CN110460090B - Water-wind-light micro-grid power supply capacity configuration method - Google Patents
Water-wind-light micro-grid power supply capacity configuration method Download PDFInfo
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Abstract
The invention discloses a method for configuring the power capacity of a water-wind-light micro-grid, which comprises the following steps: constructing a data set; acquiring data of the flow of the hydropower station in and out of the microgrid, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power station from a related database, and processing, calculating and analyzing the data; constructing a data set of the flow of the hydropower station entering and leaving the reservoir, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power station through calculation and analysis; the method can calculate the power supply capacity configuration of the water, wind and light microgrid, reflects the randomness of the warehousing flow of the hydropower station, the wind speed of the wind power station and the change of the sunlight intensity of the photovoltaic power station, provides theoretical guidance for the power supply capacity configuration, the power generation output prediction and the operation scheduling of the water, wind and light microgrid, and provides necessary technical support for distributed new energy power generation and intelligent power grid scheduling operation.
Description
Technical Field
The invention relates to the technical field of power systems and automation thereof, in particular to a method for configuring power supply capacity of a water-wind-light micro-grid.
Background
The micro-grid is a grid form in which distributed sources (small hydropower, small wind power, photovoltaic power generation) -loads (water, electricity, gas, cold and heat loads) are integrated in a certain way. The micro-grid is connected with a main grid in 380V, 10kV, 35kV and other voltage levels, is in grid-connected operation with the main grid under the normal operation condition, absorbs power from the main grid during heavy load, and can inject power into the main grid during light load; the micro-grid can be operated in isolated network under the condition of local fault of the main grid or under the condition of fault of an adjacent micro-grid, and on the premise of ensuring the quality of electric energy, the electric quantity is provided for a load by a part of distributed power supplies in the micro-grid, so that the normal power supply state of the micro-grid without fault is realized, the power failure time is reduced, and the power supply reliability is improved.
The aim of the construction and operation of the micro-grid is to sustainably and efficiently utilize/consume part of distributed power supply electric quantity in the micro-grid and minimize the electric quantity exchanged with a main grid.
A distributed small hydropower station-based micro-grid is a micro-grid which takes small hydropower stations as a main form for power supply. In the water, wind, light and micro-grid, most hydropower stations are of a runoff type, dams generally have no water storage function, reservoirs have no water storage and water transfer capacity, the utilization of water energy of small hydropower stations completely depends on the inflow of the reservoirs, and the power generation state and the output scale of small hydropower generating units also completely depend on the inflow of the reservoirs. Under the condition, in order to realize the efficient utilization of the water energy to generate electricity in the small hydropower station, the small hydropower station needs to make sure how much water is used for generating more or less electricity. The water inflow amount of the small hydropower station reservoir is random, the water inflow amount is completely different in different hydrological cycles, the water inflow amount is large in a rich water period, and the water inflow amount is small in a dry water period. Thus, small hydropower farm basin river flows tend to be represented in tabular form as minimum flow, maximum flow, average flow, annual average flow, calculated average flow, weighted average flow, mathematical average flow, and the like. By adopting a meter form with different flow rates, small hydropower stations can obtain different installed capacity levels. The generated power and generated energy of the small hydropower station are different in different hydrologic periods at different installed capacity levels, and the optimal generated power and generated energy result in different hydropower station water energy utilization rates, generating equipment utilization rates and generating equipment annual maximum utilization hours.
The small hydropower station-wind micro-grid is a micro-grid which integrates two distributed power sources of small hydropower stations and small wind power stations, has a certain capacity load and connects the distributed power sources and the load in a certain mode. In the small hydropower station-wind microgrid, not only the water inflow amount, the reservoir flow, the power generation flow and the like of a reservoir of a small hydropower station have uncertainty and randomness, but also the wind speed of a small wind power plant has uncertainty and randomness. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine generator set does not output power; when the wind speed is higher than the cut-in wind speed and lower than the rated wind speed, the output power of the wind turbine generator is lower than the rated power; and when the wind speed is higher than the rated wind speed and lower than the cut-out wind speed, the wind turbine generator outputs rated power. The wind speed is completely different in different seasons of the year, in different time periods of the day, and has randomness, volatility and intermittence. Thus, the small wind farm wind speed is also often represented in tabular form as a minimum wind speed, a maximum wind speed, an average wind speed, a multi-year average wind speed, a calculated average wind speed, a weighted average wind speed, a mathematical average wind speed, and so forth. By adopting a meter form with different wind speeds, the small wind power plant can obtain different installed capacity levels. The wind power generation system has the advantages that the generated power and the generated energy of small wind power plants are different in different seasons due to different installed capacity levels, and the optimal wind energy utilization rate of the small wind power plants, the utilization rate of power generation equipment and the maximum annual utilization hours of the power generation equipment are different.
