DG-FMS mode optimization design method, device, equipment and medium

Abstract

The present invention discloses a DG-FMS mode optimization design method, device, equipment and medium. The method includes the following steps: optimizing the installation position and capacity of FMS and DG in a new power system with the goal of minimizing the annual comprehensive cost, and constructing an outer optimization model; optimizing the power of FMS with the goal of minimizing the amount of electricity purchased from the power grid, and constructing an inner optimization model; solving the outer optimization model, optimizing the access position and access capacity of FMS and DG, obtaining a first optimization scheme and passing it to the inner optimization model; solving the inner optimization model, optimizing the operating power of FMS under the first optimization scheme given by the outer optimization model, obtaining a second optimization scheme and passing it to the outer optimization model, calculating the objective function of the outer optimization model and updating the access position and access capacity of FMS and DG; iteratively solving until the optimal scheme is obtained. The method can reasonably and effectively plan the access position and access capacity of FMS and DG, reduce the total cost of system operation, and improve the reliability of power supply to users of the new power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid optimization and regulation, and specifically relates to a DG-FMS mode optimization design method, device, equipment and medium. Background Art

[0002] Under the dual carbon goals of carbon neutrality and carbon peak, it is an inevitable trend to vigorously develop a new power system based on new energy. However, with the high proportion and high penetration of distributed renewable energy, new energy electric vehicles, etc., problems such as voltage over-limit and feeder balance in the distribution network have become more prominent. At the same time, due to the diversity of load types, the differentiation of load levels of different feeders, and the random volatility of distributed energy and loads, the same line in the distribution network will often switch between overload and light load states within a day. Traditional topological structure adjustment methods such as network reconstruction are limited in the number of actions and high costs, as well as their inherent rigid adjustment properties. Their ability to absorb renewable energy is quite limited. With the development of power electronics technology, Flexible Multi-state Switch (FMS) provides a solution to improve or even solve these problems. FMS not only has the function of opening and closing, but also can continuously adjust the active and reactive power of each port. By reasonably configuring the capacity and location of the FMS connected to the distribution network, DC closed-loop operation can be achieved, which can not only effectively improve the voltage and power flow over-limit problems caused by the volatility of distributed renewable energy and loads, and improve the system power flow distribution, but also play an important role in improving the absorption capacity of renewable energy. At present, domestic and foreign countries have begun to pay extensive attention to the research and application of FMS, among which the planning, configuration and optimal operation of FMS access to the distribution network are the research focus. In terms of the optimized operation of FMS access to the power grid, many studies have been carried out. For example, some literature introduces the role of FMS in improving the voltage level of the distribution network and improving the absorption capacity of distributed power sources; some literature focuses on analyzing and studying the role of FMS with energy storage in alleviating photovoltaic output fluctuations, and some literature uniformly analyzes the role of various power electronic equipment including FMS devices in supporting the access of distributed power sources, providing a good reference for the access and operation of FMS. In general, the current literature focuses on how to use FMS to improve the distribution network's ability to absorb distributed power sources, but less on the potential of FMS in improving operational reliability. In fact, FMS can achieve uninterrupted power supply to non-faulty parts when a power grid fault occurs, reducing users' power outage losses. However, existing research on FMS operation optimization and even planning and configuration rarely takes this into consideration.

[0003] On the other hand, considerable research has also been paid attention to the planning and configuration of FMS access to the distribution network, mainly through the integrated design of FMS planning and operation to achieve the optimal layout of FMS access to the power grid. For example, some literature comprehensively considers cost factors such as equipment operation, connection loss, network interaction and capacity configuration, and proposes an economic cost optimization configuration model containing FMS active distribution network to optimize FMS access location and port capacity; some literature proposes a FMS sizing method based on union-find set, which can quickly and effectively determine the real-time operation mode of FMS and improve the speed of solving the sizing problem; some literature comprehensively considers the uncertainty of system network loss and electric vehicle charging and discharging, and constructs a multi-objective FMS optimal configuration model to achieve the site selection and sizing of FMS access; Overall, although the current literature considers the joint design problem of FMS planning and configuration and operation optimization, most of the current research focuses on the optimization configuration problem of a single FMS or a single distributed power source, and it is impossible to obtain a comprehensive and balanced operation optimization solution. Summary of the invention

[0004] The purpose of the present invention is to provide a DG-FMS mode optimization design method, device, equipment and medium. Through the coordinated operation efficiency improvement and joint site selection and capacity determination of FMS and distributed power sources, not only can the joint operation capability between the two be maximized, but also the complementary potential between the two can be fully utilized to achieve better system operation.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A first aspect of the present invention provides a DG-FMS mode optimization design method, comprising the following steps:

[0007] Optimize the installation location and capacity of FMS and DG with the goal of minimizing the annual comprehensive cost, and build an outer optimization model;

[0008] The power of FMS is optimized with the goal of minimizing the amount of electricity purchased from the power grid, and an inner optimization model is constructed;

[0009] Solving the outer optimization model, optimizing the access position and access capacity of the FMS and the DG, obtaining a first optimization solution and passing it to the inner optimization model;

[0010] Solving the inner optimization model, optimizing the operating power of the FMS under the first optimization scheme given by the outer optimization model, obtaining a second optimization scheme and passing it to the outer optimization model, calculating the objective function of the outer optimization model and updating the access position and access capacity of the FMS and the DG;

[0011] The outer optimization model and the inner optimization model are solved iteratively until the optimal solution is obtained.

