Emergency Power Supply and Energy Storage Planning Method and System for Distribution System Oriented to Resilience Improvement

Abstract

The present invention discloses a method and system for emergency power supply and energy storage planning of a distribution system for resilience improvement, belonging to the technical field of power system analysis, including a simulation module, an optimization module and a solution module. The optimization module includes a constraint unit and an optimization unit, and the solution module includes a solution unit and an output unit. The simulation module simulates the photovoltaic output and the fault scenarios of the distribution system through the input historical parameters. The optimization module combines the system parameters and the data generated by the simulation module and performs optimization considering various constraints. The solution module solves the data in the optimization module to obtain the optimal plan and outputs it. By adopting the above-mentioned method and system for emergency power supply and energy storage planning of a distribution system for resilience improvement, the present invention performs single-layer planning on the distribution system in extreme disaster situations, which can not only meet the needs of important loads, but also store the excess electric energy.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a method and system for emergency power supply and energy storage planning of a distribution system for enhancing resilience. Background Art

[0002] In recent years, due to the extensive exploitation of natural resources and the increasing development and use of fossil fuels, the resulting reduction in resources and the increasing carbon dioxide emissions. Similar unreasonable human activities will trigger drastic fluctuations in the weather, leading to frequent occurrence of extreme natural disasters, making more and more regions suffer from extreme compound events. Extreme weather events are likely to damage power supply facilities, bringing great damage to the reliability of power supply of the distribution system, triggering large-scale power outages of the power grid, and then causing huge economic losses and adverse social impacts.

[0003] When the distribution system is damaged, how to reduce the resulting losses has gradually become a research topic for researchers. In order to enable the distribution network to have stronger resistance to damage and disturbances, the concept of "elastic power grid" or "resilient power grid" has emerged. A "resilient power grid" refers to a power grid that can accurately, quickly, and comprehensively judge the operating conditions of the power grid, coordinate the resources of the system, actively respond to various interferences, quickly restore important power loads, and can continuously learn and improve.

[0004] Currently, the methods for enhancing resilience mainly include three stages: pre-disaster optimization planning, in-disaster emergency control, and post-disaster rapid recovery. In the pre-disaster stage, it is mainly prediction and optimal allocation. Reasonable pre-disaster planning can improve the resilience of the power grid to a certain extent. For example, the location and capacity planning of various distributed energy sources and energy storage can reduce the loss of critical loads while ensuring relatively low costs. Distributed power sources mainly composed of diesel generators, wind power, photovoltaic power, etc. can be used as emergency power generation resources. They are relatively independent and controllable. When a disaster damages the distribution infrastructure and the upper-level power grid cannot supply power to the distribution network, these emergency power supplies can continuously supply power to the distribution network, enhancing the distribution network's ability to respond to extreme disasters and reducing losses. Moreover, in recent years, the technology of energy storage devices has gradually matured. Due to its flexible and variable configuration, stable output, and fast response speed, it plays an important role in enhancing the resilience of the distribution system and its proportion in the distribution system is also gradually increasing. However, currently, the planning schemes for ensuring the power supply capacity of critical loads of the power grid are relatively single, mostly using a multi-layer interaction mode. Therefore, a single-layer planning method for the resilience of the distribution system based on stochastic programming is needed. Summary of the Invention

[0005] The object of the present invention is to provide a method and system for emergency power supply and energy storage planning of a distribution system for resilience improvement, which conducts single-layer planning for the distribution system in extreme disaster situations, can not only meet the demands of important loads, reduce the losses of important loads, but also store the redundant electric energy.

