CN109245155A - The credible capacity evaluating method of power distribution network broad sense power supply power transformation based on uncertain theory - Google Patents
The credible capacity evaluating method of power distribution network broad sense power supply power transformation based on uncertain theory Download PDFInfo
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Abstract
The invention discloses a kind of credible capacity evaluating method of power distribution network broad sense power supply power transformation based on uncertain theory executes in calculating equipment, this method comprises: determining the maximum safe load Q of distribution network system0；Determine initial reliability level H of the distribution network system when broad sense power supply is not configured0；Calculate the reliability level H for being configured with the distribution network system of broad sense power supplyG；When reliability level is not equal to initial reliability level, the load of distribution network system is adjusted, and the reliability level H of distribution network system is redefined according to load Q adjustedG；As reliability level HGEqual to initial reliability level H0When, by the load Q of current power distribution network system and maximum safe load Q0Power transformation credible capacity of the poor Δ Q as broad sense power supply.The present invention discloses corresponding calculating equipment together.
Description
Technical Field
The invention relates to the technical field of power distribution network transformer substation capacity planning, in particular to a power distribution network generalized power supply transformation credible capacity assessment method and computing equipment based on an uncertain theory.
Background
Energy is an important basic resource on which human beings rely on survival, economic development and social progress, and is related to the national economic pulse and the national security, so that the energy resource is reasonably and effectively developed, utilized and protected, and the energy planning suitable for the national conditions of China is necessary.
The task of grid planning is to determine the corresponding optimal grid structure according to the load increase during planning and the power supply planning scheme. The traditional power grid planning method mainly comprises a mathematical planning method and a heuristic method, and has the common characteristic that a mathematical model is established on the basis of a determined future environment to obtain an optimal planning scheme.
In recent years, uncertainty factors influencing power grid planning such as power market reformation and the like are more and more, and the influence of various uncertainty factors on a planning result needs to be considered in power grid planning, but the traditional planning method cannot meet the requirement, the mathematically strict optimal scheme is not optimal for the future situation, and even a large amount of compensation investment has to be carried out due to the influence of the future uncertainty factors, so that the meaning of 'optimal' planning is reduced or even lost, and huge loss is caused. In order to overcome the defects of the traditional planning method, the influence of uncertainty factors is considered in the planning, and the method has already gained wide attention of the academic and engineering fields at home and abroad.
In order to solve uncertain factors in power grid planning, a national scholars Duncong proposes a gray method and a Niming proposes an evidence theory; the foreign scholars Borkowska b. proposed a stochastic method and Saraiva J T proposed a fuzzy method. From the application of the theories and the methods, the theories and the methods have good reference value and application prospect for processing various uncertain factors in power grid planning. Some of the power grid flexible planning methods are already applied to the actual power system planning engineering, and the satisfactory social and economic benefits are obtained.
In summary, in recent years, uncertain factors affecting power grid planning, such as power market reformation and the like, are increasing, and research on a power grid flexible planning method has gained general attention in various aspects and becomes an important research field in power system planning. However, the current flexible planning method for considering uncertainty of the power grid mainly focuses on the power transmission network, and research on the uncertainty planning method for the power distribution network is still lacking.
With the continuous improvement of the permeability of distributed Power sources, energy storage and demand side response resources, the distributed Power sources can be used as Power sources in the Power distribution network to replace transformation capacity to bear partial load, so that the distributed energy sources in the Power distribution network are collectively called Generalized Power Sources (GPS). The method has the advantages that the capacity value of the generalized power supply in the power distribution network instead of the transformation capacity is evaluated, support can be provided for planning of a transformer substation in the active power distribution network, accordingly, the utilization rate of asset equipment of the power distribution network is improved, the contribution of the generalized power supply to a power system can be effectively evaluated, and decision support is provided for the formulation of a market mechanism and an electricity price policy. How to consider the alternative capacity of the distributed power supply in the substation capacity planning stage, and carrying out site selection and volume fixing according to the deducted substation capacity can be suitable for the planning scene of larger-scale distributed energy grid connection, wherein the solution technical method of the credible capacity has important research significance.
