CN106022533B - Optimized access method based on cloud platform computing energy and information binary fusion element - Google Patents

Optimized access method based on cloud platform computing energy and information binary fusion element Download PDF

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CN106022533B
CN106022533B CN201610364609.6A CN201610364609A CN106022533B CN 106022533 B CN106022533 B CN 106022533B CN 201610364609 A CN201610364609 A CN 201610364609A CN 106022533 B CN106022533 B CN 106022533B
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CN106022533A (en
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赖祥生
黄仁乐
李蕴
王存平
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State Grid Beijing Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract

The invention discloses an optimized access method based on a binary fusion element of computing energy and information of a cloud platform, which comprises the steps of determining measurement data to be acquired by controllable equipment and prediction data to be predicted by the uncontrollable equipment; the controllable device automatically senses the measurement data at the current moment according to the measurement data; the uncontrollable equipment acquires historical measurement data of the uncontrollable equipment and adopts a least square method to perform data fitting to acquire prediction data of the uncontrollable equipment at the next moment; receiving measurement data of the controllable device at the current moment and prediction data of the uncontrollable device at the next moment, and calculating the active power and the probability of the active power of the uncontrollable device according to the prediction data of the uncontrollable device at the next moment; according to the measurement data and the particle swarm algorithm of the controllable equipment at the current moment, calculating the output of the controllable equipment at the next moment by adopting a target function with the maximum consumption energy of the distributed power supply all day; and the controllable equipment receives the output force at the next moment and adjusts the actual output force at the next moment according to the output force at the next moment.

Description

Optimized access method based on cloud platform computing energy and information binary fusion element
Technical Field
The invention relates to reasonable utilization of energy, in particular to an optimized access method based on a binary fusion element of computing energy and information of a cloud platform.
Background
With the access of a large amount of energy and information binary fusion elements into an electric power system, in order to ensure that each device of the electric power system realizes reasonable energy distribution, the conventional method is to perform uncertainty partitioning on the energy and information binary fusion elements, divide values of random variables into N regions which are completely equal, and calculate the probability, wherein the error is large, so that the charging amount or the discharging amount of controllable devices accessed into the electric power system is very inaccurate, and the voltage of the devices in the electric power system is too high or too low, so that the devices in the electric power system are damaged to different degrees.
Disclosure of Invention
Aiming at the defects in the prior art, the optimized access method based on the cloud platform computing energy and information binary fusion element provided by the invention can enable the charging amount or the discharging amount of controllable equipment accessed into a power system to be more accurate.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the optimized access method based on the cloud platform computing energy and information binary fusion element comprises the following steps:
determining measurement data to be acquired by controllable equipment and prediction data to be predicted by the uncontrollable equipment in an energy and information binary fusion element layer by adopting a cloud platform, and simultaneously constructing a target function with the maximum energy consumption of the energy distributed power supply all day;
the energy and information binary fusion element layer receives measurement data to be acquired by controllable equipment and prediction data to be predicted by the uncontrollable equipment, wherein the measurement data to be acquired by the controllable equipment and the prediction data are issued by the cloud platform, and the controllable equipment automatically senses the measurement data at the current moment according to the measurement data to be acquired; the uncontrollable equipment acquires historical measurement data of the uncontrollable equipment and adopts a least square method to perform data fitting to acquire prediction data of the uncontrollable equipment at the next moment;
the cloud platform receives measurement data of the controllable device at the current moment and prediction data of the uncontrollable device at the next moment, wherein the measurement data are uploaded by the energy and information binary fusion element layer and acquired by the controllable device, and the probability of active power and the probability of the active power of the uncontrollable device are calculated according to the prediction data of the uncontrollable device at the next moment;
the cloud platform calculates the output of the controllable equipment at the next moment by adopting a target function with the maximum energy consumption of the distributed power supply all day according to the measurement data of the controllable equipment at the current moment and a particle swarm algorithm;
the energy and information binary fusion element layer receives the output force of the controllable device issued by the cloud platform at the next moment, and the controllable device adjusts the actual output force at the next moment according to the output force at the next moment.
