A Distribution Network Self-Healing Method Considering the Access of Distributed New Energy

Abstract

The present invention belongs to the technical field of distribution network self-healing, and specifically relates to a distribution network self-healing method considering the access of distributed new energy, which includes the following steps: dividing the distribution network into resilient microgrid clusters; establishing a microgrid self-healing optimization model: in the normal operation mode, taking the maximum selling electricity profit as the objective function and considering the microgrid self-healing optimization constraint conditions, where the microgrid self-healing optimization constraint conditions include the capacity constraint of distributed new energy, the maximum installation quantity constraint of remote control switches, the operation constraint of the energy storage device system, the power constraint of each microgrid, the power flow constraint, and the network constraint; when a fault is detected, the microgrid is disconnected from the main grid and enters the island mode; isolating the fault area from the non-fault area and rescheduling the load of the non-fault area to balance the load.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network self-healing, and particularly relates to a distribution network self-healing method considering the access of distributed new energy. Background Art

[0002] In recent years, renewable energy has developed rapidly and become an important solution for countries to address energy growth and environmental problems. However, the management of a large number of heterogeneous distributed energy sources and their extensive and dynamic attributes and control points have brought huge challenges to energy management. At the same time, the progress of control and metering technologies has promoted the transformation of traditional distribution networks into modern small-scale areas (i.e., microgrids), enabling consumers to play the roles of both producers and consumers.

[0003] A microgrid is a low-voltage or medium-voltage distribution system related to heterogeneous distributed generation and energy storage systems, with a specific geographical area, and can be regarded as a subset of the power system. It can supply power to customers in a controlled and coordinated manner, thereby enhancing the reliability of the power system and its resilience in emergency situations. The self-recovery function of the microgrid enables it to restore the system to the normal state in the shortest possible time, which is crucial for dealing with preventive and corrective measures after a fault and forming a reliable and resilient system.

[0004] Existing distribution network self-healing methods mainly include strategies such as emergency control, restoration control, and island control. Although these self-healing methods can effectively cope with distribution network faults in theory, they still face some challenges and drawbacks in practical applications, such as insufficient automation, imperfect technical standards and specifications, and the impact of distributed power grid connection. In addition, the implementation of self-healing control requires a large amount of construction investment, and the evaluation of its investment benefits not only needs to consider the economic benefits of power companies, but also needs to consider the economic losses reduced by users and social benefits, etc. At present, the research on distribution network self-healing at home and abroad is mainly based on a probability coupling model, which is used to plan distributed renewable energy (DER) and reactive sources in the form of microgrids to ensure the reliability of the system, regarding the microgrid as an alternative solution rather than upgrading the power generation and transmission expansion plan. However, it mainly focuses on the interconnection operation mode of the microgrid (i.e., normal working conditions), while the island operation mode of the microgrid (i.e., self-healing action) has not been widely studied in the planning problem.

[0005] In addition, the intermittency of renewable energy poses significant challenges to the secure and reliable operation of microgrids. To overcome this challenge, effective methods are needed to handle the uncertainties associated with renewable energy production. Currently, scenario-based programming, robust optimization, information-gap decision theory, etc. are widely used to handle uncertainties, but these prior uncertainty management methods have some drawbacks in terms of computational burden and solution accuracy, and appropriate methods need to be developed to overcome them. Therefore, new metrics should be developed to evaluate the reliability and resilience of microgrids in islanded mode.

[0006] In summary, there are still some deficiencies in the current research regarding the island operation mode of microgrids, uncertainty management methods, and reliability and resilience evaluation metrics, which need to be further improved and perfected. Therefore, in order to improve the reliability of the distribution system and its ability to respond to emergencies, the present invention proposes a self-healing method for a distribution network considering the access of distributed new energy. Summary of the Invention

[0007] To overcome the problems in the prior art, the present invention proposes a self-healing method for a distribution network considering the access of distributed new energy.

