CN111555356A - Random variable analysis method for regional comprehensive energy system based on coupled Markov model - Google Patents

Random variable analysis method for regional comprehensive energy system based on coupled Markov model Download PDF

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CN111555356A
CN111555356A CN202010484656.0A CN202010484656A CN111555356A CN 111555356 A CN111555356 A CN 111555356A CN 202010484656 A CN202010484656 A CN 202010484656A CN 111555356 A CN111555356 A CN 111555356A
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CN111555356B (en
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刘亚南
梅睿
范立新
袁超
封建宝
曹佳伟
汪泓
孙永辉
翟苏巍
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Hohai University HHU
Jiangsu Fangtian Power Technology Co Ltd
Taizhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Jiangsu Fangtian Power Technology Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • 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
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention discloses a random variable analysis method of a regional comprehensive energy system based on a coupled Markov model, which comprises the following steps: determining and analyzing random variables in the comprehensive energy system; modeling photovoltaic power generation equipment; modeling a wind power generation device; modeling the multi-element load; carrying out modeling analysis of a single Markov model on each random variable in the comprehensive energy system; comprehensively analyzing the random variation condition of the comprehensive energy system on the basis of a single Markov model, and constructing a coupling Markov model of a plurality of random variables by analyzing multivariate load data; and analyzing the comprehensive energy system based on the coupled Markov model, calculating to obtain the steady-state distribution of the coupled Markov model, calculating to obtain the expectation and the variance of the comprehensive energy system, and adjusting the multi-element load and the energy storage equipment. The invention can realize the source, load and storage coordination control of the multifunctional system and ensure the safe and stable operation of the system.

Description

Random variable analysis method for regional comprehensive energy system based on coupled Markov model
Technical Field
The invention relates to the technical field, in particular to a stochastic variable analysis method of a regional comprehensive energy system based on a coupled Markov model.
Background
With the proposal of low-carbon economy and ubiquitous power internet of things concepts, various energy sources are reasonably and complementarily utilized, and renewable energy sources such as solar energy, wind energy and the like are added to form a regional comprehensive energy source system with various output functions and transportation forms, so that the comprehensive energy source system can effectively integrate and utilize various resources, improve the energy efficiency of the system, solve the problems of new energy consumption and the like, and further be widely researched.
At present, most of research aiming at the comprehensive energy system focuses on optimization, the operation mode of the comprehensive energy system is optimized mainly from the aspects of economy, environmental protection and the like, but the comprehensive energy system is taken as a highly-coupled complex system, in the actual operation process, the randomness fluctuation of renewable energy tends to cause great challenges for the operation of the multi-energy system, in order to improve the energy efficiency of the system and realize the coordinated operation among sources, networks, loads and storages, the renewable energy has higher occupation ratio in the comprehensive energy system, and with the intervention of the renewable energy with high occupation ratio, the randomness of an energy supply side and a load side brings great challenges for the stable operation of the system.
According to the invention, by constructing the Markov model of the integrated energy system comprising the renewable energy power generation system and a plurality of random variables of diversified loads, the random change characteristics of the source and the load in the integrated energy system are effectively researched, a research basis is provided for the supply and demand balance and the analysis control of the integrated energy system, and the effective control of the integrated energy system is promoted.
Disclosure of Invention
The invention aims to provide a random variable analysis method of a regional comprehensive energy system based on a coupled Markov model, which comprises the steps of constructing the random variable model of the comprehensive energy system based on the coupled Markov model by analyzing historical data and considering random variables such as the fluctuation of renewable energy output, calculating to obtain steady-state distribution and expectation and variance of the random variables in the system by combining the coupled Markov model, and adjusting multiple loads and energy storage equipment on the basis of the steady-state distribution and the expectation and variance, thereby realizing source, load and storage coordination control of a multi-energy system and ensuring safe and stable operation of the system.
In order to achieve the above purpose, with reference to fig. 1, the present invention provides a stochastic variable analysis method for a regional integrated energy system based on a coupled Markov model, where the analysis method includes:
s1, determining and analyzing random variables including distributed renewable energy power generation equipment in the comprehensive energy system;
s2, modeling the photovoltaic power generation equipment;
s3, modeling the wind power generation equipment;
s4, modeling the multiple loads;
s5, carrying out modeling analysis of a single Markov model on each random variable in the comprehensive energy system;
s6, comprehensively analyzing the random change condition of the comprehensive energy system based on the single Markov model, and constructing a coupling Markov model of a plurality of random variables by analyzing the multivariate load data;
s7, analyzing the comprehensive energy system based on the coupled Markov model, calculating to obtain the steady-state distribution of the coupled Markov model, calculating to obtain the expectation and the variance of the comprehensive energy system, and adjusting the multi-element load and the energy storage equipment to realize the source, load and storage coordinated operation of the regional comprehensive energy system;
and S8, performing simulation example verification on the coupling model of the renewable energy output random variable.
