Reliability improvement method for cooperation of power distribution network and micro-grid

Abstract

The invention relates to the technical field of power distribution networks, and discloses a power distribution network and micro-grid collaborative reliability improvement method, which specifically comprises the following three steps: constructing an SOP-based power distribution network and micro-grid operation model, constructing an SOP-containing power distribution network and micro-grid operation model, and constructing an SOP-containing power distribution network and micro-grid operation model. It is ensured that the micro-grid can operate independently or cooperatively in different fault scenes, and the reliability and flexibility of the system are improved; a multi-stage power distribution network and micro-grid collaborative optimization model is constructed, a multi-stage optimization strategy is designed for different operation stages of a power distribution network and a micro-grid, full-process collaborative optimization from prevention before a fault, scheduling during the fault to recovery after the fault is realized, and efficient distribution and management of resources are ensured; according to the rapid optimization method of the collaborative model, a rapid optimization algorithm is provided for improving the real-time performance and the application efficiency of the model, and rapid response and implementation of collaborative optimization of a large-scale power distribution network and a micro-grid are achieved by reducing the calculation complexity and improving the solving speed.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and particularly to a method for improving the reliability of the coordination between a distribution network and a microgrid. Background Art

[0002] In recent years, due to random equipment failures, especially for widely distributed user groups (such as industrial parks, commercial centers, etc.), it has brought serious losses. Such incidents indicate that in large-scale power outages caused by random failures, the recovery speed of the distribution network is slow and the emergency response ability is limited, and the resilience level needs to be improved urgently. At present, in view of the characteristics of the distribution network far from the main power source, long power supply radius, and scattered load distribution, locally configuring a microgrid in important user areas with high added value such as industrial parks and commercial core areas is an effective means to improve the resilience of the distribution network. At the same time, based on flexible soft-switching technology, it can effectively utilize user-side resources and achieve flexible mutual assistance between different feeders, enhancing the fault resistance and recovery speed of the distribution network.

[0003] Existing distribution network reliability improvement technologies mainly focus on the improvement of equipment performance and the optimization of fault management. For example, by using higher-quality equipment materials and strengthening equipment maintenance, the durability and fault resistance of distribution equipment are improved; through redundant design and optimization of the network topology structure, it is ensured that power supply can be restored through other paths when a part of the equipment fails; and through intelligent monitoring and automated control systems, rapid fault detection and location are achieved, thereby shortening the fault recovery time. These technologies have improved the stability and power supply reliability of the distribution network to a certain extent.

[0004] However, the existing solutions have certain limitations. First of all, most reliability improvement measures are based on the traditional power grid architecture and focus on local improvements within the power grid. Although the impact of some faults can be effectively reduced by increasing equipment redundancy and improving the automation level, with the access of distributed energy and the increasing requirements of power users for power supply reliability, these traditional solutions are difficult to meet more complex and diverse power supply demands. For example, the volatility and uncertainty of new energy increase the operational complexity of the power grid, and the existing technologies have limited capabilities in balancing load fluctuations and handling dynamic supply and demand changes.

[0005] In addition, the existing technical solutions mainly focus on improving the reliability within the distribution network and ignore the synergistic effect between the distribution network and external power resources. With the gradual development of distributed energy and microgrids, the power system has become more diverse and complex. The microgrid can achieve autonomous power supply in a local area and has a certain degree of flexibility and elasticity, but most of the current reliability improvement measures manage the distribution network and the microgrid as independent systems and fail to fully utilize the synergistic effect between the two. When a power supply fault occurs, the independent operation ability of the microgrid and its coordinated recovery ability with the distribution network are not effectively utilized.

[0006] Therefore, in the process of improving the reliability of the distribution network, the existing solutions lack the consideration of the coordinated operation of the distribution network and the microgrid, and cannot make full use of the flexibility of the microgrid to enhance the power supply reliability of the overall system. This design lacking a coordination mechanism limits the potential to achieve higher reliability in modern power systems with coexisting multiple energy sources and growing dynamic demands.