In recent years, some results have been obtained in domestic and foreign research aiming at evaluating the contribution of intermittent performance sources to the system capacity adequacy by using credible capacity, but the following problems still exist:
(1) at present, most of existing researches are to examine the effective capacity of intermittent energy sources to replace conventional generator sets on the level of a large power grid, and the unchanged reliability means that the power generation system is unchanged in abundance and lacks of researches on credible capacity on the level of a power distribution network.
(2) At present, in the aspect of a power distribution network, in a power supply range of a transformer substation, a research method of transformation capacity which can be replaced by distributed energy is to take a capacity-load ratio as a reliability index, after the distributed energy is accessed, under the condition that the capacity-load ratio is not changed, a load amount which can be increased is defined as a credible capacity, and the credible capacity is deducted from the capacity of the transformer substation. The method has the disadvantages that the scales of the transformer substations in the same area are different, the load transfer under the fault is not facilitated, and the structure of a high-voltage distribution network is not considered.
Disclosure of Invention
Therefore, the invention provides a method for evaluating the transformation reliability of the generalized power supply of the power distribution network based on the uncertain theory, which aims to solve or at least alleviate the problems.
According to one aspect of the invention, an uncertain theory-based power transformation reliability capacity assessment method for a generalized power supply of a power distribution network is provided and is executed in a computing device, and the method comprises the following steps: determining maximum safe load Q of power distribution network system0(ii) a Determining an initial reliability level H of the power distribution network system when the generalized power source is not configured0(ii) a Calculating reliability level H of power distribution network system configured with generalized power supplyG(ii) a When the reliability level is not equal to the initial reliability level, adjusting the load of the power distribution network system, and re-determining the reliability level of the power distribution network system according to the adjusted load Q; when the reliability level HGEqual to said initial reliability level H0Then, the load Q of the current power distribution network system is compared with the load QMaximum safe load Q0The difference delta Q of the difference is used as the power transformation reliability capacity of the generalized power supply.
Optionally, in the method for evaluating the transformation reliability capacity of the generalized power supply of the power distribution network based on the uncertain theory, the reliability level H is calculated according to the following methodG: establishing a generalized power output model; according to the generalized power output model, a sequential Monte Carlo simulation method is adopted to calculate the reliability of the high-voltage distribution network in consideration of the power supply mode so as to determine the reliability level HG。
According to another aspect of the invention, there is provided a computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method for estimating the power distribution grid generalized power source transformation reliability capacity based on the uncertainty theory as described above.
According to yet another aspect of the present invention, there is provided a readable storage medium storing program instructions, which when read and executed by a computing device, cause the computing device to execute the method for estimating the substation reliability capacity of the generalized power source of the power distribution network based on the uncertainty theory.
The invention firstly provides the definition of the transformation reliable capacity, which means that under the condition that the faults of elements such as a main transformer, a 110kV line, a circuit breaker and the like are considered on the level of a high-voltage distribution network, the generalized power supply connected into the distribution network can be equivalent to the transformation capacity according to the equal reliability principle. And then taking a power distribution area as an object, considering uncertainty and a control strategy of the generalized power supply under the condition that the predicted value of the permeability of the generalized power supply is certain in a planning horizontal year and the generalized power supply is distributed dispersedly, and providing a power transformation reliability capacity evaluation method of the generalized power supply of the power distribution network considering a power supply mode.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a schematic diagram of a computing device 100, according to one embodiment of the invention;
fig. 2 shows a flowchart of a method 200 for evaluating substation reliability capacity of a generalized power source of a power distribution network based on an uncertain theory according to an embodiment of the present invention;
fig. 3 shows a flowchart of a method 300 for evaluating the substation reliability capacity of a generalized power source of a power distribution network based on uncertain theory according to another embodiment of the invention;
FIG. 4 shows a schematic diagram of a power supply partition, according to one embodiment of the invention;
FIG. 5 illustrates an electrical wiring diagram of a direct wiring approach according to one embodiment of the present invention;
FIG. 6 illustrates an electrical wiring diagram of a single-sided power supply T-style wiring scheme in accordance with one embodiment of the present invention;
FIG. 7 shows an electrical wiring diagram of a dual power supply T-bar wiring scheme in accordance with one embodiment of the present invention;
FIG. 8 shows an electrical wiring diagram of a dual power pi-type wiring scheme according to one embodiment of the present invention;
FIG. 9 shows a schematic diagram of a geographical location of a substation according to one embodiment of the invention;
fig. 10 shows a comparison of the results of the power transformation reliability capacity in different power supply modes according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
For the purpose of more clearly describing the present invention, the terms abbreviations referred to in the present invention are explained here:
1. GPS: generalized Power Source, Generalized Power Source;
2. SOC: state Of Sharge, State Of charge;
3. DG: distributed Generation, Distributed power Generation;
4. BESS: a Battery Energy Storage System.
FIG. 1 shows a block diagram of a computing device 100, according to one embodiment of the invention. The computing device may be, for example, a server (e.g., a Web server, an application server, etc.), a personal computer such as a desktop computer and a notebook computer, a portable mobile device such as a mobile phone, a tablet computer, a smart wearable device, etc., but is not limited thereto. As shown in FIG. 1, in a basic configuration 102, a computing device 100 typically includes a system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more programs 122, and program data 124. In some implementations, the program 122 can be arranged to execute instructions on an operating system by one or more processors 104 using program data 124.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
In the computing device 100 according to the present invention, the application 122 includes a power distribution network generalized power transformation reliability capacity evaluation device 126 based on the uncertainty theory, and the device 126 includes a plurality of program instructions. The device 126 may instruct the processor 104 to execute the method 200 for evaluating the substation reliability capacity of the generalized power source of the power distribution network based on the uncertainty theory, so as to determine the substation reliability capacity of the generalized power source in the power distribution network and provide a reference for subsequent site selection and volume determination of the substation.
Fig. 2 shows a flowchart of a method 200 for evaluating transformation reliability capacity of a generalized power source of a power distribution network based on an uncertain theory according to an embodiment of the present invention. The method 200 is suitable for execution in a computing device, such as the computing device 100 described above. As shown in fig. 2, the method 200 begins at step S210.
In step S210, the maximum safety load Q of the power distribution grid system is determined0。
According to an embodiment, the maximum safety load of the system under the wiring structure is obtained through linear optimal programming according to the system high-voltage distribution network frame structure and the inter-substation contact constraint. And automatically traversing through a computer, establishing a fault mode influence analysis table library of all non-power supply elements and determining a transfer mode.
Subsequently, in step S220, an initial reliability level H of the power distribution grid system when the generalized power source is not configured is determined0。
According to one embodiment, the reliability index is an expected annual energy shortage, which is an expected number of load demand energy reductions of the system caused by power generation capacity shortage or power grid constraint in one year. Accordingly, the initial reliability level H0Reliability level H for annual Expected starved (EENS) supply of a system when a generalized power supply is Not deployedGThe system is expected to lack power supply in the year after the generalized power supply is configured. Certainly, the expected power shortage amount is only one Of the reliability indexes, in other embodiments, a person skilled in the art may also select other reliability indexes, such as a power shortage Probability (Loss Of Load Probability), a power shortage Frequency (LOLF), a power shortage Duration (Load Duration, LOLD), and the like.
The generalized power supply is a distributed power supply system, and the distributed power supply system comprises a wind power generation system and a photovoltaic power generation system. The transformation reliable capacity of the generalized power supply is the equivalent transformation capacity of the generalized power supply connected to the power distribution network according to the equal reliability principle when the power distribution network system fails.
Subsequently, in step S230, the reliability level H of the distribution network system in which the generalized power source is arranged is calculatedG. According to an embodiment, the reliability level HGIt can be calculated according to the following steps S232, S234:
step S232: and establishing a generalized power output model. And respectively setting a distributed power model (wind power and photovoltaic), an energy storage system model and a controllable load model by using an independent control strategy. FIG. 4 is a partial schematic view of a substation within a power supply bay showing the location of a GPS within the substation.
1. Distributed power supply modeling
1) Wind power generation system modeling
The output model of the wind power generation system comprises the following steps:
wherein, FwIs the output of the wind turbine generatorv，rl，rcAnd FlRespectively the cut-in wind speed, the rated wind speed, the cut-out wind speed and the rated power of the wind turbine, wherein f (r) is the wind speed at rvAnd rlAnd in the meantime, the wind speed of the wind turbine generator is in a functional relationship with the wind power.