[0006] To achieve the above object, the present invention provides an emergency power supply and energy storage planning system for a distribution system for resilience improvement, including a simulation module, an optimization module and a solution module. The optimization module includes a constraint unit and an optimization unit, and the solution module includes a solution unit and an output unit;

[0007] The simulation module simulates the photovoltaic output and the fault scenarios of the distribution system through the input historical parameters;

[0008] The optimization module aggregates the system parameters and the data generated by the simulation module, and conducts optimization considering various constraints;

[0009] The constraint unit constrains the system parameters and the data generated by the simulation module;

[0010] The optimization unit optimizes the data constrained by the constraint unit;

[0011] The solution module solves the data in the optimization module to obtain the optimal plan and outputs it;

[0012] The solution unit solves the data in the optimization module;

[0013] The output unit outputs the data obtained by the solution unit.

[0014] The present invention provides a method for emergency power supply and energy storage planning of a distribution system for resilience improvement, including the following steps:

[0015] S1. Construct the uncertain scenarios of photovoltaic units by using the Latin hypercube sampling method, and obtain the outputs of different photovoltaic units by using the Kantorovich distance method;

[0016] S2. Consider the active power and reactive power transmission of lines to establish a line power flow model and constraints;

[0017] S3. Establish an optimization model for distribution network planning for resilience improvement, construct a location and capacity model of the planning equipment, and use the equipment investment cost and load loss cost as the objective function;

[0018] S4. Use historical data to select the lines with frequent failures to form fault scenarios;

[0019] S5. Establish an extreme scenario operation model, with the goal of minimizing load loss, and the resilience of the distribution network is represented by load loss.

[0020] Preferably, there are N groups of photovoltaic random variables in S1 , where the Nth variable 's cumulative probability distribution function is described as:

[0021] ;

[0022] Scenarios constructed based on the Latin hypercube sampling method, i.e., LHS scenarios. The LHS scenario generation method includes the following steps:

[0023] S1.1.1. The sampling scale is R. Divide the distribution curve of the cumulative probability distribution function into several probability intervals with each interval range of 1 / R;

[0024] S1.1.2. Randomly select any number in each probability area; then the cumulative probability of the sampling point in the xth interval is:

[0025] ;

[0026] where, is a random number in the interval [0, 1];

[0027] S1.1.3. Substitute into the inverse cumulative probability distribution function to obtain the sampling value in the corresponding interval:

[0028] ;

[0029] S1.1.4. Repeat the sampling R times through S1.1.1~S1.1.3, that is, R sampling results regarding the photovoltaic unit will be generated;

[0030] S1.1.5. Generate an N×R-dimensional matrix, randomly sort each row, and then generate R photovoltaic output scenarios.

[0031] Preferably, in S1, the generated photovoltaic output scenarios are reduced and multiple typical representative photovoltaic output situations are retained by the Kantorovich distance method to restore the actual photovoltaic output situation. The number of generated photovoltaic output scenarios is N, and the number of reduced scenarios is n. The LHS scenario reduction method includes the following steps:

[0032] S1.2.1. Initialize the processing. The probability value of each photovoltaic power prediction value scenario is , and the initial number of reduced scenarios is ;

[0033] S1.2.2. Calculate each scenario Kantorovich distance :

[0034] ;

[0035] where, is the photovoltaic power magnitude under a certain scenario, are the photovoltaic power values under the remaining scenarios;

[0036] S1.2.3. Select the scenario with the minimum distance from the scenario and calculate the product of the Kantorovich distance and the scenario probability:

[0037] ;

[0038] where, is the scenario r probability value;

[0039] S1.2.4. Repeat S1.2.3 for each scenario, then select the minimum scenario and denote it as scenario d and delete this scenario, and at the same time update the reduced number of scenarios , then the probability value of scenario r can be updated as:

[0040] ;

[0041] where, is the probability value of scenario d;

[0042] S1.2.5. Repeat S1.2.2 to S1.2.4 until the final number of scenarios .