[0012] As an optional solution of the present invention, in the step of constructing the outer layer optimization model, the constructed outer layer optimization model is as follows:

[0013] min f=C C +C I +C O +C B

[0014] In the formula, C C is the annual electricity purchase cost, C I is the annual construction cost of FMS and DRE, C O is the operation and maintenance cost of FMS and DRE, C B Annual power outage loss costs for users.

[0015] As an optional solution of the present invention, the annual electricity purchase cost C C , annual construction cost of FMS and DRE C I , FMS and DRE operation and maintenance costs C O And the user's annual power outage loss cost C B The calculation formulas are as follows:

[0016]

[0017]

[0018]

[0019]

[0020] In the formula, c b is the electricity purchase price from the power grid, N t is the total calculation time; P1(s) is the purchased power under scenario s; p s is the occurrence probability of scene s; Δt is the calculation time step; N s is the number of scenes; c FMS and is the unit capacity investment cost of FMS and type j DG; and is the installed capacity of FMS and DG; d FMS and d DG is the discount rate of FMS and DG; y FMS and DG is the service life of FMS and DG; N FMS and N DG is the number of FMS and DG; η FMS is the operation and maintenance cost coefficient of FMS; is the unit electricity operation and maintenance cost of type j DG; is the output of the jth DG in scenario s; P is the judgment variable for whether node j in the isolated island formed after branch i fails in scenario s. If it is 1, it is cut off, and if it is 0, it is not cut off. L,j (s) is the load of node j in scenario s; T B (i) is the failure time of node i; c j is the load electricity price of node j; N L is the set of all lines; N B (i) is the set of nodes in the island formed after line i fails; is the loss cost of unit power outage of the load at node j.

[0021] As an optional solution of the present invention, the user's annual power outage loss cost C B The calculation process is as follows:

[0022] S21, initializing the failure probability and repair time of each line according to the line length, and initializing the line number l=1;

[0023] S22, after the first line fails, determine the load that loses the main grid power supply source to form an island;

[0024] S23, determining whether the sum of the port powers of the DRE and the FMS during the fault time can meet the needs of all loads in the island; if so, no power outage will be caused to users; if not, loads are removed in descending order of load power outage losses until the power supply and demand in the island are balanced;

[0025] S24. Determine whether line l traverses all lines. If so, multiply the load shedding amount of each line fault by the corresponding power outage loss and the fault probability, and sum them to obtain the final power outage loss; if not, set l=l+1 and return to step S22.

[0026] As an optional solution of the present invention, in the step of solving the outer layer optimization model, an improved particle swarm algorithm is used to solve the outer layer optimization model.

[0027] As an optional solution of the present invention, in the step of solving the inner layer optimization model, a GAMS algorithm is used to solve the inner layer optimization model.

[0028] As an optional solution of the present invention, the outer optimization model and the inner optimization model are iteratively solved until the optimal solution is obtained, and the specific steps are as follows:

[0029] S41, initializing PSO algorithm parameters and generating an initial particle swarm;

[0030] S42, transferring the particle swarm to the inner optimization model, optimizing the inner optimization model through the GAMS algorithm, and obtaining the power of the FMS and the power purchased by the power grid under the current particle swarm configuration;

[0031] S43, returning the calculation result of the inner optimization model to the outer layer, and calculating the target value of the outer optimization model;

[0032] S44, updating the speed and position of the particles in the PSO algorithm to generate new particles; at the same time, using a nonlinear decreasing weight strategy to update the inertia factor in the PSO algorithm;

[0033] S45. Determine whether the convergence condition is met. If so, end the calculation and output the result. Otherwise, return to step S42.

[0034] A second aspect of the present invention provides a DG-FMS mode optimization design device, comprising:

[0035] The first model building module is used to optimize the installation location and capacity of FMS and DG with the goal of minimizing the annual comprehensive cost and to build an outer optimization model;

[0036] The second model building module is used to optimize the power of the FMS with the goal of minimizing the amount of electricity purchased from the power grid and to build an inner optimization model;

[0037] A first model solving module is used to solve the outer optimization model, optimize the access position and access capacity of the FMS and the DG, obtain a first optimization solution and pass it to the inner optimization model;

[0038] The second model solving module is used to solve the inner optimization model, optimize the operating power of the FMS under the first optimization scheme given by the outer optimization model, obtain the second optimization scheme and pass it to the outer optimization model, calculate the objective function of the outer optimization model and update the access position and access capacity of the FMS and the DG;

[0039] The iteration module is used to iteratively solve the outer optimization model and the inner optimization model until the optimal solution is obtained.

