CN109409609A - Probability constraint modeling method and device for multi-energy flow supply and demand balance of comprehensive energy system - Google Patents
Probability constraint modeling method and device for multi-energy flow supply and demand balance of comprehensive energy system Download PDFInfo
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Abstract
The invention discloses a probability constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system, which can carry out multi-energy flow supply and demand balance modeling on the integrated energy system according to uncertain factors in the integrated energy system, and simultaneously, as the probability constraint conditions in the integrated energy system are more flexible than the rigid constraint conditions in the prior art, the actual operation requirements of the integrated energy system can be met, and the safe and stable operation of the integrated energy system is ensured. In addition, the invention also discloses a probability constraint modeling device for the balance of the multi-energy flow supply and demand of the comprehensive energy system, and the effect is as above.
Description
Technical Field
The invention relates to the field of comprehensive energy, in particular to a probabilistic constraint modeling method and device for multi-energy flow supply and demand balance of a comprehensive energy system.
Background
The comprehensive energy system mainly comprises an electric-gas coupling system, an electric-thermal coupling system, an electric-cold coupling system, an electric-gas-thermal coupling system and the like, at present, the comprehensive energy system is optimally scheduled mainly by considering network constraint, equipment output constraint, climbing energy profit constraint and the like of the system, and when the optimal scheduling is carried out, a coupled energy conversion device among different systems is modeled, and the integrated energy system mainly comprises a cogeneration unit, an electric boiler, a gas unit, an electric-to-gas device and the like. In addition, the energy hub model also provides a simplified and intuitive mode for modeling the coupling relation between the multi-energy systems.
However, when modeling is carried out on the multi-energy flow supply and demand balance of the comprehensive energy system at present, a deterministic model is mainly used, namely, a multi-energy flow supply and demand balance model of the comprehensive energy system is established according to deterministic factors in the comprehensive energy system; on one hand, renewable energy sources such as wind and light in the integrated energy system have strong randomness and fluctuation, and in addition, parameters of energy transmission and energy conversion devices in the integrated energy system also have uncertainty, and the uncertainty factors have great influence on the safe and stable operation of the system of the integrated energy system. On the other hand, when modeling the supply and demand balance of the multi-energy flow in the integrated energy system, the robust optimization method is mostly adopted to model the supply and demand balance of the multi-energy flow in the integrated energy system, and the robust optimization method requires that the integrated energy system meets a series of rigid constraints in the worst scene, so that the robust optimization method is too conservative, namely the integrated energy system cannot violate the constraint conditions in any situation, but actually, the integrated energy system can be accepted to violate some constraint conditions in a short time in the actual operation process. Therefore, when a robust optimization method is adopted to model the comprehensive energy system, the modeling is too conservative, so that the final modeling model cannot meet the actual operation requirement of the comprehensive energy system, and further the safe and stable operation of the comprehensive energy system is greatly influenced.
Therefore, how to combine the uncertainty factors in the integrated energy system to model the multi-energy flow supply and demand balance of the integrated energy system so as to ensure the safe and stable operation of the integrated energy system is a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a probabilistic constraint modeling method and a probabilistic constraint modeling device for multi-energy supply and demand balance of an integrated energy system, which are used for modeling the multi-energy supply and demand balance of the integrated energy system by combining uncertainty factors in the integrated energy system, so that the safe and stable operation of the integrated energy system is ensured.
In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:
the embodiment of the invention provides a probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system, which comprises the following steps:
acquiring constraint conditions corresponding to a plurality of pre-established energy hub models in the comprehensive energy system;
acquiring uncertainty factors in the integrated energy system and predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule;
determining probability constraint conditions in the comprehensive energy system by using the probability predicted value;
acquiring power generation cost data and energy value cost data of system equipment in the comprehensive energy system;
determining a target function of the multi-energy flow supply and demand balance of the comprehensive energy system according to the power generation cost data and the energy value data;
and establishing a multi-energy flow supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the objective function.
Preferably, the building process of each energy hub model specifically includes:
acquiring the energy type in the integrated energy system, the energy conversion equipment in the integrated energy system and the load type in the integrated energy system;
establishing an energy supply and demand balance equality relation between the energy input end and the energy output end based on the conversion efficiency of the energy conversion equipment by taking various types of energy as energy input of the energy input end and the load type as energy output of the energy output end;
the energy hub model includes the energy supply and demand balance equation relationship.