[0043] Preferably, the line power flow constraints in S2 are as follows:

[0044] For each node , there is:

[0045] (1) represents t the voltage phasor of node i at time represents t the square of the voltage magnitude of node i at time

[0046] (2) represents t the current phasor of node i at time represents t the square of the current magnitude of node i at time

[0047] (3) , respectively represent t the active power and reactive power injected into the node at the moment i ;

[0048] (4) , respectively represent t the active power and reactive power flowing through the branch at the moment ;

[0049] (5) , respectively represent the resistance and reactance values of the branch ;

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] wherein, is the set of all nodes; is the set of nodes with emergency energy storage; is the set of nodes with original energy storage; is a set of time for 24 hours; is a set of nodes with emergency power supplies; is a set of nodes with original distributed power sources; is a set of nodes with wind-solar units; represents a set of nodes with node i as the parent node; represents i a set of nodes with the node as the child node; , are respectively the lower and upper limits of the square of the voltage amplitude; , are respectively the active and reactive power demands of node i during t the time period; , are respectively the active and reactive power losses of node i during the time period t ; , are respectively the total active and reactive power outputs of all resources connected to node i during the time period t ; , are respectively the active and reactive power outputs of the power supply connected to node g during the time period t ; , are the active and reactive power outputs of the photovoltaic unit of node d during the time period t ; , are the curtailed active and reactive power of node d during the time period t ; , are respectively the charging and discharging powers of the energy storage device during the time period t ; , are the active and reactive power outputs of the wind turbine of node d during the time period t ; , are the curtailed active and reactive power of node d during the time period t ; is the power transmitted from the IES to the distribution system, is the power of the system discarding the electrical energy of the IES.

[0067] Preferably, the site selection and capacity model of the planned equipment in S3 are as follows:

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] Among them, is the set of emergency energy storage devices; is the set of nodes with original energy storage; is the time set of 24 hours; is t the electric energy stored in the energy storage device at time is the charging efficiency of the energy storage device; is the discharging efficiency of the energy storage device; is the time interval; are the charging and discharging powers of the energy storage device at the current moment respectively; is a 0-1 state variable, representing the charging and discharging states of the energy storage device at present respectively; is the planned value of the emergency energy storage device; is the minimum capacity of the emergency energy storage device; are the maximum and minimum capacities of the original energy storage device respectively; is the initial energy storage of the original energy storage device; is the initial capacity of the emergency energy storage; are the minimum and maximum powers for the energy storage device to charge respectively; , are the minimum and maximum powers for the energy storage device to discharge respectively, is the installation quantity of the emergency energy storage device;

[0080] Constraints of the original distributed power sources and emergency power sources in the system:

[0081] ;

[0082] ;

[0083] Among them, is the output of the distributed power source and the emergency power source; is the upper limit of the output of each power source; is the set of nodes containing the emergency power source; is the set of nodes containing the distributed power source.

[0084] Preferably, the objective functions for planning the equipment investment cost and the load loss cost in S3 are:

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] Among them, is the upper-level objective function; is the planned emergency power source cost; is the planned emergency energy storage cost; is the total number of disasters occurring during the life of the emergency resources, assumed to be 10 times; represents the expected load loss cost caused by one disaster; is the unit price of purchasing the fuel energy of the emergency power source; is the total amount of fuel required for the emergency power source; is the unit price of purchasing the emergency power source unit; is the output of the emergency power source; is the capacity unit price of the energy storage device; is the capacity of the emergency energy storage device; is the set of all nodes; in the formula, is the time set of 24 hours; are the sets composed of the first, second, and third-level load nodes respectively; are the unit prices of losing the first, second, and third-level load per unit power respectively; , are respectively the first-level, second-level, and third-level loads of the node i causing the load loss power at the t time period.

[0090] Therefore, the present invention adopts the above-mentioned emergency power supply and energy storage planning method and system for distribution system aiming at resilience improvement, and conducts single-layer planning for the distribution system in extreme disaster situations, which can not only meet the needs of important loads, reduce the losses of important loads, but also store the surplus electric energy.