The small hydropower station-wind-light micro-grid is a micro-grid which integrates three distributed power sources of small hydropower stations, small wind power and photovoltaic power generation and has a certain capacity load and connects the distributed power sources and the load in a certain mode. In the small hydropower station-wind-light microgrid, not only the water inflow amount, the reservoir flow, the power generation flow and the like of a reservoir of a small hydropower station have uncertainty and randomness, but also the wind speed of a small wind power station has uncertainty and randomness, and the sunlight intensity also has uncertainty and randomness. The greater the sunlight intensity is, the greater the output power of the photovoltaic power generation system is. The sunshine intensity is completely different in different seasons of the year, the sunshine intensity is completely different in different time periods of the day, and the sunshine intensity is random, fluctuating and intermittent. Therefore, the solar intensity of a photovoltaic power plant is often expressed in tabular form as a minimum solar intensity, a maximum solar intensity, an average solar intensity, an annual average solar intensity, a calculated average solar intensity, a weighted average solar intensity, a mathematical average solar intensity, and the like. By adopting a meter form with different sunshine intensities, the photovoltaic power generation station can obtain different installed capacity levels. According to different installed capacity levels, the generated power and the generated energy of the photovoltaic power station are different in different seasons, and the optimal wind energy utilization rate, the generating equipment utilization rate and the annual maximum utilization hours of the generating equipment of the photovoltaic power station are different.
Different load levels and the capacity scales of the distributed power supply are integrated in the microgrid, so that the structural form and the tidal current characteristics of the microgrid are changed. Due to the fact that various distributed power sources such as small hydropower, small wind power and photovoltaic power generation are connected, voltages of various levels can be adopted due to different connected power source capacity scales. Due to the randomness of electricity utilization, the load power always changes on different space-time scales, and the time-interval performance is obvious; meanwhile, the output of distributed power supplies such as wind power generation and photovoltaic power generation is intermittent, random and time-interval, and the output of small hydroelectric generating sets is seasonal. Therefore, the balance relation between the load power and the power supply power of the micro-grid is difficult to maintain, when the load power is greater than the power supply power, the micro-grid needs to obtain supplementary power from the main power grid, and when the load power is less than the power supply power, the residual power of the micro-grid needs to be injected into the main power grid, so that a random bidirectional power flow characteristic is formed. The random bidirectional power flow characteristic can cause the voltage of the node in the local area in the microgrid to be higher when the distributed power supply is large in output and light in load and cause the voltage of the node in the local area in the microgrid to be lower when the distributed power supply is small in output and heavy in load. Therefore, the limitation conditions and requirements of the node voltage inside the microgrid have influence and restriction on the capacity configuration, the operation mode and the voltage control strategy of the distributed power supply in the microgrid, and the limitation conditions and requirements of the node voltage inside the microgrid need to be considered. When a microgrid is connected to nodes of power distribution networks with different voltage grades, the node voltage of the power distribution network is changed to be higher or lower due to different absorption or injection power of the microgrid from or into the power distribution network, and the limit conditions and requirements of the node voltage of the power distribution network need to be considered in the capacity configuration, operation mode and voltage control strategy of a distributed power supply in the microgrid.