Preferably, the uncertain factors in the integrated energy system specifically include:
the output of photovoltaic power generation equipment in the comprehensive energy system;
correspondingly, the predicting the probability prediction value corresponding to the uncertainty factor by using the sample data corresponding to the uncertainty factor according to the predefined rule specifically includes:
acquiring photovoltaic output power sample data corresponding to the output of the photovoltaic power generation equipment;
and predicting a nuclear density predicted value corresponding to the output of the photovoltaic power generation equipment as the probability predicted value by using the photovoltaic output power sample data based on a nonparametric nuclear density estimation method.
Preferably, the determining the probability constraint condition in the integrated energy system by using the probability prediction value comprises:
acquiring a power route in the integrated energy system, a maximum power limit corresponding to the power route and a probability that an allowable power corresponding to the circuit route is out of limit;
correspondingly, the probability constraint condition is specifically represented by the following formula:
wherein,predicting a power probability PL in the integrated energy system for a period tl,tLess than or equal to the maximum power limitProbability of (α)lIs the probability that the allowed power is out of limit.
Preferably, the determining the probabilistic constraint condition in the integrated energy system using the probabilistic predictive value further includes:
acquiring a natural gas pipeline in the integrated energy system, a maximum airflow limit value corresponding to the natural gas pipeline and the probability of out-of-limit allowable flow corresponding to the natural gas pipeline;
correspondingly, the probability constraint condition is specifically represented by the following formula:
in the above formula, the first and second carbon atoms are,the predicted value of the airflow probability in the comprehensive energy system is less than or equal to the maximum airflow limit value corresponding to the natural gas pipeline in the period tProbability of (α)plIs the probability that the allowed flow rate is out of limit.
Preferably, the determining the probabilistic constraint condition in the integrated energy system using the probabilistic predictive value further includes:
acquiring the probability of out-of-limit total emission, maximum emission and allowable emission of traditional system equipment, combined heat and power generation equipment and gas boiler equipment in the comprehensive energy system;
correspondingly, the probability constraint condition is specifically represented by the following formula:
wherein,for the t period of time total emission in the integrated energy systemLess than or equal to the maximum discharge amountProbability of (β)tIs the probability that the allowable emission amount is out of limit.
Preferably, the objective function of the multi-energy flow supply and demand balance of the integrated energy system is specifically represented by the following formula:
wherein t is a time interval sequence number and gammatEnergy value cost data for time period t, GtEnergy amount of energy source for t period, ag,t、bg,tAnd cg,tGenerating cost coefficient for system equipment, EG for set of system equipment group, Eg,tThe generated power of the system equipment in the time period t is shown, and H is the number of the time periods.
Preferably, the constraint condition specifically includes:
a multi-energy flow supply and demand balance constraint of the integrated energy system and a safe operation constraint of system equipment of the integrated energy system;
the multi-energy flow supply and demand balance constraint of the comprehensive energy system specifically comprises an electric power balance constraint and a natural gas balance constraint, and the safe operation constraint specifically comprises a heat and power cogeneration equipment safe operation constraint;
the power balance constraint is specifically represented by the following formula:
wherein l is the serial number of the power line in the integrated energy system, EL is the set of the power line in the integrated energy system, PLl,tIs the power of the power line l, θ, for a period of tlf,tIs the phase angle of the starting end of the power line l in the period tle,tIs the terminal phase angle, x, of the power line l during a period tlIs the reactance of the power line l;
the natural gas balance constraint is specifically represented by the following formula:
wherein Q isi,tThe net injection amount and f of natural gas of a node i in the t period in the comprehensive energy systemim,tAnd fnm,tRespectively injecting natural gas into the node i at the t period in the comprehensive energy system, flowing out the natural gas, and Fj.tGas, G consumed by compressor in t period branch j in the comprehensive energy systemijThe values of gas taking coefficients of the compressors in the branch i and the branch j of the integrated energy system are set to be 1 when gas is taken from the node i;
the safe operation constraint of the cogeneration equipment specifically comprises an electric output operation constraint of the cogeneration equipment of an energy hub node i where the energy hub model is located and a thermal output operation constraint of the cogeneration equipment;
the electrical output operation constraint is specifically represented by the following formula:
wherein N is the number of nodes, H is the number of time periods,The electric energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The upper limit of the electric energy output of the cogeneration equipment in the node i in the time period t,Efficiency of converting natural gas into electrical energy for a cogeneration unit, kappa the distribution coefficient of natural gas between a gas boiler plant and a cogeneration plant, Pgas,i,tNatural gas input to an energy hub node i in the comprehensive energy system at the time period t;
the thermodynamic operating constraint is specifically represented by the following formula:
wherein,the heat energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The upper limit of the thermal energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The efficiency of converting natural gas into heat energy in the thermoelectric cogeneration equipment in the node i in the time period t is shown.