Further, the step S1 of determining and analyzing the main random variables in the integrated energy system means that the integrated energy system includes a plurality of distributed energy power generation devices, and therefore, the integrated energy system includes a plurality of random variables, and determining the number of variables in the system has an important meaning for the overall analysis of the system. The distributed renewable energy power generation system is mainly cited as the random variable in the description of the patent, but the invention is not limited to the random variable, and the invention is also applicable to the situation of more random variables. Similarly, the invention only lists two types of photovoltaic power generation and wind power generation, and in fact, the invention is also applicable to other new energy sources.
Further, in step S2, the most common photovoltaic power generation system at present is considered, which mainly consists of three parts: the photovoltaic array is used as a direct current power supply, a direct current-direct current converter with maximum power point tracking control and a direct current-alternating current inverter with a controller.
The photovoltaic array power generation model comprises the following steps:
Figure BDA0002518681460000021
in the formula, VpvRespectively representing the output voltage of the photovoltaic cell, IpvRepresenting the illumination current, I0Representing the current through the drain, q is the coulomb constant, T represents the Fahrenheit temperature, a represents the diode quality factor, d represents a constant, RsRepresents the equivalent series resistance, RpRepresents the equivalent parallel resistance, NsRepresenting the number of photovoltaic cells in series.
The DC-DC converter with maximum power point tracking control is mainly composed of a switch tube, and realizes maximum power tracking by adjusting the duty ratio of the switch tube, and the mathematical formula is as follows:
Figure BDA0002518681460000022
wherein C represents the capacitance of the DC side, L represents the inductance of the DC side, D represents the duty ratio of the control switch tube, vDCAnd iDCRespectively instantaneous voltage and current values, vmpAnd impAnd respectively outputting voltage and current values by the photovoltaic array.
The dc-ac inverter with controller is also composed of a plurality of switching tubes, and its main function is to convert dc into ac, and a typical mathematical formula for converting dc into three-phase ac is as follows:
Figure BDA0002518681460000031
in the formula, edAnd eqRespectively representing the voltage values, i, of the dq coordinate system after the AC coordinate transformationdAnd iqRespectively representing the current values, u, of the dq coordinate system after the AC coordinate transformationdcAnd iLRespectively, the voltage and current values on the DC side, SdAnd SqRespectively, representing the control signals of the dq coordinate system after the ac coordinate transformation.
Further, in step S3, considering the most common wind power generation at present, the power model of the wind turbine is as follows:
Pw＝0.5πρf2V3Cp
where ρ is the density of air, f is the radius of the rotor, V is the wind speed, CpRepresenting the availability of wind energy, CpDirectly determining the efficiency of the system, the expression is as follows:
Figure BDA0002518681460000032
in the formula, λiRepresenting the intermediate variable, β representing the blade elevation angle, and λ representing the tip speed ratio.
Further, in step S4, the multi-component load is divided into a translatable load and a translatable load according to the characteristics of the energy consumption.
The translatable load is that the load requirement is met only in a certain time period, generally has fixed load duration and habitual use time, is not prone to be interrupted once started, and is modeled in a unified mode as follows:
Figure BDA0002518681460000033
Figure BDA0002518681460000034
Figure BDA0002518681460000035
wherein: w is ai,j,tAnd
Figure BDA0002518681460000036
respectively representing the power value and the rated power of the ith translatable load of the user j at the moment t, wherein the power value is an electric power value for the electric load, and the power value is a cold/hot power value for the cold/hot load;i,j,ta variable 0-1 representing the i-th class translatable class load enable state for user j at time t,i,j,t0 andi,j,t1 denotes start and no start, respectively;
Figure BDA0002518681460000037
and HiRespectively the start use time, the end use time and the load duration of the ith type translatable load habit.
