An energy storage type intelligent soft switch double-layer optimization site selection method based on improved whale algorithm and second-order cone programming cooperation

Abstract

The application discloses a kind of based on improved whale algorithm and second-order cone programming collaborative energy storage type intelligent soft switch double-layer optimization site selection method, belong to distribution network and optimization operation technical field, method includes: acquisition distribution network basic data and pre-processing;Establish the double-layer optimization model with minimum system daily operation cost as target, the installation position of energy storage type intelligent soft switch is decision variable in upper layer, the three-port power transmission value is decision variable in lower layer and is based on second-order cone relaxation after tide flow equation and energy storage operation limit and constructs constraint;Improved whale optimization algorithm introducing adaptive inertia weight is used to solve the site selection problem in upper layer, and second-order cone programming method is used to solve the operation problem in lower layer, and the global collaborative optimization of planning and operation is realized through double-layer interaction, the solution accuracy and efficiency of site selection problem are improved, and the system operation cost and network loss are effectively reduced.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network and optimized operation technology, and in particular relates to a two-layer optimized location method for energy storage-type intelligent soft switch based on the synergy of improved whale algorithm and second-order cone planning. Background Technology

[0002] Traditional smart soft switches (SOPs), as a key flexible interconnection technology, primarily function to achieve instantaneous and flexible power flow control between feeders to address voltage exceedance and network congestion issues. However, SOPs lack the ability to transfer energy over time. SOPs can only spatially redistribute real-time power, essentially acting as an "instantaneous power router." When faced with drastic fluctuations in renewable energy and load, the SOP's regulation capability is limited by the net power difference between the feeders it connects to at any given moment. When both sides experience power shortages or surpluses, its regulation capability quickly saturates or even fails. This means that relying solely on SOPs is insufficient to address prolonged power imbalances and cannot smooth the net load curve through peak shaving and valley filling, thus limiting its potential in improving renewable energy absorption and achieving global economic dispatch.

[0003] Energy storage systems integrated with soft openpoints (ESOPs), as flexible power distribution devices integrating energy storage units, can achieve flexible power flow control and spatiotemporal energy transfer among multiple feeders. This effectively alleviates voltage exceedance and increased network losses caused by distributed generation fluctuations, improving the operational economy and reliability of the distribution network. Currently, the planning methods for ESOPs in distribution networks mainly fall into two categories: one is heuristic methods based on sensitivity analysis, such as voltage sensitivity methods and network loss sensitivity methods, which are computationally simple and easy to implement in engineering; the other is optimization methods based on mathematical programming, such as mixed-integer linear programming and second-order cone programming, which have advantages in model rigor and theoretical optimality.

[0004] However, existing ESOP planning methods still have significant limitations. Heuristic methods based on sensitivity analysis struggle to accurately characterize the source-load-storage coupling relationships across multiple time scales, often leading to optimization results deviating from the global optimum. Traditional mathematical optimization methods suffer from high computational complexity and poor convergence when dealing with non-convex power flow constraints, especially in distribution networks of IEEE 33 nodes and above, where the success rate is low. Furthermore, existing studies often treat the ESOP location problem separately from its operational optimization problem, failing to fully consider the synergistic effects between the two, resulting in significant room for optimization in terms of system operating costs and network losses in the final configuration. Therefore, a collaborative ESOP planning method that can balance computational efficiency and global optimization performance is urgently needed. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a dual-layer optimized addressing method for energy storage-type smart soft switches based on the combined use of an improved whale algorithm and second-order cone programming, thereby resolving the issues present in the existing technologies.

[0006] To achieve the above objectives, this invention provides a two-layer optimized addressing method for energy storage-type smart soft switching based on the combined use of an improved whale algorithm and second-order cone programming, comprising:

[0007] S1. Collect basic data of the power distribution network and perform normalization preprocessing;

[0008] S2. Establish a two-layer optimization model with the objective function of minimizing the daily operating cost of the system; wherein, the upper-layer model uses the installation location of the energy storage intelligent soft switch as the decision variable; the lower-layer model uses the three-port power transmission value of the energy storage intelligent soft switch as the decision variable, and constructs constraints based on the power flow equation after second-order cone relaxation and energy storage operation constraints.