[0007] Although the existing solutions for improving the reliability of the distribution network enhance the stability of the power system to a certain extent, there are still many deficiencies. In the context of the gradual development of the distribution network and the microgrid, the existing technical solutions still fail to provide sufficient support in dealing with distributed energy sources, microgrid autonomy, and coordinated operation. This makes the flexibility and reliability of the overall power supply system fail to reach the expected level in practical applications, especially in the face of complex grid environments and load fluctuations. The following are the main defects of the existing solutions:

[0008] 1. Lack of autonomous operation ability within the microgrid: The existing solutions rely more on centralized control, ignoring the autonomous operation ability of the microgrid, which limits the local power supply ability in the event of grid faults or emergencies.

[0009] 2. Difficulty in coordinating the distribution network and the microgrid: The existing solutions lack an effective mechanism to coordinate the operation of the distribution network and the microgrid, resulting in great operational difficulties in resource scheduling and power supply restoration, affecting the overall reliability of the system.

[0010] 3. Limited ability to handle complex multi - energy systems: The existing technologies are difficult to handle diverse energy systems. Especially in the case of the access of distributed new energy, the system has insufficient scheduling optimization and flexibility, and cannot make full use of the advantages of the microgrid and distributed energy sources. Summary of the Invention

[0011] To solve the above - mentioned technical problems, the present invention is achieved through the following technical solutions:

[0012] A method for improving the reliability of the coordinated operation of a distribution network and a microgrid, comprising the following three steps:

[0013] Step 1: Construct an operation model of the distribution network and the microgrid based on SOP, enabling the microgrid to operate independently or coordinately under different fault scenarios;

[0014] Step 2: Construct a multi - level coordinated optimization model of the distribution network and the microgrid to achieve full - process coordinated optimization from pre - fault prevention, in - fault scheduling to post - fault restoration;

[0015] Step 3: A rapid optimization method for the coordinated model to achieve rapid response and implementation of large - scale coordinated optimization of the distribution network and the microgrid.

[0016] In the first step, an operation model of the distribution network and the microgrid based on SOP is constructed, including:

[0017] In the distribution network, a microgrid is constructed under the constraints of energy storage and distributed energy output, specifically as follows:

[0018]

[0019] Where: s and m represent the indices of the photovoltaic and energy storage units respectively; t is the time index; represents the output power of the microgrid; is the output power of each photovoltaic; and are the charging and discharging powers of the energy storage unit respectively; is the load demand; is the reactive power output of the microgrid; is the reactive power output of the photovoltaic; k i,t is the proportional coefficient of active power and reactive power; S pv is the apparent power of the photovoltaic unit; and are the maximum output and input powers of the energy storage respectively; is the state factor of energy storage charge and discharge; SoC m,t is the state of charge of the energy storage unit at time t; and are the minimum and maximum states of charge of the energy storage respectively;

[0020] The operation limitations of the distribution network are as follows:

[0021]

[0022] Where: P i,j,t and Q i,j,t represent the active power and reactive power of the line, and represent the active power, reactive power, and U of the distribution network power supply at node i i,t represents the node voltage, R i,j and X i,j represent the line resistance and reactance coefficient, U i and represent the minimum and maximum ranges of the voltage;

[0023] The SOP operation limitations are as follows:

[0024]

[0025]

[0026] Where: P isop and represent the active power of two ports of the SOP for nodes i and j; is the maximum reactive power of the SOP, represent the reactive power of the SOP at node i; S sop represents the capacity of the SOP.

[0027] In step 2, a collaborative optimization model of the multi-level distribution network and the microgrid is constructed as follows:

[0028]

[0029] where: l = 1 / 2 / 3 represents three stages: pre-fault prevention, in-fault scheduling, and post-fault restoration; C m represents the scheduling cost of personnel and resources, represents the amount of load loss; where:

[0030] 1) Pre-fault prevention stage:

[0031] The pre-fault prevention stage model is as follows:

[0032]

[0033] where: represents the energy storage unit m deployed to node i in the pre-disaster stage; the above constraint indicates that each energy storage unit can be configured for at most one node;

[0034] 2) In-fault scheduling stage:

[0035] The switch scheduling model during the fault is as follows:

[0036]

[0037] where: z i,j,t represents the line status; a i,j = 1 indicates that the line is equipped with an automatic switch, otherwise it is 0; when the switch fails (i.e., u i,j,t = 1), the switch status remains unchanged;

[0038] 3) Post-fault restoration stage:

[0039] The post-fault restoration stage model is as follows:

[0040]

[0041]

[0042] where: represents whether the construction team c repairs the fault point or operates the switch i at time t; Indicate whether construction team c has completed the repair of fault point i; tr i,j Indicate the moving time of construction team c between points i and j, rep i,j Indicate the time required for construction team c to repair fault point i.

[0043] The fast optimization method of the collaborative model in the third step is as follows:

[0044] The distribution network fault repair model is constructed as a "min-min" optimization model, and the two-layer optimization model is represented in the matrix form of equations:

[0045]

[0046] s.t.(1)-(18),(21)-(39) (27)

[0047]

[0048] s.t.Ax≤g (41.a)

[0049] Gy+Ex≤s (41.b) (28)

[0050] Where: x is the decision vector of the upper-layer model, representing the scheduling variables of the three stages, and y is the decision vector in the objective of the lower-layer model; y represents the operation variables of the distribution network, a and b are the corresponding coefficients respectively, and T is the transpose symbol; A, G, and E are the corresponding constraint coefficient matrices, and g and s are the corresponding right-side constant vectors subject to constraints.

[0051] Beneficial effects

[0052] The present invention constructs a collaborative optimization framework for the distribution network and the microgrid covering three stages of pre-fault prevention, in-fault scheduling, and post-fault recovery. By optimizing the operation strategy of SOP, the full-link reliability of the distribution network under fault conditions is improved. This method can not only ensure the power supply of the distribution network, but also effectively reduce the power outage loss and improve the overall operation efficiency and reliability of the system in a complex environment. Among them:

[0053] Distribution network and microgrid operation model based on SOP: A distribution network and microgrid operation model including SOP is constructed to ensure that the microgrid can operate independently or collaboratively under different fault scenarios, improving the reliability and flexibility of the system.

[0054] Multi-level distribution network and microgrid collaborative optimization model: For different operation stages of the distribution network and the microgrid, multi-level optimization strategies are designed to achieve full-process collaborative optimization from pre-fault prevention, in-fault scheduling to post-fault recovery, ensuring the efficient allocation and management of resources.

[0055] Fast Optimization Method for Cooperative Model: To improve the real-time performance and application efficiency of the model, a fast optimization algorithm is proposed to achieve fast response and implementation of the cooperative optimization of large-scale distribution networks and microgrids by reducing the computational complexity and enhancing the solution speed. Brief Description of the Drawings

[0056] Figure 1 It is a schematic diagram of the active prevention measures of the present invention. Detailed Embodiments

[0057] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0058] To make the objectives, technical solutions and advantages of the present invention clearer, the following further describes in detail the embodiments of the present invention.

[0059] Embodiment 1

[0060] A method for improving the reliability of the cooperation between a distribution network and a microgrid includes the following three steps:

[0061] Step 1: Construct an operation model of the distribution network and the microgrid based on SOP to enable the microgrid to operate independently or cooperatively under different fault scenarios;

[0062] Step 2: Construct a multi-level cooperative optimization model of the distribution network and the microgrid to achieve full-process cooperative optimization from pre-fault prevention, in-fault scheduling to post-fault recovery;

[0063] Step 3: A fast optimization method for the cooperative model to achieve fast response and implementation of the cooperative optimization of large-scale distribution networks and microgrids.

[0064] Operation Model of the Distribution Network and the Microgrid Based on SOP:

[0065] SOP can optimize the power flow distribution, alleviate the load imbalance problem, and improve the power supply reliability under the operation of uninterruptible power supplies. Introducing SOP into the distribution network can flexibly switch the power sources in the fault area, improving the operation efficiency of the distribution network and the recovery speed in the fault state.

