Power distribution network self-healing method considering distributed new energy access

Abstract

The invention belongs to the technical field of power distribution network self-healing, and particularly relates to a power distribution network self-healing method considering distributed new energy access, which comprises the following steps: dividing a power distribution network into elastic micro-grid clusters; establishing a micro-grid self-healing optimization model: in a normal operation mode, taking electricity selling profit maximization as an objective function, and considering micro-grid self-healing optimization constraint conditions; the micro-grid self-healing optimization constraint conditions comprise a distributed new energy capacity constraint, a remote control switch maximum installation number constraint, an energy storage device system operation constraint, and a power constraint, a power flow constraint and a network constraint of each micro-grid; when a fault is detected, disconnecting the micro-grid from the main power grid, and entering an island mode; and 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 in particular 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 has become an important solution for the country to cope with energy growth and environmental problems. However, the management of a large number of heterogeneous distributed energy resources and their extensive and dynamic properties and control points have brought great challenges to energy management. At the same time, advances in control and metering technologies have promoted the transformation of traditional distribution networks to modern small-scale areas (i.e., microgrids), allowing consumers to play the role of both producers and consumers.

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

[0004] Existing distribution network self-healing methods mainly include emergency control, recovery control and island control strategies. Although these self-healing methods can effectively deal with distribution network failures in theory, they still face some challenges and shortcomings 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 its investment benefit evaluation should not only consider the economic benefits of the power company, but also the economic losses and social benefits of users. At present, the research on distribution network self-healing at home and abroad is mainly based on a probabilistic coupling model for planning distributed renewable energy (DER) and reactive sources in the form of microgrids to ensure the reliability of the system, and regard microgrids as an alternative solution instead of upgrading power generation and transmission expansion planning. However, it mainly focuses on the interconnected operation mode of microgrids (i.e., normal working conditions), while the island operation mode of microgrids (i.e., self-healing actions) has not been widely studied in planning problems.

[0005] In addition, the intermittency of renewable energy poses a major challenge to the safe and reliable operation of microgrids. To overcome this challenge, effective methods are needed to deal with the uncertainties associated with renewable energy production. Currently, scenario-based programming, robust optimization, information gap decision theory, etc. are widely used to deal with uncertainty, but these a priori uncertainty management methods have some shortcomings in terms of computational burden and solution accuracy, and suitable methods need to be developed to overcome them. Therefore, new indicators should be developed to evaluate the reliability and resilience of microgrids in island mode.

[0006] In summary, the current research still has some deficiencies in the island operation mode, uncertainty management method, reliability and resilience evaluation index of microgrid, which need further improvement and perfection. Therefore, in order to improve the reliability of the distribution system and its ability to cope with emergencies, the present invention proposes a distribution network self-healing method considering the access of distributed new energy. Summary of the invention

[0007] In order to overcome the problems in the prior art, the present invention proposes a distribution network self-healing method considering the access of distributed renewable energy.

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

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

[0010] Divide the distribution network into clusters of resilient microgrids;

[0011] Establish a microgrid self-healing optimization model: Under normal operation mode, the objective function is to maximize the profit of electricity sales, and consider the microgrid self-healing optimization constraints, which include the capacity constraints of distributed new energy, the maximum number of remote switches installed, the operation constraints of the energy storage device system, the power constraints of each microgrid, the flow constraints, and the network constraints;

[0012] When a fault is detected, the microgrid is disconnected from the main grid and enters island mode; the fault area is isolated from the non-fault area, and the load in the non-fault area is rescheduled to balance the load.

[0013] Furthermore, the process of dividing the distribution network into elastic microgrid clusters includes: first, upgrading the passive distribution system to an active distribution system by integrating distributed new energy and energy storage devices; second, clarifying the electrical and geographical boundaries of the microgrid by setting remote control switches to form an elastic microgrid cluster.

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

[0015] Furthermore, isolating the fault area from the non-fault area and rescheduling the load in the non-fault area to balance the load specifically includes: dividing the fault area by controlling the remote control switch, reallocating the controllable distributed power generation device to the non-fault area, establishing the objective function of the island mode, minimizing the cost as the goal, and rescheduling the load in the non-fault area to balance the load.

