Active power distribution network fault recovery method and device considering participation of temperature control load

Abstract

The invention discloses an active power distribution network fault recovery method and device considering participation of a temperature control load, and the method comprises the steps: analyzing the operation characteristics of a load side air conditioner and heat storage type electric heating in an active power distribution network fault recovery frame, and depicting an operation characteristic curve at different power failure time, the influence of the regulatable capability of the two kinds of loads on the operation state is explored at the same time; starting and stopping time of the two loads is calculated based on the operation characteristic curve, and the power requirement of load aggregation can be solved according to the law of large numbers; representing power requirements of the temperature control load in different time periods by using the state characteristic quantity, and calculating a dynamic load recovery weight based on the power requirements; an active power distribution network fault recovery mathematical model can be established according to the dynamic load weight, an objective function is to maximize recovery of a power-losing load and minimize the number of times of switching actions, and constraint conditions comprise node voltage constraint, branch power flow constraint and active power distribution network safe operation constraint; and on the basis of the fault recovery mathematical model of the active power distribution network, a power-losing load is recovered through a multi-period network reconstruction technology. The device comprises a processor and a memory.

Description

Technical Field

[0001] The present invention relates to the field of active distribution networks, and particularly to a method and device for active distribution network fault recovery considering the participation of temperature-controlled loads. Background Art

[0002] With the rapid economic growth and the modernization of cities, the power demand is continuously rising, which not only increases the load on the power grid but also raises the risk of power grid failures. [1-2] Traditional distribution networks do not contain flexible resources, with low automation levels, insufficient network redundancy configurations, and limited control and protection means. It is difficult to achieve rapid response and self-healing in the event of sudden failures. Therefore, they can only passively wait for the superior power grid to be restored and then rely on the large power grid to support the load restoration. [3] Compared with traditional distribution networks, in active distribution networks, entities such as load aggregators and dispatch centers can flexibly coordinate and dispatch various flexible resources. [4] Therefore, how to effectively coordinate various flexible resources in the active distribution network to restore the lost load is of great significance for improving the power supply reliability of the power grid and ensuring the economic operation of the power system.

[0003] At present, most of the active distribution network fault recovery analyzes fixed loads, and at the same time coordinates flexible resources such as mobile energy storage, electric vehicles, and intelligent soft switches to restore the lost load through network reconfiguration technology and island operation methods. [5] However, nowadays, with the high proportion of temperature-controlled loads connected to the distribution network, [6] temperature-controlled loads with rich characteristics and various types (such as air conditioners, electric heating, water heaters, etc.) will exhibit diverse operating characteristics after a fault. Existing fault recovery methods have not deeply analyzed the characteristics of temperature-controlled loads during the recovery period and cannot fully exploit the adjustable potential of temperature-controlled loads in the power supply interruption scenario. In the face of temperature-controlled loads with multiple operating states after a fault, it is difficult to make corresponding adjustments to the recovery method based on their dynamically changing power demands, and the applicable range is relatively narrow, unable to meet the recovery of temperature-controlled loads in multiple operating states. Therefore, existing fault recovery methods are only applicable to scenarios with a constant load recovery priority. [7]

[0004] To accurately evaluate the impact of temperature-controlled loads on fault recovery, it is necessary to analyze the characteristics of temperature-controlled loads in the fault scenario. Existing methods for analyzing the characteristics of temperature-controlled loads after a fault mainly include: exponential analysis method and physical analysis method. [8-9] The exponential analysis method fits the power demand of temperature-controlled loads after a fault with an exponential decay curve and presents its operating characteristics in the form of a mathematical expression. The physical analysis method simulates the physical process of temperature-controlled loads after a fault, which involves multiple physical quantities such as power outage time and indoor temperature of users, and the power demand can be obtained based on the above physical quantities.

[0005] However, most of the above two methods are used to analyze the loss of diversity of temperature-controlled loads after a fault. The surge in power demand caused by the loads that have lost their operating states makes the restoration difficult.

[0006] Therefore, it is particularly important to characterize the multiple operating states of temperature-controlled loads after a fault and propose a reasonable fault restoration method based on this. Summary of the Invention

[0007] The present invention provides a method and device for active distribution network fault restoration considering the participation of temperature-controlled loads. The present invention can be used for fault restoration in scenarios with a high proportion of temperature-controlled load access, and can maximize the restoration of power-off loads on the premise of ensuring user comfort. It can not only improve the speed and efficiency of fault restoration, but also optimize the system resource allocation, reduce the power outage time, and thus significantly improve the reliability of the active distribution network. See the following description for details:

[0008] In a first aspect, a method for active distribution network fault restoration considering the participation of temperature-controlled loads, the method includes:

[0009] Analyze the operating characteristics of air conditioners and thermal storage electric heating on the load side in the active distribution network fault restoration framework, characterize the operating characteristic curves under different power outage times, and explore the influence of the controllability of the two types of loads on the operating states;

[0010] Calculate the start-up and shutdown times of the two types of loads based on the operating characteristic curves, and obtain the power demand of the load aggregation according to the law of large numbers;

[0011] Use state characteristic quantities to represent the power demand of temperature-controlled loads at different time periods, and calculate the dynamic load restoration weights based on this;

[0012] According to the dynamic load weights, establish a mathematical model for active distribution network fault restoration, with the objective function of maximizing the restoration of power-off loads and minimizing the number of switch operations, and the constraint conditions including: node voltage constraints, branch power flow constraints, and active distribution network safe operation constraints;

[0013] Based on the mathematical model of active distribution network fault restoration, restore the power-off loads through multi-period network reconfiguration technology.