[0091] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 is a structural schematic diagram of the present invention;

[0093] Figure 2 is a flow schematic diagram of the present invention;

[0094] Figure 3 is a generated diagram of the photovoltaic scenario of the present invention;

[0095] Figure 4 is a processing scenario diagram of three photovoltaic units of the present invention;

[0096] Figure 5 is a line topology diagram of the 62-node distribution system of the present invention;

[0097] Figure 6 is a node load demand diagram of the present invention;

[0098] Figure 7 is a load loss diagram after a disaster occurs without planning of the present invention;

[0099] Figure 8 is a diagram of the electric energy that the system can provide after a disaster occurs in the present invention;

[0100] Figure 9 is an output diagram of the emergency power supply after planning of the present invention;

[0101] Figure 10 is the change of the energy storage state of various types in the present invention;

[0102] Figure 11 is a diagram of the electric energy provided by the system after planning in the present invention;

[0103] Figure 12 is a load loss diagram after planning in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0104] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0105] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the technical field to which the present invention belongs.

[0106] Embodiment 1

[0107] As shown Figure 1 in the figure, the present invention provides a distribution system emergency power supply and energy storage planning system for enhancing resilience, including a simulation module, an optimization module, and a solution module. The optimization module includes a constraint unit and an optimization unit, and the solution module includes a solution unit and an output unit.

[0108] The simulation module simulates the photovoltaic output and the fault scenarios of the distribution system through the input historical parameters.

[0109] The optimization module combines the system parameters and the data generated by the simulation module and performs optimization considering various constraints. The constraint unit constrains the system parameters and the data generated by the simulation module. The optimization unit optimizes the data constrained by the constraint unit.

[0110] The solution module solves the data in the optimization module to obtain the optimal plan and outputs it. The solution unit solves the data in the optimization module. The output unit outputs the data obtained by the solution unit.

[0111] As shown Figure 2 in the figure, the present invention provides a distribution system emergency power supply and energy storage planning method for enhancing resilience, including the following steps:

[0112] S1. Construct uncertain scenarios of photovoltaic units using the Latin hypercube sampling method, and obtain the outputs of different photovoltaic units using the Kantorovich distance method;

[0113] There are N groups of photovoltaic random variables , where the Nth variable 's cumulative probability distribution function is described as:

[0114] ;

[0115] Based on the scenarios constructed by the Latin hypercube sampling method, i.e., the LHS scenarios, the LHS scenario generation method includes the following steps:

[0116] S1.1.1. The sampling scale is R, and the distribution curve of the cumulative probability distribution function is divided into several probability intervals with each interval range of 1 / R;

[0117] S1.1.2. Randomly select any number in each probability area; then the sampling point in the xth interval's cumulative probability is:

[0118] ;

[0119] Among them, is a random number in the interval [0, 1];

[0120] S1.1.3. Substitute into the inverse cumulative probability distribution function to obtain the sampling value within the corresponding interval :

[0121] ;

[0122] S1.1.4. Repeat the sampling R times through S1.1.1 to S1.1.3, that is, R sampling results regarding the photovoltaic unit will be generated;

[0123] S1.1.5. Generate an N×R-dimensional matrix, randomly sort each row, and generate R photovoltaic output scenarios.

[0124] In the model constructed by the present invention, 1000 different scenarios obtained based on the historical data of photovoltaic are as shown in Figure 3 shown.

[0125] After generating a large number of photovoltaic output scenarios, a high proportion of the scenarios have high similarity; in order to eliminate the high-proportion similar scenarios or extremely low-probability scenarios, the generated photovoltaic output scenarios are reduced and multiple typical representative photovoltaic output situations are retained by using the Kantorovich distance method to restore the actual photovoltaic output situation. The number of generated photovoltaic output scenarios is N, and the number of reduced scenarios is n. The LHS scenario reduction method includes the following steps:

[0126] S1.2.1. Initialization processing, the probability value of each photovoltaic power prediction value scenario is , and the initial number of reduced scenarios is ;

[0127] S1.2.2. Calculate the Kantorovich distance of each scenario :

[0128] ;

[0129] Among them, is the photovoltaic power magnitude under a certain scenario, is the photovoltaic power value under the remaining scenarios;

[0130] S1.2.3. Select the scenario with the smallest distance from the scenario, and calculate the product of the Kantorovich distance and the scenario probability:

[0131] ;

[0132] Among them, is the probability value of scenario r ;

[0133] S1.2.4. For each scenario, repeat S1.2.3, then select the smallest scenario, denote it as scenario d, delete this scenario, and at the same time update the reduced number of scenarios. , then the probability value of scenario r can be updated to:

[0134] ;

[0135] where is the probability value of scenario d.