[0016] Furthermore, in the normal operation mode, the objective function is to maximize the profit from electricity sales, and the objective function is:

[0017] Pnft = REV-cost;

[0018]

[0019] cost=Z DER +Z SW ;

[0020] Where: Pnft represents profit, REV represents revenue, cost represents cost; t represents optimization time; m represents microgrid index; is the price of electricity sold to consumers at time t, is the price of electricity sold to the electricity market at time t, is the price of electricity purchased from the electricity market at time t; is the amount of electricity sold to consumers at time t, is the amount of electricity sold to the electricity market at time t, is the amount of electricity purchased from the electricity market at time t; is a 0-1 variable indicating the state of power sold by the microgrid, is a 0-1 variable indicating the state of the microgrid purchasing electricity, where if electricity is sold or purchased at time t, the value is 1, otherwise it is 0; Z DER is the investment cost of distributed renewable energy; Z SW The operating cost of distributed renewable energy.

[0021] Furthermore, the objective function f of the island mode is island :

[0022]

[0023] In the above formula, i is the distributed renewable energy DER unit index; m is the microgrid index; C i,m is the operating cost coefficient of distributed renewable energy; is the active power of distributed renewable energy; The added price for power storage, The price of electricity reserves has fallen; The added value of electricity provided by distributed generation devices, The drop value provided for distributed generation devices; d cl,m is the controllable load of the microgrid; u cl,m is a 0-1 variable representing the load state; κ cl,m is the load value per unit of the microgrid; is the load shedding cost coefficient; LS t,m is the load shedding amount in island mode; is the spillover cost coefficient of renewable energy; RS t,m is the renewable energy overflow in island mode.

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

[0025]

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

[0027] Furthermore, the adjustable interval optimization method based on the C&CG framework is used to analyze the uncertainty problems caused by the access of distributed renewable energy.

[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, which meets the economy and reliability of the system, while minimizing the planning cost and achieving seamless islanding.

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

[0031] (3) The distribution network self-healing problem of the present invention is expressed as an accurate and efficient mixed integer linear programming problem with high solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A flow chart of a distribution network self-healing method considering access to distributed renewable energy sources according to the present invention;

[0034] Figure 2 This is a diagram illustrating the self-healing strategy in the emergency mode proposed by the present invention;

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

[0036] Figure 4 It is the daily economic dispatch result of the microgrid in the distribution network self-healing method considering the access of distributed new energy in the present invention. DETAILED DESCRIPTION

[0037] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. The specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by technicians in the technical field of the present invention.

[0038] In order to improve the reliability of the distribution system and its ability to respond to emergencies, this paper proposes a distribution network self-healing method considering the access of distributed renewable energy. First, the distribution network is divided into a small-scale elastic microgrid cluster; secondly, a microgrid self-healing optimization model is established, and under normal operation mode, the objective function is to maximize the profit of electricity sales; consider the capacity constraints of DER, the maximum installation quantity constraints of remote control switches (RCS), the operation constraints of energy storage systems (ESS), the power constraints of each microgrid, the flow constraints, the network constraints and other constraints; then a self-healing strategy is proposed, and a new reliability index for evaluating microgrids in 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 the access of distributed renewable energy on the self-healing of the distribution network, 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 sources specifically includes the following steps:

[0040] Divide the distribution network into clusters of resilient microgrids;

[0041] In the normal operation mode, the objective function is to maximize the profit of electricity sales, and the microgrid self-healing optimization constraints are considered. The microgrid self-healing optimization constraints include the capacity constraints of distributed new energy, the maximum number of remote switches installed, the energy storage device system operation constraints, the power constraints of each microgrid, the flow constraints, and the network constraints, so as to establish a microgrid self-healing optimization model;

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

[0043] Refer to the following Figure 1-Figure 4 Expand each of the above steps in detail:

[0044] Step 100: Initialization, input distribution network line parameters, distributed new energy access capacity and nodes, and load forecast benchmark values.

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

[0046] The most important and fundamental step to achieve 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. Microgrids with convenient and intelligent control characteristics are considered to be an ideal solution to this critical problem. Microgrids can sell their surplus power to the main grid, and in emergency situations such as failures, microgrids can also be separated from the network and independently supply their own needs locally, that is, autonomous mode.

[0047] Therefore, the best way to manage a complex system is to decompose the distribution network into small-scale microgrids. Microgrids have outstanding self-recovery capabilities and can effectively restore the system to normal in a short time, thereby enhancing the reliability of the distribution system and its resilience in emergency situations.