[0066] In the distribution network, users can construct a microgrid under the constraints of energy storage and distributed energy output, etc.:

[0067]

[0068] Where: s and m respectively represent the indices of the photovoltaic and energy storage units; t is the time index. Represents the output power of the microgrid; Is the output power of a photovoltaic; And are the charging and discharging powers of the energy storage unit, respectively; is the load demand. is the reactive power output of the microgrid; is the reactive power output of the PV; k i,t is the proportionality coefficient of the active power to the reactive power; S pv is the apparent power of the PV unit. and are the maximum output and input powers of the energy storage, respectively; is the state factor of the energy storage charge and discharge; SoC m,t is the state of charge of the energy storage unit at time t; and are the minimum and maximum states of charge of the energy storage, respectively.

[0069] Operating limits of the distribution network:

[0070]

[0071] Among them: P i,j,t and Q i,j,t represent the active power and reactive power of the line, and represent the active power, reactive power, and U of the distribution network power source at node i i,t represents the node voltage, R i,j and X i,j represent the line resistance and reactance coefficient, U i and represent the minimum and maximum ranges of the voltage. The above constraints ensure the balance of the active power and reactive power of the line on the one hand, and also ensure the voltage balance on the other hand. The voltage must be within the deviation range.

[0072] Operating limits of the SOP:

[0073]

[0074] Among them: P i sop and represent the active powers of the two ports of the SOP at nodes i and j. is the maximum reactive power of the SOP, represents the reactive power of the SOP at node i. S sop represents the capacity of the SOP.

[0075] Collaborative optimization model of the multi-level distribution network and the microgrid

[0076] The present invention constructs a distributed micro collaborative optimization model covering multiple stages under extreme disasters. In the pre-fault prevention, in-fault scheduling, and post-fault recovery models: the pre-fault prevention model realizes the pre-configuration of energy storage and the adjustment of the microgrid operation mode; the in-fault scheduling realizes the adjustment of the distribution network power and the switching operation of the distribution network switches; the post-fault recovery model involves the scheduling of repair teams and the optimization of the repair order. The goal of the multi-level distribution network and microgrid collaborative optimization model is to achieve the minimum load loss through the minimum resource scheduling and personnel scheduling costs:

[0077]

[0078] where: l = 1 / 2 / 3 represents the three stages of pre-fault prevention, in-fault scheduling, and post-fault recovery. C m represents the scheduling cost of personnel and resources, represents the amount of load loss.

[0079] 1) Pre-fault prevention stage:

[0080] Purpose of the pre-fault prevention stage

[0081] The main goal of the pre-fault prevention model is to reduce the risk of faults through the reasonable pre-configuration of energy storage. In this stage, it is first necessary to evaluate the system state to determine potential fault risk points. Based on this information, a pre-configuration strategy for the energy storage system is formulated to ensure effective support during high load periods. The pre-fault prevention stage model can be expressed as:

[0082]

[0083] where: represents the energy storage unit m deployed to node i in the pre-disaster stage. The above constraint indicates that each energy storage unit can be configured to at most one node.

[0084] 2) In-fault scheduling stage:

[0085] When a fault occurs, the scheduling system needs to respond quickly to adjust the power of the distribution network and switch the switch operation to ensure the reliability and security of power supply. First, based on the real-time obtained fault information, the fault source is located. Next, according to the nature and location of the fault, the output of each power generation unit in the microgrid is adjusted to compensate for the load loss caused by the fault. This involves adjusting the charge and discharge strategy of the energy storage system. At the same time, the distribution network needs to coordinate the switching of switches to effectively isolate the fault area and ensure that the power supply in other areas is not affected. The switch scheduling model during the fault can be expressed as:

[0086]

[0087] where: zi,j,t Indicates the line status; a i,j = 1 indicates that the line is equipped with an automatic switch, otherwise it is 0. When the switch fails (i.e., u i,j,t = 1), the switch status remains unchanged.