The transferable load means that the load meets a certain load requirement in a specified time interval and has a certain virtual energy storage characteristic. For electrical loads, which mainly include electric vehicles and the like, mathematical models are as follows:
Figure BDA0002518681460000041
Figure BDA0002518681460000042
Figure BDA0002518681460000043
Figure BDA0002518681460000044
Figure BDA0002518681460000045
wherein:
Figure BDA0002518681460000046
and
Figure BDA0002518681460000047
the charging power and the state of charge of the kth electric vehicle at the moment t are respectively;
Figure BDA0002518681460000048
and EEV,kCharging efficiency and battery capacity of the kth EV, respectively;
Figure BDA0002518681460000049
and
Figure BDA00025186814600000410
minimum and maximum battery states allowed for the kth automobile battery, respectively;
Figure BDA00025186814600000411
and
Figure BDA00025186814600000412
the actual and expected battery states of the kth electric vehicle during off-grid are respectively; t is tari,kAnd tdep,kThe moment when the kth electric vehicle is connected to and separated from the power system respectively;
Figure BDA00025186814600000413
the maximum charging power of the kth car.
Further, in step S5, the Markov process is a stochastic process, and is characterized in that the state at the next time is dependent only on the state at the current time and is independent of the previous state. Markov modeling is respectively carried out on each random variable in the comprehensive energy system, so that the change characteristics of the random variables can be described and recorded as Zτ(τ -1, 2, …, T), state space S-1, 2, …, k, ZτOn behalf of each model event τ, the state space S is a collection of grouped event groups that have collected similar model events. For model event τ and all states S1，S2，S3，…，SτCurrent state SτIs only compared with the previous state Sτ-1In this regard, the discrete markov process has stability and homogeneity over time, i.e. for any state r to S:
P＝{Zr＝s|Zr-1＝r}
conditional probability p ═ { Z ═ Zr＝s|Zr-1R is defined as the transition probability of state r to state S. The physical meaning of the transition probability is the probability that a model event in state r at the current time τ -1 transitions to the next model event S at time τ. The matrix containing all transition probabilities is called the transition probability matrix P:
Figure BDA0002518681460000051
the transition probability matrix has the following properties:
(1) all elements are non-negative.
(2) The sum of the elements in each row is 1, namely:
Figure BDA0002518681460000052
further, in step S6, the coupling M is constructed by including a plurality of random variables in the integrated energy systemThe arkov model is only explained in the two cases of the photovoltaic power generation device and the wind power generation device, and the construction method of the Markov model for the load random variable is similar. Analyzing the random variation of the system by analyzing the load data and dividing into S according to the illumination intensity by clustering analysis1In each interval, the wind speed is analyzed by adopting a clustering method to obtain S2The random characteristics of multiple renewable energy sources in each interval can generate S1×S2And (3) a situation. This yields the coupled Markov chain S ═ {1,21×s2And each Markov mode corresponds to the output condition of one renewable energy source, and the whole Markov chain comprises all possible working conditions of the renewable energy source power generation. Meanwhile, transition probability matrixes in Markov are used for describing the transition situation among the states, and the transition probability expressions are as follows:
pij(k-1)＝p{r(k)＝j|r(k-1)＝i},ij∈S
where k and k-1 represent the current time and the previous time, respectively, and i and j correspond to two states in the Markov chain, respectively.
All transition probabilities make up a transition probability matrix as follows:
P(k-1)＝{pij(k-1)},ij∈S
wherein S is a Markov chain.
Because a plurality of variables are independent of each other, the transition probability of the single Markov model and the transition probability of the coupled Markov model have the following relation:
Figure BDA0002518681460000053
in the formula, pmnRepresenting the transition probabilities of the coupled Markov models,
Figure BDA0002518681460000054
and
Figure BDA0002518681460000055
respectively representing the transition probabilities of the single Markov models of the two renewable energy systems.
Further, in step S7, the integrated energy system may be analyzed according to the coupled Markov model, and the corresponding steady distribution pi ═ is (pi ═ pi1,π2,...,πs) The following conditions are satisfied:
Figure BDA0002518681460000061
Figure BDA0002518681460000062
wherein s represents the total number of states contained in the coupled Markov model, P represents the transition probability corresponding to the coupled Markov model, and X represents the initial state.
The mean value of the system can be calculated according to the coupled Markov model
Figure BDA0002518681460000063
And variance d is as follows:
Figure BDA0002518681460000064
Figure BDA0002518681460000065
wherein, pijAnd piiRepresenting individual state quantities, y, in coupled Markov modelsjAnd xiRespectively shows the output situations of two renewable energy sources,
Figure BDA0002518681460000066
represents the expectation of the coupled Markov model and d represents the variance of the coupled Markov model. The system expectation and variance obtained by calculation can characterize the comprehensive situation of random change of the system, and the operation mode and equipment of the system are adjusted and controlled according to the comprehensive situation.