The embodiment of the invention provides a probability constraint modeling device for multi-energy flow supply and demand balance of an integrated energy system, which comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring constraint conditions corresponding to a plurality of pre-established energy hub models in the comprehensive energy system;
the second acquisition module is used for acquiring uncertainty factors in the integrated energy system and predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule;
the first determination module is used for determining probability constraint conditions in the comprehensive energy system by using the probability predicted value;
the third acquisition module is used for acquiring power generation cost data and energy value cost data of system equipment in the comprehensive energy system;
the second determination module is used for determining a target function of multi-energy supply and demand balance of the comprehensive energy system according to the power generation cost data and the energy value data;
and the establishing module is used for establishing a multi-energy flow supply and demand balance model of the comprehensive energy system by utilizing the constraint conditions, the probability constraint conditions and the objective function.
The embodiment of the invention provides another probability constraint modeling device for multi-energy flow supply and demand balance of an integrated energy system, which comprises:
a memory for storing a computer program;
a processor for executing a computer program stored in the memory to implement the steps of any of the above-mentioned probabilistic constraint modeling methods of integrated energy system multi-energy flow supply and demand balance.
The probability constraint modeling method for the multi-energy-flow supply and demand balance of the comprehensive energy system disclosed by the embodiment of the invention comprises the steps of firstly obtaining constraint conditions corresponding to a plurality of energy junction models which are established in advance in the comprehensive energy system, then obtaining uncertainty factors in the comprehensive energy system, predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule, then determining the probability constraint conditions in the comprehensive energy system by using the probability predicted values, then establishing a target function of the multi-energy-flow supply and demand balance of the comprehensive energy system according to power generation cost data and energy value cost data of system equipment in the comprehensive energy system, and finally establishing the multi-energy-flow supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the target function. Therefore, by adopting the scheme, the multi-energy flow supply and demand balance modeling can be carried out on the comprehensive energy system according to uncertain factors in the comprehensive energy system, and meanwhile, probability constraint conditions in the comprehensive energy system are more flexible than rigid constraint conditions in the prior art, so that the actual operation requirements of the comprehensive energy system can be met, and the safe and stable operation of the comprehensive energy system is ensured. In addition, the embodiment of the invention also discloses a probability constraint modeling device for the balance of the multi-energy flow supply and demand of the comprehensive energy system, and the effect is as above.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a probabilistic constraint modeling device for balancing supply and demand of multiple energy flows of an integrated energy system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another probabilistic constraint modeling device for multi-energy flow supply and demand balance of an integrated energy system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a probability constraint modeling method and a probability constraint modeling device for multi-energy flow supply and demand balance of an integrated energy system, wherein the multi-energy flow supply and demand balance of the integrated energy system is modeled by combining uncertainty factors in the integrated energy system, so that the safe and stable operation of the integrated energy system is ensured.
Referring to fig. 1, fig. 1 is a schematic flow chart of a probabilistic constraint modeling method for balancing supply and demand of multiple energy flows of an integrated energy system, according to an embodiment of the present invention, where the method includes:
s101, obtaining constraint conditions corresponding to a plurality of pre-established energy hub models in the comprehensive energy system.
In this embodiment, first, an energy hub model of the integrated energy system is explained:
the energy hub is a unit for describing input and output, mutual conversion and storage of different energy flows, and can also be regarded as a network node of a comprehensive energy system, various energy flows are input from an inlet section of the energy hub and pass through related energy transmission and conversion equipment to meet various types of loads at an outlet end of the energy hub, and a basic idea of a mathematical model is to describe a relation between input energy flows and output energy by constructing a coupling matrix, wherein the mathematical model can be specifically represented by the following formula:
the above formula can be easily expressed, and specifically, the following formula is provided:
L＝C·P
the elements in the L matrix are output power vectors of the energy hub, the elements in the P matrix are input power vectors of the energy hub, the C matrix is a coupling matrix between the input power vectors and the output power vectors, and the values of the elements in the coupling matrix C depend on the component composition and the mutual connection mode inside the energy hub.
Based on the mathematical model, in the embodiment of the invention, the energy types in the integrated energy system comprise natural gas and electric energy, the load types in the integrated energy system comprise thermal load and electric load, and the energy conversion equipment in the integrated energy system comprises a gas turbine-based cogeneration unit for generating electric energy and heat energy by natural gas according to a certain proportion; a gas boiler for burning natural gas to generate heat energy; a transformer: the transmission of electric energy is realized; and an AC/AC and DC/AC conversion device for realizing the access of wind power and photovoltaic power.
Therefore, the energy input end vector in the embodiment of the present invention includes various types of electric energy and natural gas energy, and may specifically be represented by the following formula:
wherein, in the above formula, PwindFor the electrical energy, P, of a wind power plantpvFor the electrical energy, P, of a photovoltaic power plante_netFor electric energy, P of large power grid in comprehensive energy systemgasThe power is the power of natural gas power generation equipment.