[0136] S1.2.5. Repeat S1.2.2 - S1.2.4 until the final number of scenarios .

[0137] In the constructed model, 1000 scenarios are generated according to the uncertainty of photovoltaic power output, and the three scenarios with the most reasonable probability distributions are retained as the power outputs of the three photovoltaic units, as Figure 4 shown.

[0138] S2. Consider the active and reactive power transmission of the line to establish the line power flow model and constraints;

[0139] The line power flow constraints are as follows:

[0140] For each node , there is:

[0141] (1) represents t the voltage phasor of node i at time represents t the square of the voltage amplitude of node i at time

[0142] (2) represents t the current phasor of node i at time represents t the square of the current amplitude of node i at time

[0143] (3) , respectively represent t the active power and reactive power injected into node i at time

[0144] (4) , respectively represent t the active power and reactive power flowing through branch at time

[0145] (5) , respectively represent the resistance and reactance values on the branch ;

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] Among them, is the set of all nodes; is the set of nodes with emergency energy storage; is the set of nodes with original energy storage; is the time set of 24 hours; is the set of nodes with emergency power supply; is the set of nodes with original distributed power sources; is the set of nodes with wind-solar units; represents the node set with node i as the parent node; represents i the node set with the node as the child node; , are the lower and upper limits of the square of the voltage amplitude, respectively; , are the active and reactive power demands of node i during t period, respectively; , are the active and reactive power losses of node i during period t , respectively; , are the total active and reactive power outputs of all resources connected to node i during period t , respectively; , are the active and reactive power outputs of the power source connected to node g during period t , respectively; 、 are the active and reactive power outputs of the photovoltaic unit at node d during period t , respectively; 、 are the active and reactive power of curtailed light at node d during period t , respectively; , are the charging and discharging powers of the energy storage device during period t , respectively; 、 are the active and reactive power outputs of the wind turbine at node d during period t , respectively; 、 are the active and reactive power of curtailed wind at node d during period t , respectively; is the power transmitted from the IES to the distribution system, is the power of the system discarding the electric energy of the IES.

[0163] S3. Establish a distribution network planning optimization model for resilience improvement, construct a siting and capacity model for the planning equipment, and take the planning equipment investment cost and load loss cost as the objective function;

[0164] The siting and capacity model of the planning equipment is as follows:

[0165] ;

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] ;

[0171] ;

[0172] ;

[0173] ;

[0174] ;

[0175] ;

[0176] wherein, is the set of emergency energy storage devices; is the set of nodes with original energy storage; is the time set of 24 hours; is t the electric energy stored in the energy storage device at time is the charging efficiency of the energy storage device; is the discharging efficiency of the energy storage device; is the time interval; are respectively the charging and discharging powers of the energy storage device at the current moment; is a 0-1 state variable, representing respectively the charging and discharging states of the energy storage device at present; is the planned value of the emergency energy storage device; is the minimum capacity of the emergency energy storage device; are respectively the maximum and minimum capacities of the original energy storage device; is the initial energy storage of the original energy storage device; is the initial capacity of the emergency energy storage; are respectively the minimum and maximum powers for the energy storage device to be charged; , are respectively the minimum and maximum powers for the energy storage device to discharge, is the installation quantity of the emergency energy storage device.

[0177] Constraints of the original distributed power sources and emergency power sources in the system:

[0178] ;

[0179] ;

[0180] wherein, is the output of distributed power sources and emergency power sources; is the upper limit of the output of each power source; is the set of nodes containing emergency power sources; is the set of nodes containing distributed power sources.