[0008] The technical solution of the present invention to solve the above technical problems is as follows:

[0009] The present invention provides a self-healing method for a distribution network considering the access of distributed new energy, including the following steps:

[0010] Divide the distribution network into resilient microgrid clusters;

[0011] Establish a microgrid self-healing optimization model: In the normal operation mode, with the maximization of electricity sales profit as the objective function, and considering the microgrid self-healing optimization constraints, the microgrid self-healing optimization constraints include the capacity constraints of distributed new energy, the maximum installation quantity constraints of remote control switches, the operation constraints of energy storage device systems, the power constraints of each microgrid, the power flow constraints, and the network constraints;

[0012] When a fault is detected, the microgrid disconnects from the main grid and enters the islanded mode; isolate the fault area from the non-fault area, and re-schedule the load of the non-fault area to balance the load.

[0013] Further, the process of dividing the distribution network into resilient microgrid clusters includes: First, upgrade the passive distribution system to an active distribution system by integrating distributed new energy and energy storage devices; Second, clarify the electrical and geographical boundaries of the microgrid by setting remote control switches to form resilient microgrid clusters.

[0014] Further, each microgrid must have at least one dispatchable distributed generation device.

[0015] Further, the steps of isolating the fault area from the non-fault area and rescheduling the load in the non-fault area to balance the load specifically include: dividing the fault area by controlling remote switches, redistributing controllable distributed generation devices to the non-fault area, establishing an objective function for the islanding mode, and rescheduling the load in the non-fault area to balance the load with the goal of minimizing cost.

[0016] Further, in the normal operation mode, with the maximization of the electricity sales profit as the objective function, the objective function is:

[0017] Pnft = REV - cost;

[0018]

[0019] cost = Z DER +Z SW ;

[0020] In the formula: Pnft represents profit, REV represents revenue, cost represents cost; t is the optimization time; m is the microgrid index; is the price of selling electricity to consumers at time t, is the price of selling electricity to the electricity market at time t, is the price of purchasing electricity from the electricity market at time t; is the electricity quantity sold to consumers at time t, is the electricity quantity sold to the electricity market at time t, is the electricity quantity purchased from the electricity market at time t; is a 0-1 variable of the microgrid's electricity selling state, is a 0-1 variable of the microgrid's electricity purchasing state, where if electricity is sold or purchased at time t, the value is 1, otherwise the value is 0; Z DER is the investment cost of distributed new energy; Z SW is the operating cost of distributed new energy.

[0021] Further, the objective function f of the islanding mode island :

[0022]

[0023] In the above formula, i is the unit index of distributed new energy DER; m is the microgrid index; C i,m is the operating cost coefficient of distributed new energy; is the active power of distributed new energy; is the price of increasing the electricity reserve, is the price of decreasing the electricity reserve; is the increased electricity quantity provided by the distributed generation device, The value of the drop provided for the distributed power generation device; d cl,m The controllable load of the microgrid; u cl,m The 0-1 variable representing the load status; κ cl,m The per-unit load value of the microgrid; The load shedding cost coefficient; LS t,m The amount of load shedding in the islanding mode; The renewable energy spillage cost coefficient; RS t,m The amount of renewable energy spillage in the islanding mode.

[0024] Furthermore, it also includes evaluating the reliability of the microgrid in the islanding mode, and the indicators of the reliability in the islanding mode include the island expected energy interruption IEEI and the island expected energy deficiency IEED:

[0025]

[0026] In the formula: TS is the action time in the islanding mode; SD TS The load interruption amount of the microgrid in the islanding mode; Δ TS The duration of the microgrid in the islanding mode; D TS The load amount in the islanding mode; P TS The available power in the islanding mode; NI is the number of islands in a year; IEED Target The target energy value in the islanding mode.

[0027] Furthermore, based on the adjustable interval optimization method of the C&CG framework, the uncertain problems caused by the access of distributed new energy are analyzed.

[0028] Compared with the prior art, the present invention has the following technical effects:

[0029] (1) The present invention selects various DERs for appropriate combination, meets the economy and reliability of the system, while minimizing the planning cost and achieving seamless islanding.

[0030] (2) By assuming that the uncertain parameters belong to a bounded convex uncertain set and minimizing the deviation between the objective function and the expected value, the present invention corrects the data uncertainty in the operation stage. Compared with the traditional deterministic method, performing interval optimization can protect the microgrid from the risks brought by uncertainty.