[0040] According to a third aspect of the present invention, there is provided an electronic device, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned DG-FMS mode optimization design method.

[0041] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the above-mentioned DG-FMS mode optimization design method is implemented.

[0042] The beneficial effects of the present invention are as follows:

[0043] 1) The DG-FMS mode optimization design method provided by the present invention comprises the steps of: optimizing the installation position and capacity of the FMS and the DG with the goal of minimizing the annual comprehensive cost, and constructing an outer optimization model; optimizing the power of the FMS with the goal of minimizing the amount of electricity purchased from the power grid, and constructing an inner optimization model; solving the outer optimization model, optimizing the access position and access capacity of the FMS and the DG, obtaining a first optimization scheme and passing it to the inner optimization model; solving the inner optimization model, optimizing the operating power of the FMS under the first optimization scheme given by the outer optimization model, obtaining a second optimization scheme and passing it to the outer optimization model, calculating the objective function of the outer optimization model and updating the access position and access capacity of the FMS and the DG; iteratively solving the outer optimization model and the inner optimization model until the optimal scheme is obtained. The method of the present invention considers in detail the coordinated optimization configuration of flexible multi-state switches and distributed power sources, fully takes into account the reliability benefits brought by the access of flexible multi-state switches, and proposes an FMS and DG site selection and sizing optimization configuration method based on double-layer nested optimization. It can reasonably and effectively plan the access location and access capacity of FMS and DG, reduce the total system operation cost, and improve the user's power supply reliability.

[0044] 2) The DG-FMS mode optimization design method provided by the present invention has better economic benefits by coordinating and optimizing the configuration of FMS and DG compared with the traditional single FMS configuration or DG configuration. It can not only reduce the annual comprehensive cost of the power grid to a greater extent, but also effectively reduce the power outage losses of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0046] Figure 1 It is a schematic diagram of the process flow of the DG-FMS mode optimization design method of the present invention.

[0047] Figure 2 Schematic diagram of the process of calculating power outage losses for users.

[0048] Figure 3 Schematic diagram of the solution principle of the two-layer optimization model.

[0049] Figure 4 Schematic diagram of the multi-port FMS structure.

[0050] Figure 5 Schematic diagram of the VSC operation boundary structure.

[0051] Figure 6 Schematic diagram of the solution process of the two-layer optimization model.

[0052] Figure 7 Schematic diagram of IEEE33 node system.

[0053] Figure 8 WT, PV and three types of load data diagram; among them: (a) PV data, (b) WT data, (c) commercial load data (d) industrial load data, (e) residential load data.

[0054] Fig. 9 Schematic diagram of DG related scenes.

[0055] Fig.10 Schematic diagram of annual power loss of various loads under different schemes.

[0056] Fig.11 The structure diagram of a DG-FMS mode optimization design device of the present invention.

[0057] Fig.12 The present invention is a structural block diagram of an electronic device. DETAILED DESCRIPTION

[0058] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0059] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0060] The present invention provides a DG-FMS mode optimization design method, device, equipment and medium. In the planning and configuration stage, the coordinated optimization operation of FMS and DG is considered in detail, and the reliability benefits brought by FMS access are fully taken into account. A two-layer optimization configuration model for FMS and DG site selection and capacity determination based on nested optimization is constructed; in view of the complexity of solving the two-layer nested model, a two-layer optimization configuration model solution strategy based on the combination of improved particle swarm algorithm and GAMS is proposed. Taking the modified IEEE33 node example system as an example, the effectiveness of the proposed model and method is verified. The DG-FMS mode optimization design method provided in the first aspect of this embodiment can be used as a separate module in the power system and participate in many applications of the power system, such as as an important reference for the power distribution and scheduling of important users of the power system, or to realize intelligent linkage analysis with other modules in the power system.

[0061] Example 1

[0062] like Figure 1 As shown, Embodiment 1 of the present invention provides a DG-FMS mode optimization design method, comprising the following steps:

[0063] S1. Model the FMS.

[0064] Multi-port FMS structure such as Figure 4 As shown in the figure, the AC sides of multiple VSCs are connected to different feeders, and the DC sides are interconnected back to back. Multiple feeders achieve mutual assistance of active power through FMS, and each VSC can independently provide reactive power for the connected feeders. VSC can control active power and reactive power separately, and there is no coupling relationship between the two. In different control modes, there is a coupling relationship between the DC bus voltage and active power, and there is a coupling relationship between the AC bus voltage and reactive power. There are 6 control methods:

[0065] 1) Determine active power and reactive power;

[0066] 2) Constant DC voltage and constant reactive power;

[0067] 3) DC voltage droop and constant reactive power;

[0068] 4) Constant DC voltage and constant AC voltage;

[0069] 5) Determine active power and AC voltage;

[0070] 6) DC voltage droop and constant AC voltage. When different control methods are used, the VSC is equivalent to different nodes in the power flow calculation process.