A microgrid distributed power system is a system with both complex and interactive stochastic and fuzzy uncertainty events or parameters. Under the influence of various uncertain random and fuzzy events or parameters, the power generation power and the power generation amount of the micro-grid distributed power supply become more random and fuzzy, and the capacity configuration of the micro-grid distributed power supply is greatly influenced by the characteristics. In the past, the generated power and the generated energy of a micro-grid distributed power system usually adopt a deterministic calculation method, and some of the generated power and the generated energy also adopt an uncertain calculation method of probability analysis. The deterministic calculation method is generally used for calculating the generated power, the generated energy and the installed capacity of the micro-grid distributed power supply system under the condition that the water inflow and the flow of a small hydropower station, the sunlight intensity in an area and the wind speed are all determined, the influences of factors such as the voltage regulation requirements of the micro-grid and a power distribution network and a flexible control mode are not considered, the calculation result is unique and deterministic, and the actual conditions of the generated power, the generated energy and the installed capacity of the micro-grid distributed power supply system cannot be reflected. The calculation method of probability analysis is generally to calculate the generated power, the generated energy and the installed capacity of the microgrid distributed power supply system under the condition that only single factors such as the water inflow and the flow of a small hydropower station, the sunlight intensity in an area, the wind speed and the like are assumed as uncertainty factors, and the calculation result is a probability value with a certain confidence level. In fact, the generated power, the generated energy and the installed capacity of the microgrid distributed power supply system are influenced by various uncertainty factors. Moreover, these influencing factors are typically random uncertainties or fuzzy uncertainties, or they are random and fuzzy uncertainties, often present as random and fuzzy uncertainty events or quantities. Therefore, the uncertainty and randomness of the influence factors are not considered comprehensively in the prior art of calculating the generated power, the generated energy and the installed capacity of the microgrid distributed power supply system, and the applicability, the practicability and the applicability of the calculation method are difficult to meet.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for configuring the power supply capacity of a water-wind-light microgrid, which aims at a microgrid system consisting of small hydropower stations, small wind power and photovoltaic power generation, considers the uncertainty and the randomness of the warehousing flow of the small hydropower stations, the wind speed of a wind power station and the sunlight intensity of a photovoltaic power station, and also considers the uncertainty and the randomness of sunlight time, sunlight shadow, sunlight deflection angle and the like.
The purpose of the invention is realized by the following technical scheme:
a method for configuring the power capacity of a water-wind-light micro-grid comprises the following steps:
s1, constructing a data set; acquiring data of the flow of the hydropower station in and out of the microgrid, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power station from a related database, and processing, calculating and analyzing the data; constructing a data set of the flow of the hydropower station entering and leaving the reservoir, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power station through calculation and analysis;
s2, calculating the probability of the warehousing flow of the small hydropower station; calculating and determining the warehousing flow of the small hydropower stations in the microgrid within 1, 2,mean value mu of the change in the distribution of states QIt Sum variance σ QIt (ii) a Mean value mu of total warehousing flow of small hydropower stations in T and changing according to normal distribution rule QI Sum variance σ QI ；
S3, calculating the probability of the wind speed of the wind power plant; calculating and determining the mean value mu of the wind speed of the wind power plant in the microgrid changing according to the normal distribution rule within 1, 2, once and T time by using a probability analysis method and utilizing a wind speed data set Wt Sum variance σ Wt (ii) a Mean value mu of wind speed of wind power plant changing according to normal distribution rule in T W Sum variance σ W ；
S4, calculating the probability of the sunlight intensity of the photovoltaic power station; calculating and determining a mean value mu of the sunlight intensity of the photovoltaic power station in the microgrid within 1, 2, once and T time according to a normal distribution rule by using a probability analysis method and utilizing a sunlight intensity data set of the photovoltaic power station PVt Sum variance σ PVt (ii) a Mean value mu of sunlight intensity of photovoltaic power station changing according to normal distribution rule in T PV Sum variance σ PV ；
S5, determining the allowable maximum distributed power source capacity P of the microgrid according to the section area of the connecting wire of the microgrid and the power distribution network and the operation constraint conditions MGF ；
S6, determining the allowed maximum distributed power source capacity P of the micro-grid according to the connection relation between the micro-grid and the power distribution network, the power flow distribution characteristics and the voltage constraint conditions MGV ；
S7, calculating the available capacity P of the small hydropower station in the micro-grid AH ；
S8, calculating the installed capacity P of the small wind power plant in the microgrid AW ；
S9, calculating the installed capacity P of the photovoltaic power station in the microgrid APV ；
S10, calculating and determining installed capacity P of the small hydropower stations in the microgrid according to the allowable numerical relationship among the maximum distributed power capacity of the microgrid, the installed capacity of the small hydropower stations, the installed capacity of the small wind power plants and the installed capacity of the photovoltaic power stations SH Installed capacity P of small wind power plant SW Installed capacity P of photovoltaic power station SPV ：
Wherein k is AH 、k AW 、k APV Is a weight coefficient and satisfies k AH +k AW +k APV ＝1。
Preferably, the installed capacity P of the small hydropower station in the microgrid in the S7 AH And (3) calculating, comprising the following calculating steps:
s7.1, calculating the probability that the reservoir warehousing flow of the small hydropower stations in the microgrid changes according to a normal distribution rule within 1, 2, 1.