[0014] Among them, the analysis of the operating characteristics of air conditioners and thermal storage electric heating on the load side in the active distribution network fault restoration framework is:

[0015]

[0016] P e =P w +P hs -P hl

[0017] In the formula, T in (t) and Tout (t) represents the indoor and outdoor temperatures at time t; C ac and R ac are the specific heat capacity and thermal resistance of the air conditioner; P ac is the electric power of the air conditioner, η ac is the refrigeration efficiency; λ ac (t) is a binary variable representing the operating state of the air conditioner at time t. ρ and μ are the heat dissipation coefficient and temperature rise coefficient of the house with heat storage electric heating; P e is the electric power of the heat storage electric heating; P w is the operating power of the heat storage electric heating; P hs and P hl are the heat storage and heat release powers of the heat storage electric heating respectively; λ eh (t) is a binary variable representing the start-up and shutdown of the electric heating.

[0018] Among them, the start-up and shutdown times of the two types of loads are calculated based on the operating characteristic curve, and according to the law of large numbers, the power demand of the load aggregation can be obtained as:

[0019] 1) Solving the differential equation of the first-order equivalent thermal parameter model can obtain the start-up and shutdown times of the temperature-controlled load in each operating state:

[0020]

[0021] In the formula, and are the start-up time and shutdown time of the air conditioner, and are the start-up time and shutdown time of the heat storage electric heating; T over,max and T over,min are the temperatures corresponding to the start-up and shutdown of the air conditioner during the fault recovery period; is a binary variable. When the air conditioner fails when it is in the on state, and when the air conditioner fails when it is in the off state;

[0022] 2) According to the law of large numbers, the aggregated power of multiple temperature-controlled loads can be obtained:

[0023]

[0024] In the formula: is the aggregated power of N air conditioners at time t, is the aggregated power of N heat storage electric heaters at time t; P ac,n,t is the electric power of the air conditioner in node n at time t, P eh,n,t is the electric power of the heat storage electric heating in node n at time t.

[0025] Among them, the dynamic load weight is:

[0026] The load weight of each node:

[0027]

[0028] In a second aspect, an active distribution network fault recovery device taking into account the participation of temperature-controlled loads, the device comprising: a processor and a memory, the memory storing program instructions, the processor calling the program instructions stored in the memory to enable the device to execute any one of the methods described in the first aspect.

[0029] In a third aspect, a computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes any one of the methods described in the first aspect.

[0030] The beneficial effects of the technical solution provided by the present invention are:

[0031] 1. The present invention describes the various operating states of two types of temperature control loads based on an improved physical analysis method, while fully exploring the adjustable potential of the temperature control loads during the recovery period;

[0032] 2. The present invention gives full play to the flexible adjustment capability of the electric heating heat storage device, effectively achieving the goal of not stopping heating during power outages, and providing a strong guarantee for fault recovery in winter scenarios;

[0033] 3. Based on the multiple operating states of two types of temperature control loads, the present invention formulates a dynamic weighted recovery method, which promotes the optimization of the temperature control load control method during the recovery period, so that the power demand can be adaptively corrected when dealing with sudden faults, laying the foundation for the rapid recovery of the active distribution network;

[0034] 4. The present invention comprehensively utilizes grid-load flexibility resources to coordinate and optimize the operation of the distribution network, further improving the management capability of the active distribution network while ensuring maximum load restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a framework diagram for active distribution network fault recovery;

[0036] Figure 2 It is a flow chart of active distribution network fault recovery taking into account the participation of temperature control load;

[0037] Figure 3 The IEEE 33-node power distribution system diagram used in the example of the present invention;

[0038] Figure 4 24 power distribution curve diagram for clean energy and base load;

[0039] Figure 5 It is a comparison diagram of node voltages under the first three methods in the summer scenario;

[0040] Figure 6 It is a node voltage diagram during the recovery period of Method 3 and the method of the present invention in the summer scenario;

[0041] Figure 7 It is a switching operation plan during the recovery period of Method 3 and the method of the present invention in the summer scenario;

[0042] Figure 8 It is a comparison diagram of node voltages under the first three methods in the winter scenario;

[0043] Figure 9 It is a node voltage diagram during the recovery period of Method 3 and the method of the present invention in the winter scenario;

[0044] Figure 10 It is a switching operation plan during the recovery period of Method 3 and the method of the present invention in the winter scenario. Detailed implementation manner

[0045] To make the objectives, technical solutions and advantages of the present invention clearer, the following further describes the implementation manners of the present invention in detail.