[0048] The transformation of a large distribution system into a microgrid consists of two stages: transforming a passive distribution system into an active distribution system by integrating various distributed renewable energy sources (DER) and energy storage systems (ESS); and forming a networked microgrid by assigning remote control switches to specify the electrical and geographical boundaries of the microgrid. In addition, the division of the distribution network into a group of microgrids must meet the following criteria:

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

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

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

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

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

[0054] Step 300: Establish a microgrid self-healing optimization model. Under normal operation mode, the objective function is to maximize the profit of electricity sales, and consider the microgrid self-healing optimization constraints, which include the capacity constraints of distributed new energy DER, the maximum installation quantity constraints of remote control switches RCS, the operation constraints of energy storage system ESS, the power constraints of each microgrid, the flow constraints, and the network constraints.

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

[0056] Step 310: In the normal operation mode, the objective function is to maximize the profit from electricity sales:

[0057] Pnft = REV-cost;

[0058]

[0059] cost=Z DER +Z SW ;

[0060] Where: Pnft represents profit, REV represents revenue, cost represents cost; t represents optimization time; m represents microgrid index; is the price of electricity sold to consumers at time t, is the price of electricity sold to the electricity market at time t, is the price of electricity purchased from the electricity market at time t; is the amount of electricity sold to consumers at time t, is the amount of electricity sold to the electricity market at time t, is the amount of electricity purchased from the electricity market at time t; is a 0-1 variable indicating the state of power sold by the microgrid, is a 0-1 variable indicating the state of the microgrid purchasing electricity, where if electricity is sold or purchased at time t, the value is 1, otherwise it is 0; Z DER is the investment cost of distributed renewable energy DER; Z SW The operating cost of distributed renewable energy DER;

[0061]

[0062]

[0063] Where: T is the total optimization time; n is the node index; γ t,m,n is a 0-1 variable representing the state of the distributed generation device DG; is the operating cost of the distributed generation unit DG; is the power generation of distributed generation device DG; e is the energy storage device system ESS index; The unit operating cost of the energy storage system ESS; is a 0-1 variable indicating the charging state of the energy storage system ESS, A 0-1 variable indicating the discharge state of the energy storage system ESS; Charging power for the energy storage system ESS, N is the discharge power of the energy storage system ESS; DER is the number of distributed renewable energy DERs; i is the distributed renewable energy DER unit index; x i is a 0-1 variable indicating whether to invest in distributed renewable energy DER; ε i is an integer variable that determines the type of distributed renewable energy DER; is the investment cost per unit of distributed renewable energy DER; is the capacity of the unit distributed renewable energy DER; τ sw is a 0-1 variable indicating whether to invest in a section switch (SW), tw K is a 0-1 variable indicating whether to invest in a tie switch (TW); sw is the load shedding cost, K tw Leakage costs for renewable energy.

[0064] Step 320: Establishing microgrid self-healing optimization constraints includes: capacity constraints of distributed new energy DER, maximum installation quantity constraints of remote control switches RCS, operation constraints of energy storage system ESS, power constraints of each microgrid, power flow constraints, and network constraints, which are described in order as follows:

[0065] Capacity constraints of distributed renewable energy DER:

[0066]

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

[0068] The maximum number of installation constraints of remote control switch RCS:

[0069]

[0070] Where: is the maximum number of connected sectionalizer switches SW, The maximum number of connections for the switch TW.

[0071] Energy storage system ESS operation constraints:

[0072]

[0073] Where: is the charging power of the energy storage device e at time t, is the discharge power of the energy storage device e at time t; and are 0-1 variables representing the charging and discharging status of the energy storage device e at time t respectively; is the maximum charging power of the energy storage device e, is the maximum discharge 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 permissible state of charge of the energy storage system ESS, is the maximum allowable state of charge of the energy storage system ESS, is the initial charge state of the energy storage system ESS; is the initial capacity of the energy storage device; A 0-1 variable indicating whether the energy storage system ESS is idling; is the efficiency of the energy storage system ESS in charging mode; is the energy storage system ESS efficiency in discharge mode.

[0074] Power constraints for each microgrid:

[0075]

[0076]

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

[0078] Power flow constraints:

[0079]

[0080] Where: 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 at node n, V n+1 is the voltage of node n+1; ω n,n+1 is cos(δ n -δn+1 ) is a piecewise linear representation; δ n is the voltage phase angle at node n, δ n+1 is the voltage phase angle of node n+1.