Where erf (y) is an error function expressed as:
s7.2, calculating the average value of warehousing flow of the small hydropower stations in the micro-grid:
in the formula, Q QIi 、p QIi Respectively the ith warehousing flow and the probability of the variation thereof according to the normal distribution rule, N QI The number of considered warehousing traffic;
s7.3, calculating the capacity of the small hydropower station small hydroelectric generator assembling machine:
P AH ＝0.0098kHQ I
wherein k is the generating efficiency of the small hydropower station small hydropower unit, and H is the small hydropower station water head.
Preferably, the installed capacity P of the small wind farm in the microgrid in S8 AW And (3) calculating, comprising the following calculating steps:
s8.1, calculating 1, 2, 1 W The probability that the wind speed of the small wind power station in the directional microgrid changes according to a normal distribution rule is as follows:
in the formula k W For coefficients, erf (y) is an error function expressed as:
s8.2, calculating the average value of the wind speeds of the small wind power plants in the microgrid:
in the formula, v Wi 、p Wi Respectively the ith direction wind speed and the probability of the change according to the normal distribution rule, N W The number of wind speeds and wind directions typical to the wind power plant;
s8.3, calculating the capacity of the small wind power generator assembling machine of the small wind power plant:
in the formula, v ci 、v co Respectively cut-in and cut-out wind speed k of the wind turbine W0 、k W1 、k W2 、k W3 Respectively, the coefficients relating to the wind speed.
Preferably, the installed capacity P of the photovoltaic power station in the microgrid in the S9 APV And (3) calculating, comprising the following calculating steps:
s9.1, calculating the probability that the sunlight intensity of a photovoltaic power station in the microgrid changes according to a normal distribution rule in the ith time interval (i =1, 2,.. And T) in a day by adopting a probability analysis method:
in the formula k PV For coefficients, erf (y) is an error function expressed as:
s9.2, calculating the average value of the sunlight intensity of the photovoltaic power station in the microgrid:
in the formula, E PVi 、p PVi Respectively indicating the solar intensity of the photovoltaic power station and the probability of the solar intensity changing according to a normal distribution rule in the ith time period;
s9.3, calculating the installed capacity of the photovoltaic power station:
wherein k is PV0 、k PV1 、k PV2 Respectively, coefficients relating to the intensity of sunlight.
Compared with the prior art, the invention has the following beneficial effects:
the method can calculate the power supply capacity configuration of the water, wind and light microgrid, reflects the randomness of the warehousing flow of the hydropower station, the wind speed of the wind power station and the change of the sunlight intensity of the photovoltaic power station, provides theoretical guidance for the power supply capacity configuration, the power generation output prediction and the operation scheduling of the water, wind and light microgrid, and provides necessary technical support for distributed new energy power generation and intelligent power grid scheduling operation.