[0181] The objective function for planning equipment investment costs and load loss costs is:

[0182] ;

[0183] ;

[0184] ;

[0185] ;

[0186] Among them, is the upper-level objective function; is the planned cost of emergency power sources; is the planned cost of emergency energy storage; is the total number of disasters occurring during the life of emergency resources, assumed to be 10 times; represents the expected load loss cost caused by one disaster; is the unit price of purchasing fuel energy for emergency power sources; is the total amount of fuel required for emergency power sources; is the unit price of purchasing emergency power source units; is the output of emergency power sources; is the capacity unit price of energy storage equipment; is the capacity of emergency energy storage equipment; is the set of all nodes; in the formula, is the time set of 24 hours; are the sets composed of first-, second-, and third-level load nodes respectively; are the unit prices of losing unit power of first-, second-, and third-level loads respectively; , are respectively i the first-level, second-level, and third-level loads of node t the load loss power caused during the time period.

[0187] S4. Use historical data to select the lines with frequent failures to form a fault scenario;

[0188] As Figure 5 shown, based on the existing historical data, establish a line topology diagram of a 62-node distribution system. As Figure 6 shown, construct a node load demand diagram.

[0189] Select the lines with frequent failures to form different scenarios:

[0190] Table 1 Summary of line break situations

[0191] ;

[0192] S5. Establish an extreme scenario operation model with the goal of minimizing load loss. The resilience of the distribution network is represented by load loss;

[0193] Take the line break situation 1 as an example to establish an extreme scenario operation model. When no emergency equipment is planned, the load loss is as Figure 7 shown. Without planning, once a disaster occurs, almost all the load in the system will be lost. The electric energy provided by the system after the disaster is as Figure 8 shown. When no planning is carried out, the electric energy that the system itself can provide is extremely small and it is difficult to support the load demand of the nodes.

[0194] From Figure 7 and Figure 8 it can be seen that when no emergency resources are planned, when an extreme natural disaster occurs, most of the load in the system will be lost.

[0195] Table 2 Location and capacity of planned equipment

[0196] ;

[0197] The output of the emergency power supply and emergency energy storage is as Figure 9 shown. It can be known that the emergency power supply approximately meets the high output during the day and has less output in the early morning and at night, similar to the trend of the node load demand curve, indicating that the emergency power supply also always follows the change of the node load demand. The state changes of various types of energy storage are as Figure 10 shown., The node load demand is less in the early morning period, so the energy storage equipment mainly stores electric energy during this period. When passing through the noon period, due to the increase in load demand, some energy storage equipment starts to discharge energy to meet the power gap of the nodes. After evening, in order to minimize the load loss as much as possible, the energy storage equipment releases all its energy.

[0198] The energy storage equipment is mainly in the state of charging or not discharging in the morning and at noon. The reason is that the load demand is not high in the morning, and at this time the distributed power supply can meet the demand of important loads and store the excess electric energy at the same time. Although the load demand is large at noon, because the photovoltaic units also generate power at this time and provide electric energy, the photovoltaic units and the distributed power supply jointly generate power, reducing the loss of important loads. If there is a surplus, it can be stored in the energy storage equipment.

[0199] At this time, the electric energy provided by the system and the load loss are as Figure 11 and Figure 12As shown by Figure 11 it can be seen that, compared with the power supply diagram of the system before planning, there is an obvious increase in the power supply. From Figure 12 compared with Figure 7 it can be seen that the load loss of the system has decreased, but a considerable part of the load is still discarded. This is mainly because some electric energy is difficult to be transmitted to the next node due to system disconnection, and the system mainly gives priority to meeting the needs of important loads, and the tertiary loads will be appropriately discarded.