The renewable energy power generation is uncontrollable, so in order to guarantee stable and safe operation of the system, the energy storage device and flexible load adjustment can be realized, redundant or lacking energy can be adjusted by absorption or release of the energy storage device and translation of the load, the charge and discharge control and the charge and discharge power of the energy storage device depend on the power electronic device, typically a bidirectional direct-direct conversion circuit, and when the energy storage device works in a charge mode, the mathematical expression is as follows:
Figure BDA0002518681460000067
when the energy storage device is operating in the discharge mode, the mathematical expression is as follows:
Figure BDA0002518681460000071
wherein, C1Representing the capacitance, L, in a bidirectional DC-DC converter circuit1Representing inductance, R representing equivalent resistance, d (t)1Indicating switch S1Duty ratio of d (t)2Indicating switch S2The duty cycle of (a) is,
Figure BDA0002518681460000072
indicates the DC voltage in the discharge mode,
Figure BDA0002518681460000073
Indicating the magnitude of the current, V, flowing through the inductordcIndicating the magnitude of the voltage.
The heat storage device is controlled in a similar manner, and the energy storage and release power of the energy storage device is controlled by adjusting the inflow and outflow of the energy storage device.
Further, in step S8, in combination with the foregoing, a system model including multiple renewable energy power generation and multiple loads may be constructed based on historical data, a single Markov model may be constructed taking into account the stochastic characteristics of the renewable energy, a coupled Markov model may be established on the basis, the steady state distribution thereof may be calculated, the expectation and variance of the system may be obtained, the source, load, and storage coordinated operation of the regional integrated energy system may be realized in combination with the energy storage device and the multiple loads, the safe and stable operation of the system may be ensured, and a theoretical basis and a reference basis may be provided for the planning design and operation regulation of the system.
Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:
(1) the invention can comprehensively consider all factors (such as energy storage equipment and multi-element load) in the whole energy system, effectively realize the source, load and storage coordination control of the multi-energy system, ensure the safe and stable operation of the system and provide effective theoretical basis and reference basis for the planning design and operation regulation and control of the system.
(2) The construction method of the coupled Markov model is suitable for various random variables including distributed renewable energy power generation equipment, has strong applicability, and can meet the continuously increasing demand of the random variables caused by the further development of a comprehensive energy system.
(3) The effectiveness and the practicability of the method are successfully verified by adopting a simulation example and through data analysis and test.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the method for analyzing the stochastic variables of the regional integrated energy system based on the coupled Markov model.
FIG. 2 is a schematic diagram of wind power output.
Fig. 3 is a schematic diagram of photovoltaic power generation output power.
Fig. 4 is a schematic diagram of a dc-dc converter.
Fig. 5 is a schematic diagram of a dc-to-ac converter.
Fig. 6 is a schematic diagram of a simulation control result.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
The invention provides a random variable analysis method of a regional comprehensive energy system based on a coupled Markov model, which comprises the following specific steps as shown in figure 1:
1. primary random variable determination and analysis in an integrated energy system
And determining the number of variables in the system aiming at the comprehensive energy system in the target area, and carrying out research and analysis based on historical data.
2. Photovoltaic power generation equipment modeling
The photovoltaic power generation equipment comprises a photovoltaic array, a direct current-direct current converter and a direct current-alternating current inverter, wherein a photovoltaic array power generation model is as follows:
Figure BDA0002518681460000081
in the formula, VpvRespectively representing the output voltage of the photovoltaic cell, IpvRepresenting the illumination current, I0Representing the current through the drain, q is the coulomb constant, T represents the Fahrenheit temperature, a represents the diode quality factor, d represents a constant, RsRepresents the equivalent series resistance, RpRepresents the equivalent parallel resistance, NsRepresenting the number of photovoltaic cells in series.
Fig. 2 shows a dc-dc converter, the mathematical formula is as follows:
Figure BDA0002518681460000082
wherein C represents the capacitance of the DC side, L represents the inductance of the DC side, D represents the duty ratio of the control switch tube, vDCAnd iDCRespectively instantaneous voltage and current values, vmpAnd impAnd respectively outputting voltage and current values by the photovoltaic array.