Drawings
FIG. 1 is a block flow diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
The invention discloses a basic principle of water-wind-light microgrid power supply capacity configuration based on expected calculation average flow, which is characterized in that a probability analysis method is adopted for calculating the probability and the average value of the small hydropower station warehouse-in flow, the wind speed of a wind power plant and the sunlight intensity of a photovoltaic power plant according to a normal distribution rule aiming at the uncertainty of the small hydropower station warehouse-in flow, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power plant, calculating the initial values of the installed capacities of the small hydropower station, the wind power plant and the photovoltaic power plant, reducing the size of the installed capacity according to the proportion of the initial values of the installed capacities of the small hydropower station, the wind power plant and the photovoltaic power plant according to the limitation of the voltage change of nodes in a microgrid and connected with a power distribution network, and achieving the maximization of the utilization rate of new energy, the utilization rate of power generation equipment and the annual utilization hours of the power generation equipment and increasing the generated energy in operation periods of days, months, years and the like.
The installed capacity of the small hydropower station is determined by the warehousing flow of the small hydropower station, the installed capacity of the wind power station is determined by the wind speed of the wind power station, and the installed capacity of the photovoltaic power station is determined by the sunlight intensity. The system is determined by the utilization rate of new energy, the generated energy, the utilization rate of power generation equipment and the annual utilization hours, is determined by a micro-grid, the transmission capacity of a power distribution network, the voltage regulation requirement, the network loss control and the like, and has great influence on the installed capacity of a photovoltaic power generation system in different time and space due to the sunlight intensity, the sunlight time, the sunlight shadow, the sunlight deflection angle, the uncertainty and the randomness thereof. The time-space characteristics of water, wind and light are independent from each other, but the power capacity configurations of the micro-grid are mutually influenced and restricted when the micro-grid is integrated in the micro-grid, and the micro-grid and a power distribution network have more complicated water and wind power capacity configurations due to transmission capacity, voltage regulation requirements, network loss control and the like.
Step 1 in FIG. 1 describes the process and method of data set construction. And acquiring data of the flow of the hydropower station in and out of the microgrid, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power station from the related database, and processing, calculating and analyzing the data. And (4) constructing a data set of the flow of the hydropower station entering and leaving the reservoir, the wind speed of the wind power plant and the sunlight intensity of the photovoltaic power station through calculation and analysis.
Step 2 in fig. 1 describes the process and method of small hydropower station warehousing flow probability calculation. Calculating and determining the mean value mu of the warehouse-in flow of the small hydropower station in the microgrid within 1, 2, once and T time according to the normal distribution rule by using a probability analysis method and utilizing a warehouse-in flow data set QIt Sum variance σ QIt . Mean value mu of total warehousing flow of small hydropower stations in T and changing according to normal distribution rule QI Sum variance σ QI 。
Step 3 in FIG. 1 describes the process and method of wind farm wind speed probability calculation. Calculating and determining the mean value mu of the wind speed of the wind power plant in the microgrid changing according to the normal distribution rule within 1, 2, once and T time by using a probability analysis method and utilizing a wind speed data set Wt Sum variance σ Wt . Mean value mu of wind speed of wind power plant changing according to normal distribution rule in T W Sum variance σ W 。
Step 4 in fig. 1 describes the process and method of photovoltaic power plant probability calculation. Calculating and determining the mean value mu of the sunlight intensity of the photovoltaic power station in the microgrid within 1, 2, 1 and T time according to the normal distribution rule by using the sunlight intensity data set of the photovoltaic power station by adopting a probability analysis method PVt Sum variance σ PVt . Mean value mu of sunlight intensity of photovoltaic power station changing according to normal distribution rule in T PV Sum variance σ PV 。
Step 5 in fig. 1 describes the process and method of microgrid maximum distributed power source capacity determination. Determining allowable microgrid maximum according to section area of connecting wire of microgrid and distribution network and operation constraint conditionsLarge distributed power capacity P MGF 。
Step 6 in fig. 1 describes the process and method of allowed maximum distributed power capacity determination for the microgrid. Determining the allowable maximum distributed power capacity P of the microgrid according to the connection relation between the microgrid and the power distribution network, the power flow distribution characteristics and the voltage constraint conditions MGV 。
Step 7 in fig. 1 describes the process and method of calculating the installed capacity of a small hydropower station in a microgrid. The method comprises the following steps:
(1) Calculating the probability that the reservoir warehousing flow of the small hydropower stations in the microgrid changes according to a normal distribution rule within 1, 2, 1.