[0088] 3) Post-fault recovery stage:

[0089] The post-fault recovery model focuses on the scheduling optimization of repair teams during the system recovery process. First, during the fault repair stage, the repair requirements and resource allocation are determined. Based on the evaluation results, the scheduling system will optimize the scheduling of repair teams to ensure that the repair work is completed quickly and efficiently. This includes determining the number of repair personnel and the work sequence, and reasonably allocating resources to minimize the recovery time. In addition, by optimizing the emergency repair sequence, areas with a greater impact on power supply can be given priority to restore normal power supply as soon as possible. Ultimately, the goal is to achieve the lowest load loss, ensure that users can restore power services in the shortest time, and improve the overall system recovery ability. The post-fault recovery stage model can be expressed as:

[0090]

[0091]

[0092] Where: Indicates whether construction team c repairs the fault point or operates switch i at time t. Indicates whether construction team c has completed the repair of fault point i. tr i,j Indicates the moving time of construction team c between points i and j, rep i,j Indicates the time required for construction team c to repair fault point i.

[0093] Fast optimization method for the collaborative model

[0094] Mathematically, the multi-level distribution network and microgrid collaborative optimization model is a mixed integer programming model. Traditional centralized solution methods have high requirements for hardware equipment, and there are numerous distribution network nodes. Solving large-scale mixed integer programming requires facing a huge time burden. Therefore, the present invention constructs the above multi-level distribution network and microgrid collaborative optimization model into a two-layer mixed integer linear programming model. The upper layer model minimizes the scheduling cost by optimizing pre-fault prevention, in-fault scheduling, and post-fault recovery scheduling, while the lower layer model ensures the minimum load loss for emergency resources. The proposed distribution network fault repair model can be constructed as a "min-min" optimization model. The two-layer optimization model can be represented in the matrix form of equations.

[0095]

[0096] s.t. (1)-(18), (21)-(39) (27)

[0097]

[0098] s.t. Ax ≤ g (41.a)

[0099] Gy + Ex ≤ s (41.b) (28)

[0100] Where: x is the decision vector of the upper-layer model, representing the scheduling variables in three stages, and y is the decision vector in the objective of the lower-layer model. y represents the operating variables of the distribution network, a and b are the corresponding coefficients respectively, T is the transpose symbol; A, G, and E are the corresponding constraint coefficient matrices, and g and s are the corresponding right-side constant vectors subject to constraints.

[0101] Example 2

[0102] The specific steps of the solution method based on the above model are as follows:

[0103] 1) Initialization. Set the initial values, set the lower limit LB = 0, the upper limit UB = +∞, the convergence index ε = 0.01, and set the initial iteration number k = 1.

[0104] 2) Solve the upper-layer model to obtain the decision variable x and the objective function value F based on the upper-layer model. LB = minimum{LB, F}.

[0105] 3) Solve the lower-layer model. Obtain the emergency resource outflow and the sub-problem objective function value f based on the given emergency scheduling strategy UB = max{UB, f}.

[0106] 4) Convergence verification If UB - LB ≤ ε, the solution ends, and the scheduling variable x in the last iteration is output.

[0107] Example 3

[0108] The improved IEEE-30 bus test system is used to verify the effectiveness of the proposed invention. In addition, the test system is configured with two SOPs with capacities of 50 kW and 40 kW respectively. Table 1 describes the parameters of each component of the distribution network.

[0109] Table 1

[0110]

[0111] Before the random fault occurs, the reliability of the power grid can be enhanced by pre-arranging mobile power sources and adjusting the operation mode. The schematic diagram of the proactive preventive measures is as Figure 1As shown. To reduce the power shortage losses caused by line faults, two energy storages are pre-deployed at nodes 15 and 23 respectively. In addition, by opening the switches on lines 3-23 and 6-26 to isolate the critical loads, and using the mobile storage capacity to provide power support for the critical loads, the power outage losses caused by faults can be reduced.

[0112] To further verify the superiority of the method proposed by the present invention, the method proposed by the present invention is compared with other methods including two methods A and B. Method A is a three-stage scheduling method that does not consider fault isolation; Method B is a three-stage scheduling method that does not consider mobile storage.