[0046] Therefore, in order to better address the challenges brought by the high proportion of temperature-controlled loads connected to the distribution network to fault recovery, achieve efficient operation and reliable power supply of the power system, and enhance the active management ability of the distribution system and the resilience to cope with sudden faults. The present invention proposes an active distribution network fault recovery method based on dynamic load weights, which obtains the dynamic load weights in each time period by depicting various operating states of temperature-controlled loads after a fault, and realizes the efficient recovery of powered-off loads based on network reconfiguration technology.

[0047] Embodiment 1

[0048] In order to achieve the maximum recovery of powered-off loads and ensure user comfort, the embodiment of the present invention proposes an active distribution network fault recovery method considering the participation of temperature-controlled loads, and this method includes the following steps:

[0049] 101: Propose an active distribution network fault recovery framework that takes into account the adjustable temperature-controlled loads on the user side and the flexible resources on the network side;

[0050] Among them, this framework combines dynamic network reconfiguration, the characteristics of temperature-controlled loads, and the power supply capacity of clean energy, aiming to maximize the recovery of powered-off loads while minimizing the number of switching operations.

[0051] 102: Analyze the operating characteristics of air conditioners and heat storage electric heating on the load side in the active distribution network fault recovery framework, depict the operating characteristic curves under different power outage times, and explore the influence of the adjustable capabilities of these two types of loads on the operating state;

[0052] 103: Calculate the start-up and shut-down times of the two types of loads based on the operating characteristic curve described above. According to the law of large numbers, the power demand of the aggregated load can be obtained.

[0053] Among them, for the heat storage electric heating, the minimum heating power during the recovery period needs to be calculated according to the PMV index, and then the N electric heating loads are aggregated to facilitate the calculation of the node load power demand. This method ensures the comfort of users during the recovery period and effectively implements the policy requirement of "no interruption of heating during power outages".

[0054] 104: Represent the power demand of the temperature-controlled load in different time periods with state characteristic quantities, and calculate the dynamic load recovery weight based on this.

[0055] Specifically, for the air conditioner, introduce the temperature state characteristic quantity to represent the state of the indoor temperature; for the heat storage electric heating, introduce the heat storage state characteristic quantity to represent the stored heat energy. Based on the above temperature-controlled load state characteristic quantities, combined with the fixed load weight, the weights of each temperature-controlled load are obtained, and the weighted average of the weights of various loads is used to obtain the weight of each node.

[0056] 105: According to the dynamic load weight, a mathematical model for the fault recovery of the active distribution network can be established. The objective function is to maximize the recovery of the lost load while minimizing the number of switch operations. The constraint conditions include: node voltage constraint, branch power flow constraint, and the safe operation constraint of the active distribution network, etc.

[0057] 106: Based on the mathematical model for the fault recovery of the active distribution network, the lost load is recovered through the multi-period network reconfiguration technology.

[0058] Since the network-side mathematical model contains quadratic terms and discrete variables and is difficult to solve, the Big-M method and second-order cone relaxation are needed to transform the original mixed-integer non-linear programming problem into a mixed-integer second-order cone programming problem. On this basis, the formulation of the active distribution network fault recovery method considering the participation of temperature-controlled loads is completed.

[0059] Among them, during the network reconfiguration process, the branch switch state needs to be set as a 0-1 variable, and the dynamic network reconfiguration needs to consider the influence of time periods on this basis.

[0060] In summary, through the above steps 101 - 106, the embodiment of the present invention realizes the rapid recovery of the lost load through the fault recovery method with dynamic weights, improves the load recovery amount, and ensures the user comfort at the same time.

[0061] Embodiment 2

[0062] Next, the solution in Embodiment 1 will be further introduced in combination with specific calculation formulas and examples, as detailed in the following description:

[0063] 201: Propose an active distribution network fault recovery framework that takes into account both the adjustable temperature control load on the user side and the flexible resources on the network side, as follows Figure 2 as shown.

[0064] When a fault occurs, the main network is disconnected from the distribution network, and the system's recovery ability cannot meet the power demand of all the lost loads. During the recovery period, the load aggregator transmits information such as the power demand for scheduling various loads after the power loss and the state characteristic quantities of the temperature control load to the dispatching center. The dispatching center first adjusts the temperature control load according to the feedback information to make the system's recovery ability meet the power demand of the lost load. Secondly, it calculates the recovery weights of various loads and formulates the corresponding switch operation plan. Finally, the distributed power supply supplies power to the lost load according to the recovery weights. The user comfort can be guaranteed until the recovery is completed.