[0009] S3. The improved whale optimization algorithm is used to solve the location selection problem of the upper-level model, and the second-order cone programming method is used to solve the power optimization problem of the lower-level model. The two-level interaction mechanism realizes collaborative optimization and outputs the optimal location scheme. The improved whale optimization algorithm introduces an adaptive inertia weight mechanism into the standard whale algorithm.

[0010] Preferably, in step S1, the basic data of the distribution network includes distribution network topology parameters, load curves, and distributed generation output prediction data.

[0011] Preferably, in step S2, the interaction mechanism of the two-layer optimization model is as follows: the upper-layer model passes the location scheme to the lower-layer model, the lower-layer model performs power optimization solution according to the location scheme, and feeds back the calculated daily operating cost as the fitness value to the upper-layer model.

[0012] Preferably, in step S2, the objective function includes the power purchase cost of the distribution network, the network loss cost, the charging and discharging cost of the energy storage device, and the cost of wind and solar curtailment penalties.

[0013] Preferably, in step S2, the constraints of the lower-level model include the active power balance constraints, reactive power constraints, capacity constraints, loss constraints, and operation constraints of the energy storage intelligent soft switch.

[0014] Preferably, the active power balance constraint of the energy storage intelligent soft switch satisfies the following relationship:

[0015] ;

[0016] In the formula: Indicates the number of SOP ports connected. Indicates SOP in time The active power flowing through the converter at the end, This indicates the charging power of the energy storage connected to the DC side of the SOP. and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. This indicates the power loss of the SOP. This indicates the discharge power of the energy stored on the DC side of the SOP.

[0017] Preferably, the power flow equations after second-order cone relaxation are based on the DistFlow model, and the non-convex branch power flow equations are transformed into second-order cone constraint forms.

[0018] Preferably, in step S3, the adaptive inertia weight is non-linear, and its value is dynamically adjusted according to the current iteration state, the distance between the current optimal position and the current position.

[0019] Preferably, the adaptive inertia weight formula is:

[0020] ;

[0021] ;

[0022] In the formula, It is an adaptive weight. This represents the current iteration number; This indicates the best current whale position; Indicates the current position of the whale. For the coefficient vector, This represents the distance between the current whale position and the best possible whale position. For random variables, Determines how whales move; This represents the distance between the current whale position and the historical best whale position; b is the logarithmic spiral shape constant; for A random number between [a certain number of points].

[0023] Preferably, after step S3, the optimal addressing scheme is evaluated using the daily operating cost saving rate and network loss reduction rate indicators.

[0024] Compared with the prior art, the present invention has the following advantages and technical effects:

[0025] This invention jointly iteratively optimizes the location decision and operation strategy of an energy storage-type intelligent soft switch (ESOP). This technique overcomes the suboptimal problem caused by the separation of planning and operation in existing technologies, enabling the final location scheme to be directly coupled with the optimal operation strategy, thereby minimizing the system's daily operating cost at the global level. Implementation results show that this method can effectively reduce the system's daily operating cost.

[0026] This invention employs an improved whale optimization algorithm (AWOA) that incorporates adaptive inertia weights in the upper-level optimization. By dynamically adjusting the search strategy, it effectively balances the algorithm's global exploration and local development capabilities, overcoming the defect of standard algorithms that are prone to getting trapped in local optima. This significantly improves the solution accuracy and global optimality of the discrete combinatorial optimization problem of finding the optimal installation location of ESOPs in complex power distribution networks.

[0027] This invention employs a second-order cone programming method to perform convex relaxation on the DistFlow-based power flow equations in the lower-level optimization, transforming the originally non-convex and difficult-to-solve power flow constraints into a convex optimization problem that can be solved efficiently. Furthermore, it utilizes a commercial solver for rapid computation. This not only ensures the convergence and solution efficiency of the lower-level operational strategy model but also provides stable and rapid fitness evaluation feedback for the upper-level addressing algorithm, ensuring the feasibility and efficiency of the entire collaborative optimization process.