The invention has the beneficial effects that: according to the scheme, the active power and the probability of the active power are obtained through the prediction data of the uncontrollable equipment, then the output of the controllable equipment at the next moment is calculated by adopting the objective function with the maximum energy absorbed by the distributed power supply all day by combining the measurement data of the controllable equipment at the current moment and the particle swarm algorithm, and the actual output of the controllable equipment at the next moment is adjusted through the calculated output at the next moment.
By the optimized access method, the charging amount/discharging amount/power size of controllable equipment accessed into the power system can be more accurate, and the voltage of the equipment in the power system is ensured to be always kept in a normal working range, so that the service life of the equipment in the power system is prolonged; in addition, the method can enable the distributed power supply to have the maximum energy consumption and the uncertainty to be expressed more accurately, reasonably and representatively.
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Fig. 1 is a flowchart of an optimized access method based on a cloud platform computing energy and information binary fusion element.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Referring to fig. 1, fig. 1 shows a flow chart of an optimized access method based on a cloud platform computing energy and information binary fusion element; as shown in fig. 1, the method 100 includes steps 101 to 105:
in step 101, a cloud platform is adopted to determine measurement data to be acquired by controllable equipment and prediction data to be predicted by the uncontrollable equipment in an energy and information binary fusion element layer, and meanwhile, an objective function with the maximum energy consumption of the energy distributed power supply all day is constructed.
The energy and information binary fusion element layer consists of an energy and information binary fusion element, and the energy and information binary fusion element consists of a controllable device and an uncontrollable device part; the controllable equipment comprises an electric automobile, an energy storage device and a capacitor bank; the uncontrollable equipment comprises distributed energy sources and loads, and the distributed energy sources are composed of wind power supplies and photovoltaic power supplies.
The photovoltaic power supply to-be-acquired prediction data are photovoltaic output expectation and variance, and the wind power supply to-be-acquired prediction data are wind output expectation and variance; the method comprises the steps that measured data to be acquired by the electric automobile are battery energy state quantity and charging power, measured data to be acquired by the energy storage device are energy state quantity and charging and discharging power, and predicted data to be acquired by the capacitor bank are reactive state quantity, switchable capacity and switched times.
In step 102, the energy and information binary fusion element layer receives measurement data to be acquired by the controllable device and prediction data to be predicted by the uncontrollable device, which are issued by the cloud platform, and the controllable device automatically senses the measurement data at the current moment according to the measurement data to be acquired; the uncontrollable equipment acquires historical measurement data of the uncontrollable equipment and adopts a least square method to perform data fitting to acquire prediction data of the uncontrollable equipment at the next moment;
and a PnP protocol layer and two transmission layers, namely a gateway intelligent node and a transmission layer, are also arranged between the energy and information binary fusion element layer and the cloud platform, and when information interaction is carried out between the energy and information binary fusion element layer and the cloud platform, the transfer must be carried out through the PnP protocol layer and the gateway intelligent node and the transmission layer.
In step 103, the cloud platform receives the measurement data of the current moment and the prediction data of the next moment of the uncontrollable device, which are obtained by the controllable device and uploaded by the energy and information binary fusion element layer, and calculates the active power and the probability of the active power of the uncontrollable device according to the prediction data of the next moment of the uncontrollable device.
In an embodiment of the present invention, calculating the active power and the probability of the active power of the uncontrollable device according to the prediction data of the uncontrollable device at the next moment may further include:
A. calculating the active power and the probability of the active power of a photovoltaic power supply
a1, because the active power output of the photovoltaic power supply is mainly influenced by the intensity of the solar illumination, and the solar illumination obeys Beta distribution, when the active power and the probability of the active power of the photovoltaic power supply are obtained, firstly, parameters α and β of the Beta distribution need to be obtained through the photovoltaic power output expectation and the variance of the photovoltaic power supply.