Fig. 3 shows a dc-ac inverter, the mathematical formula is as follows:
Figure BDA0002518681460000083
in the formula, edAnd eqRespectively representing the voltage values, i, of the dq coordinate system after the AC coordinate transformationdAnd iqRespectively representing the current values, u, of the dq coordinate system after the AC coordinate transformationdcAnd iLRespectively, the voltage and current values on the DC side, SdAnd SqRespectively, representing the control signals of the dq coordinate system after the ac coordinate transformation.
3. Modeling of wind power plants
Considering the most common wind power generation at present, the power model of a wind turbine is as follows:
Pw＝0.5πρf2V3Cp
where ρ is the density of air, f is the radius of the rotor, V is the wind speed, CpRepresenting the availability of wind energy, omegatRepresenting angular velocity of the wind turbine, CpDirectly determining the efficiency of the system, the expression is as follows:
Figure BDA0002518681460000091
in the formula, λiRepresenting the intermediate variable, β representing the blade elevation angle, and λ representing the tip speed ratio.
4. Multivariate load modeling
The characteristics of the energy usage in an integrated energy system can be divided into translatable and translatable loads. The translatable load is that the load requirement is met only in a certain time period, generally has fixed load duration and habitual use time, is not prone to be interrupted once started, and is modeled in a unified mode as follows:
Figure BDA0002518681460000092
Figure BDA0002518681460000093
Figure BDA0002518681460000094
wherein: w is ai,j,tAnd
Figure BDA0002518681460000095
respectively representing the power value and the rated power of the ith translatable load of the user j at the moment t, wherein the power value is an electric power value for the electric load, and the power value is a cold/hot power value for the cold/hot load;i,j,ta variable 0-1 representing the i-th class translatable class load enable state for user j at time t,i,j,t0 andi,j,t1 denotes start and no start, respectively;
Figure BDA0002518681460000096
and HiRespectively the start use time, the end use time and the load duration of the ith type translatable load habit.
The transferable load means that the load meets a certain load requirement in a specified time interval and has a certain virtual energy storage characteristic. For electrical loads, which mainly include electric vehicles and the like, mathematical models are as follows:
Figure BDA0002518681460000101
Figure BDA0002518681460000102
Figure BDA0002518681460000103
Figure BDA0002518681460000104
Figure BDA0002518681460000105
wherein:
Figure BDA0002518681460000106
and Sk,tThe charging power and the state of charge of the kth electric vehicle at the moment t are respectively;
Figure BDA0002518681460000107
and EEV,kCharging efficiency and battery capacity of the kth EV, respectively;
Figure BDA0002518681460000108
and
Figure BDA0002518681460000109
minimum and maximum battery states allowed for the kth automobile battery, respectively;
Figure BDA00025186814600001010
and
Figure BDA00025186814600001011
the actual and expected battery states of the kth electric vehicle during off-grid are respectively; t is tari,kAnd tdep,kThe moment when the kth electric vehicle is connected to and separated from the power system respectively;
Figure BDA00025186814600001012
the maximum charging power of the kth car.
5. Single Markov chain model construction
Respectively entering random variables in the integrated energy systemLine Markov modeling, for model event τ and all states S1，S2，S3，…，SτCurrent time SτIs only compared with the previous state Sτ-1In connection with, i.e. for any state r to state S:
P＝{Zr＝s|Zr-1＝r}
the probability p that a model event in state r at the current time is tau-1 transitions to a model event S at the next time is taurs＝{Zτ＝s|Zτ-1R, the transition probability matrix P is obtained as:
Figure BDA00025186814600001013
the transition probability matrix has the following properties:
(1) all elements are non-negative.
(2) The sum of the elements in each row is 1, namely:
Figure BDA0002518681460000111
6. coupled Markov model construction
Because the comprehensive energy system comprises a plurality of random variables, the coupled Markov model is constructed, and clustering analysis is adopted to divide the coupled Markov model into S according to the illumination intensity1In each interval, the wind speed is analyzed by adopting a clustering method to obtain S2The random characteristics of multiple renewable energy sources in each interval can generate S1×S2And (3) a situation. This yields the coupled Markov chain S ═ {1,21×s2Each Markov mode corresponds to the output condition of one renewable energy source, the whole Markov chain comprises all possible working conditions of the renewable energy source power generation, and a transition probability matrix expression of the Markov chain is as follows:
pij(k-1)＝p{r(k)＝j|r(k-1)＝i},ij∈S
where k and k-1 represent the current time and the previous time, respectively, and i and j correspond to two states in the Markov chain, respectively.
All probabilities make up a transition probability matrix as follows:
P(k-1)＝{pij(k-1)},ij∈S
wherein S is a Markov chain.