[0081] Network constraints:

[0082]

[0083] Where: is the minimum voltage, is the maximum voltage; is the minimum phase angle of node n, is the maximum value of the phase angle of node n; is the maximum power allowed to flow through the branch.

[0084] Step 400: Propose a self-healing strategy: When a fault or other event occurs, the microgrid is disconnected from the grid and enters an island mode, controls the remote control switch RCS to divide the fault area, and re-dispatches the non-fault area to balance the load to meet the system reliability and safety requirements.

[0085] Due to the change in demand and generation conditions caused by the fault, the controllable distributed generation devices DG are reallocated to the non-fault area. After all faults are cleared, the system will return to normal operation mode and continue to 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 microgrid consumption 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 redistributed to minimize the cost function.

[0087] The objective function f in island mode island for:

[0088]

[0089] Where: i is the distributed renewable energy DER unit index; m is the microgrid index; C i,m is the operating cost coefficient of distributed renewable energy; is the active power of distributed renewable energy; The added price for power storage, The price of electricity reserves has fallen; The added value of electricity provided by distributed generation devices DG, is the drop 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 load value per unit of the microgrid; is the load shedding cost coefficient; LS t,m is the load shedding amount in island mode; is the spillover cost coefficient of renewable energy; RS t,m is the renewable energy overflow in island mode.

[0090] Step 500: Establishing a reliability index for evaluating the microgrid in the island mode, so as to evaluate the reliability of the microgrid in the island mode.

[0091] Although the IEEE standard has defined various reliability indices for calculating the reliability level of the distribution network, these indices cannot properly represent the system behavior in the island mode. To this end, the present invention proposes two new indicators to evaluate the reliability of the microgrid in the island mode. The new indicators include the operational reliability index in the island mode - the island expected energy interruption (IEEI) and the user-based reliability index - the island expected energy deficiency (IEED), which are as follows:

[0092]

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

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

[0095] (1) Adjustable interval optimization;

[0096] Adjustable interval optimization, uses interval optimization method to study the impact of uncertain parameters on the problem, expands the adjustable interval model to eliminate the conservatism of the traditional interval model, and adds a penalty factor to the objective function of the island mode to balance the load and generation.

[0097] In order to apply the interval optimization method in the problem, firstly, the proposed deterministic microgrid planning model with inequality and equality constraints is expressed symbolically based on the standard form, namely,

[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 Then the minimum value of the objective function F - (Ψ) and the maximum value F + (Ψ) can be calculated by the following method,

[0103]

[0104] After the operation transformation, the interval objective function will be replaced by the initial function to optimize its boundary with the given input interval. avg (Ψ) and deviation (F div (Ψ)) are optimized simultaneously until the microgrid becomes more robust to uncertainties.

[0105] Furthermore, by increasing the deviation from the uncertain parameter estimates, the model may become very conservative. To this end, the present invention extends the adjustable interval model instead of the traditional interval model to eliminate this concern. In this approach, an additional penalty factor is added to the objective function of the islanding mode, representing the cost associated with renewable energy spillover:

[0106]

[0107] In the above formula, i is the distributed renewable energy DER unit index; m is the microgrid index; C i,m is the operating cost coefficient of distributed renewable energy; is the active power of distributed renewable energy; The added price for power storage, The price of electricity reserves has fallen; The added value of electricity provided by distributed generation devices, The drop value provided for distributed generation devices; d cl,m is the controllable load of the microgrid; u cl,m is a 0-1 variable representing the load status; is the load shedding cost coefficient; LS t,m is the load shedding amount in island 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] Since the proposed problem is a min-max-min problem, it cannot be solved explicitly using an existing solver, so the C&CG algorithm is proposed. The problem is decomposed into a main problem and sub-problems, and the sub-problems are transformed into complementary constraints using the KKT optimal conditions (Karush-Kuhn-Tucker conditions), and then transformed into a linear form using the big-M method. The optimal value of the M parameter is determined through iteration, and the constraints of the sub-problems are transformed into a mixed integer linear programming model using the linearization method, and the solution is iterated continuously.

[0110] The main problem can be stated as:

[0111]

[0112]

[0113] In the formula: 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 stated as:

[0115]

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

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

[0118]

[0119] Where: and λ are the dual variables of the defined problem. 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 main problem (i.e., the investment problem) and two sub-problems (i.e., one for interconnected operation and the other for isolated operation).