[0031] (3) The self-healing problem of the distribution network of the present invention is formulated as a mixed integer linear programming problem with accurate calculation and high efficiency, and has a high solution efficiency. Description of the Drawings

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 Flowchart of the distribution network self-healing method considering distributed new energy access of the present invention;

[0034] Figure 2 Illustration of the self-healing strategy in the emergency mode proposed by the present invention;

[0035] Figure 3 Topological schematic diagram of the 33-node distribution network example in the distribution network self-healing method considering distributed new energy access of the present invention;

[0036] Figure 4 Daily economic dispatch result of the microgrid in the distribution network self-healing method considering distributed new energy access of the present invention. Detailed implementation manners

[0037] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of the technical solutions proposed according to the present invention. The specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0038] To improve the reliability of the distribution system and the ability to respond to emergencies, the present invention proposes a distribution network self-healing method considering distributed new energy access. First, the distribution network is divided into a small-scale elastic microgrid cluster; second, a microgrid self-healing optimization model is established. In the normal operation mode, the objective function is to maximize the electricity sales profit; considering constraints such as the capacity constraint of DER, the maximum installation quantity constraint of remote control switches (RCS), the operation constraint of the energy storage system (ESS), the power constraint of each microgrid, the power flow constraint, and the network constraint; then a self-healing strategy is proposed, and a new reliability index for evaluating the microgrid in the island mode is proposed; finally, an adjustable interval optimization method based on the C&CG framework is used to study the impact of the uncertainty problem caused by distributed new energy access on the distribution network self-healing, and the commercial solver Gurobi is called to solve this problem.

[0039] In this embodiment, a distribution network self-healing method considering the access of distributed new energy specifically includes the following steps:

[0040] Divide the distribution network into elastic microgrid clusters;

[0041] In the normal operation mode, with the maximum selling electricity profit as the objective function and considering the microgrid self-healing optimization constraint conditions, the microgrid self-healing optimization constraint conditions include the capacity constraint of distributed new energy, the maximum installation quantity constraint of remote control switches, the operation constraint of the energy storage device system, the power constraint of each microgrid, the power flow constraint, and the network constraint, and thus establish a microgrid self-healing optimization model;

[0042] When a fault is detected, the microgrid disconnects from the main grid and enters the island mode; isolate the fault area from the non-fault area, and reschedule the load in the non-fault area to balance the load.

[0043] The following refers to Figures 1 - 4 Expand each of the above steps in detail:

[0044] Step 100: Initialize, input the distribution network line parameters, the access capacity and nodes of distributed new energy, and the load prediction reference value.

[0045] Step 200: Divide the distribution network into small-scale elastic microgrid clusters to ensure that the distribution network can effectively respond to disturbances.

[0046] The most important and fundamental step to achieve the self-healing of the distribution network is to establish an independent module to control the system to achieve the global optimum of the entire system. The microgrid with convenient and intelligent control characteristics is considered an ideal solution to solve this key problem. The microgrid can sell its surplus power to the main grid, and in case of emergencies such as faults, the microgrid can also be separated from the network and independently supply its own demand locally, that is, the autonomous mode.

[0047] Therefore, the best way to manage a complex system is to decompose the distribution network into small-scale microgrids. The microgrid has outstanding self-recovery functions and can effectively restore the system to the normal state in a short time, thereby enhancing the reliability of the distribution system and the recovery resilience in case of emergencies.

[0048] Converting a large-scale distribution system into a microgrid includes two stages: converting a passive distribution system into an active distribution system by integrating various distributed energy resources DER and energy storage device systems ESS; forming a networked microgrid by allocating remote control switches to specify the electrical boundary and geographical boundary of the microgrid. In addition, dividing the distribution network into a group of microgrids needs to meet the following criteria:

[0049] (1) Each microgrid must have at least one dispatchable distributed generation (DG) unit.

[0050] (2) Each microgrid must have sufficient generation capacity to supply critical loads.

[0051] (3) In islanded mode (self-healing), each microgrid must meet the required reliability limits.

[0052] (4) Each microgrid should be able to match generation and consumption to maintain frequency stability.

[0053] (5) Each microgrid should have sufficient voltage / reactive power control capabilities to maintain voltage profile.