[0028] This invention focuses on ESOPs (Energy Provider Operating Centers) as the optimization target and establishes a comprehensive model that incorporates multiple costs and unique constraints. It comprehensively considers electricity purchase costs, grid loss costs, energy storage loss costs, and wind / solar curtailment penalties. Simultaneously, it rigorously incorporates the active power balance, capacity, and operational constraints of ESOP-energy storage coupling. This ensures that the optimization results not only meet the requirements for safe grid operation but also provide a more comprehensive and accurate assessment of operational economics, resulting in more valuable engineering guidance for the generated site selection and dispatch schemes. Attached Figure Description

[0029] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0030] Figure 1 This is a connection diagram of a three-port ESOP according to an embodiment of the present invention;

[0031] Figure 2 This is a flowchart of a two-layer optimized location method for energy storage smart soft switches based on the improved whale algorithm and second-order cone planning in an embodiment of the present invention.

[0032] Figure 3 This is a schematic diagram showing the optimal ESOP location for the IEEE 33-node system and the IEEE 69-node system according to embodiments of the present invention;

[0033] Figure 4 This is a 24-hour system wind and solar power output diagram according to an embodiment of the present invention;

[0034] Figure 5 This is a 24-hour load curve diagram of a 33-node and a 69-node system according to an embodiment of the present invention;

[0035] Figure 6 The graphs show the 24-hour power scheduling optimization results of this invention, where (a) is the active power flow curve of the ESOP three-port and (b) is the reactive power flow curve of the ESOP three-port.

[0036] Figure 7 The following are voltage diagrams corresponding to different nodes in the embodiments of the present invention: (a) is the voltage diagram of 33 nodes, and (b) is the voltage diagram of 69 nodes.

[0037] Figure 8 This is a comparison diagram of network loss between ESOP and SOP interconnection systems according to an embodiment of the present invention. Detailed Implementation

[0038] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0040] Example 1

[0041] The power flow in traditional distribution networks is determined by line parameters and natural load distribution, making it uncontrollable. However, an energy storage system integrated with a soft open point (ESOP) achieves precise and active control of power injection / absorption at specific nodes through its ports. The three ports of an ESOP typically connect to three different AC feeders, which is the traditional function of a soft open point (SOP) to achieve power flow balance between feeders. The energy storage system is directly connected to the common DC bus of the ESOP via a bidirectional DC / DC converter. A connection diagram of a three-port ESOP is shown below. Figure 1 As shown.

[0042] The ESOP interconnection system and SOP interconnection system are described as follows:

[0043] 1) The Standard Operating Procedure (SOP) is used to interconnect the IEEE 33-node system and the IEEE 69-node system, and its active power constraints are as follows:

[0044] (1)

[0045] in, Indicates the number of SOP ports connected. Indicates SOP in time The active power flowing through the converter at the end, This indicates the loss of SOP.

[0046] 2) Since the ESOP has an energy storage system connected to its DC side, its active power constraints are as follows:

[0047] (2)

[0048] In the formula: Indicates the number of SOP ports connected. Indicates SOP in time The active power flowing through the converter at the end, This indicates the charging power of the energy storage connected to the DC side of the SOP. and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively, both taken as 0.9. Indicates the loss of SOP. This indicates the discharge power of the energy stored on the DC side of the SOP.

[0049] like Figure 2 As shown, this embodiment provides a two-layer optimized location method for energy storage smart soft switching based on the combined use of an improved whale algorithm and second-order cone programming, including:

[0050] S1. Collect basic data of the power distribution network and perform normalization preprocessing;

[0051] Furthermore, in step S1, the basic data of the distribution network includes distribution network topology parameters, load curves, and distributed generation output prediction data.

[0052] Specifically, topology parameters of the IEEE 33-node and IEEE 69-node distribution network systems are collected, including basic data such as line impedance and node load; 24-hour load curves and distributed generation output prediction data are obtained; and the collected data are normalized to eliminate the influence of dimensions.

[0053] S2. Establish a two-layer optimization model with the objective function of minimizing the daily operating cost of the system; wherein, the upper-layer model uses the installation location of the energy storage intelligent soft switch as the decision variable; the lower-layer model uses the three-port power transmission value of the energy storage intelligent soft switch as the decision variable, and constructs constraints based on the power flow equation after second-order cone relaxation and energy storage operation constraints.

