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:
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:
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:
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:
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:
wherein: w is a
i,j,tAnd
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 and
i,j,t1 denotes start and no start, respectively;
and H
iRespectively 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:
wherein:
and
the charging power and the state of charge of the kth electric vehicle at the moment t are respectively;
and E
EV,kCharging efficiency and battery capacity of the kth EV, respectively;
and
minimum and maximum battery states allowed for the kth automobile battery, respectively;
and
the actual and expected battery states of the kth electric vehicle during off-grid are respectively; t is t
ari,kAnd t
dep,kThe moment when the kth electric vehicle is connected to and separated from the power system respectively;
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:
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:
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:
in the formula, p
mnRepresenting the transition probabilities of the coupled Markov models,
and
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:
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
And variance d is as follows:
wherein, pi
jAnd pi
iRepresenting individual state quantities, y, in coupled Markov models
jAnd x
iRespectively shows the output situations of two renewable energy sources,
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:
when the energy storage device is operating in the discharge mode, the mathematical expression is as follows:
wherein, C
1Representing the capacitance, L, in a bidirectional DC-DC converter circuit
1Representing inductance, R representing equivalent resistance, d (t)
1Indicating switch S
1Duty ratio of d (t)
2Indicating switch S
2The duty cycle of (a) is,
indicates the DC voltage in the discharge mode,
Indicating the magnitude of the current, V, flowing through the inductor
dcIndicating 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.
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:
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:
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:
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:
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:
wherein: w is a
i,j,tAnd
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 and
i,j,t1 denotes start and no start, respectively;
and H
iRespectively 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:
wherein:
and S
k,tThe charging power and the state of charge of the kth electric vehicle at the moment t are respectively;
and E
EV,kCharging efficiency and battery capacity of the kth EV, respectively;
and
minimum and maximum battery states allowed for the kth automobile battery, respectively;
and
the actual and expected battery states of the kth electric vehicle during off-grid are respectively; t is t
ari,kAnd t
dep,kThe moment when the kth electric vehicle is connected to and separated from the power system respectively;
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:
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:
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:
in the formula, p
mnRepresenting the transition probabilities of the coupled Markov models,
and
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:
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
And variance d is as follows:
wherein, pi
jAnd pi
iRepresenting individual state quantities, y, in coupled Markov models
jAnd x
iRespectively shows the output situations of two renewable energy sources,
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:
when the energy storage device is operating in the discharge mode, the mathematical expression is as follows:
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.