Because a plurality of variables are independent of each other, the transition probability of the single Markov model and the transition probability of the coupled Markov model have the following relation:
Figure BDA0002518681460000112
in the formula, pmnRepresenting the transition probabilities of the coupled Markov models,
Figure BDA0002518681460000113
and
Figure BDA0002518681460000114
respectively representing the transition probabilities of the single Markov models of the two renewable energy systems.
7. Analysis and coordinated operation method based on coupled Markov model
According to the coupled Markov model, the comprehensive energy system can be analyzed, and the corresponding steady-state distribution pi-phi (pi-phi)1,π2,...,πs) The following conditions are satisfied:
Figure BDA0002518681460000115
Figure BDA0002518681460000116
wherein s represents the total number of states contained in the coupled Markov model, P represents the transition probability corresponding to the coupled Markov model, and X represents the initial state.
The mean value of the system can be calculated according to the coupled Markov model
Figure BDA0002518681460000121
And variance d is as follows:
Figure BDA0002518681460000122
Figure BDA0002518681460000123
wherein, pijAnd piiRepresenting individual state quantities, y, in coupled Markov modelsjAnd xiRespectively shows the output situations of two renewable energy sources,
Figure BDA0002518681460000124
represents the expectation of the coupled Markov model and d represents the variance of the coupled Markov model.
The system expectation and variance obtained by calculation can characterize the comprehensive situation of random change of the system, and the operation mode and equipment of the system are adjusted and controlled according to the comprehensive situation. The renewable energy power generation is uncontrollable, so in order to guarantee stable and safe operation of the system, the energy storage device and flexible load adjustment can be realized, redundant or lacking energy can be adjusted by absorption or release of the energy storage device and translation of the load, the charge and discharge control and the charge and discharge power of the energy storage device depend on the power electronic device, typically a bidirectional direct-direct conversion circuit, and when the energy storage device works in a charge mode, the mathematical expression is as follows:
Figure BDA0002518681460000125
when the energy storage device is operating in the discharge mode, the mathematical expression is as follows:
Figure BDA0002518681460000126
wherein, C1Representing the capacitance, L, in a bidirectional DC-DC converter circuit1Representing inductance, R representing equivalent resistance, d (t)1Indicating switch S1Duty ratio of d (t)2Indicating switch S2The duty cycle of (c).
The heat storage device is controlled in a similar manner, and the energy storage and release power of the energy storage device is controlled by adjusting the inflow and outflow of the energy storage device.
8. Example analysis
The testing example comprises a distributed renewable energy wind-solar power generation system model, the capacities of wind and light power generation systems are respectively 100kW, single Markov chain models of wind power generation and photovoltaic power generation are respectively constructed through cluster analysis, a cluster center point corresponding to each Markov state is calculated, a coupling Markov model and a corresponding transition probability are constructed on the basis, expectation and variance of the system are obtained according to the steady distribution of the coupled Markov chain models, and source, load and storage coordinated operation of a regional comprehensive energy system is realized by combining energy storage equipment and multiple loads.
Finally, the validity of the analysis and operation strategy is verified through example analysis, and the result is shown in fig. 6. The invention promotes the safe and stable operation of the system, and provides effective theoretical basis and reference basis for the planning design and operation regulation of the system.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims (9)

1. A stochastic variable analysis method of a regional integrated energy system based on a coupled Markov model is characterized by comprising the following steps:
s1, determining and analyzing random variables including distributed renewable energy power generation equipment in the comprehensive energy system;
s2, modeling the photovoltaic power generation equipment;
s3, modeling the wind power generation equipment;
s4, modeling the multiple loads;
s5, carrying out modeling analysis of a single Markov model on each random variable in the comprehensive energy system;
s6, comprehensively analyzing the random change condition of the comprehensive energy system based on the single Markov model, and constructing a coupling Markov model of a plurality of random variables by analyzing the multivariate load data;
s7, analyzing the comprehensive energy system based on the coupled Markov model, calculating to obtain the steady-state distribution of the coupled Markov model, calculating to obtain the expectation and the variance of the comprehensive energy system, and adjusting the multi-element load and the energy storage equipment to realize the source, load and storage coordinated operation of the regional comprehensive energy system;
and S8, performing simulation example verification on the coupling model of the renewable energy output random variable.