Since the parameters α and β of the Beta distribution are solved by using the conventional and relatively conventional examples, how to solve the Beta distribution is not described herein.
a2, calculating the output density of the photovoltaic power supply:
Figure BDA0001002170760000041
wherein α and β refer to parameters of Beta distribution, Gamma represents Gamma function, P refers to actual output of photovoltaic power supply, and P refers to actual output of photovoltaic power supplymaxThe maximum output power of the photovoltaic power supply is obtained; p, P for photovoltaic power supplymaxAnd PminThe photovoltaic power supply active sensing device can be obtained through active sensing and identification of the photovoltaic power supply and historical data analysis.
a3, calculating the active power consumed by the photovoltaic power supply:
Figure BDA0001002170760000042
in the formula, PminRefers to the minimum output power, P, of the photovoltaic power supplymaxRefers to the maximum possible output power, p, of the photovoltaic power supplyx,i-1The active output of the i-1 state is referred to; ns refers to the total number of states;
calculating the probability of active power consumed by the photovoltaic power supply:
Figure BDA0001002170760000043
in the formula, Pi，lRefers to the minimum active output of the ith state; pi，rRefers to the maximum active power output of the ith state.
B. Calculating active power and probability of active power of fan power supply
b1, calculating the output density of the wind power supply by adopting Weibull distribution:
Figure BDA0001002170760000051
in the formula, pj is wind speed; k and c are two parameters of weibull distribution, namely k is a shape parameter and c is a scale parameter;
b2, calculating the active power absorbed by the wind power supply:
Figure BDA0001002170760000052
in the formula, PminRefers to the minimum possible output power, P, of the wind power supplymaxRefers to the maximum possible output power, p, of the wind power supplyx,j-1The active output representative value of the j-1 state is obtained; ns refers to the total number of states;
Figure BDA0001002170760000053
in the formula, Px，lRefers to the minimum active output of the x state; px，rRefers to the maximum active output of the ith state; and f (x) is the output density of the wind power supply.
In step 104, the cloud platform calculates the output of the controllable device at the next moment by using the objective function of the distributed power supply with the maximum energy consumption all day according to the measurement data of the controllable device at the current moment and the particle swarm optimization.
In one embodiment of the invention, the objective function of the distributed power supply for maximum energy consumption all day is:
Figure BDA0001002170760000054
wherein n is the number of n time periods divided from one day to less than or equal to n; pt,pvAnd Pt,wtRespectively representing the active power absorbed by the photovoltaic power supply and the active power absorbed by the wind power supply in the ith time period, Pt,pv≤Pi，Pt,wt≤Pj；gc,tIs the probability of the c-th state of the t period, gc,t＝gi*gjC is 1,2, … N; Δ t is any time period.
In order to optimize the target function, the constraint conditions which need to be met by the target function with the maximum energy consumption of the distributed power supply all day are equality constraint conditions, inequality constraint conditions and probability constraint conditions;
wherein the equality constraint is:
Figure BDA0001002170760000061
in the formula: pi、PLiRespectively indicating the active output and the active load at the node i; qi、QLiRespectively indicating the magnitude of reactive output and reactive load at a node i; u shapei、UkRespectively refer to the voltage amplitudes of the node i and the node k; deltaikRefers to the voltage phase angle difference between node i and node k; gik、BikRespectively refers to a real part and an imaginary part of a system admittance matrix;
inequality constraints and probability constraints:
Figure BDA0001002170760000062
Figure BDA0001002170760000063
Figure BDA0001002170760000064
wherein P { A } represents the probability of occurrence of event A, αUAnd αSConfidence levels for voltage and capacity, respectively; u shapeiIs the node voltage; sijIs the branch capacity; omeganodeRefers to a collection of nodes of the system;
Figure BDA0001002170760000065
the actual reactive power of the ith state micro gas turbine in the t period is shown;
Figure BDA0001002170760000066
is the actual existence of the energy storage device in the ith state in the t periodThe magnitude of the output force;
Figure BDA0001002170760000067
is the actual output of the photovoltaic of the ith state in the t period.