[0054] Step 300: Establish a microgrid self-healing optimization model. In normal operation mode, with the maximization of selling electricity profit as the objective function and considering the microgrid self-healing optimization constraints, the microgrid self-healing optimization constraints include the capacity constraint of distributed new energy DER, the maximum installation number constraint of remote control switches RCS, the operation constraint of energy storage device system ESS, the power constraint of each microgrid, the power flow constraint, and the network constraint.

[0055] In some embodiments, Step 300 includes the following sub-steps:

[0056] Step 310: In normal operation mode, with the maximization of selling electricity profit as the objective function:

[0057] Pnft = REV - cost;

[0058]

[0059] cost = Z DER +Z SW ;

[0060] Where: Pnft represents profit, REV represents revenue, cost represents cost; t is the optimization time; m is the microgrid index; is the price of selling electricity to consumers at time t, is the price of selling electricity to the electricity market at time t, is the price of purchasing electricity from the electricity market at time t; is the electricity quantity sold to consumers at time t, is the electricity quantity sold to the electricity market at time t, is the electricity quantity purchased from the electricity market at time t; is a 0-1 variable of the microgrid's electricity selling state, A 0-1 variable for the power purchase status of the microgrid, where if power is sold or purchased at time t, the value is 1, otherwise the value is 0; Z DER The investment cost of distributed new energy DER; Z SW The operating cost of distributed new energy DER;

[0061]

[0062]

[0063] In the formula: T is the total optimization time; n is the node index; γ t,m,n A 0-1 variable representing the status of distributed generation device DG; The operating cost per unit of distributed generation device DG; The power generation of distributed generation device DG; e is the index of energy storage device system ESS; The operating cost per unit of energy storage device system ESS; A 0-1 variable for the charging status of energy storage device system ESS, A 0-1 variable for the discharging status of energy storage device system ESS; The charging power of energy storage device system ESS, The discharging power of energy storage device system ESS; N DER The number of distributed new energy DER; i is the unit index of distributed new energy DER; x i A 0-1 variable representing whether to invest in distributed new energy DER; ε i An integer variable determining the type of distributed new energy DER; The investment cost per unit of distributed new energy DER; The capacity per unit of distributed new energy DER; τ sw A 0-1 variable representing whether to invest in a section switch (SW), ζ tw A 0-1 variable for whether to invest in a tie switch (TW); K sw The load shedding cost, K tw The renewable energy leakage cost.

[0064] Step 320: Establish the microgrid self-healing optimization constraint conditions, including: the capacity constraint of distributed new energy DER, the maximum installation quantity constraint of remote control switch RCS, the operation constraint of energy storage device system ESS, the power constraint of each microgrid, the power flow constraint, and the network constraint, which are explained as follows:

[0065] The capacity constraint of distributed new energy DER:

[0066]

[0067] Where: w is the unit index of the wind turbine WT; N WT is the number of wind turbines WT; is the power generation of the wind turbine, is the minimum capacity of the wind turbine, is the maximum capacity of the wind turbine; v is the unit index of the photovoltaic; N PV is the number of photovoltaic devices; is the power generation of the photovoltaic, is the minimum capacity of the photovoltaic, is the maximum capacity of the photovoltaic; d is the unit index of the distributed generation device DG; N DG is the number of distributed load generation devices DG; is the power generation of the distributed generation device DG, is the minimum capacity of the distributed generation device DG, is the maximum capacity of the distributed generation device DG; e is the unit index of the energy storage device ESS; N ESS is the number of energy storage devices ESS; is the power of the energy storage device system ESS, is the minimum capacity of the energy storage device system ESS, is the maximum capacity of the energy storage device system ESS; is a 0-1 variable of the operating state of the wind turbine, is a 0-1 variable of the operating state of the photovoltaic, is a 0-1 variable of the operating state of the distributed generation device DG, is a 0-1 variable of the operating state of the energy storage device system ESS; is the active power of the wind turbine w in the microgrid m; is the active power of the photovoltaic v in the microgrid m; is the active power of the distributed generation device d in the microgrid m; is the active power of the energy storage device e in the microgrid m; PI DER is the penetration rate of the distributed new energy DER; l is the load node index; is the load quantity.

[0068] Remote control switch RCS maximum installation quantity constraint:

[0069]

[0070] Where: is the maximum access quantity of the sectionalizing switch SW, is the maximum access quantity of the connecting switch TW.