[0065] 202: Analyze the multiple operating states of the temperature control load during the recovery period and characterize it in detail through a first-order equivalent thermal parameter model;

[0066] Among them, the first-order equivalent thermal parameter models of air conditioners and heat storage electric heating are as follows:

[0067]

[0068] In the formula, T in (t) and T out (t) are the indoor and outdoor temperatures at time t; C ac and R ac are the specific heat capacity and thermal resistance of the air conditioner; P ac is the electric power of the air conditioner, η ac is the refrigeration efficiency; λ ac (t) is a binary variable representing the operating state of the air conditioner at time t. ρ and μ are the house heat dissipation coefficient and temperature rise coefficient of the heat storage electric heating; P e is the electric power of the heat storage electric heating; P w is the operating power of the heat storage electric heating; P hs and P hl are the heat storage and heat release powers of the heat storage electric heating respectively; λ eh (t) is a binary variable representing the start-up and shutdown of the electric heating.

[0069] 203: Based on the multiple operating states of the temperature control load during the recovery period, calculate the start-up and shutdown times in each operating state, and then complete the aggregation of the power demands of multiple temperature control loads:

[0070] 1) Solving the differential equation of the first-order equivalent thermal parameter model can obtain the start-up and shutdown times of the temperature control load in each operating state:

[0071]

[0072] In the formula, and are the air conditioner startup time and shutdown time, and are the startup time and shutdown time of the heat storage electric heating; T over,max and T over,min are the temperatures corresponding to the air conditioner startup and shutdown during the fault recovery period; is a binary variable. When the air conditioner fails while in the startup state, and when the air conditioner fails while in the shutdown state.

[0073] 2) According to the law of large numbers, the aggregated power of multiple temperature-controlled loads can be obtained:

[0074]

[0075] In the formula: is the aggregated power of N air conditioners at time t, is the aggregated power of N heat storage electric heaters at time t; P ac,n,t is the electric power of the air conditioner in node n at time t, P eh,n,t is the electric power of the heat storage electric heater in node n at time t.

[0076] 204: Calculate the dynamic load weight of each node by defining the air conditioner temperature state characteristic quantity and the heat storage state characteristic quantity of the heat storage electric heating;

[0077] 1) Air conditioner temperature state characteristic quantity:

[0078]

[0079] In the formula: K ac (t) represents the air conditioner temperature state characteristic quantity, and respectively represent the upper and lower limits of the interval corresponding to the indoor set temperature when restoring the power-off air conditioner, T in (t) represents the indoor temperature of the user at time t.

[0080] 2) Heat storage state characteristic quantity of the heat storage electric heating:

[0081]

[0082] In the formula, K eh (t) represents the state characteristic quantity of the heat energy stored in the heat storage device of the electric heating, P re,min is the minimum heat retained by the heat storage device, P heat is the minimum heating electric power during the recovery period.

[0083] 3) Calculate the load weight of each node

[0084] Under normal circumstances, since the priority of the temperature-controlled load in fault recovery is lower than that of the base load, the user comfort can be appropriately reduced in the summer scenario to meet the needs of load recovery. The set of air-conditioning load weights for node i is specifically as follows:

[0085]

[0086] In the formula, ω ac,i,t is the set of air-conditioning load weights for node i at time t, that is, ω ac,i,t ={ω ac,i,t,1 , ω ac,i,t,2 ,…ω ac,i,t,k}; λ ac,i,t is the set of air-conditioning load switches for node i at time t, that is, λ ac,i,t ={λ ac,i,t,1 , λ ac,i,t,2 ,…λ ac,i,t,k}; N ac,i is the number of air-conditioning loads contained in node i; ω 0,i,t is the base load weight of node i. This formula indicates that during fault recovery, it is first necessary to determine whether the indoor temperature is within the normal range. If it is within the normal range, the recovery weight is determined according to the air-conditioning switch state in the previous period. If it is outside the normal range, its load weight is increased.

[0087] By summing up the obtained load weights for each, the load weight of each node can be obtained:

[0088]

[0089] 205: Based on the temperature-controlled load model with multiple operating states and dynamic load weights, establish the objective function for the fault recovery of the active distribution network, that is:

[0090] Among them, the objective function including the load recovery amount and the number of switch operations:

[0091]

[0092] In the formula, α i,t represents a 0-1 variable indicating whether the load of node i is recovered at time t, is the active power demand of the fixed load of node i at time t, P ac / eh,i,t is the power demand of the air-conditioning and heat storage electric heating of node i at time t. x ij,t is a 0-1 variable indicating the opening and closing of branch ij; Ω n is the set of all nodes in the distribution network; Ω l is the set of all branches in the distribution network.

[0093] 206: Constraints are imposed on each variable in the established objective function, including network reconfiguration constraints, distribution network safe operation constraints, power flow constraints, etc.;

[0094] 1) Power flow constraint of distribution network branches after relaxation transformation:

[0095]

[0096] In the formula: R ij and X ij respectively represent the resistance and reactance of branch ij; L ij,t is the square of the current of branch ij at time t; P ij,t and Q ij,t respectively represent the active power and reactive power flowing through branch ij at time t; V i,t is the square of the voltage of node i at time t, and are respectively the active power output and reactive power output of the photovoltaic at time t, and are respectively the active power output and reactive power output of the wind power at time t, and M is a very large positive number.