The load types included in the energy output end L in the embodiment of the present invention are an electrical load and a thermal load, where the energy output end L can be represented by the following formula:
wherein L iseleFor an electrical load, LheatIs the heat load.
For the relationship between various input energies at the energy input end and various output loads at the energy output end and the requirement for establishing the supply and demand balance when energy is transmitted between the energy conversion devices, the following formula can be specifically adopted for expression:
wherein, ηwFor the conversion efficiency of the converter, ηnetIn order to be efficient for the transformer,for the efficiency of cogeneration plants in converting natural gas to electricity,natural gas for use in cogeneration plants.
Wherein,in order to improve the heat energy conversion efficiency of the gas boiler,for the efficiency of the cogeneration unit in converting natural gas into heat energy,is the natural gas used by the gas-fired boiler,natural gas for use in cogeneration plants.
In the above formulaAndcan be represented using the following formulae, respectively:
in the above formula, κ is a distribution coefficient of natural gas input to the energy hub model between the gas boiler and the cogeneration plant, PgasIs the total natural gas input into the energy hub, wherein, kappa PgasIs used by a cogeneration plant, and the (1-kappa) PgasThe natural gas of (2) is used by a gas boiler.
The input-output coupling matrix parameters of the energy hub model are determined by the various conversion efficiency parameters and the distribution coefficients, and can be specifically represented by the following formula:
it should be noted that the energy hub model is a single energy hub model, and in addition, each single energy hub model may be associated with a natural gas network through a power network to obtain an interconnected multi-energy hub model. For the interconnected multi-energy hub system, the energy required to be input from the upper-level power grid and the air grid is distributed at the input port of each energy hub, and the following formula can be adopted in a mathematical expression:
wherein, the above formula can be simply expressed as follows:
Pα＝Sα·fα
in the above formula, PαVector, S, of energy flows α required to describe each energy hub modelαFor the network topology connection matrix corresponding to the power flow α, the elements in the matrix take on values {0,1, -1}, fαThe energy flow of the corresponding branch in the comprehensive energy system.
In summary, as a preferred embodiment of the present invention, the process of establishing each energy hub model specifically includes:
acquiring energy types in the comprehensive energy system, energy conversion equipment in the comprehensive energy system and load types in the comprehensive energy system;
establishing an energy supply and demand balance equality relation between the energy input end and the energy output end based on the conversion efficiency of the energy conversion equipment by taking various types of energy as the energy input of the energy input end and the energy output of the load type as the energy output of the energy output end;
the energy hub model includes an energy supply and demand balance equality relationship.
Next, the constraint conditions in step S101 are explained as follows:
the constraint conditions in the integrated energy system include the constraint conditions of balance of energy flow supply and demand in the integrated energy system, and the operation constraints and safety intervals of system equipment in the integrated energy system, in addition to the constraint conditions of balance of energy flow supply and demand in the energy flow hub model mentioned above.
As a preferred embodiment, the multi-energy-flow supply and demand balance constraint of the integrated energy system specifically includes a power balance constraint and a natural gas balance constraint, the safe operation constraint specifically includes a cogeneration equipment safe operation constraint, the power balance constraint is that the power system adopts a direct current power flow model, that is, a branch power flow and a node voltage phase angle are in a linear relationship, and the power balance constraint is specifically represented by the following formula:
wherein, l is the serial number of the power line in the integrated energy system, EL is the collection of the power line in the integrated energy system, PLl,tIs the power of the power line l, θ, for a period of tlf,tIs the phase angle of the starting end of the power line l in the period tle,tIs the terminal phase angle, x, of the power line l during a period tlIs the reactance of the power line l;
for a natural gas pipe network, the gas quantity balance of each node needs to be considered, and the natural gas balance constraint is specifically represented by the following formula:
wherein Q isi,tThe net injection amount and f of natural gas of a t-period node i in the comprehensive energy systemim,tAnd fnm,tAre respectively provided withNatural gas injection flow and natural gas outflow flow F of t-period node i in the comprehensive energy systemj.tGas, G, consumed by the compressor in the t-period branch j in the integrated energy systemijThe values of gas taking coefficients of compressors in a branch i and a branch j of the comprehensive energy system are 1 when gas is taken from a node i;
in the above formula, the natural gas injection flow f of the pipe network branchim,tThe calculation can be performed according to the Weymouth equation, which is specifically given by the following formula:
in the above formula, WimCoefficient of friction, p, for natural gas pipe networki,tAnd pm,tAnd (4) the pressure of the ith pipe network branch and the mth pipe network branch in the t period.
