraints including energy storage are: Where, P SOP,i,t , Q SOP,i,t is the active power and reactive power output by the i-th port of the three-terminal SOP during period t; S SOP,i,max is the maximum capacity of the VSC of the i-th port of the three-terminal SOP; P loss,i,t is the active power loss of the ith port of the three-terminal SOP in period t; P ESS,t is the charge and discharge power of ESS during period t; Q SOP,i,min , Q SOP,i,max is the upper and lower limits of reactive power injected by SOP at node i; A SOP,i is the converter loss coefficient in the SOP at node i; is the power loss in the SOP at node i at time t.

9. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 8, characterized in that: The constraints of the economic model objective function in step 1 also include: power flow constraints, energy storage charge constraints, and SVC and CB constraints.

10. The method for optimizing reactive power configuration of a distribution network taking into account a three-terminal intelligent soft switch with energy storage as claimed in claim 2 or 7, characterized in that: The reactive power optimization configuration of the distribution network including the three-terminal intelligent soft switch of the ESS in step 4 is specifically as follows: a multi-objective optimization capacity configuration model, in which the economic objective constitutes the objective function f1; and the technical objective constitutes the objective function f2:

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