[0097] 2) Distribution network security constraints:

[0098]

[0099] In the formula, L ij,max is the maximum value of the square of the current, V i,max is the maximum value of the square of the voltage, P ij,max and Q ij,max are respectively the maximum values of the active power and reactive power of branch ij.

[0100] 3) Wind and light output constraints:

[0101]

[0102] In the formula, and and are respectively the upper and lower limits of the active and reactive power outputs of the photovoltaic and wind power.

[0103] 4) Distribution network radial operation constraints and switch operation times constraints:

[0104]

[0105] In the formula, C k , P kDenote any path between an arbitrary power supply loop and the root node in the distribution network, \(C\) represents the set of all power supply loops in the distribution network, \(P\) represents the set of all paths between the root nodes of the distribution network, \(N\) represents the number of all nodes, and \(R\) represents the number of root nodes.

[0106] In the modeling process of the embodiments of the present invention, by using the key switch screening technology, key switches are selected according to the influence degree of switch actions on the optimization results during the network reconfiguration process, and non-key switches are set to be normally open or normally closed, so as to reduce the 0-1 variables, achieve the effect of reducing the problem scale and accelerating the solution speed, and realize the fast solution of the multi-period network reconfiguration problem.

[0107] In summary, through the above steps 201-step 206, the embodiments of the present invention construct an active distribution network fault recovery framework, propose an active distribution network dynamic load weight fault recovery method involving temperature-controlled loads, improve the amount of lost load recovery, prevent the occurrence of node voltage over-limit, and ensure the user comfort during the recovery period.

[0108] Embodiment 3

[0109] The following combines specific Figures 3 to 6 and Table 1, Table 2 to verify the feasibility of the solutions in Embodiment 1 and Embodiment 2, as detailed in the following description:

[0110] Based on the active distribution network fault recovery framework of this example, a simulation analysis is carried out on the IEEE33-node distribution network. The system structure is as Figure 3 shown. Distributed power sources used for load recovery connect photovoltaic power at nodes 16, 23, and 26, and connect wind power at nodes 10 and 30. The power prediction curves of each clean energy during the fault recovery period are as Figure 4 shown. To simulate the regulation ability of temperature-controlled loads during the fault recovery period, the power supply restoration is set to 10 time periods, and the step size of each time period is 30 minutes. The initial temperature of the temperature-controlled load and the remaining heat in the electric heating energy storage device are generated according to the normal distribution, and the initial switch state is randomly generated. The outdoor temperature is set to 34 °C in summer and -15 °C in winter. The distribution network nodes consist of basic loads and temperature-controlled loads. The power distribution curve of the basic load is as Figure 4 shown. The temperature-controlled load is set to 800 average air conditioners connected to the distribution network in summer and 200 average heat storage electric heating devices connected to the distribution network in winter. The specific load parameters can be found in References

[10] and

[11] .

[0111] To test the optimization results of the embodiments of the present invention and verify the effectiveness and feasibility of the proposed method, optimization methods under different scenarios as shown in Table 1 are set. Figure 4 Figure 28 shows the comparison of node voltages during the midday peak period under different optimization methods, and Table 2 shows the comparison of optimization results under different optimization methods. Combining Figure 4As can be seen from Table 2, the optimization method proposed in the embodiments of the present invention can fully mobilize the adjustable resources in the system, while balancing the power flow distribution and reducing the network loss of the distribution system, improving the node voltage level, and greatly enhancing the operation safety and reliability of the distribution network.

[0112] Table 1 Optimization methods under different scenarios

[0113]

[0114] Summer restoration scenario:

[0115] During the process of using the limited resources in the distribution network to restore power after a large-area power outage caused by a fault, explore the influence of the air conditioner regulation ability and multiple operating states on the fault restoration strategy and results. As the object of distribution network fault restoration, the load will be affected by the reflux power generated by the air conditioner load, which will affect the power demand of each node. For the convenience of comparative analysis, the fifth time period is selected to analyze the node voltage levels under the first three methods. As Figure 7 shown, the node voltages in this time period exceed the limit under the three methods. Among them, in Method 2, due to the sudden increase in load demand caused by power reflux and the lack of consideration of the regulation ability of temperature-controlled loads, some nodes lose power and the node voltages exceed the limit after restoration; Method 1 does not consider the multiple operating states of air conditioners, so the number of power-loss nodes and voltage-overlimit nodes is less than that of Method 2, but Method 1 regards the load demand as a constant quantity and cannot effectively analyze the fault restoration of the current high proportion of temperature-controlled loads connected to the distribution network; Method 3 considers the regulation ability of air conditioners. Although the voltage exceeds the limit, the restoration effect is better than that of Method 1 and Method 2.

[0116] Table 1 shows the situation of node voltage over-limit under the above three methods. It can be seen that the node voltages of the three methods all exceed the limit to varying degrees. Among them, Method 1 and Method 2 have more serious voltage over-limit because they do not consider the air conditioner power regulation ability, and Method 3 has fewer voltage-overlimit nodes because it considers the air conditioner power regulation ability.