For branch j, F containing compressorj.tCan be represented by the following formula:
in the above formula, α, k1、k2And ω denotes the correlation constant, ηcCompressor efficiency is indicated.
For the safe operation constraint of the cogeneration equipment, the safe operation constraint of the cogeneration equipment specifically comprises the electric output operation constraint of the cogeneration equipment of the energy hub node i where the energy hub model is located and the thermal output operation constraint of the cogeneration equipment;
the electrical output operating constraints are specifically represented by the following formula:
wherein N is the number of nodes, H is the number of time periods,The electric energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The upper limit of the electric energy output of the cogeneration equipment in the node i in the time period t,Efficiency of converting natural gas into electrical energy for a cogeneration unit, kappa the distribution coefficient of natural gas between a gas boiler plant and a cogeneration plant, Pgas,i,tNatural gas input to a t-period energy hub node i in the comprehensive energy system;
the thermodynamic operating constraints are specifically represented by the following formula:
wherein,the heat energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The upper limit of the thermal energy output of the thermoelectric cogeneration equipment in the node i in the time period t,Is node i in heatEfficiency of the cogeneration plant to convert natural gas to heat energy at time t.
Further, in addition to the above-mentioned constraints, the following constraints are also included:
for any generator g in the integrated energy system, the following constraints apply:
wherein E isg,tThe generated electric quantity of the generator in the time period t,And EG is the set of generators and is the maximum power generation capacity of the generators in the t period.
For a gas boiler of an arbitrary energy hub (node) i, the following constraints apply:
in the above formula, the first and second carbon atoms are,the thermal energy output of the gas boiler in the node i in the time period t,and (4) the upper limit of the thermal energy output of the gas boiler in the node i in the time period t.
For the wind power accessed to any node i, the following constraint conditions are provided:
in the above formula, Pwind,i,tThe output of wind power connected to an energy hub (node) i in a time period t,and connecting the output upper limit of the wind power at the time t for the energy hub (node) i.
For photovoltaic access to any node i, the following constraints apply:
in the above formula, Ppv,i,tThe output of the photovoltaic connected to the energy hub (node) i in the time period t,and connecting the output upper limit of the photovoltaic connected to the energy hub (node) i in the time period t.
And S102, acquiring uncertainty factors in the comprehensive energy system and predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule.
Specifically, in this embodiment, the uncertainty factor in the integrated energy system is first explained:
the safe and stable operation of the comprehensive energy system is influenced by the loads from different single energy systems and random factors of various aspects of accessed renewable energy sources such as wind power, light and the like. Under the consideration of the random variation of the series of renewable energy sources as input variables, not only the optimization goal of the comprehensive energy source system needs to be achieved, but also the safety constraint of the operation of the comprehensive energy source system in an uncertain environment needs to be met, and the feasibility of the scheme is ensured. Therefore, it is essential to analyze uncertainty factors in the integrated energy system. In the embodiment of the invention, the main uncertain factor sources of the electric-heat-gas coupling comprehensive energy system containing renewable energy in the comprehensive energy system in the injection amount aspect comprise the output of wind power and photovoltaic and the uncertainty of load.
Firstly, the uncertainty of wind power output is caused, wind energy is renewable energy with the highest global reserves, and wind is mainly used for blowing blades of a fan to rotate, converting the wind energy into mechanical energy and further converting the mechanical energy into electric energy. The output power of the fan depends on multiple factors, such as wind speed, fan speed, etc. Meanwhile, the wind speed is influenced by a series of factors such as climate and terrain, so that the output of wind power has strong randomness and uncertainty, and the wind speed is generally considered to be distributed according to Weibull in the existing research.
Secondly, the uncertainty of the output of photovoltaic power generation, the photovoltaic power generation is a new energy power generation technology which is developed rapidly in recent years, the main principle of the photovoltaic power generation is that solar energy is converted into electric energy through a photoproduction Ford effect, and the photovoltaic power generation has high flexibility. Similar to wind power, the output condition of photovoltaic power generation is also influenced by a series of factors such as the sunlight radiation intensity and the surface condition of the battery plate, so that the photovoltaic power generation has randomness. Generally, the solar illumination intensity obeys Beta distribution, and according to the solar hanging power generation output power expression, the photovoltaic output is positively correlated with the solar illumination intensity, so that the solar illumination intensity obeys the Beta distribution.