[0071] Under normal operating conditions, one VSC in the FMS sets the DC voltage and reactive power, while the other VSCs set the active power and reactive power, and power mutual assistance is carried out between the interconnected feeders, which can improve the system operation level in many aspects such as power quality, economy and reliability. When a connected feeder fails, the VSC of the FMS can quickly switch the control mode to provide voltage and frequency support for the non-fault area, thereby achieving uninterrupted power supply.

[0072] The schematic diagram of the operation boundary of each VSC in FMS is as follows Figure 5 shown.

[0073] The VSC operating power cannot exceed the rated power:

[0074]

[0075] Where P vsc and Q vsc are the active power and reactive power of VSC respectively; S vscmax is the rated power of the VSC.

[0076] In FMS, multiple VSCs interconnected back to back must not only meet their own power constraints, but also ensure active power balance in the interconnected state, that is, the sum of the active power and loss of the interconnected VSCs is 0. The operating constraints of FMS are as follows:

[0077]

[0078]

[0079]

[0080] Where P vsc,i and Q vsc,i is the active power and reactive power of the ith port of FMS; S vsc,i is the rated capacity of the ith port of FMS; is the active power loss of the ith port of FMS; N FMS is the port number of FMS; is the loss coefficient of the i-th port of FMS.

[0081] The active power loss of VSC is small compared with the network loss of distribution network, so its active power loss can be ignored, which can be expressed by formula (5):

[0082]

[0083] S2. Construct a two-layer optimization model for the coordinated optimization configuration of flexible multi-state switch FMS and distributed generation device DG, in which the outer optimization model optimizes the installation location and capacity of FMS and DG with the goal of minimizing the annual comprehensive cost, and the inner optimization model optimizes the power of FMS with the goal of minimizing the amount of electricity purchased from the power grid. The two-layer model iterates each other to obtain the optimal configuration solution. The specific steps are as follows:

[0084] Constructing the outer optimization model

[0085] (1) Objective function

[0086] The objective function of the outer optimization model is to minimize the annual comprehensive cost of the system:

[0087] min f=C C +C I +C O +C B (6)

[0088] In the formula, C C is the annual electricity purchase cost; C I is the annual construction cost of FMS and DRE; C O The operation and maintenance costs of FMS and DRE; C BIt is the annual power outage loss cost for users.

[0089]

[0090]

[0091]

[0092]

[0093] In the formula, c b is the electricity purchase price from the power grid, N t is the total calculation time; P1(s) is the purchased power under scenario s; p s is the occurrence probability of scene s; Δt is the calculation time step; N s is the number of scenes; c FMS and is the unit capacity investment cost of FMS and type j DG; and is the installed capacity of FMS and DG; d FMS and d DG is the discount rate of FMS and DG; y FMS and DG is the service life of FMS and DG; N FMS and N DG is the number of FMS and DG; η FMS is the operation and maintenance cost coefficient of FMS; is the unit electricity operation and maintenance cost of type j DG; is the output of the jth DG in scenario s; P is the judgment variable for whether node j in the isolated island formed after branch i fails in scenario s. If it is 1, it is cut off, and if it is 0, it is not cut off. L,j (s) is the load of node j in scenario s; T B (i) is the failure time of node i; c j is the load electricity price of node j; N L is the set of all lines; N B (i) is the set of nodes in the island formed after line i fails; is the loss cost of unit power outage of the load at node j.

[0094] For users, the annual power outage loss cost C B In the calculation of N-1 fault, since the system does not lose the main grid power supply source when the FMS or DRE fails, the user will not experience power outage in this case, and only the user power outage under line fault is considered. Figure 2 As shown in the figure, the calculation process of user power outage loss is as follows:

[0095] S21, initializing the failure probability and repair time of each line according to the line length, and initializing the line number l=1;

[0096] S22, after the first line fails, determine the load that loses the main grid power supply source to form an island;

[0097] S23, determining whether the sum of the port powers of the DRE and the FMS during the fault time can meet the needs of all loads in the island; if so, no power outage will be caused to users; if not, loads are removed in descending order of load power outage losses until the power supply and demand in the island are balanced;

[0098] S24. Determine whether line l traverses all lines. If so, multiply the load shedding amount of each line fault by the corresponding power outage loss and the fault probability, and sum them to obtain the final power outage loss; if not, set l=l+1 and return to step S22.