2) Photovoltaic power generation system modeling
The relationship between the active power output of the photovoltaic power generation system and the solar radiation intensity is as follows:
wherein, FblFor the active power of the photovoltaic power generation system, T is the effective area of the photovoltaic cell panel, P (w) is the light intensity received by the cell panel at the moment w, and pcThe conversion efficiency of the solar power panel; l iskIs a threshold constant.
2. Energy storage system modeling
The energy storage charging and discharging power is as follows:
wherein,andrespectively representing the charging power and the discharging power of the energy storage battery at the moment w, FexIs the difference between the load demand and the distributed power output, defined as the net exchange power, pc、pdRespectively, the charge-discharge efficiency, T, of the energy storage systemnomTo rated capacity, gammacTo the charge conversion rate, gammadIs the discharge conversion rate.
Different from the normal operation state of the system, the net exchange power Fex in the outage region during the fault needs to consider the transfer load, as shown in the formula (4):
wherein, Fex.w、Fload.w，Floss.w，FDG.i.wNet exchange power, total load power, network loss power and internet power of the ith group of distributed power sources in the power outage area at the moment w respectively; fT.wThe load amount which can be supplied for the power failure area; zDGThe total number of distributed power sources in the blackout area.
And the charging and discharging power of the energy storage system during the fault is jointly determined by the formula (3) and the formula (4).
The charge and discharge power and the charge state of the energy storage system meet the following constraint of the formula (5):
wherein, Toc.minTo a minimum charge capacity, Toc.maxTo maximum charge capacity, Toc.wTo charge capacity at time w, Toc.w-1The charge capacity at time w-1, Δ T is the capacity difference.
3. Controllable load modeling
The load under the same transformer belongs to one subarea, and when a fault occurs and power failure occurs, the controllable load in the same power supply subarea has direct control capability, so that the load can be timely reduced. Preferentially ensuring the DG power supply, secondly considering the size of the load which can be supplied, when the two measures still cannot meet the power consumption of all loads in a fault area, the energy storage executes a fault operation strategy, and the total controllable load which needs to be reduced by the computing system is as follows (6):
wherein, FIL.wFor a controlled total load to be reduced in the system, ToutThe power failure time. When the total controllable load of the area is more than FIL.wIn time, no load power failure is caused; on the contrary, when the total controllable load of the area is less than FIL.wIn time, namely, after all controllable loads implement direct load control to reduce the loads, partial loads still lose power, and the system power failure loads are as follows:
wherein N isDTotal number of controllable loads in blackout area, FD.i.wThe total amount of load that can be reduced for the time w controllable load i. And (3) setting the time-varying rule of each load point in the system to be the same as the total load, and determining by a time-varying load model:
N(w)＝Ny×Fwk×Fd×Fh(w) (8)
wherein N (w) is the total annual load, NyIs annual peak load, FwkIs the average peak charge, F, at 52 weeksdIs the daily average peak load in percent of the weekly peak load, Fh(w) is the day of operationThe average peak load at 24 hours is the percentage of the daily peak load.
Step S234: according to the generalized power output model, a sequential Monte Carlo simulation method is adopted to calculate the reliability of the high-voltage distribution network in consideration of the power supply mode so as to determine the reliability level HG。
The power supply mode of the high-voltage distribution network comprises the following steps: direct connection, unilateral power T-type connection, dual power pi-type connection, and electrical connection diagrams are respectively shown in fig. 5-8. When the high-voltage distribution network fails to cause load power loss, the power loss load is firstly transferred by the high-voltage distribution network; when the transfer supply line is in fault or the transfer supply load is out of limit, the transfer supply is performed through the communication between the intermediate-voltage side stations. The high voltage supply and the medium voltage supply together determine the reliability level of the system.
The reliability evaluation method is based on a sequential Monte Carlo simulation method, a power distribution network reliability calculation time sequence model is established on the basis of considering the four different power supply modes, and the reliability evaluation indexes are the annual expected power supply shortage amount:
wherein R represents the total number of simulated years; ersRepresenting the amount of power supply lacking in the primary fault condition; q and f are the number of power element fault state simulations and the number of non-power element fault state simulations, respectively.