In step 105, the energy and information binary fusion element layer receives the output magnitude of the controllable device at the next moment issued by the cloud platform, and the controllable device adjusts the actual output magnitude at the next moment according to the output magnitude at the next moment.
The output magnitude of the electric automobile at the next moment refers to the charging power magnitude at the next moment, the output magnitude of the energy storage device at the next moment refers to the energy storage charging and discharging power magnitude at the next moment, and the output magnitude of the capacitor bank at the next moment refers to the switchable capacity of the capacitor at the next moment.
And after the controllable equipment adjusts the actual output force at the next moment, the adjustment information is transmitted to the cloud platform through the gateway intelligent node, the transmission layer and the PnP protocol layer, and after the execution is finished, the next cycle is executed.
In order to realize the high sharing and the optimized distribution of the energy of the information of the massive and diversified energy and information binary fusion elements in the power system, the scheme realizes the real-time bidirectional intercommunication and sharing of the massive energy and information binary fusion elements and the cloud platform by sensing and monitoring the relevant information of the various energy and information binary fusion elements required by the cloud platform calculation in real time, and realizes the active discovery, the optimized configuration and the intelligent scheduling of the cloud computing platform in the relevant information period of the energy and information binary fusion elements. The manager can also dynamically increase or decrease the power of the controllable equipment in the energy and information binary fusion element anytime and anywhere according to application requirements, so that each equipment of the power system can realize reasonable energy distribution.

Claims (4)

1. An optimized access method based on a cloud platform computing energy and information binary fusion element is characterized by comprising the following steps:
determining measurement data to be acquired by controllable equipment and prediction data to be predicted by the uncontrollable equipment in an energy and information binary fusion element layer by adopting a cloud platform, and simultaneously constructing a target function with the maximum energy consumption of the energy distributed power supply all day;
the energy and information binary fusion element layer receives measurement data to be acquired by controllable equipment and prediction data to be predicted by the uncontrollable equipment, wherein the measurement data to be acquired by the controllable equipment and the prediction data are issued by the cloud platform, and the controllable equipment automatically senses the measurement data at the current moment according to the measurement data to be acquired; the uncontrollable equipment acquires historical measurement data of the uncontrollable equipment and adopts a least square method to perform data fitting to acquire prediction data of the uncontrollable equipment at the next moment;
the cloud platform receives measurement data of the controllable device at the current moment and prediction data of the uncontrollable device at the next moment, wherein the measurement data are uploaded by the energy and information binary fusion element layer and acquired by the controllable device, and the probability of active power and the probability of the active power of the uncontrollable device are calculated according to the prediction data of the uncontrollable device at the next moment;
the cloud platform calculates the output of the controllable equipment at the next moment by adopting a target function with the maximum energy consumption of the distributed power supply all day according to the measurement data of the controllable equipment at the current moment and a particle swarm algorithm;
the energy and information binary fusion element layer receives the output force of the controllable equipment at the next moment issued by the cloud platform, and the controllable equipment adjusts the actual output force at the next moment according to the output force at the next moment;
the uncontrollable equipment comprises a distributed power supply, and the distributed energy sources are a wind power supply and a photovoltaic power supply; the controllable equipment comprises an electric automobile, an energy storage device and a capacitor bank;
the objective function of the distributed power supply with the maximum energy consumption all day is as follows:
Figure FDA0002331243490000011
wherein n means that a day is divided into n time periods; pt,pvAnd Pt,wtRespectively representing the active power absorbed by the photovoltaic power supply and the active power absorbed by the wind power supply in the ith time period, Pt,pv≤Pi，Pt,wt≤Pj；gc,tThe probability of the c-th state for the t period,gc,t＝gi*gjc is 1,2, … N; Δ t is any time period; giProbability of active power dissipated for the photovoltaic power supply; gjProbability of active power being dissipated for the wind power supply;
the constraint conditions which need to be met by the objective function with the maximum energy consumption of the distributed power supply all day are equality constraint conditions, inequality constraint conditions and probability constraint conditions;
wherein the equality constraint is:
Figure FDA0002331243490000021
in the formula: pi、PLiRespectively indicating the active output and the active load at the node i; qi、QLiRespectively indicating the magnitude of reactive output and reactive load at a node i; u shapei、UkRespectively refer to the voltage amplitudes of the node i and the node k; deltaikRefers to the voltage phase angle difference between node i and node k; gik、BikRespectively refers to a real part and an imaginary part of a system admittance matrix;
inequality constraints and probability constraints:
Figure FDA0002331243490000022
Figure FDA0002331243490000023
wherein P { A } represents the probability of occurrence of event A, αUAnd αSConfidence levels for voltage and capacity, respectively; u shapeiIs the node voltage; sijIs the branch capacity; omeganodeRefers to a collection of nodes of the system;
Figure FDA0002331243490000024
the actual reactive power of the ith state micro gas turbine in the t period is shown;
Figure FDA0002331243490000025
the value is the actual active power output of the energy storage device in the ith state in the t period;
Figure FDA0002331243490000026
is the actual output of the photovoltaic of the ith state in the t period.