[0071] Energy storage device system ESS operation constraint:

[0072]

[0073] In the formula: is the charging power of the energy storage device e at time t, is the discharging power of the energy storage device e at time t; and are 0-1 variables representing the charging and discharging states of the energy storage device e at time t; is the maximum charging power of the energy storage device e, is the maximum discharging power of the energy storage device e; is the state of charge of the energy storage device e at time t, is the state of charge of the energy storage device e at time t-1, is the minimum allowable state of charge of the energy storage device system ESS, is the maximum allowable state of charge of the energy storage device system ESS, is the initial state of charge of the energy storage device system ESS; is the initial capacity of the energy storage device; is a 0-1 variable representing whether the energy storage device system ESS is idling; is the efficiency of the energy storage device system ESS in the charging mode; is the efficiency of the energy storage device system ESS in the discharging mode.

[0074] Power constraint of each microgrid:

[0075]

[0076]

[0077] In the formula: is the active power of the wind turbine w at time t, is the active power of the photovoltaic v at time t, is the active power of the distributed generation device d at time t.

[0078] Power flow constraint:

[0079]

[0080] In the formula: is the active power of branch b, is the reactive power of branch b; G b is the conductance of branch b, B b is the susceptance of branch b; V n is the voltage magnitude of node n, V n+1 is the voltage magnitude of node n+1; ω n,n+1 is cos(δ n -δn+1 ) piecewise linear representation; δ n is the voltage phase angle of node n, δ n+1 is the voltage phase angle of node n + 1.

[0081] Network constraints:

[0082]

[0083] In the formula: is the minimum voltage, is the maximum voltage; is the minimum value of the phase angle of node n, is the maximum value of the phase angle of node n; is the maximum power that the branch allows to flow through.

[0084] Step 400: Propose a self-healing strategy: When events such as faults occur, the microgrid disconnects from the power grid and enters the islanding mode. Control the remote control switch RCS to divide the fault area, and reschedule the non-fault area to balance the load, meeting the requirements of system reliability and security.

[0085] Due to changes in demand and generation conditions caused by faults, reallocate the controllable distributed generation device DG to the non-fault area. After clearing all faults, the system will return to the normal operation mode and continuously check for faults.

[0086] In the self-healing mode, the problem of fast economic dispatch is solved, and load dispatch measures are taken to control the consumption of the microgrid to avoid frequency and voltage instability. That is, the controllable part can be optimized as a control variable to stabilize the power generation of each microgrid and maximize the restored load. On the other hand, the power generation of the controllable distributed generation device DG must be reallocated to minimize the cost function.

[0087] Objective function f in the islanding mode island is:

[0088]

[0089] In the formula: i is the unit index of the distributed new energy DER; m is the microgrid index; C i,m is the operating cost coefficient of the distributed new energy; is the active power of the distributed new energy; is the price of the increase in power reserve, is the price of the decrease in power reserve; is the increased value of the power provided by the distributed generation device DG, is the decreased value provided by the distributed generation device DG; cl is the controllable load index; d cl,m is the controllable load of the microgrid; ucl,m is a 0-1 variable representing the load state; κ cl,m is the per-unit load value of the microgrid; is the load shedding cost coefficient; LS t,m is the amount of load shedding in islanding mode; is the renewable energy spillage cost coefficient; RS t,m is the amount of renewable energy spillage in islanding mode.

[0090] Step 500: Set up reliability indicators for evaluating the microgrid in islanding mode to evaluate the reliability of the microgrid in islanding mode.

[0091] Although IEEE standards have defined various reliability indices for calculating the reliability level of distribution networks, these indices do not properly represent the system behavior in islanding mode. For this reason, the present invention proposes two new indices to evaluate the reliability of the microgrid in islanding mode. The new indices include the operating reliability index in islanding mode - Island Expected Energy Interruption (IEEI) and the user-based reliability index - Island Expected Energy Deficiency (IEED), as follows:

[0092]

[0093] In the formula: TS is the action time in islanding mode; SD TS is the load interruption amount of the microgrid in islanding mode; Δ TS is the duration of the microgrid in islanding mode; D TS is the load amount in islanding mode; P TS is the available power in islanding mode; NI is the number of islandings in a year; IEED Target is the target energy value in islanding mode.