[0117] Table 2 Node voltages under the three methods

[0118]

[0119] Table 3 shows the load loss and restoration amounts under the above three scenarios. It should be noted that the load loss includes air conditioners and basic loads, and the air conditioner load loss is the load that fails to be restored outside the normal temperature range. After analysis, it can be seen that Method 2 considers multiple operating states of air conditioners compared with Method 1, and the load restoration amount has also been correspondingly improved; however, compared with Method 3 with the ability to regulate air conditioner loads, the load model of Method 2 is too conservative and cannot be adjusted according to the actual system restoration level. Therefore, the analysis of temperature-controlled loads needs to fully consider their multiple operating states and power regulation abilities.

[0120] Comparison of Load Recovery Results under Three Methods in Table 3

[0121]

[0122] To explore the influence of load weight on the recovery result, Method 3 adopts the method of fixing the recovery weight of load nodes for recovery, while Method 4 adopts a dynamic recovery strategy for recovery. The node voltages of the two scenarios during the recovery period are as Figure 5 shown. It can be seen from the figure that Method 4 can keep the node voltages within the normal range during the recovery period, and the voltage levels are all higher than those of Method 3. Since Method 3 recovers according to the fixed priority of load nodes and cannot effectively cope with the sudden increase in power generated by air conditioners, there are more cases of node voltage over-limit.

[0123] The switching operation scheme based on network reconfiguration during the fault recovery period is as Figure 6 shown. Although the number of switching operations under both methods is within the normal range, the number of switching operations of Method 4 is significantly less than that of Method 3, that is, considering the load dynamic weight recovery strategy can achieve better temperature control load recovery effect.

[0124] Winter Recovery Scenario:

[0125] Similar to the analysis of the summer scenario, in winter, the influence of heat storage electric heating on fault recovery is mainly considered. For the convenience of comparative analysis, the voltage levels under the first three methods are also selected for analysis in the fifth time period, as Figure 7 shown. There are over-limit phenomena in the node voltages under all three methods. Among them, the comparative analysis of Methods 1 and 2 is the same as before. Since Method 3 does not consider the dynamic recovery strategy, power outages occur in some basic loads.

[0126] Table 4 shows the over-limit situations of node voltages under the above three methods. It can be seen that there are over-limit phenomena in the node voltages under all three methods. Among them, since the temperature control loads of Methods 1 and 2 do not have the power regulation ability, the voltage over-limit is more serious. Since the temperature control load of Method 3 has the regulation ability, the number of nodes with voltage over-limit is less.

[0127] Table 4 Node Voltages under Three Methods

[0128]

[0129] Table 5 shows the load missing and recovery amounts under the above three methods. It should be noted that the load missing amount includes heat storage electric heating and basic loads. The missing amount of heat storage electric heating is the load that fails to be recovered after departing from the normal temperature range. After analysis, it can be seen that the load model of Method 2 can reflect the multiple operating states of heat storage electric heating to a certain extent compared with Method 1, but compared with Method 3 with power regulation ability, its load recovery ratio needs to be improved.

[0130] Comparison of Load Restoration Results under Three Methods in Table 5

[0131]

[0132] To explore the influence of load weights on the restoration results, Method 3 conducts restoration by fixing the restoration weights of load nodes. Method 4, on the other hand, adopts a dynamic restoration strategy. The node voltages during the restoration of the two methods are as Figure 8 shown. It can be seen from the figure that the node voltages in Method 4 are within the normal range, showing a trend of lower voltage levels in the early stage of restoration and higher voltage levels in the later stage of restoration. Since Method 3 does not consider the dynamic restoration strategy, there are more cases of node voltage violations in the middle and early stages of restoration. However, in the later stage of restoration, as the minimum heating power of the thermal storage electric heating is guaranteed, the node voltages gradually return to the normal level.

[0133] The switching action plan based on network reconfiguration during the fault restoration period is as Figure 9 shown. Although the number of switching actions under both methods is within the normal range, the number of switching actions in Method 4 is significantly less than that in Method 3. That is, the effect of realizing the restoration of temperature-controlled loads by considering the dynamic load weight restoration strategy is better.

[0134] The above analysis shows that: Method 4, that is, this method, based on considering the multi-operation states of the power of temperature-controlled loads, proposes a fault restoration method with dynamic load weights, which can effectively handle fault restorations in different seasonal scenarios, quickly restore the power-off loads, reduce the risk of voltage instability, and ensure the user comfort during the restoration.