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

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

[0126]

[0127]

[0128] Table 1 shows the results of the adjustable interval method and the deterministic interval method on the self-healing problem of the distribution network. From the results, it can be seen that the distribution network self-healing method proposed in the present invention has a smaller system fluctuation than the traditional method, although the system investment cost and operation cost have increased; on the other hand, compared with the deterministic method, the renewable energy overflow of the method of the present invention is greatly reduced, and the reliability index is significantly improved, so 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 the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A distribution network self-healing method considering the access of distributed renewable energy, characterized in that: The following steps are involved: Divide the distribution network into clusters of resilient microgrids; Establish a microgrid self-healing optimization model: Under normal operation mode, the objective function is to maximize the profit of electricity sales, and consider the microgrid self-healing optimization constraints, which include the capacity constraints of distributed new energy, the maximum number of remote switches installed, the operation constraints of the energy storage device system, the power constraints of each microgrid, the flow constraints, and the network constraints; When a fault is detected, the microgrid is disconnected from the main grid and enters island mode; Isolate the faulty area from the non-faulty area and reschedule the load in the non-faulty area to balance the load.

2. A distribution network self-healing method considering the access of distributed new energy according to claim 1, characterized in that: The process of dividing the distribution network into elastic microgrid clusters includes: first, upgrading the passive distribution system to an active distribution system by integrating distributed new energy and energy storage devices; second, clarifying the electrical and geographical boundaries of the microgrid by setting remote control switches to form an elastic microgrid cluster.

3. A distribution network self-healing method considering the access of distributed new energy according to claim 2, characterized in that: Each microgrid must have at least one dispatchable distributed generation device.

4. A distribution network self-healing method considering the access of distributed new energy according to claim 3, characterized in that: The method of isolating the fault area from the non-fault area and rescheduling the load in the non-fault area to balance the load specifically includes: dividing the fault area by controlling the remote control switch, reallocating the controllable distributed power generation device to the non-fault area, establishing the objective function of the island mode, minimizing the cost as the goal, and rescheduling the load in the non-fault area to balance the load.

5. 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 from electricity sales, and the objective function is: Pnft = REV-cost; cost=Z DER +Z SW ; Where: Pnft represents profit, REV represents revenue, cost represents cost; t represents optimization time; m represents microgrid index; is the price of electricity sold to consumers at time t, is the price of electricity sold to the electricity market at time t, is the price of electricity purchased from the electricity market at time t; is the amount of electricity sold to consumers at time t, is the amount of electricity sold to the electricity market at time t, is the amount of electricity purchased from the electricity market at time t; is a 0-1 variable indicating the state of power sold by the microgrid, is a 0-1 variable indicating the state of the microgrid purchasing electricity, where if electricity is sold or purchased at time t, the value is 1, otherwise it is 0; Z DER is the investment cost of distributed renewable energy; Z SW The operating cost of distributed renewable energy.

6. A distribution network self-healing method considering the access of distributed new energy according to claim 4, characterized in that: The objective function of the island mode is f island : In the above formula, i is the distributed renewable energy DER unit index; m is the microgrid index; C i,m is the operating cost coefficient of distributed renewable energy; is the active power of distributed renewable energy; The added price for power storage, The price of electricity reserves has fallen; The added value of electricity provided by distributed generation devices, The drop value provided for distributed generation devices; d cl,m is the controllable load of the microgrid; u cl,m is a 0-1 variable representing the load state; κ cl,m is the load value per unit of the microgrid; is the load shedding cost coefficient; LS t,m is the load shedding amount in island mode; is the spillover cost coefficient for renewable energy; RS t,m is the renewable energy overflow in island mode.

7. A distribution network self-healing method considering the access of distributed new energy according to claim 6, characterized in that: It also includes evaluating 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; SD TS is the load interruption of the microgrid in island mode; Δ TS is the duration of the microgrid in island mode; D TS is the load in island mode; P TS is the available power in island mode; NI is the number of islands in a year; IEED Target is the target energy value in island mode.

8. A distribution network self-healing method considering the access of distributed renewable energy according to claim 6, characterized in that: Based on the adjustable interval optimization method of C&CG framework, the uncertainty problems caused by the access of distributed new energy are analyzed.

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