[0094] Step 600: Analyze the uncertainty problem caused by the access of distributed new energy based on the adjustable interval optimization method of the C&CG (Column-and-Constraint Generation) framework.

[0095] (1) Adjustable interval optimization;

[0096] Adjustable interval optimization is to use the interval optimization method to study the influence of uncertain parameters on the problem, expand the adjustable interval model to eliminate the conservatism of the traditional interval model, and add a penalty factor to the objective function in islanding mode to balance load and power generation.

[0097] To apply the interval optimization method to the problem, first, on the basis of the standard form, represent the proposed deterministic microgrid planning model with inequality and equality constraints using symbols, that is

[0098] Max F = Profit[Ψ, ξ];

[0099] X(M, N, Ψ) ≤ 0;

[0100] Y(M, N, Ψ) = 0;

[0101] Where: F is the objective function of the deterministic microgrid planning model; Ψ is the variable of the model; ξ is the uncertain parameter; X(M, N, Ψ) ≤ 0 is the inequality constraint of the model; Y(M, N, Ψ) = 0 is the equality constraint of the model.

[0102] Assume that the lower and upper bounds of the uncertain parameter ξ are respectively Then the minimum value F - (Ψ) and the maximum value F + (Ψ) can be calculated by the following method.

[0103]

[0104] After operation transformation, the interval objective function will be replaced by the initial function to optimize its boundary with the given input interval. The average objective (F avg (Ψ) and the deviation (F div (Ψ)) are optimized simultaneously until the microgrid has stronger robustness to uncertainty.

[0105] In addition, by increasing the deviation from the estimated value of the uncertain parameter, the model may become very conservative. For this reason, the present invention extends the adjustable interval model rather than the traditional interval model to eliminate this concern. In this method, an additional penalty factor is added to the objective function of the islanding mode, representing the cost associated with renewable energy spillage:

[0106]

[0107] In the above formula, i is the unit index of the distributed new energy DER; m is the microgrid index; C i,m is the operating cost coefficient of the distributed new energy; is the active power of the distributed new energy; is the price increase of the power reserve; is the price decrease of the power reserve; is the increased power value provided by the distributed generation device; is the decreased value provided by the distributed generation device; d cl,m is the controllable load of the microgrid; u cl,m is a 0-1 variable representing the load state; is the load shedding cost coefficient; LS t,m is the amount of load shedding in the islanding mode; is the spillover cost coefficient of renewable energy; RS t,m is the renewable energy overflow in island mode.

[0108] (2) C&CG algorithm;

[0109] Because the proposed problem is a min-max-min problem that cannot be solved explicitly using off-the-shelf solvers, the C&CG algorithm is proposed. This problem is decomposed into a main problem and subproblems. The subproblems are transformed into complementary constraints using the KKT optimality conditions (Karush-Kuhn-Tucker conditions). These are then converted into a linear form using the big-M method. The optimal value of the M parameter is determined through iteration. A linearization method is then applied to transform the subproblem constraints into a mixed-integer linear programming model, which is then solved iteratively.

[0110] The main problem can be stated as:

[0111]

[0112]

[0113] Where: vectors A0, B0, C0, q1, q2 and matrices A1, B1, C1, A2, B2, C2 are all constants; f, u, z are decision variables; ξ is the objective function of the subproblem.

[0114] The sub-problem can be expressed as:

[0115]

[0116] Where: is any given non-optimal solution.

[0117] Using the KKT optimal conditions, the constraints are transformed into complementary constraints, that is,

[0118]

[0119] Where: and λ are the dual variables of the problem defined. To ensure convexity, the KKT condition can be transformed into a linear form using the big-M method:

[0120]

[0121] 0≤λ≤M.(1-θ);

[0122] Where: M is a larger value, and θ is a 0-1 variable.

[0123] Therefore, the C&CG algorithm decomposes the original problem into a master problem (i.e., the investment problem) and two sub-problems (i.e., one for interconnected operation and the other for island operation).

[0124] Step 700: Invoke the commercial solver Gurobi to solve the mixed integer linear programming problem established and transformed in the previous steps, and output the self-healing optimization result.