[0135] Embodiment 4

[0136] An active distribution network fault restoration device considering the participation of temperature-controlled loads, which includes: a processor and a memory. Program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute the following method steps in Embodiment 1:

[0137] Analyze the operating characteristics of air conditioners and thermal storage electric heating on the load side in the active distribution network fault restoration framework, depict the operating characteristic curves under different power outage times, and explore the influence of the controllability of the two types of loads on the operating state;

[0138] Calculate the start-up and shutdown times of the two types of loads based on the operating characteristic curves, and the power demand of load aggregation can be obtained according to the law of large numbers;

[0139] Represent the power demand of temperature-controlled loads at different time periods with state characteristic quantities, and calculate the dynamic load restoration weights based on this;

[0140] According to the dynamic load weights, a mathematical model for the fault restoration of active distribution networks can be established. The objective function is to maximize the restored lost load while minimizing the number of switch operations. The constraint conditions include: node voltage constraints, branch power flow constraints, and the safe operation constraints of active distribution networks;

[0141] Based on the mathematical model of the fault restoration of active distribution networks, the lost load is restored through multi-period network reconstruction technology.

[0142] Among them, the operating characteristics of air conditioners and thermal storage electric heating on the load side in the fault restoration framework of active distribution networks are analyzed as follows:

[0143]

[0144] P e =P w +P hs -P hl

[0145] In the formula, T in (t) and T out (t) are the indoor and outdoor temperatures at time t; C ac and R ac are the specific heat capacity and thermal resistance of the air conditioner; P ac is the electric power of the air conditioner, η ac is the refrigeration efficiency; λ ac (t) is a binary variable representing the operating state of the air conditioner at time t. ρ and μ are the heat dissipation coefficient and temperature rise coefficient of the house with thermal storage electric heating; P e is the electric power of the thermal storage electric heating; P w is the operating power of the thermal storage electric heating; P hs and P hl are the heat storage and heat release powers of the thermal storage electric heating respectively; λ eh (t) is a binary variable representing the start-up and shutdown of the electric heating.

[0146] Among them, based on the operating characteristic curves, the start-up and shutdown times of the two types of loads are calculated, and according to the law of large numbers, the power demand of the load aggregation can be obtained as:

[0147] 1) Solving the differential equation of the first-order equivalent thermal parameter model can obtain the start-up and shutdown times of the temperature-controlled loads in each operating state:

[0148]

[0149] In the formula, and are the start-up time and shutdown time of the air conditioner, and are the start-up time and shutdown time of the thermal storage electric heating; T over,max and T over,minis the temperature corresponding to the start and stop of the air conditioner during fault recovery; is a binary variable. When a fault occurs when the air conditioner is in the on state, and when a fault occurs when the air conditioner is in the off state;

[0150] 2) According to the law of large numbers, the aggregated power of multiple temperature-controlled loads can be obtained:

[0151]

[0152] In the formula: is the aggregated power of N air conditioners at time t, is the aggregated power of N heat storage electric heating devices at time t; P ac,n,t is the electric power of the air conditioner in node n at time t, P eh,n,t is the electric power of the heat storage electric heating device in node n at time t.

[0153] Among them, the dynamic load weight is:

[0154] The load weight of each node:

[0155]

[0156] It should be noted here that the device description in the above embodiments corresponds to the method description in the embodiments, and the embodiments of the present invention will not be elaborated here.

[0157] The execution subjects of the above-mentioned processor and memory can be devices with computing functions such as computers, single-chip microcomputers, and microcontrollers. Specifically, in implementation, the embodiments of the present invention do not limit the execution subject, and it is selected according to the needs in actual applications.

[0158] Data signals are transmitted between the memory and the processor through a bus, and the embodiments of the present invention will not elaborate on this.

[0159] Based on the same inventive concept, the embodiments of the present invention also provide a computer-readable storage medium. The storage medium includes a stored program that controls the device where the storage medium is located to execute the method steps in the above embodiments when the program runs.

[0160] The computer-readable storage medium includes but is not limited to flash memory, hard disk, solid-state drive, etc.

[0161] It should be noted here that the description of the readable storage medium in the above embodiments corresponds to the method description in the embodiments, and the embodiments of the present invention will not be elaborated here.

[0162] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part.

[0163] The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium or a semiconductor medium, etc.

[0164] References

[0165] [1] Wang Ying, Ma Jiajun, Wang Xiaojun, et al. Method for restoring important loads in distribution networks with intelligent soft switches [J]. Automation of Electric Power Systems, 2021, 45(08): 104-111.

[0166] [2] Gao Haixiang, Chen Ying, Huang Shaowei, et al. Research progress on distribution network resilience and related aspects [J]. Automation of Electric Power Systems, 2015, 39(23): 1-8.

[0167] [3] Xu Yin, He Jinghan, Wang Ying, et al. Review and prospect of research on distribution network fault restoration under the background of resilience [J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3416-3429.

[0168] [4] Xu Yin, Wang Ying, He Jinghan, et al. Multi-source collaborative optimization decision-making method for multi-period load restoration in distribution networks [J]. Automation of Electric Power Systems, 2020, 44(2): 123-131.

[0169] [5] Zhu Xiaorong, Si Yu. Multi-period dynamic power supply restoration strategy for distribution networks considering the coupling of physical-information-traffic networks [J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3306-3320.