Where erf (y) is an error function expressed as:
(2) Calculating the average value of warehousing flow of small hydropower stations in the micro-grid:
in the formula, Q QIi 、p QIi Respectively the ith warehousing flow and the probability that the warehousing flow changes according to the normal distribution rule, N QI Is the amount of binned traffic considered.
3) Calculating the capacity of the small hydropower station small hydroelectric generator assembling machine:
P AH ＝0.0098kHQ I
and k is the generating efficiency of the small hydropower station small hydropower unit, and H is the water head of the small hydropower station.
Step 8 in fig. 1 describes a process and a method for calculating the installed capacity of the small wind power station in the microgrid. The method comprises the following steps:
(1) Calculating 1, 2, N by using a probability analysis method W The probability that the wind speed of the small wind power station in the directional microgrid changes according to a normal distribution rule is as follows:
in the formula k W For coefficients, erf (y) is an error function expressed as:
(2) Calculating the average value of the wind speeds of the small wind power plants in the microgrid:
in the formula, v Wi 、p Wi Respectively the ith direction wind speed and the probability of the change according to the normal distribution rule, N W The number of typical wind speeds and directions of the wind power plant.
(3) Calculating the capacity of the small wind power generator assembling machine of the small wind power plant:
in the formula, v ci 、v co Respectively cut-in wind speed and cut-out wind speed k of the wind turbine W0 、k W1 、k W2 、k W3 Respectively, coefficients related to wind speed.
Step 9 in fig. 1 describes a process and method for calculating the installed capacity of a photovoltaic power plant in a microgrid. The method comprises the following steps:
(1) Calculating the probability that the sunlight intensity of a photovoltaic power station in the microgrid changes according to a normal distribution rule in the ith time period (i =1, 2,.. And T) in a day by adopting a probability analysis method:
in the formula k PV For coefficients, erf (y) is an error function expressed as:
(2) Calculating the average value of the sunlight intensity of a photovoltaic power station in the microgrid:
in the formula, E PVi 、p PVi The solar radiation intensity of the photovoltaic power station in the ith time period and the probability of the solar radiation intensity changing according to the normal distribution rule are respectively shown.
(3) Calculating the installed capacity of the photovoltaic power station:
wherein k is PV0 、k PV1 、k PV2 Respectively, coefficients relating to the intensity of sunlight.
Step 10 in fig. 1 describes the process and method of installed capacity calculation for small hydropower stations, small wind farms and photovoltaic power plants in a microgrid. Calculating and determining installed capacity P of the small hydropower stations in the microgrid according to the allowable numerical relationship among the maximum distributed power capacity of the microgrid, the installed capacity of the small hydropower stations, the installed capacity of the small wind power plants and the installed capacity of the photovoltaic power stations SH Installed capacity P of small wind power plant SW Installed capacity P of photovoltaic power station SPV ：
Wherein k is AH 、k AW 、k APV Is a weight coefficient and satisfies k AH +k AW +k APV ＝1。
The method can calculate the power supply capacity configuration of the water, wind and light microgrid, reflects the randomness of the warehousing flow of the hydropower station, the wind speed of the wind power station and the change of the sunlight intensity of the photovoltaic power station, provides theoretical guidance for the power supply capacity configuration, the power generation output prediction and the operation scheduling of the water, wind and light microgrid, and provides necessary technical support for distributed new energy power generation and intelligent power grid scheduling operation.
The present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and are included in the scope of the present invention.