[0125] Table 1 Influence results of the adjustable interval method and the deterministic interval method on the distribution network self-healing problem

[0126]

[0127]

[0128] Table 1 shows the influence results of the adjustable interval method and the deterministic interval method on the distribution network self-healing problem. As can be seen from the results, although the investment cost and operation cost of the system increase for the distribution network self-healing method proposed in this invention compared with the traditional method, the system fluctuation is smaller; on the other hand, compared with the deterministic method, the renewable energy spillage of the method of this invention is reduced more, and the reliability index is significantly improved. Therefore, this method is more reliable.

[0129] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distribution network self-healing method considering the integration of distributed renewable energy, characterized in that: The following steps are involved: Initialization: input distribution network line parameters, distributed new energy access capacity and nodes, and load forecast benchmark values; Divide the distribution network into resilient microgrid clusters, including: first, upgrading the passive distribution system to an active distribution system by integrating distributed renewable energy and energy storage devices; second, defining the electrical and geographical boundaries of the microgrids by setting remote switches to form resilient microgrid clusters; each microgrid must have at least one dispatchable distributed generation device; Establish a microgrid self-healing optimization model: Under normal operating mode, the objective function is to maximize electricity sales profits, and consider the microgrid self-healing optimization constraints, which include the capacity constraints of distributed renewable energy, the maximum number of remote switches installed, the operation constraints of the energy storage device system, the power constraints of each microgrid, the power flow constraints, and the network constraints. When a fault is detected, the microgrid is disconnected from the main grid and enters island mode. This isolates the faulty area from the non-faulty area and re-dispatches the load in the non-faulty area to balance the load. This includes: dividing the faulty area by controlling remote switches, re-allocating controllable distributed generation devices to the non-faulty area, establishing an objective function for island mode to minimize cost, and re-dispatching the load in the non-faulty area to balance the load. Establish reliability indicators for evaluating the microgrid in island mode, which are used to evaluate the reliability of the microgrid in island mode. The reliability indicators in island mode include island expected energy interruption (IEEI) and island expected energy deficiency (IEED): ； ； Where: TS is the action time in island mode; is the load interruption amount of the microgrid in island mode; is the duration of the microgrid in island mode; is the load in island mode; is the available power in island mode; NI is the number of islands in a year; is the target energy value in island mode; An adjustable interval optimization method based on the C&CG framework is used to analyze the uncertainty issues caused by the integration of distributed renewable energy. The commercial solver Gurobi is called to solve the mixed integer linear programming problem established and transformed in the above steps, and the self-healing optimization result is output.

2. A distribution network self-healing method considering the access of distributed new energy according to claim 1, characterized in that: In the normal operation mode, the objective function is to maximize the profit of electricity sales, which is: ； ； ； Where: Pnft Indicates profit, REV Indicates income, cost indicates cost; t To optimize time; m Indexing for microgrids; for t The price at which electricity is sold to consumers, for t The price of electricity sold to the electricity market at that time, For t The price of electricity purchased in the electricity market; for t The amount of electricity sold to consumers at for t The amount of electricity sold to the electricity market at for t The amount of electricity purchased from the electricity market at the time of purchase; is a 0-1 variable indicating the state of the microgrid selling electricity. t When the microgrid sells electricity, the value is 1, otherwise the value is 0; is a 0-1 variable indicating the state of the microgrid purchasing electricity, where t When the microgrid purchases electricity, the value is 1, otherwise the value is 0; The investment cost of distributed new energy; The operating cost of distributed new energy.

3. A distribution network self-healing method considering the access of distributed new energy according to claim 2, characterized in that: The objective function of the island model : ； In the above formula, i Index of distributed renewable energy DER units; m is the microgrid index; d is the unit index of the distributed generation device DG; is the operating cost coefficient of distributed new energy; is the active power of distributed renewable energy; The price added for power storage, The price of electricity reserves has fallen; The added value of electricity provided by distributed generation devices, the derating value provided for distributed generation installations; is the controllable load of the microgrid; is a 0-1 variable representing the load state; is the load value per unit of the microgrid; is the load shedding cost coefficient; is the load shedding amount in island mode; is the spillover cost coefficient for renewable energy; is the renewable energy overflow in island mode.

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