For load uncertainty, loads are mainly divided into electrical loads and thermal loads, load fluctuation can be influenced by environmental conditions and energy prices, and at present, in the operation problem of an integrated energy system, the probability characteristic of the loads is generally described by normal distribution. In the embodiment of the invention, in a renewable energy access integrated energy system, a data-driven method is adopted to analyze the probability characteristics of renewable energy output and various types of loads, namely wind power of the integrated energy system, wind power station/photovoltaic power station measured data of a photovoltaic access point and required point load data are directly taken and researched, and a nonparametric analysis method based on actual sample data is utilized, preferably a nonparametric kernel density estimation method is adopted in the embodiment of the invention to model wind power/photovoltaic output power and electric heating loads.
Because the uncertainty of the output of the photovoltaic power generation equipment is far higher than the influence of other uncertainty factors on the safe and stable operation of the comprehensive energy system, as a preferred embodiment of the invention, the uncertainty factors in the comprehensive energy system specifically include:
the output of photovoltaic power generation equipment in the comprehensive energy system;
correspondingly, step S102 specifically includes:
acquiring photovoltaic output power sample data corresponding to the output of the photovoltaic power generation equipment;
and predicting a nuclear density predicted value corresponding to the output of the photovoltaic power generation equipment as a probability predicted value by using the photovoltaic output power sample data based on a nonparametric nuclear density estimation method.
Specifically, in the preferred embodiment, the probability density function of the output power of the photovoltaic is PDF (p)pv)，ppvFor the photovoltaic output power sample vector, P can be usedpv＝[p1p2…pn]Is shown, wherein PDF (p)pv) The kernel estimation expression of (a) may be represented by the following formula:
where h is the bandwidth, n is the number of samples, K (-) is the kernel function, piIs the output power of the ith photovoltaic power generation device.
The bandwidth h and the kernel function will affect the accuracy of the kernel density estimation, and therefore, the optimal bandwidth needs to be selected to avoid affecting the estimation of the kernel density. The optimal bandwidth can be selected by minimizing the integral mean square error of the probability density function estimation under the condition of different kernel density function selection, and the mathematical optimization model can be specifically represented by the following formula:
s.t.
Dh≤Dc
in the above formula, the first and second carbon atoms are,andfor kernel estimation of corresponding two different kernel functions,estimating χ for kernel density2The statistical quantity is tested and the statistical quantity is tested,estimating χ for kernel density2Test threshold value, DhFor K-S test statistics, DcIs K-S test critical value, wherein, the calculation method of X2 test statistic can adopt the following formula to calculate:
in the above formula, oiIs the observation frequency of the ith interval, piAnd (4) assuming a theoretical probability value of the probability distribution in the interval i for the data, wherein k is the number of the interval sections.
The statistical quantity calculation method of the K-S test can be represented by the following formula:
at this time, the assumed distribution function F is considered at the same timeo(pi) And an empirical distribution function Fn(pi) And obtaining a final nuclear density estimated value of the photovoltaic power generation equipment as follows:
further, to verify the predicted kernel density value, the root mean square error e may be usedrootAnd percent error of average emeanThe kernel density estimation value is verified to ensure that the error of the kernel density estimation value is kept in a proper range, and the verification method specifically comprises the following steps:
in the above formula, the first and second carbon atoms are,the distribution is estimated for the density of the nuclei,is the probability of the histogram in bin i.
In addition, for uncertain factors, the influence of the environmental temperature and the gas composition on the natural gas pipeline parameters and even the operation of the pipeline is also considered, and therefore, the embodiment of the invention introduces the analysis of the network uncertainty. Historical data of sufficient high quality natural gas pipeline parameters are difficult to acquire relative to the injection quantity, and therefore can be simulated with known distributions. In the embodiment of the invention, uncertainty analysis is carried out by adopting a mode of combining normal distribution and uniform distribution.
Flow equation of pipeline in air networkFor example, if the parameter W is subject to a normal distribution, the probability density function can be expressed as follows:
in the above formula,. mu.WAnd σWMean and standard deviation of the parameter W.
μWThe most probable value of the parameter W is, but in practice, rarely a certain point but a certain interval. The uncertainty of describing the parameter W with a uniform distribution can therefore be taken into account. The evenly distributed interval endpoint value is taken as muW-3 σ and μW+3σWThen the probability density function of the parameter W can be finally expressed by the following formula:
in addition, the probability prediction value in the embodiment of the present invention is different according to the calculation target in the integrated energy system, and the probability prediction value is also different.
And S103, determining probability constraint conditions in the comprehensive energy system by using the probability predicted values.
Specifically, in this embodiment, the probability constraint condition is determined by an accumulated distribution function obtained by accumulating according to the probability distribution functions obtained in the above embodiments, where the probability constraint condition in the integrated energy system includes a probability constraint of the power line, a natural gas probability constraint, and an environmental constraint.