[0170] [6] Medina D R, Rappold E, Sanchez O, et al. Fast assessment of frequency response of cold load pickup in power system restoration[J]. IEEE Transactions on Power System, 2016, 31(4): 3249 - 3256.

[0171] [7] Liu Libang, Wu Chuantao, Sui Quan, et al. Dynamic power supply restoration strategy for active distribution network considering the participation of controllable loads[J]. Power System Protection and Control, 2020, 48(09): 27 - 35.

[0172] [8] J.E. Mcdonald and A.M. Bruning. Cold load pickup, IEEE Transactions on Power System, 1979, PAS - 98(4): 1384 - 1386.

[0173] [9] M. Gilvanejad, H.A. Abyaneh, and K. Mazlumi. Estimation of cold - load pickup occurrence rate in distribution systems, IEEE Transactions on Power Delivery, 2013, 28(2): 1138–1147.

[10] Fan Rui, Sun Runjia, Liu Yutian. Method for reducing the load restoration amount considering the demand response of air - conditioning loads[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2869 - 2877.

[0174]

[11] Zhang Jiarui, Mu Yunfei, Jia Hongjie, et al. Day - ahead optimal scheduling method for heat - storage electric heating considering users' heat demand during power outage[J]. Automation of Electric Power Systems, 2020, 44(21): 15 - 22.

[0175] In the embodiments of the present invention, except for those with special specifications for the models of each device, the models of other devices are not limited, and any device that can perform the above - mentioned functions is acceptable.

[0176] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred embodiment, and the serial numbers of the embodiments of the present invention above are only for description and do not represent the superiority or inferiority of the embodiments.

[0177] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A fault recovery method for active distribution network considering the participation of temperature-controlled loads, characterized in that The method includes: Analyze the operating characteristics of air conditioners and heat storage electric heating on the load side in the active distribution network fault recovery framework, depict the operating characteristic curves under different power outage times, and explore the influence of the controllability of the two types of loads on the operating state; Calculate the start-up and shutdown times of the two types of loads based on the operating characteristic curves, and the aggregated power demand of the loads can be obtained according to the law of large numbers; Represent the power demand of the temperature-controlled loads at different times with state characteristic quantities, and calculate the dynamic load recovery weights based on this; A mathematical model for active distribution network fault recovery can be established according to the dynamic load weights, with the objective function of maximizing the restoration of the lost load and minimizing the number of switch operations, and the constraints include: node voltage constraints, branch power flow constraints, and active distribution network safe operation constraints; Based on the mathematical model of active distribution network fault recovery, the lost load is restored through multi-period network reconstruction technology.

2. The active distribution network fault recovery method considering the participation of temperature-controlled loads according to claim 1, wherein, The analysis of the operating characteristics of air conditioners and heat storage electric heating on the load side in the active distribution network fault recovery framework is as follows: P e = P w + P hs - P hl Where, T in (t) and T out (t) are the indoor and outdoor temperatures at time t; C ac and R ac are the specific heat capacity and thermal resistance of the air conditioner; P ac is the electric power of the air conditioner, η ac is the refrigeration efficiency; λ ac (t) is a binary variable representing the operating state of the air conditioner at time t. ρ and μ are the heat dissipation coefficient and temperature rise coefficient of the house with heat storage electric heating; P e is the electric power of the heat storage electric heating; P w is the operating power of the heat storage electric heating; P hs and P hl are the heat storage and heat release powers of the heat storage electric heating respectively; λ eh (t) is a binary variable representing the start-up and shutdown of the electric heating.

3. The active distribution network fault recovery method considering the participation of temperature-controlled loads according to claim 1, wherein The calculation of the start-up and shutdown times of the two types of loads based on the operating characteristic curves, and the aggregated power demand of the loads can be obtained according to the law of large numbers is as follows: 1) Solve the differential equation of the first-order equivalent thermal parameter model to obtain the start-up and shutdown times of the temperature-controlled loads in each operating state: Wherein, and are the air conditioner startup time and shutdown time, and are the startup time and shutdown time of the heat storage electric heating; T over,max and T over,min are the temperatures corresponding to the air conditioner startup and shutdown during the fault recovery period; is a binary variable. When , a fault occurs when the air conditioner is in the startup state. When , a fault occurs when the air conditioner is in the shutdown state; 2) The aggregated power of multiple temperature-controlled loads can be obtained according to the law of large numbers: Wherein: is the aggregated power of N air conditioners at time t, is the aggregated power of N heat storage electric heating systems at time t; P ac,n,t is the electric power of the air conditioner in node n at time t, P eh,n,t is the electric power of the heat storage electric heating system in node n at time t.

4. A method for active distribution network fault recovery considering the participation of temperature-controlled loads, characterized in that The dynamic load weights are: The load weight of each node:

5. An active distribution network fault recovery device considering the participation of temperature-controlled loads, characterized in that, The device includes: a processor and a memory. Program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute the method described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the processor executes the method described in any one of claims 1-4.

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