[0200] Table 3 Comparison of load loss and cost before and after planning

[0201] ;

[0202] Through the results described above, it can be known that:

[0203] The emergency power supply is not always in a full-output state, and the emergency energy storage does not always discharge energy, because the use of emergency resources also requires a certain cost. During disasters, the needs of important loads are mainly guaranteed, and there is an appropriate situation of discarding tertiary loads;

[0204] The primary loads hardly have losses after planning emergency resources, while some of the secondary loads still cannot be met, because due to the limitation of emergency resource costs, it is impossible to ensure that emergency resources are set at each important load node on the line, and there is an upper limit on the transmission power of the line, and the secondary loads far from the planned equipment may not be able to ensure power supply;

[0205] After setting emergency resources in the distribution system and conducting site selection and capacity planning for the emergency resources, all the primary important loads in the system can be guaranteed power supply, the loss of secondary important loads has decreased, the system loss has decreased, and the total cost has decreased significantly.

[0206] Therefore, the present invention adopts the above-mentioned method and system for planning emergency power supply and energy storage of a distribution system for resilience improvement, and conducts single-layer planning on the distribution system in extreme disaster situations, which can not only meet the needs of important loads, reduce the losses of important loads, but also store the excess electric energy.

[0207] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that: they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. Emergency power supply and energy storage planning method for distribution system with improved resilience, based on the emergency power supply and energy storage planning system for distribution system with improved resilience, the emergency power supply and energy storage planning system for distribution system with improved resilience includes a simulation module, an optimization module and a solution module, the optimization module includes a constraint unit and an optimization unit, and the solution module includes a solution unit and an output unit; The simulation module simulates the photovoltaic output and distribution system failure scenarios by inputting historical parameters; The optimization module combines system parameters and data generated by the simulation module, taking into account multiple constraints for optimization; The constraint unit constrains the system parameters and the data generated by the simulation module; The optimization unit optimizes the data constrained by the constraint unit; The solution module solves the data in the optimization module, obtains the optimal plan and outputs it; The solving unit solves the data in the optimization module; The output unit outputs the data obtained by the solving unit; The emergency power supply and energy storage planning method for distribution system with improved resilience specifically includes the following steps: S1. The Latin hypercube sampling method is used to construct the uncertain scenario of photovoltaic units, and the Kantorovich distance method is used to obtain the output of different photovoltaic units; There are N sets of photovoltaic random variables , where the Nth variable The cumulative probability distribution function of Described as: ； The scene constructed based on the Latin hypercube sampling method is called the LHS scene. The LHS scene generation method includes the following steps: S1.1.

1. The sampling scale is R, and the cumulative probability distribution function The distribution curve of is divided into several probability intervals, each of which has a range of 1 / R; S1.1.

2. Randomly select any number in each probability interval; then the sampling point of the xth interval is The cumulative probability for: ； in, is a random number in the interval [0, 1]; S1.1.

3. Substitute into the inverse cumulative probability distribution function In the corresponding interval, the sampling value is obtained : ； S1.1.4, repeat the sampling R times through S1.1.1~S1.1.3, that is, R sampling results about the photovoltaic unit will be generated; S1.1.5, generate an N×R dimensional matrix, randomly sort the rows and generate R photovoltaic output scenarios; S2, consider the line active power and reactive power transmission to establish the line power flow model and constraints; S3. Establish a distribution network planning optimization model for improving resilience, build a site selection and capacity model for planning equipment, and take the planning equipment investment cost and load loss cost as the objective function; S4. Use historical data to select lines that often fail and form a failure scenario; S5. Establish an extreme scenario operation model with the goal of minimizing load loss. The resilience of the distribution network is expressed by load loss.

2. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 1, characterized in that: In S1, the generated photovoltaic output scenario is reduced by using the Kantorovich distance method and multiple typical representative photovoltaic output situations are retained to restore the actual photovoltaic output situation. The number of photovoltaic output scenarios generated is N, and the number of scenarios after reduction is n. The LHS scenario reduction method includes the following steps: S1.2.1, Initialization processing, the probability value of each photovoltaic power prediction value scenario is , the initial reduction number of scenes is ; S1.2.