Claims (1)
1. A method for configuring the power capacity of a water-wind-light micro-grid is characterized by comprising the following steps:
s1, constructing a data set; acquiring data of warehousing flow of small hydropower stations in the microgrid, wind speed of the small wind power stations and sunlight intensity of photovoltaic power stations from a related database, and processing, calculating and analyzing the data; constructing a data set of flow of the small hydropower station entering and leaving the reservoir, wind speed of the small wind power plant and sunlight intensity of the photovoltaic power station through calculation and analysis;
s2, calculating the probability of the warehousing flow of the small hydropower station; calculating and determining the mean value mu of the warehouse-in flow of the small hydropower station in the microgrid within 1, 2, once and T time according to the normal distribution rule by using a probability analysis method and utilizing a warehouse-in flow data set QIt Sum variance σ QIt (ii) a Mean value mu of total warehousing flow of small hydropower stations in T and changing according to normal distribution rule QI Sum variance σ QI ；
S3, calculating the probability of the wind speed of the small wind power plant; calculating and determining the mean value mu of the wind speed of the small wind power plant in the microgrid changing according to the normal distribution rule within 1, 2, once and T time by using a probability analysis method and utilizing a wind speed data set Wt Sum variance σ Wt (ii) a Mean value mu of wind speed of small wind power plant within T and changing according to normal distribution rule W Sum variance σ W ；
S4, calculating the probability of the sunlight intensity of the photovoltaic power station; calculating and determining a mean value mu of the sunlight intensity of the photovoltaic power station in the microgrid within 1, 2, once and T time according to a normal distribution rule by using a probability analysis method and utilizing a sunlight intensity data set of the photovoltaic power station PVt Sum variance σ PVt (ii) a Mean value mu of sunlight intensity of photovoltaic power station changing according to normal distribution rule in T PV Sum variance σ PV ；
S5, determining the allowable maximum distributed power source capacity P of the microgrid according to the section area of the connecting wire of the microgrid and the power distribution network and the operation constraint conditions MGF ；
S6, determining the allowed maximum distributed power source capacity P of the micro-grid according to the connection relation between the micro-grid and the power distribution network, the power flow distribution characteristics and the voltage constraint conditions MGV ；
S7, calculating the available capacity P of the small hydropower station in the micro-grid AH The method comprises the following calculation steps:
s7.1, calculating the probability that the reservoir warehousing flow of the small hydropower stations in the microgrid changes according to a normal distribution rule within 1, 2, 1.
Where erf (y) is an error function expressed as:
s7.2, calculating the average value of warehousing flow of the small hydropower stations in the micro-grid:
in the formula, Q QIi 、p QIi Respectively the ith warehousing flow and the probability of the variation thereof according to the normal distribution rule, N QI Is the number of considered warehousing traffic;
s7.3, calculating the installed capacity of the small and medium hydropower stations in the micro-grid:
P AH ＝0.0098kHQ I
wherein k is the generating efficiency of the small hydropower station small hydropower unit, and H is the water head of the small hydropower station;
s8, calculating the installed capacity P of the small wind power plant in the microgrid AW The method comprises the following calculation steps:
s8.1, calculating 1, 2, 1 W The probability that the wind speed of the small wind power station in the directional microgrid changes according to a normal distribution rule is as follows:
in the formula k W For coefficients, erf (y) is an error function expressed as:
s8.2, calculating the average value of the wind speeds of the small wind power plants in the microgrid:
in the formula, v Wi 、p Wi Respectively the wind speed in the ith direction and the probability that the wind speed changes according to the normal distribution rule, N W The number of typical wind speeds and wind directions of a small wind power plant;
s8.3, calculating the installed capacity of the small wind power plant in the microgrid:
in the formula, v ci 、v co Respectively cut-in wind speed and cut-out wind speed k of the wind turbine W0 、k W1 、k W2 、k W3 Respectively, coefficients related to wind speed;
s9, calculating the installed capacity P of the photovoltaic power station in the microgrid APV The method comprises the following calculation steps:
s9.1, calculating the probability that the sunlight intensity of the photovoltaic power station in the microgrid changes according to a normal distribution rule in the ith time interval i =1, 2,.. And T in the day by adopting a probability analysis method:
in the formula k PV For coefficients, erf (y) is an error function expressed as:
s9.2, calculating the average value of the sunlight intensity of the photovoltaic power station in the microgrid:
in the formula, E PVi 、p PVi Respectively indicating the solar intensity of the photovoltaic power station and the probability of the solar intensity changing according to a normal distribution rule in the ith time period;
s9.3, calculating the machine-installable capacity of the photovoltaic power station in the microgrid:
wherein k is PV0 、k PV1 、k PV2 Respectively, coefficients related to the intensity of sunlight;
s10, calculating and determining installed capacity P of the small hydropower stations in the microgrid according to the allowable numerical relationship among the maximum distributed power capacity of the microgrid, the installed capacity of the small hydropower stations, the installed capacity of the small wind power plants and the installed capacity of the photovoltaic power stations SH Installed capacity P of small wind power plant SW Installed capacity P of photovoltaic power station SPV ：
Wherein k is AH 、k AW 、k APV Is a weight coefficient and satisfies k AH +k AW +k APV ＝1。
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103151798A (en) * | 2013-03-27 | 2013-06-12 | 浙江省电力公司电力科学研究院 | Optimizing method of independent microgrid system |
| CN106329554A (en) * | 2016-09-07 | 2017-01-11 | 中国南方电网有限责任公司电网技术研究中心 | Method of calculating energy storage rated capacity applied to zone-type microgrid |
| CN106655248A (en) * | 2016-10-21 | 2017-05-10 | 中国电建集团中南勘测设计研究院有限公司 | Power capacity allocation method of grid-connected microgrid |
| CN107732960A (en) * | 2017-09-18 | 2018-02-23 | 国网甘肃省电力公司电力科学研究院 | Micro-grid energy storage system capacity configuration optimizing method |
| CN109217293A (en) * | 2018-09-04 | 2019-01-15 | 北京科诺伟业科技股份有限公司 | A kind of light storage micro-grid system capacity collocation method |
| CN109636140A (en) * | 2018-11-27 | 2019-04-16 | 广东电网有限责任公司韶关供电局 | Consider the micro-capacitance sensor medium-small hydropower plants abandoning energy calculation method of run-off |
| CN109687506A (en) * | 2018-11-27 | 2019-04-26 | 广东电网有限责任公司韶关供电局 | Micro-capacitance sensor medium-small hydropower plants generated energy prediction technique |
| CN109711605A (en) * | 2018-12-10 | 2019-05-03 | 中国电力科学研究院有限公司 | A kind of polymorphic type power grid constant volume method and apparatus |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9148019B2 (en) * | 2010-12-06 | 2015-09-29 | Sandia Corporation | Computing architecture for autonomous microgrids |
| TWI401611B (en) * | 2010-05-26 | 2013-07-11 | Univ Yuan Ze | Method for optimizing installation capacity of hybrid energy generation system |
-
2019
- 2019-07-22 CN CN201910660596.0A patent/CN110460090B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103151798A (en) * | 2013-03-27 | 2013-06-12 | 浙江省电力公司电力科学研究院 | Optimizing method of independent microgrid system |
| CN106329554A (en) * | 2016-09-07 | 2017-01-11 | 中国南方电网有限责任公司电网技术研究中心 | Method of calculating energy storage rated capacity applied to zone-type microgrid |
| CN106655248A (en) * | 2016-10-21 | 2017-05-10 | 中国电建集团中南勘测设计研究院有限公司 | Power capacity allocation method of grid-connected microgrid |
| CN107732960A (en) * | 2017-09-18 | 2018-02-23 | 国网甘肃省电力公司电力科学研究院 | Micro-grid energy storage system capacity configuration optimizing method |
| CN109217293A (en) * | 2018-09-04 | 2019-01-15 | 北京科诺伟业科技股份有限公司 | A kind of light storage micro-grid system capacity collocation method |
| CN109636140A (en) * | 2018-11-27 | 2019-04-16 | 广东电网有限责任公司韶关供电局 | Consider the micro-capacitance sensor medium-small hydropower plants abandoning energy calculation method of run-off |
| CN109687506A (en) * | 2018-11-27 | 2019-04-26 | 广东电网有限责任公司韶关供电局 | Micro-capacitance sensor medium-small hydropower plants generated energy prediction technique |
| CN109711605A (en) * | 2018-12-10 | 2019-05-03 | 中国电力科学研究院有限公司 | A kind of polymorphic type power grid constant volume method and apparatus |
Non-Patent Citations (1)
| Title |
|---|
| 综合考虑风光水储的微网容量优化配置;曹蓓 等;《水电能源科学》;20150630;第33卷(第6期);第209-212、150页 * |
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