Level of reliability HGThe specific calculation steps of (a) are as follows (as shown in fig. 3):
1. a sequence of time-varying sequences is generated. And generating a time sequence change sequence of the wind power and the photovoltaic within the range of the total simulation duration W according to the wind power and photovoltaic output model. And generating a time sequence change sequence of the charge state of the energy storage system according to the operation strategy under the normal operation state of the energy storage, and setting the load sequence to be constant.
2. Sequential simulation of both power and non-power elements in the system is performed simultaneously, regardless of double and above faults, with (a) selected, f plus 1, for fault evaluation at step ③ when a non-power element fault condition occurs and (b) selected, q plus 1, for fault evaluation at step ③ when a power element fault condition occurs.
3. And (4) fault evaluation. Depending on the faulty component, there are two ways in which:
(a) according to the fault condition of the non-power supply element, inquiring a fault mode analysis table library to determine a load loss area, further determining the output condition of the DG in the load loss area during the system fault period according to the DG output sequence obtained in the step ①, determining the size of the load needing to be transferred and a transfer path, when the transfer cannot be completed, implementing a fault operation strategy on the energy storage in the load loss area to supply part of the load, and when the electricity consumption of all the loads cannot be met, determining the controllable load quantity needing to be reduced, and after all the measures are implemented, the load which still cannot be supplied is the electricity loss load of the fault, and recording the power loss quantity E of the fault staters。
(b) And (4) determining a fault area according to the fault condition of the power supply element, removing the fault power supply, and determining the output condition of the residual DG in the fault process of the fault area according to the DG output sequence obtained in the step ①.
4. And (4) index evaluation. And unifying the calculation indexes of the fault states of the power supply element and the non-power supply element, as shown in formula (9), and obtaining the system reliability index after the generalized power supply is connected.
Subsequently, in step S240, the reliability level H is judgedGWhether or not to match the initial reliability level H0Are equal. If not, executing step S250, continuously adjusting the load of the power distribution network system by using a string cutting method, and re-determining the reliability level H of the power distribution network system according to the adjusted load QG。
If the reliability level HGEqual to the initial reliability level H0Then step S260 is executed to determine the load Q of the current distribution network system and the maximum safe load Q0And the difference delta Q is used as the transformation reliability capacity of the generalized power supply, and the transformation reliability capacity of the generalized power supply under different wiring modes is evaluated through the calculation result of the method.
An example analysis of the invention is given below:
selecting a planned floor area of 8.41km2The load of the development area is divided into 4 cells for load prediction, and the annual load peak value of each cell is 25.0 MW. Assuming that the capacity of the 110kV/10kV transformer substation in the region is 2 × 40MVA, the load is equally distributed in four cells. The intra-site contact constraint is 40MVA and the inter-site contact constraint is 10 MVA. If the spare power automatic switching is adopted for the in-station transfer, the transfer time can be ignored, and the inter-station transfer time is set to be 1 hour. The four examples correspond to four typical power wiring modes: direct connection, unilateral power T-shaped connection, dual power T-shaped connection and dual power Pi-shaped connection. In each example, four total substations of 220kV and 110kV were wired according to the geographical location of fig. 9 to ensure that the line lengths of each example were substantially equal.
The rated installed capacity of photovoltaic power generation in the region is planned to be 100MW, the number of the wind driven generators is 358, and the rated capacity is 335 kW. The cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind driven generator are respectively as follows: 2, 12 and 25m/s, and the average wind speed is 13.75 km/h. The non-power supply elements are classified into four types, 110kV overhead line, 10kV breaker, 110kV breaker, 110/10kV transformer, and failure rate thereof is set to 0.01/time (a.km)-10.005 times (a, platform)-10.02 times (a table)-10.005 times (a, platform)-1(ii) a The repair time after the failure is 12, 4, 7.5 and 48 hours respectively.