[0099] (2) Constraints

[0100] a. FMS capacity constraints

[0101]

[0102]

[0103] In the formula, is the upper limit of FMS capacity; is the unit capacity of the FMS installation; k FMS It is an integer variable, representing the number of FMS installations.

[0104] b. DG capacity constraints

[0105]

[0106]

[0107] In the formula, Install capacity cap for DG; is the unit capacity of DG installation, k DG It is an integer variable, representing the number of DG installations.

[0108] Constructing inner optimization model

[0109] (1) Objective function

[0110] The objective function of the inner optimization model is to minimize the amount of electricity purchased from the power grid in each scenario:

[0111] min f=P1(s) (15)

[0112] (2) Constraints

[0113] a. System power flow constraints

[0114]

[0115]

[0116] Where P i (s) and Q i (s) is the active power and reactive power injected by node i in scenario s; U i (s) is the voltage amplitude of node i in scenario s; G ij and B ij are the real and imaginary parts of the node admittance matrix; N(i) is the set of nodes adjacent to node i; θ ij (s) is the phase angle difference between node i and node j in scene s, where

[0117]

[0118]

[0119] In the formula, is the reactive power of node i connected to DG in scenario s; and is the active and reactive power of FMS under scenario s; and is the active load and reactive load of node i in scenario s.

[0120] b.FMS power constraints

[0121]

[0122]

[0123]

[0124] In the formula, Ω FMS is the set of access nodes for each FMS; is the active power loss of node i connected to FMS in scenario s; γ FMS is the loss coefficient of FMS; is the capacity of node i accessing FMS.

[0125] c. Voltage Constraint

[0126] U min ≤U i (s)≤U max (twenty three)

[0127] Where U max and Umin are the upper and lower limits of the node voltage respectively.

[0128] d. Line capacity constraints

[0129]

[0130] In the formula, is the rated transmission power capacity between line i and line j.

[0131] S3. Solve the outer optimization model, optimize the access position and access capacity of the FMS and the DG, obtain the first optimization solution and pass it to the inner optimization model. Solve the inner optimization model, optimize the operating power of the FMS under the first optimization solution given by the outer optimization model, obtain the second optimization solution and pass it to the outer optimization model, calculate the objective function of the outer optimization model and update the access position and access capacity of the FMS and the DG. The outer optimization model and the inner optimization model are solved iteratively until the optimal solution is obtained.

[0132] The method for solving the model in this embodiment is as follows:

[0133] The coordinated optimization configuration model of FMS and DG in the distribution network constructed above is a typical mixed integer nonlinear programming model. The model is relatively complex. This paper proposes to realize the decoupling of integer variables and continuous variables through a two-layer optimization algorithm. The outer optimization model adopts the improved particle swarm optimization (IPSO) algorithm to solve, optimize the access position and access capacity of integer variables FMS and DG, and pass the optimization results to the lower model. The inner optimization model adopts the GAMS algorithm to solve, optimize the operating power of FMS under the planning scheme given by the outer optimization model, and pass the results to the outer optimization model for calculating the objective function of the outer optimization model and updating the optimization variables. The solution is iterated until the optimal configuration and operation scheme are obtained. The solution process is as follows: Figure 3 and 6 shown.

[0134] S41, initializing PSO algorithm parameters and generating an initial particle swarm;

[0135] S42, transferring the particle swarm to the inner optimization model, optimizing the inner optimization model through the GAMS algorithm, and obtaining the power of the FMS and the power purchased by the power grid under the current particle swarm configuration;

[0136] S43, returning the calculation result of the inner optimization model to the outer layer, and calculating the target value of the outer optimization model;

[0137] S44, determine whether the convergence condition is met, if so, end the calculation and output the result, otherwise return to step S42.

[0138] In order to further illustrate and prove the effectiveness and technical effects of the embodiments of the present invention, a specific example is provided below for illustration.

[0139] Study Parameters

[0140] Taking the modified IEEE 33-bus system as an example, the coordinated optimization configuration of FMS and DG is carried out. Figure 7 As shown in Table 1, the system voltage level is 12.66 kV. Considering that the access location of FMS is generally at the end of the line, there are two candidate locations for setting FMS, and there are also two candidate locations for wind turbines (WT) and photovoltaics (PV), as shown in Table 1. The loads in the system are divided into three categories: industrial, commercial and residential loads. The load categories of each node and the loss costs caused by power outages per unit of electricity are shown in Table 2. The configuration parameters of FMS and DG are shown in Table 3.

[0141] Table 1 Candidate locations for FMS and DRE

[0142]

[0143]

[0144] Table 2 Load categories and power outage losses

[0145]

[0146] Table 3 Economic configuration parameters of FMS and DG

[0147]

[0148] Results and Discussion

[0149] According to the measured annual operation data of WT, PV and three types of loads, such as Figure 8 As shown in Figure 1, the kernel density estimation method is used to fit the corresponding probability distribution, and then the scene sampling of Latin square sampling and the scene reduction of synchronous back substitution method are combined to generate WT, PV and other cross-sectional power scenarios, as shown in Figure 1. Fig. 9 The occurrence probability of different cross-section power scenarios is shown in Table 4.