2. The method for analyzing stochastic variables of regional integrated energy systems based on coupled Markov models according to claim 1, wherein the step S2 comprises the following steps:
s21, dividing the photovoltaic power generation equipment into three parts: the photovoltaic array, the DC-DC converter with maximum power point tracking control and the DC-AC inverter with the controller are connected with the photovoltaic array;
s22, constructing a photovoltaic array power generation model as follows:
Figure FDA0002518681450000011
in the formula, VpvRespectively representing the outputs of the photovoltaic cellsOutput voltage, IpvRepresenting the illumination current, I0Representing the current through the drain, q is the coulomb constant, T represents the Fahrenheit temperature, a represents the quality factor of the diode, d represents the constant, RsRepresents the equivalent series resistance, RpRepresents the equivalent parallel resistance, NsRepresenting the number of the photovoltaic cells connected in series; psThe output power of the photovoltaic array is represented, and I represents the current of the photovoltaic array;
s23, constructing a direct current-direct current converter model with maximum power point tracking control as follows:
Figure FDA0002518681450000012
wherein C represents the capacitance of the DC side, L represents the inductance of the DC side, D represents the duty ratio of the control switch tube, vDCAnd iDCRespectively instantaneous voltage and current values, vmpAnd impVoltage and current values output by the photovoltaic array respectively;
s24, constructing a direct current-alternating current inverter model with the controller as follows:
Figure FDA0002518681450000021
in the formula, edAnd eqRespectively representing the voltage values, i, of the dq coordinate system after the AC coordinate transformationdAnd iqRespectively representing the current values, u, of the dq coordinate system after the AC coordinate transformationdcAnd iLRespectively, the voltage and current values on the DC side, SdAnd SqRespectively, representing the control signals of the dq coordinate system after the ac coordinate transformation.
3. The coupled Markov model based area integrated energy system stochastic variable analysis method of claim 1, wherein the wind power plant modeling process in step S3 comprises:
the power model of the wind turbine is constructed as follows:
Pw＝0.5πρf2V3Cp
where ρ is the density of air, f is the radius of the rotor, V is the wind speed, CpRepresenting the availability of wind energy, CpDirectly determining the efficiency of the system, the expression is as follows:
Figure FDA0002518681450000022
in the formula, λiRepresenting the intermediate variable, β representing the blade elevation angle, and λ representing the tip speed ratio.
4. The method for analyzing stochastic variables of regional integrated energy systems based on coupled Markov models according to claim 1, wherein the step S4 comprises the steps of:
s41, dividing the multi-element load into a translatable load and a translatable load according to the characteristics of the energy;
s42, for translatable loads, the model is constructed as follows:
Figure FDA0002518681450000023
Figure FDA0002518681450000024
Figure FDA0002518681450000025
wherein: w is ai,j,tAnd
Figure FDA0002518681450000026
respectively representing the power value and the rated power of the ith translatable load of the user j at the moment t, wherein the power value is an electric power value for the electric load, and the power value is a cold/hot power value for the cold/hot load;i,j,t0-1 change of i-th type translatable type load starting state of user j representing time tThe amount of the compound (A) is,i,j,t0 andi,j,t1 denotes start and no start, respectively;
Figure FDA0002518681450000031
and HiRespectively setting the starting use time, the ending use time and the load duration time of the ith type translatable load habit;
s43, for transferable loads, the model is constructed as follows:
Figure FDA0002518681450000032
Figure FDA0002518681450000033
Figure FDA0002518681450000034
Figure FDA0002518681450000035
Figure FDA0002518681450000036
wherein:
Figure FDA0002518681450000037
and Sk,tThe charging power and the state of charge of the kth electric vehicle at the moment t are respectively;
Figure FDA0002518681450000038
and EEV,kCharging efficiency and battery capacity of the kth EV, respectively;
Figure FDA0002518681450000039
and
Figure FDA00025186814500000310
minimum and maximum battery states allowed for the kth automobile battery, respectively;
Figure FDA00025186814500000311
and
Figure FDA00025186814500000312
the actual and expected battery states of the kth electric vehicle during off-grid are respectively; t is tari,kAnd tdep,kThe moment when the kth electric vehicle is connected to and separated from the power system respectively;
Figure FDA00025186814500000313
representing the maximum charging power of the kth car.