As a preferred embodiment, the determining the probability constraint condition in the integrated energy system by using the probability prediction value includes:
acquiring an electric power route in the comprehensive energy system, a maximum power limit value corresponding to the electric power route and the probability of out-of-limit allowable power corresponding to a circuit route;
correspondingly, the probability constraint condition is specifically expressed by the following formula:
wherein,for power probability predicted value PL in t period comprehensive energy systeml,tLess than or equal to the maximum power limitProbability of (α)lTo allow for the probability of power violations.
As a preferred embodiment, the determining the probability constraint condition in the integrated energy system by using the probability prediction value further includes:
acquiring a natural gas pipeline in the comprehensive energy system, a maximum airflow limit value corresponding to the natural gas pipeline and the probability of out-of-limit allowable flow corresponding to the natural gas pipeline;
correspondingly, the probability constraint condition is specifically expressed by the following formula:
in the above formula, the first and second carbon atoms are,the predicted value of the airflow probability in the comprehensive energy system at the t period is less than or equal to the maximum airflow limit value corresponding to the natural gas pipelineProbability of (α)plTo allow for the probability of the flow crossing the limit.
As a preferred embodiment, the determining the probability constraint condition in the integrated energy system by using the probability prediction value further includes:
acquiring the probability of out-of-limit total emission, maximum emission and allowable emission of traditional system equipment, combined heat and power generation equipment and gas boiler equipment in the comprehensive energy system;
correspondingly, the probability constraint condition is specifically expressed by the following formula:
wherein,for total emission in the t-period integrated energy systemLess than or equal to the maximum dischargeProbability of (β)tThe probability of allowing emissions to exceed limits.
Specifically, in the present embodiment,specifically, the following formula can be used:
wherein, in the above formula, Cele(·)、CCHP(. and C)FurShown are the emission functions of a conventional unit, a cogeneration unit and a gas boiler, respectively.
And S104, acquiring power generation cost data and energy value cost data of system equipment in the comprehensive energy system.
And S105, establishing a multi-energy flow supply and demand balance objective function of the comprehensive energy system according to the power generation cost data and the energy value data.
Specifically, in this embodiment, the power generation cost data is the power generation cost of the power generator, and the energy value data is the gas purchase price.
As a preferred embodiment of the present invention, the objective function of the multi-energy flow supply and demand balance of the integrated energy system can be specifically expressed by the following formula:
wherein t is a time interval sequence number and gammatEnergy value cost data for time period t, GtEnergy amount of energy source for t period, ag,t、bg,tAnd cg,tGenerating cost coefficient for system equipment, EG for set of system equipment group, Eg,tThe generated power of the system equipment in the time period t is shown, and H is the number of the time periods.
And S106, establishing a multi-energy flow supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the objective function.
The probability constraint modeling method for the multi-energy-flow supply and demand balance of the comprehensive energy system disclosed by the embodiment of the invention comprises the steps of firstly obtaining constraint conditions corresponding to a plurality of energy junction models which are established in advance in the comprehensive energy system, then obtaining uncertainty factors in the comprehensive energy system, predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule, then determining the probability constraint conditions in the comprehensive energy system by using the probability predicted values, then establishing a target function of the multi-energy-flow supply and demand balance of the comprehensive energy system according to power generation cost data and energy value cost data of system equipment in the comprehensive energy system, and finally establishing the multi-energy-flow supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the target function. Therefore, by adopting the scheme, the multi-energy flow supply and demand balance modeling can be carried out on the comprehensive energy system according to uncertain factors in the comprehensive energy system, and meanwhile, probability constraint conditions in the comprehensive energy system are more flexible than rigid constraint conditions in the prior art, so that the actual operation requirements of the comprehensive energy system can be met, and the safe and stable operation of the comprehensive energy system is ensured.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a probabilistic constraint modeling apparatus for balancing supply and demand of multiple energy flows of an integrated energy system according to an embodiment of the present invention, including:
a first obtaining module 201, configured to obtain constraint conditions corresponding to a plurality of energy hub models that are established in advance in the integrated energy system;
the second obtaining module 202 is configured to obtain uncertainty factors in the integrated energy system and predict probability predicted values corresponding to the uncertainty factors according to predefined rules by using sample data corresponding to the uncertainty factors;
the first determining module 203 is used for determining probability constraint conditions in the comprehensive energy system by using the probability predicted value;
a third obtaining module 204, configured to obtain power generation cost data and energy value cost data of system equipment in the integrated energy system;
the second determining module 205 is configured to determine an objective function of multi-energy supply and demand balance of the integrated energy system according to the power generation cost data and the energy value data;
and the establishing module 206 is used for establishing a multi-energy flow supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the objective function.