[0150] Table 4 Occurrence probability of each scenario

[0151]

[0152] Optimize the configuration of FMS and DG for the following three solutions:

[0153] Solution 1: Only optimize the configuration of DG;

[0154] Solution 2: Only optimize the configuration of FMS;

[0155] Solution 3: Coordinate and optimize the configuration of FMS and DG.

[0156] The proposed method is used to solve the optimization model of the three schemes, and the optimization configuration results of the three schemes are shown in Table 5. The cost comparison of different planning schemes is shown in Table 6.

[0157] Table 5 Configuration results of three solutions

[0158]

[0159] Table 6 Comparison of different planning schemes

[0160]

[0161] From Table 5 and Table 6, it can be seen that optimizing the configuration of DG alone can reduce the annual comprehensive cost by 617,200 yuan, optimizing the configuration of FMS alone can reduce the annual comprehensive cost by 164,200 yuan, and coordinating and optimizing the configuration of FMS and DG can reduce the annual comprehensive cost by 686,800 yuan. The result of coordinating and optimizing the configuration of FMS and DG has the best economy. Since it is difficult for DG to control the system voltage in the isolated island formed by the fault, it is difficult to reduce the annual power outage loss cost of users by optimizing the configuration of DG alone. FMS can realize the voltage support of the isolated island. Therefore, after FMS is connected, the annual power outage loss of users can be reduced. The coordinated configuration of DG and FMS can further reduce the power outage loss of users. At the same time, the access of DG can reduce the power purchase cost of the system. Therefore, the scheme of coordinating and optimizing the configuration of FMS and DG has better economy, which can minimize the annual comprehensive cost of the power grid and reduce the loss of users caused by power outage.

[0162] The annual power loss of various loads under different schemes is as follows: Fig.10 As shown in the figure, it can be seen that since renewable energy DRE is difficult to supply power to the load in the isolated island alone, the load loss in Scheme 1 is the largest. The access of FMS in Scheme 2 greatly reduces the power loss of users. In Scheme 3, through the coordinated and optimized configuration of FMS and DRE, the power loss of industrial and commercial loads with the highest unit power outage loss cost is further reduced, effectively ensuring the power supply reliability of users.

[0163] From the above content, it can be seen that the DG-FMS mode optimization design method proposed in the embodiment of the present invention refers to the double-layer nested optimization configuration, can reasonably and effectively plan the access location and access capacity of FMS and DG, reduce the total cost of system operation, and improve the power supply reliability of users. The coordinated optimization configuration of FMS and DG has better economic benefits than the traditional single FMS configuration or DG configuration.

[0164] The DG-FMS mode optimization design method provided by the embodiment of the present invention has a good application prospect. When applied to the power system, through the coordinated operation efficiency enhancement and joint site selection and capacity determination of FMS and distributed power sources, it can not only maximize the joint operation capability between the two, but also make full use of the complementary potential between the two to achieve better system operation. It can be used to support the power allocation and dispatching of the important user perception guarantee system of the new power system. As a module of the overall power system, it has the function of integration and fusion with other functional sub-modules in the system to realize intelligent linkage analysis in subsequent work.

[0165] Example 2

[0166] like Fig.11 As shown, a DG-FMS mode optimization design device comprises:

[0167] The first model building module is used to optimize the installation location and capacity of FMS and DG with the goal of minimizing the annual comprehensive cost, and to build an outer optimization model; wherein the constructed outer optimization model is:

[0168] min f=C C +C I +C O +C B

[0169] In the formula, C C is the annual electricity purchase cost, C I is the annual construction cost of FMS and DRE, C O is the operation and maintenance cost of FMS and DRE, C B Annual power outage loss costs for users.

[0170] The user's annual power outage loss cost C B The calculation process is as follows:

[0171] S21, initializing the failure probability and repair time of each line according to the line length, and initializing the line number l=1;

[0172] S22, after the first line fails, determine the load that loses the main grid power supply source to form an island;

[0173] S23, determining whether the sum of the port powers of the DRE and the FMS during the fault time can meet the needs of all loads in the island; if so, no power outage will be caused to users; if not, loads are removed in descending order of load power outage losses until the power supply and demand in the island are balanced;

[0174] S24. Determine whether line l traverses all lines. If so, multiply the load shedding amount of each line fault by the corresponding power outage loss and the fault probability, and sum them to obtain the final power outage loss; if not, set l=l+1 and return to step S22.

[0175] The second model building module is used to optimize the power of the FMS with the goal of minimizing the amount of electricity purchased from the power grid and to build an inner optimization model.