5. The method for analyzing stochastic variables of regional integrated energy systems based on coupled Markov models according to claim 1, wherein the step S5 of performing modeling analysis of single Markov models on each stochastic variable in the integrated energy system comprises:
markov modeling is respectively carried out on each random variable in the integrated energy system to depict the change characteristics of the random variables, and the change characteristics are recorded as Zτ(τ -1, 2, …, T), state space S-1, 2, …, k, ZτRepresenting each model event tau, the state space S is a collection of grouped event groups of collected similar model events;
for model event τ and all states S1，S2，S3，…，SτCurrent state SτIs only compared with the previous state Sτ-1Regarding from the arbitrary state r to the state S, there are:
P＝{Zr＝s|Zr-1＝r}
the probability p that a model event in state r at the current time is tau-1 transitions to a model event S at the next time is taurs＝{Zτ＝s|Zτ-1R, the transition probability matrix P is obtained as:
Figure FDA0002518681450000041
the transition probability matrix is a matrix containing all transition probabilities and has the following properties:
(1) all elements are non-negative;
(2) the sum of the elements in each row is 1, namely:
Figure FDA0002518681450000042
6. the method for analyzing the stochastic variables of the regional integrated energy system based on the coupled Markov model according to claim 1, wherein the step S6 of constructing the coupled Markov models of the plurality of the stochastic variables comprises the steps of:
analyzing the multivariate load data, and dividing into S according to the illumination intensity by cluster analysis1Individual interval and S obtained by analyzing wind speed division by clustering method2Calculating to obtain S by combining random characteristics of various renewable energy sources in each interval1×S2This results in the coupled Markov chain S ═ 1,21×s2Each Markov mode corresponds to the output condition of one renewable energy source, and the whole Markov chain comprises all possible working conditions of the renewable energy source for power generation;
transition probabilities are used for describing the jumping situation between the Markov modes:
pij(k-1)＝p{r(k)＝j|r(k-1)＝i},ij∈S
in the formula, k and k-1 respectively represent the current time and the last time, and i and j respectively correspond to two states in a Markov chain;
and (3) forming all transition probabilities into a transition probability matrix:
P(k-1)＝{pij(k-1)},ij∈S
wherein S is a Markov chain;
the relationship between the transition probability of the single Markov model and the transition probability of the coupled Markov model is calculated as follows:
Figure FDA0002518681450000043
in the formula, pmnRepresenting the transition probabilities of the coupled Markov models,
Figure FDA0002518681450000044
and
Figure FDA0002518681450000045
respectively representing the transition probabilities of the single Markov models of the two renewable energy systems.
7. The method for analyzing stochastic variables of regional integrated energy systems based on coupled Markov models according to claim 1, wherein the step S7 of calculating the steady state distribution of the coupled Markov models comprises:
analyzing the comprehensive energy system according to the coupled Markov model, wherein the corresponding steady-state distribution pi-is (pi)1,π2,...,πs) The following conditions are satisfied:
Figure FDA0002518681450000051
Figure FDA0002518681450000052
wherein s represents the total number of states contained in the coupled Markov model, P represents the transition probability corresponding to the coupled Markov model, and X represents the initial state.
8. The method for analyzing stochastic variables of regional integrated energy systems based on coupled Markov models according to claim 7, wherein the step S7 of calculating the expectation and variance of the integrated energy system comprises:
Figure FDA0002518681450000053
Figure FDA0002518681450000054
wherein, pijAnd piiRepresenting individual state quantities, y, in coupled Markov modelsjAnd xiRespectively shows the output situations of two renewable energy sources,
Figure FDA0002518681450000055
represents the expectation of the coupled Markov model and d represents the variance of the coupled Markov model.
9. The method for analyzing the stochastic variables of the regional integrated energy system based on the coupled Markov model according to claim 7, wherein the step of adjusting the multiple loads and the energy storage device to realize the source, load and storage coordinated operation of the regional integrated energy system comprises the following steps:
s71, constructing an energy storage device model:
when the energy storage device is operating in the charging mode, the mathematical expression is as follows:
Figure FDA0002518681450000056
when the energy storage device is operating in the discharge mode, the mathematical expression is as follows:
Figure FDA0002518681450000057
d(t)1representing the switch S in a DC-DC converter circuit1Duty ratio of d (t)2Representing the switch S in a DC-DC converter circuit2The duty cycle of (a) is,
Figure FDA0002518681450000061
indicating direct current in discharge modePressing,
Figure FDA0002518681450000062
Indicating the magnitude of the current, V, flowing through the inductordcIndicating the voltage magnitude;
and S72, absorbing or releasing the energy storage device and translating the load to adjust the surplus or the lack of energy, and controlling the energy storage and release power of the energy storage device by adjusting the inflow and the outflow of the energy storage device.
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