2. The optimized access method based on the binary fusion element of cloud platform computing energy and information as claimed in claim 1,
the photovoltaic power supply to-be-obtained prediction data are photovoltaic output expectation and variance; the prediction data to be acquired by the wind power supply are wind output expectation and variance;
the electric vehicle comprises a capacitor bank, a battery, an energy storage device, a capacitor bank and a power storage device, wherein the electric vehicle comprises a battery, the energy storage device comprises a power storage device, the power storage device comprises a power storage device, the.
3. The optimized access method for computing the energy and information binary fusion element based on the cloud platform according to claim 2, wherein the calculating the active power and the probability of the active power according to the predicted data of the uncontrollable device at the next moment further comprises:
A. calculating the active power and the probability of the active power of a photovoltaic power supply
a1, acquiring parameters α and β of Beta distribution through photovoltaic output expectation and variance of a photovoltaic power supply;
a2, calculating the output density of the photovoltaic power supply:
Figure FDA0002331243490000031
wherein α and β refer to parameters of Beta distribution, Gamma represents Gamma function, P refers to actual output of photovoltaic power supply, and P refers to actual output of photovoltaic power supplymaxThe maximum output power of the photovoltaic power supply is obtained;
a3, calculating the active power consumed by the photovoltaic power supply:
Figure FDA0002331243490000032
in the formula, PminRefers to the minimum possible output power, P, of the photovoltaic power supplymaxRefers to the maximum possible output power, p, of the photovoltaic power supplyx,i-1The active output representative value of the i-1 state is obtained; ns refers to the total number of states of the photovoltaic power supply;
calculating the probability of active power consumed by the photovoltaic power supply:
Figure FDA0002331243490000033
in the formula, Pi，lRefers to the minimum active output of the ith state; pi，rRefers to the maximum active output of the ith state; f (x) is the output density of the photovoltaic power supply;
B. calculating active power and probability of active power of fan power supply
b1, calculating the output density of the wind power supply by adopting Weibull distribution:
Figure FDA0002331243490000041
in the formula, pj is wind speed; k and c are two parameters of weibull distribution, k is a shape parameter, and c is a scale parameter;
b2, calculating the active power absorbed by the wind power supply:
Figure FDA0002331243490000042
in the formula, PminRefers to the minimum possible output power, P, of the wind power supplymaxRefers to the maximum possible output power, p, of the wind power supplyx,j-1The active output representative value of the j-1 state is obtained; n is a radical ofcThe state number refers to the total state number of the wind power supply;
Figure FDA0002331243490000043
in the formula, Px，lRefers to the minimum active output of the x state; px，rRefers to the maximum active output of the ith state; and f (x) is the output density of the wind power supply.
4. The optimal access method for computing the energy and information binary fusion element based on the cloud platform according to any one of claims 1 to 3, wherein a PnP protocol layer, a gateway intelligent node and a transmission layer are sequentially arranged between the cloud platform and the energy and information binary fusion element layer when the cloud platform and the energy and information binary fusion element layer perform data transmission.
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