Setting the reliability index R of the second example in the calculation of each example when the second example does not contain GPS and the load is 25MW0And calculating the credible capacity change condition corresponding to each instance under different GPS permeability as a reference. Meanwhile, the step length of the iterative calculation of the permeability of each power supply is 5 percent for wind power, photovoltaic and energy storage. The controllable load is a constant value of 10%, and the total permeability is not counted. Trusted capacity of four examples at different GPS permeabilitiesFor example, in fig. 10, when the GPS penetration rates in the graph are 0%, 30%, 60%, 90%, 120%, 150%, 180%, 210%, 240%, and 270%, respectively, at intervals of 30%, the simulation result is compared with the GPS penetration rates in the graph to obtain data comparisons of the trusted capacities of the four power supply modes, which are shown in the following table. Therefore, the influence of different power supply modes on the transformation reliability capacity of the generalized power supply in the power distribution network needs to be considered.
TABLE 1. four power supply modes with different generalized power supply permeabilities, variable power reliability capacity data (MW)
The method for evaluating the transformation credible capacity considering different power supply modes can be used for evaluating the value of replacing the transformation capacity by the generalized power supply in the power distribution network and provides reference for the transformation capacity planning of the generalized power supply connected to the power distribution network.
A9: the method of A6, wherein the photovoltaic power generation system output model includes:
wherein, FblFor the active power of the photovoltaic power generation system, T is the effective area of the photovoltaic cell panel, P (w) is the light intensity received by the cell panel at the moment w, and pcThe conversion efficiency of the solar power panel; l iskIs a threshold constant.
A10: the method of A6, wherein the energy storage system model comprises:
wherein,andrespectively representing the charging power and the discharging power of the energy storage battery at the moment w, FexIs the difference between the load demand and the distributed power output, defined as the net exchange power, pc、pdRespectively, the charge-discharge efficiency, T, of the energy storage systemnomTo rated capacity, gammacFor charge conversion efficiency, gammadTo the discharge conversion efficiency;
net exchange power F in fault outage area when said distribution grid system failsexComprises the following steps:
wherein, Fex.w、Fload.w，Floss.w，FDG.i.wNet exchange power, total load power, network loss power and internet power of the ith group of distributed power sources in the power outage area at the moment w respectively; fT.wThe load amount which can be supplied for the power failure area; zDGThe total number of distributed power sources in the blackout area.
A11: the method A10, wherein the charging and discharging power and the state of charge of the energy storage system satisfy the following constraints:
Toc.min≤Toc.w≤Toc.max
wherein, Toc.minIs minimum charging capacity, Toc.maxIs maximum charge capacity, Toc.wCharging capacity at time w, Toc.w-1The charge capacity at the time w-1 and Δ T are the capacity difference.
A12: the method of a10, wherein the controllable load model comprises:
wherein, FIL.wFor a controlled total load to be reduced in the system, ToutThe power failure time;
system power failure load Fout.wComprises the following steps:
wherein N isDTotal number of controllable loads in blackout area, FD.i.wThe total load amount which can be reduced for the w moment controllable load i;
N(w)＝Ny×Fwk×Fd×Fh(w)
wherein N (w) is the total annual load, NyIs annual peak load, FwkIs the average peak charge, F, at 52 weeksdIs the daily average peak load in percent of the weekly peak load, Fh(w) is the average peak load to peak load per day as a percentage of the average peak load at 24 hours of the day of operation.