[0054] Furthermore, in step S2, the interaction mechanism of the two-layer optimization model is as follows: the upper-layer model passes the location scheme to the lower-layer model, the lower-layer model performs power optimization solution according to the location scheme, and feeds back the calculated daily operating cost as the fitness value to the upper-layer model.

[0055] Specifically, the upper-level model initializes the access location of the three ports of the ESOP using the whale algorithm, takes the ESOP installation location as the decision variable, and sets the candidate node voltage deviation to no more than ±5% and the minimum daily operating cost as constraints.

[0056] The lower-layer model calculates the network loss and node voltage deviation based on the installation location of the upper layer, and returns the calculation results to the upper layer. The interaction between the upper and lower layers enables the optimal operation of the ESOP access system.

[0057] Objective function:

[0058] Constraints: Minimize system network loss and minimize node voltage deviation.

[0059] Furthermore, in step S2, the objective function includes the power purchase cost of the distribution network, the network loss cost, the charging and discharging cost of the energy storage device, and the cost of wind and solar curtailment penalties.

[0060] Specifically, the objective function is to minimize the daily operating cost of the interconnected system: the daily operating cost of the interconnected system consists of four parts: the power purchase cost of the distribution network, the network loss cost, the charging and discharging cost of energy storage devices, and the cost of wind and solar curtailment penalties.

[0061] (3)

[0062] Daily operating costs consist of the following four components.

[0063] 1) Electricity purchase cost for distribution network :

[0064] (4)

[0065] In the formula: The system adopts a time-of-use pricing mechanism for electricity purchase prices, namely, the price is 0.27 yuan / kWh from 0:00 to 9:00, 0.83 yuan / kWh from 13:00 to 20:00, and 0.52 yuan / kWh at other times; Purchase power for the system.

[0066] 2) Daily power supply loss cost of distribution network :

[0067] (5)

[0068] In the formula: The power supply loss cost coefficient for the distribution network is set to 0.1. for Time-based SOP at node Active power loss at the location, The unit cost of SOP operation is taken as 0.05 in this paper; The number of nodes; For The set of starting nodes; For nodes and The maximum current flowing between them is 10kA.

[0069] 3) Charging and discharging costs of energy storage devices :

[0070] (6)

[0071] In the formula: The charge / discharge cost factor for ESS is set to 0.08. and They are respectively in time The charging and discharging power of the ESS at the node.

[0072] 4) Penalty costs for wind and solar power curtailment :

[0073] (7)

[0074] In the formula: The penalty cost per unit of wind and solar power curtailment is taken as 0.4. This refers to the power of wind and solar power that has been curtailed.

[0075] Furthermore, in step S2, the constraints of the lower-level model include the active power balance constraints, reactive power constraints, capacity constraints, loss constraints, and operational constraints of the energy storage intelligent soft switch.

[0076] Specifically, 1) The active power balance constraint of ESOP is shown in formula (2);

[0077] 2) ESOP reactive power constraint:

[0078] (8)

[0079] In the formula: and Representing access nodes The upper and lower limits of reactive power at SOP. This means that all SOP access nodes must satisfy this equation constraint.

[0080] 3) ESOP capacity constraints:

[0081] MERGEFORMAT (9)

[0082] In the formula: It is a Standard Operating Procedure (SOP) The maximum port capacity of the ESOP in this paper is 2.2MW, which is the capacity of the converters on both sides of the terminal.

[0083] 4) ESOP loss constraint:

[0084] (10)

[0085] In the formula: and Indicates in The active power loss and loss factor of the converter at the terminal are given, with the loss factor set to 0.02. This indicates that all SOP access nodes should satisfy this equation constraint.

[0086] 5) Constraints on energy storage devices:

[0087] MERGEFORMAT (11)

[0088] (12)

[0089] (13)

[0090] (14)

[0091] In the formula: and for Variable, representing Time Node It is in the charging and discharging operation state of energy storage; and Represents a node The energy storage charging and discharging efficiency is set to 0.9. and express Time Node Maximum charging and discharging power of the energy storage system during operation; and These represent the minimum energy storage capacity and the full charge capacity of the energy storage system, respectively, which are 2.2MW and 0.22MW.