[0176] The inner optimization model is as follows:

[0177] min f=P1(s)

[0178] The first model solving module is used to solve the outer optimization model, optimize the access position and access capacity of the FMS and the DG, obtain a first optimization solution and pass it to the inner optimization model.

[0179] The second model solving module is used to solve the inner optimization model, optimize the operating power of the FMS under the first optimization scheme given by the outer optimization model, obtain the second optimization scheme and pass it to the outer optimization model, calculate the objective function of the outer optimization model and update the access position and access capacity of the FMS and the DG.

[0180] The iteration module is used to iteratively solve the outer optimization model and the inner optimization model until the optimal solution is obtained.

[0181] The iterative solution process is as follows:

[0182] S41, initializing PSO algorithm parameters and generating an initial particle swarm;

[0183] S42, transferring the particle swarm to the inner optimization model, optimizing the inner optimization model through the GAMS algorithm, and obtaining the power of the FMS and the power purchased by the power grid under the current particle swarm configuration;

[0184] S43, returning the calculation result of the inner optimization model to the outer layer, and calculating the target value of the outer optimization model;

[0185] S44, updating the speed and position of the particles in the PSO algorithm to generate new particles; at the same time, using a nonlinear decreasing weight strategy to update the inertia factor in the PSO algorithm;

[0186] S45. Determine whether the convergence condition is met. If so, end the calculation and output the result. Otherwise, return to step S42.

[0187] Example 3

[0188] like Fig.12 As shown, the present invention also provides an electronic device 100 for implementing the DG-FMS mode optimization design method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the DG-FMS mode optimization design method of embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0189] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0190] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a DG-FMS mode optimization design method, and the processor 102 can execute the plurality of instructions to implement:

[0191] Optimize the installation location and capacity of FMS and DG with the goal of minimizing the annual comprehensive cost, and build an outer optimization model;

[0192] The power of FMS is optimized with the goal of minimizing the amount of electricity purchased from the power grid, and an inner optimization model is constructed;

[0193] Solving the outer optimization model, optimizing the access position and access capacity of the FMS and the DG, obtaining a first optimization solution and passing it to the inner optimization model;

[0194] Solving the inner optimization model, optimizing the operating power of the FMS under the first optimization scheme given by the outer optimization model, obtaining a second optimization scheme and passing it to the outer optimization model, calculating the objective function of the outer optimization model and updating the access position and access capacity of the FMS and the DG;

[0195] The outer optimization model and the inner optimization model are solved iteratively until the optimal solution is obtained.