2. Calculate each scene Kantorovich distance : ； in, is the photovoltaic power size in a certain scenario, is the photovoltaic power value in other scenarios; S1.2.

3. Select the scene with the smallest distance to the scene, and calculate the product of the Kantorovich distance and the scene probability: ； in, For the scene r The probability value of S1.2.

4. Repeat S1.2.3 for each scenario, then select the smallest scenario as scenario d and delete it, and update the number of reduced scenarios. , then the probability value of scene r can be updated as: ； in, is the probability value of scene d; S1.2.5, repeat S1.2.2~S1.2.4 until the final number of scenes is .

3. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 1, characterized in that: The line flow constraints in S2 are as follows: For each node ,have: (1) represent Time Node The voltage phasor, represent Time Node The square of the voltage amplitude; (2) represent Time Node The current phasor, represent Time Node The square of the current amplitude; (3) , Respectively represent Inject nodes at all times Active power and reactive power; (4) , Respectively represent Time Branch Active power and reactive power flowing through; (5) , Represents branches Resistance and reactance values ​​on the ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； in, is the set of all nodes; is a collection of nodes containing emergency energy storage; is the set of nodes containing original energy storage; A time collection of 24 hours; is a collection of nodes containing emergency power supplies; is a collection of nodes containing the original distributed power sources; is a collection of nodes including wind and solar generators; Represents a node The node set that is the parent node; Indicates Node is a node collection whose child nodes are; , are the lower and upper limits of the square of the voltage amplitude respectively; , Node exist Active and reactive power requirements of loads during the time period; , Node In the period Lost load active and reactive power; , Node In the period The total active and reactive output of all resources connected to it; , Node In the period The active and reactive output of the power supply connected to it; , For Node In the period The active and reactive output of the photovoltaic unit; , For Node In the period The abandoned active and reactive power under , The time periods The charging and discharging power of the energy storage device; , For Node In the period Active and reactive output of downstream wind turbines; , For Node In the period The abandoned wind active and reactive power; is the power delivered by IES to the distribution system, The power of IES energy discarded to the system.

4. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 1, characterized in that: The location and capacity model of the planned equipment in S3 is as follows: ； ； ； ； ； ； ； ； ； ； ； in, A collection of emergency energy storage equipment; is the set of nodes containing original energy storage; A time collection of 24 hours; for The electrical energy stored in the energy storage device at all times; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the time interval; are respectively the charging and discharging power of the energy storage device at the current moment; are 0-1 state variables, representing the current charging and discharging status of the energy storage device; is the planned value of the emergency energy storage equipment; The minimum capacity of the emergency energy storage equipment; are the maximum and minimum capacities of the original energy storage equipment respectively; It is the initial energy storage of the original energy storage equipment; is the initial capacity of emergency energy storage; are the minimum and maximum power for charging the energy storage device respectively; , are the minimum and maximum power of energy storage equipment. The number of emergency energy storage devices installed; Constraints of existing distributed power and emergency power in the system: ； ； in, Output of distributed power supply and emergency power supply; The output limit of each power source; A collection of nodes containing emergency power supplies; is a set of nodes containing distributed power sources.

5. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 1, characterized in that: The objective function for planning equipment investment cost and load loss cost in S3 is: ； ； ； ； in, is the upper objective function; Cost of emergency power supply for planning; Cost of planned emergency energy storage; is the total number of disasters that occur within the life of emergency resources, assumed to be 10; It represents the expectation of the load loss cost caused by a disaster; The unit price of purchasing the fuel energy of a unit of emergency power supply; The total amount of fuel required for emergency power supply; The unit price for purchasing the emergency power supply unit; Provide emergency power supply; is the capacity unit price of the energy storage equipment; The capacity of the emergency energy storage equipment; is the set of all nodes; where A time collection of 24 hours; They are the sets consisting of the first, second and third level load nodes respectively; They are the unit prices of the first, second and third level loads for the unit power loss respectively; , Node The primary, secondary and tertiary loads are The load loss power caused by the period.

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