[0113] Table 2 shows the comparison results of the load shedding between the proposed method and methods A and B. The load shedding of the method proposed by the present invention is 1.94 MW less than that of method B. The load shedding rate is 27.4% lower. This shows that the distribution micro multi-stage collaborative strategy model based on SOP significantly improves the reliability of the power system under extreme faults. This model ensures continuous power supply for critical loads under fault conditions by deploying SOP and mobile energy storage at critical nodes and isolating the fault area from the power supply area through switching operations, thus improving the reliability of the distribution network.

[0114] Table 2

[0115]

[0116] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A reliability improvement method for a distribution network and a microgrid, characterized in that: It includes the following three steps: Step 1: Build a distribution network and microgrid operation model based on SOP, so that the microgrid can operate independently or collaboratively under different fault scenarios; Step 2: Build a multi-level distribution network and microgrid collaborative optimization model to achieve full-process collaborative optimization from pre-fault prevention, fault scheduling to post-fault recovery; Step 3: Rapid optimization method of collaborative model to achieve rapid response and implementation of collaborative optimization of large-scale distribution networks and microgrids.

2. The reliability improvement method of a distribution network and a microgrid according to claim 1, characterized in that: In the step 1, a distribution network and microgrid operation model based on SOP is constructed, including: In the distribution network, a microgrid is constructed under the constraints of energy storage and distributed energy output, as follows: Where: s, m represent the indexes of the photovoltaic and energy storage units respectively; t is the time index; represents the output power of the microgrid; is the output power of each photovoltaic; and are the charging and discharging power of the energy storage unit respectively; is the load demand; is the reactive power output of the microgrid; is the reactive power output of photovoltaic; k i,t is the proportionality coefficient of active power to reactive power; S pv is the apparent power of the PV unit; and are the maximum output and input power of energy storage respectively; SoC is the energy storage charge and discharge state factor; m,t is the state of charge of the energy storage unit at time t; and They are the minimum and maximum states of charge for energy storage, respectively; The operating restrictions of the distribution network are as follows: Where: P i,j,t and Q i,j,t Indicates the active power and reactive power of the line. and represents the active power, reactive power, and U of the power supply in the distribution network at node i. i,t represents the node voltage, R i,j and X i,j represents the line resistance and reactance coefficient, U i and Indicates the minimum and maximum range of voltage; SOP operating restrictions are as follows: in: and represents the active power of the two ports of the SOP of nodes i and j; is the maximum reactive power of SOP, represents the reactive power of SOP at node i; S sop Indicates the capacity of SOP.

3. The reliability improvement method of a distribution network and a microgrid according to claim 1, characterized in that: In the step 2, a multi-level distribution network and microgrid collaborative optimization model is constructed, as follows: Where: l = 1 / 2 / 3 represents the three stages of pre-failure prevention, fault dispatch and post-failure recovery; C m Indicates the scheduling cost of personnel and resources, Represents the amount of load loss; where: 1) Pre-failure prevention stage: The pre-failure prevention stage model is as follows: in: represents the energy storage unit m deployed to node i in the pre-disaster stage; the above constraints indicate that each energy storage unit can be configured as at most one node; 2) Fault dispatching phase: The switch scheduling model during a fault is as follows: Where: z i,j,t Indicates line status; a i,j =1 means the line is equipped with an automatic switch, otherwise it is 0; when the switch fails (i.e.: u i,j,t =1), the switch state remains unchanged; 3) Post-failure recovery phase: The post-failure recovery phase model is as follows: in: Indicates whether construction team c repairs the fault point or operates switch i at time t; Indicates whether construction team c has completed the repair of fault point i; tr i,j represents the moving time of construction team c between points i and j, rep i,j It represents the time required by construction team c to repair fault point i.

4. The reliability improvement method of a distribution network and a microgrid according to claim 1, characterized in that: The rapid optimization method of the collaborative model in step 3 is as follows: The distribution network fault repair model is constructed as a "minimum-minimum" optimization model. The two-layer optimization model is expressed in the matrix form of the equation: Where: x is the decision vector of the upper model, representing the dispatch variables of the three stages, y is the decision vector in the target of the lower model; y represents the operating variable of the distribution network, a and b are the corresponding coefficients, and T is the transposed sign; A, G, E are the corresponding constraint coefficient matrices, and g and s are the corresponding constrained right-hand constant vectors.

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