[0196] Example 4

[0197] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0198] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0200] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0201] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A DG-FMS mode optimization design method, characterized in that: The steps include: The access location and access capacity of the flexible multi-state switch FMS and distributed generation DG are optimized with the goal of minimizing the annual comprehensive cost, and an outer optimization model is constructed; The operating power of the flexible multi-state switch FMS is optimized with the goal of minimizing the amount of electricity purchased from the power grid, and an inner optimization model is constructed; Solving the outer optimization model, optimizing the access position and access capacity of the flexible multi-state switch FMS and the distributed generation DG, obtaining a first optimization solution and passing it to the inner optimization model; Solving the inner optimization model, optimizing the operating power of the flexible multi-state switch FMS under the first optimization scheme given by the outer optimization model, obtaining the second optimization scheme and passing it to the outer optimization model, calculating the objective function of the outer optimization model and updating the access position and access capacity of the flexible multi-state switch FMS and the distributed generation source DG; The outer optimization model and the inner optimization model are solved iteratively until the optimal solution is obtained; In the step of constructing the outer layer optimization model, the constructed outer layer optimization model is as follows: min f=C C +C I +C O +C B In the formula, C C is the annual electricity purchase cost, C I is the annual construction cost of flexible multi-state switch FMS and DRE, C O is the operation and maintenance cost of the flexible multi-state switch FMS and DRE, C B Annual power outage loss costs for users; Annual electricity purchase cost C C , the annual construction cost of flexible multi-state switch FMS and DRE C I , Operation and maintenance costs of flexible multi-state switch FMS and DRE C O And the user's annual power outage loss cost C B The calculation formulas are as follows: In the formula, cb is the power purchase price of the power grid, N t is the total calculation time; P1(s) is the purchased power under scenario s; p s is the occurrence probability of scene s; Δt is the calculation time step; N s is the number of scenes; c FMS and is the unit capacity investment cost of FMS and type j DG; and is the installed capacity of FMS and DG; d FMS and dDG are the discount rates of FMS and DG; y FMS and DG is the service life of FMS and DG; N FMS and N DG is the number of FMS and DG; ηFMS is the operation and maintenance cost coefficient of FMS; is the unit electricity operation and maintenance cost of type j DG; is the output of the jth DG in scenario s; is the judgment variable for whether node j in the isolated island formed after branch i fails in scenario s should be cut off; P L,j (s) is the load of node j in scenario s; TB(i) is the failure time of node i; cj is the load electricity price of node j; N L is the set of all lines; N B (i) is the set of nodes in the island formed after line i fails; is the loss cost of load unit outage at node j; The user's annual power outage loss cost C B The calculation process is as follows: S21, initializing the failure probability and repair time of each line according to the line length, and initializing the line number l=1; S22, after the first line fails, determine the load that loses the main grid power supply source to form an island; S23, determining whether the sum of the port powers of the DRE and the FMS during the fault time can meet all load requirements in the island; if so, no power outage will be caused to users; If it cannot be met, the loads will be removed in ascending order of load power outage losses until the power supply and demand in the island are balanced. S24, determine whether line l traverses all lines. If so, multiply the load shedding amount of each line fault by the corresponding power outage loss and the fault probability, and sum them to obtain the final power outage loss; if not, set l=l+1 and return to step S22; Constraints of the outer optimization model: a. FMS capacity constraints In the formula, is the upper limit of FMS capacity; is the unit capacity of the FMS installation; k FMS is an integer variable, representing the number of FMS installations; b. DG capacity constraints In the formula, Install capacity cap for DG; is the unit capacity of DG installation, k DG is an integer variable, representing the number of DG installations; The objective function of the inner optimization model is to minimize the amount of electricity purchased from the power grid in each scenario: min f=P1(s) Constraints of the inner optimization model: a. System power flow constraints Where P i (s) and Q i (s) is the active power and reactive power injected by node i in scenario s; U i (s) is the voltage amplitude of node i in scenario s; G ij and B ij are the real and imaginary parts of the node admittance matrix; N(i) is the set of nodes adjacent to node i; θ ij (s) is the phase angle difference between node i and node j in scene s, where In the formula, is the reactive power of node i connected to DG in scenario s; and is the active and reactive power of FMS under scenario s; and is the active load and reactive load of node i in scenario s; b.FMS power constraints In the formula, Ω FMS is a set of access nodes for each FMS; is the active power loss of node i connected to FMS in scenario s; γ FMS is the loss coefficient of FMS; is the capacity of node i accessing FMS; c. Voltage Constraint Umin≤Ui(s)≤Umax, where U max and U min are the upper and lower limits of the node voltage respectively; d. Line capacity constraints In the formula, is the rated transmission power capacity between line i and line j.

2. The DG-FMS mode optimization design method according to claim 1, characterized in that: In the step of solving the outer layer optimization model, an improved particle swarm algorithm is used to solve the outer layer optimization model.

3. The DG-FMS mode optimization design method according to claim 1, characterized in that: In the step of solving the inner layer optimization model, a GAMS algorithm is used to solve the inner layer optimization model.

4. The DG-FMS mode optimization design method according to claim 1, characterized in that: The steps of iteratively solving the outer optimization model and the inner optimization model until the optimal solution is obtained are as follows: S41, initializing PSO algorithm parameters and generating an initial particle swarm; S42, transferring the particle swarm to the inner optimization model, optimizing the inner optimization model through the GAMS algorithm, and obtaining the power of the FMS and the power purchased by the power grid under the current particle swarm configuration; S43, returning the calculation result of the inner optimization model to the outer layer, and calculating the target value of the outer optimization model; S44, updating the speed and position of the particles in the PSO algorithm to generate new particles; at the same time, using a nonlinear decreasing weight strategy to update the inertia factor in the PSO algorithm; S45. Determine whether the convergence condition is met. If so, end the calculation and output the result. Otherwise, return to step S42.

5. A DG-FMS mode optimization design device, used to implement the DG-FMS mode optimization design method according to any one of claims 1 to 4, characterized in that: include: The first model building module is used to optimize the access position and access capacity of the flexible multi-state switch FMS and the distributed generation DG with the goal of minimizing the annual comprehensive cost, and to build an outer optimization model; The second model building module is used to optimize the operating power of the flexible multi-state switch FMS with the goal of minimizing the amount of electricity purchased from the power grid and to build an inner optimization model; A first model solving module is used to solve the outer optimization model, optimize the access position and access capacity of the flexible multi-state switch FMS and the distributed power source DG, obtain a first optimization solution and pass it to the inner optimization model; The second model solving module is used to solve the inner optimization model, optimize the operating power of the flexible multi-state switch FMS under the first optimization scheme given by the outer optimization model, obtain the second optimization scheme and pass it to the outer optimization model, calculate the objective function of the outer optimization model and update the access position and access capacity of the flexible multi-state switch FMS and the distributed power source DG; The iteration module is used to iteratively solve the outer optimization model and the inner optimization model until the optimal solution is obtained.

6. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the DG-FMS mode optimization design method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the DG-FMS mode optimization design method as described in any one of claims 1 to 4 is implemented.

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