[0092] 6) System power flow constraints:

[0093] To ensure the stable operation of the distribution network, the system must meet the branch power flow constraints based on the DistFlow model. This model accurately describes the physical relationship between inter-node voltage drop and line power loss, and its equations are as follows:

[0094] (15)

[0095] (16)

[0096] (17)

[0097] (18)

[0098] (19)

[0099] (20)

[0100] Where: nodes It is a node Downstream nodes; and Representing nodes respectively , Voltage at point; , Indicates that by node flow direction node Active power and reactive power; and Represents a node The active and reactive power at the net load; and Representing nodes respectively To the node The resistance and reactance values ​​of the lines between them; and Represents a node The power of active and reactive loads; Represents a node The active power output of the wind turbine; It is a node Active power of photovoltaic unit. , They are nodes The upper and lower limits of voltage amplitude; , They are respectively Time-of-day branch The current amplitude and upper limit. Among them, constraints... This ensured that the voltage levels at each node in the system remained within safe limits, constraining... It was ensured that the current of each line did not exceed its thermal stability limit, and the square of the maximum current flowing through the line did not exceed 10kA, with the voltage range being 0.95-1.05.

[0101] 7) Second-order cone relaxation

[0102] use replace ,use replace Then the power flow constraint is equivalent to:

[0103] (twenty one)

[0104] (twenty two)

[0105] By convexly relaxing the loss and capacity constraints of the SOP, a rotating cone constraint is obtained:

[0106] (twenty three)

[0107] (twenty four)

[0108] Furthermore, the power flow equations after the second-order cone relaxation are based on the DistFlow model, and the non-convex branch power flow equations are transformed into second-order cone constraint forms.

[0109] The multi-terminal SOP network loss constraint and capacity constraint are transformed into a rotating cone constraint to obtain convex constraint conditions that are easy to solve:

[0110] (25)

[0111] (26)

[0112] in, The maximum value shall not exceed 2.2MW. The value is 0.02.

[0113] S3. The improved whale optimization algorithm is used to solve the location selection problem of the upper-level model, and the second-order cone programming method is used to solve the power optimization problem of the lower-level model. The two-level interaction mechanism realizes collaborative optimization and outputs the optimal location scheme. The improved whale optimization algorithm introduces an adaptive inertia weight mechanism into the standard whale algorithm.

[0114] Furthermore, in step S3, the adaptive inertia weight is non-linear, and its value is dynamically adjusted according to the current iteration state, the distance between the current optimal position and the current position.

[0115] Specifically, the Adaptive Inertia Weight Whale Optimization Algorithm (AWOA) described in step S3 mainly introduces a nonlinear adaptive inertia weight mechanism on top of the standard whale algorithm, which is used to enhance the global exploration and local development capabilities of the dynamic balancing algorithm.

[0116] In metaheuristic algorithms, maintaining a dynamic balance between exploration and exploitation capabilities is crucial for ensuring optimization effectiveness. The standard Whale Algorithm (WOA) demonstrates excellent exploitation capabilities during the encirclement and predation phase, but suffers from premature convergence in the global search. Inertial weighting mechanisms can effectively adjust the algorithm's search range; therefore, this method employs an AWOA algorithm that combines nonlinear adaptive inertial weighting with the standard WOA for solution.

[0117] The basic idea of ​​the Whale Algorithm is to find the optimal solution in the search space by simulating the bubble-web hunting behavior of humpback whales. It mainly includes three stages: encirclement hunting, bubble-web attack, and random search.

[0118] 1) Encirclement and Predation Phase: Whales will approach either the whale in the optimal position or a randomly selected whale. This process simulates the behavior of whales encircling their prey. The position update formula is as follows:

[0119] (27)

[0120] in, Indicates the current position of the whale. This indicates the optimal position for the whale.

[0121] 2) Bubble Web Attack Phase: The whale will perform a spiral search based on the current optimal solution's position and its own position. The position update formula is:

[0122] (28)

[0123] in, This represents the distance between the current whale and the optimal whale; b is a constant used to control the shape of the spiral. It is a random number between [-1, 1].