A13: a5-12, wherein the calculation of high voltage distribution network reliability taking into account power supply patterns is performed using sequential Monte Carlo simulations to determine the reliability level HGComprises the following steps:
setting a simulation total duration W, and respectively generating time sequence change sequences of the wind power generation system output, the photovoltaic power generation system output and the energy storage system charge state within the range of the total duration W according to the wind power generation system output model, the photovoltaic power generation system output model and the energy storage system model;
setting the load sequence to a constant value;
sequentially simulating power supply elements and non-power supply elements in the power distribution network system at the same time, not considering double or more faults, and determining the power supply shortage E of each faultrs；
According to the amount of power supply lost per fault ErsTo determine a reliability level H of an electric distribution network systemG。
A14: the method according to A13, wherein the electric power supply E is cut offrsThe method comprises the following steps:
when the power supply element fails, determining a failure region according to the failure condition of the power supply element, removing a failure power supply, and determining the output condition of the residual distributed power supplies in the failure region during the failure according to the output sequence of the distributed power supplies;
when a non-power element fails, inquiring a failure mode analysis table library according to the failure condition of the non-power element, determining a loss load area, and determining the output condition of a distributed power supply in the loss load area during the system failure according to the output sequence of the distributed power supply;
determining the size of the load needing to be transferred and a transfer path; when the transfer cannot be completed, the stored energy in the load loss area implements a fault operation strategy to supply part of the load; when the power utilization of all loads cannot be met, determining the controllable load quantity of the power utilization to be reduced; after all the measures are implemented, the load which still can not realize power supply is the power-off load of the fault, and the power supply shortage E of the fault state is recordedrs。
A15: a13 or 14, wherein the reliability level H of the distribution network systemGCalculated according to the following formula:
wherein R represents the total number of simulated years; ersRepresenting the amount of power supply lacking in the primary fault condition; q and f are the number of power element fault state simulations and the number of non-power element fault state simulations, respectively.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard drives, U.S. disks, floppy disks, CD-ROMs, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to execute the uncertainty theory-based power transformation reliability capacity evaluation method of the generalized power source of the power distribution network according to the instructions in the program codes stored in the memory.
By way of example, and not limitation, readable media may comprise readable storage media and communication media. Readable storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of readable media.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with examples of this invention. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense with respect to the scope of the invention, as defined in the appended claims.
Claims (10)
1. An uncertain theory-based method for evaluating transformation reliability capacity of generalized power sources of a power distribution network, executed in computing equipment, comprises the following steps:
determining maximum safe load Q of power distribution network system0；
Determining an initial reliability level H of the power distribution network system when the generalized power source is not configured0；
Calculating reliability level H of power distribution network system configured with generalized power supplyG；
When the reliability level is not equal to theAdjusting the load of the distribution network system at the initial reliability level, and re-determining the reliability level H of the distribution network system according to the adjusted load QG；
When the reliability level HGEqual to said initial reliability level H0Then, the load Q of the current power distribution network system and the maximum safe load Q are compared0The difference delta Q of the difference is used as the power transformation reliability capacity of the generalized power supply.
2. The method of claim 1, wherein the generalized power source is a distributed power system comprising a wind power generation system and a photovoltaic power generation system.
3. The method as claimed in claim 1 or 2, wherein the transformation reliability capacity of the generalized power source is a transformation capacity equivalent to that of the generalized power source connected to the distribution network according to the equal reliability principle when the distribution network system fails.
4. The method of any one of claims 1-3, wherein the maximum safety load Q0The method comprises the following steps:
determining the maximum safe load Q of the power distribution network system by adopting linear optimal programming according to the grid structure of the power distribution network system and the contact constraint among all transformer substation stations in the power distribution network system0。
5. The method according to any one of claims 1-4, wherein the calculating calculates a reliability level H of the distribution network system in which the generalized power source is deployedGComprises the following steps:
establishing a generalized power output model;
according to the generalized power output model, a sequential Monte Carlo simulation method is adopted to calculate the reliability of the high-voltage distribution network in consideration of the power supply mode so as to determine the reliability level HG。
6. The method of claim 5, wherein the generalized power output model comprises a distributed power model, an energy storage system model, and a controllable load model, the distributed power model comprising a wind power generation system output model and a photovoltaic power generation system output model.
7. The method of claim 5, wherein the power modes include direct wiring, single-sided power T-wiring, dual-power pi-wiring.
8. The method of claim 6, wherein the wind power system output model comprises:
wherein, FwIs the output of the wind turbine generatorv，rl，rcAnd FlRespectively the cut-in wind speed, the rated wind speed, the cut-out wind speed and the rated power of the wind turbine, wherein f (r) is the wind speed at rvAnd rlAnd in the meantime, the wind speed of the wind turbine generator is in a functional relationship with the wind power.
9. A computing device, comprising:
at least one processor; and
a memory having stored thereon program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for executing the method for estimating the substation reliability capacity of a generalized power source of an electric power distribution grid based on uncertainty theory according to any of claims 1-8.
10. A readable storage medium storing program instructions which, when read and executed by a computing device, cause the computing device to execute the method for estimating the substation reliability capacity of a generalized power source of a power distribution network based on uncertainty theory according to any of claims 1-8.
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