[0124] 3) Random Search Phase: When individual whales are far from the optimal solution, they will conduct a random search throughout the solution space. The position update formula is as follows:

[0125] (29)

[0126] in, The whale's location was randomly selected. This represents the distance between the current whale and a random whale.

[0127] 4) Adaptive inertia weights:

[0128] (30)

[0129] (31)

[0130] In the formula, It is an adaptive weight. This represents the current iteration number; This indicates the best current whale position; Indicates the current position of the whale. For the coefficient vector, Let be the distance between the current whale position and the current best whale position. During whale hunting, the two behaviors of shrinking encirclement and spiraling closer are performed simultaneously. Therefore, in each iteration, it is assumed that there is a 50% probability of choosing one of these behaviors, and a random variable is used. This determines how whales move. This represents the distance between the current whale position and the historical best whale position; It is the logarithmic spiral shape constant; for A random number between [a certain number of points].

[0131] Furthermore, after step S3, the optimal addressing scheme is evaluated using the daily operating cost saving rate and network loss reduction rate indicators.

[0132] Specifically, using the indicators of daily operating cost savings rate and network loss reduction rate, its characteristics are described as follows:

[0133] Daily operating cost savings rate: ;

[0134] Evaluation dimensions: Reflecting the overall practicality of the solution;

[0135] Network loss reduction rate: ;

[0136] Evaluation dimensions: characterizing the degree of improvement in system energy efficiency;

[0137] In the formula, and The larger the value, the better the embodiment is at reducing system operating costs and improving system energy efficiency, and the more practical its value.

[0138] This embodiment uses an IEEE 33-node system and an IEEE 69-node system for verification. The optimal ESOP location diagram for the IEEE 33-node system and the IEEE 69-node system in this embodiment is shown below. Figure 3 As shown. Wind and solar power outputs are represented using typical daily power curves, with wind power output using the Weibull model and solar power output using the Beta model. The 24-hour system wind and solar power outputs are shown below. Figure 4 As shown. The loads of the IEEE 33-node system and the IEEE 69-node system are as follows. Figure 5 As shown, the relevant parameters for the whale algorithm are set as follows: population size is set to 20, and the number of iterations is set to 100 generations. The parameters of the improved whale algorithm remain consistent with those of the original algorithm.

[0139] This embodiment adopts a dual-layer optimization location method for energy storage-type smart soft switching based on the improved whale algorithm and second-order cone programming. The daily operating cost (unit: yuan) of the two models is shown in Table 1, and the system loss (kW) of the two models is shown in Table 2.

[0140] In an ESOP interconnected system, the active and reactive power transmitted by a soft-switching three-port switch are respectively as follows: Figure 6 (a) and Figure 6 As shown in (b), when using the ESOP interconnect system of the present invention, the voltage of the IEEE33 node of the interconnect system is as follows: Figure 7 As shown in (a) above, the voltage at IEEE 69 node is as follows: Figure 7 As shown in (b) above, a comparison of network loss results between the ESOP interconnection system of this invention and the traditional SOP interconnection system is presented. Figure 8 As shown.

[0141] The optimized results show that, overall, for the two installation schemes solved by the two algorithms, using the ESOP interconnect system not only has advantages in terms of cost, but also has significant advantages in terms of reducing system network losses.

[0142] Table 1

[0143]

[0144] Table 2

[0145]

[0146] Tables 1 and 2 show the results of the improved whale algorithm for the two interconnection scenarios. The tables clearly demonstrate that the improved whale algorithm is significantly more effective in reducing system operating costs and network losses. Comparatively, when using the SOP interconnection method for the IEEE 33-node and IEEE 69-node systems, the original whale algorithm resulted in a daily operating cost of 2204.6 yuan and a network loss of 4224.5 kW. In contrast, the improved whale algorithm reduced the daily operating cost to 2147.4 yuan and the network loss to 4012.9 kW. This represents a 2.59% reduction in daily operating cost compared to the original whale algorithm. ， Network loss was reduced by 5.01%. When using ESOP interconnection of IEEE 33-node and IEEE 69-node systems, the original whale algorithm for solving the installation scheme resulted in a daily operating cost of 1962.6 yuan and a network loss of 3869.8 kW. The improved whale algorithm reduced this to 1809.8 yuan and 3838.6 kW, representing a 7.79% reduction in daily operating cost and a 0.81% reduction in network loss compared to the original. However, under the same installation scheme, using the whale algorithm resulted in a 10.98% reduction in daily operating cost and an 8.40% reduction in network loss when using ESOP interconnection compared to SOP interconnection; and a 15.72% reduction in daily operating cost and a 4.34% reduction in network loss when using the improved whale algorithm compared to SOP interconnection.

[0147] The two interconnection system schemes illustrate that, compared with the original SOP interconnection system, the ESOP interconnection IEEE 33-node system and IEEE 69-node system of the present invention are more practical, simpler and more efficient, and have stronger adaptability.

[0148] In summary, utilizing ESOP interconnection systems can reduce daily operating costs and network losses, and provides some guidance for using soft switches for system interconnection in practical engineering applications.

[0149] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A two-layer optimized location method for energy storage-type intelligent soft switching based on the combined use of an improved whale algorithm and second-order cone programming, characterized in that, Includes the following steps: S1. Collect basic data of the power distribution network and perform normalization preprocessing; S2. Establish a two-layer optimization model with the objective function of minimizing the daily operating cost of the system; wherein, the upper-layer model uses the installation location of the energy storage intelligent soft switch as the decision variable; the lower-layer model uses the three-port power transmission value of the energy storage intelligent soft switch as the decision variable, and constructs constraints based on the power flow equation after second-order cone relaxation and energy storage operation constraints. S3. The improved whale optimization algorithm is used to solve the location selection problem of the upper-level model, and the second-order cone programming method is used to solve the power optimization problem of the lower-level model. The two-level interaction mechanism realizes collaborative optimization and outputs the optimal location scheme. The improved whale optimization algorithm introduces an adaptive inertia weight mechanism into the standard whale algorithm.

2. The method according to claim 1, characterized in that, In step S1, the basic data of the distribution network includes distribution network topology parameters, load curves, and distributed generation output prediction data.

3. The method according to claim 1, characterized in that, In step S2, the interaction mechanism of the two-layer optimization model is as follows: the upper-layer model passes the location scheme to the lower-layer model, the lower-layer model performs power optimization solution according to the location scheme, and feeds back the calculated daily operating cost as the fitness value to the upper-layer model.

4. The method according to claim 1, characterized in that, In step S2, the objective function includes the power purchase cost of the distribution network, the network loss cost, the charging and discharging cost of the energy storage device, and the cost of wind and solar curtailment penalties.

5. The method according to claim 1, characterized in that, In step S2, the constraints of the lower-level model include the active power balance constraints, reactive power constraints, capacity constraints, loss constraints, and operation constraints of the energy storage intelligent soft switch.

6. The method according to claim 5, characterized in that, The active power balance constraint of the energy storage intelligent soft switch satisfies the following relationship: ； In the formula: Indicates the number of SOP ports connected. Indicates SOP in time The active power flowing through the converter at the end, This indicates the charging power connected to the DC side of the SOP energy storage. and Let represent the charging efficiency and discharging efficiency of the energy storage device, respectively, both taken as 0.

9. This indicates the power loss of the SOP. This indicates the discharge power of the energy stored on the DC side of the SOP.

7. The method according to claim 1, characterized in that, The power flow equations after second-order cone relaxation are based on the DistFlow model, and the non-convex branch power flow equations are transformed into second-order cone constraint forms.

8. The method according to claim 1, characterized in that, In step S3, the adaptive inertia weight is non-linear, and its value is dynamically adjusted according to the current iteration state, the distance between the current optimal position and the current position.

9. The method according to claim 1, characterized in that, The formula for adaptive inertia weight is: ； ； In the formula, It is an adaptive weight. This represents the current iteration number; This indicates the best current whale position; Indicates the current position of the whale. For the coefficient vector, This represents the distance between the current whale position and the best possible whale position. For random variables, Determines how whales move; This represents the distance between the current whale position and the historical best whale position; b is the logarithmic spiral shape constant; for A random number between [a certain number of points].

10. The method according to claim 1, characterized in that, After step S3, the optimal addressing scheme is evaluated using the daily operating cost saving rate and network loss reduction rate indicators.

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