The probability constraint modeling device for the multi-energy supply and demand balance of the comprehensive energy system disclosed by the embodiment of the invention comprises the following steps of firstly obtaining constraint conditions corresponding to a plurality of energy junction models which are established in advance in the comprehensive energy system, then obtaining uncertainty factors in the comprehensive energy system, predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule, then determining the probability constraint conditions in the comprehensive energy system by using the probability predicted values, then establishing a target function for the multi-energy supply and demand balance of the comprehensive energy system according to power generation cost data and energy value cost data of system equipment in the comprehensive energy system, and finally establishing the multi-energy supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the target function. Therefore, by adopting the scheme, the multi-energy flow supply and demand balance modeling can be carried out on the comprehensive energy system according to uncertain factors in the comprehensive energy system, and meanwhile, probability constraint conditions in the comprehensive energy system are more flexible than rigid constraint conditions in the prior art, so that the actual operation requirements of the comprehensive energy system can be met, and the safe and stable operation of the comprehensive energy system is ensured.
Referring to fig. 3, fig. 3 is a schematic structural diagram of another probabilistic constraint modeling apparatus for balancing multiple energy flows of an integrated energy system according to an embodiment of the present invention, including:
a memory 301 for storing a computer program;
a processor 302 for executing a computer program stored in a memory to implement the steps of the probabilistic constraint modeling method for balance of multi-energy flow supply and demand of an integrated energy system as set forth in any of the above embodiments.
In another probabilistic constraint modeling apparatus for multi-energy supply and demand balance of an integrated energy system provided in this embodiment, a computer program stored in a memory may be called by a processor to implement the steps of the probabilistic constraint modeling method for multi-energy supply and demand balance of an integrated energy system provided in any one of the above embodiments, so that the modeling apparatus has the same practical effects as the probabilistic constraint modeling method for multi-energy supply and demand balance of an integrated energy system.
The probability constraint modeling method and device for the multi-energy flow supply and demand balance of the comprehensive energy system are introduced in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Claims (10)
1. A probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system is characterized by comprising the following steps:
acquiring constraint conditions corresponding to a plurality of pre-established energy hub models in the comprehensive energy system;
acquiring uncertainty factors in the integrated energy system and predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule;
determining probability constraint conditions in the comprehensive energy system by using the probability predicted value;
acquiring power generation cost data and energy value cost data of system equipment in the comprehensive energy system;
determining a target function of the multi-energy flow supply and demand balance of the comprehensive energy system according to the power generation cost data and the energy value data;
and establishing a multi-energy flow supply and demand balance model of the comprehensive energy system by using the constraint conditions, the probability constraint conditions and the objective function.
2. The probabilistic constraint modeling method for multi-energy flow supply and demand balance of the integrated energy system according to claim 1, wherein the building process of each energy hub model specifically comprises:
acquiring the energy type in the integrated energy system, the energy conversion equipment in the integrated energy system and the load type in the integrated energy system;
establishing an energy supply and demand balance equality relation between the energy input end and the energy output end based on the conversion efficiency of the energy conversion equipment by taking various types of energy as energy input of the energy input end and the load type as energy output of the energy output end;
the energy hub model includes the energy supply and demand balance equation relationship.
3. The probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system according to claim 1, wherein the uncertainty factors in the integrated energy system specifically include:
the output of photovoltaic power generation equipment in the comprehensive energy system;
correspondingly, the predicting the probability prediction value corresponding to the uncertainty factor by using the sample data corresponding to the uncertainty factor according to the predefined rule specifically includes:
acquiring photovoltaic output power sample data corresponding to the output of the photovoltaic power generation equipment;
and predicting a nuclear density predicted value corresponding to the output of the photovoltaic power generation equipment as the probability predicted value by using the photovoltaic output power sample data based on a nonparametric nuclear density estimation method.
4. The probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system according to claim 1, wherein the determining the probabilistic constraint condition in the integrated energy system using the probabilistic predictive value comprises:
acquiring a power route in the integrated energy system, a maximum power limit corresponding to the power route and a probability that an allowable power corresponding to the circuit route is out of limit;
correspondingly, the probability constraint condition is specifically represented by the following formula:
wherein,predicting a power probability PL in the integrated energy system for a period tl,tLess than or equal to the maximum power limitProbability of (α)lIs the probability that the allowed power is out of limit.
5. The probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system according to claim 4, wherein the determining the probabilistic constraint condition in the integrated energy system using the probabilistic predictive value further comprises:
acquiring a natural gas pipeline in the integrated energy system, a maximum airflow limit value corresponding to the natural gas pipeline and the probability of out-of-limit allowable flow corresponding to the natural gas pipeline;
correspondingly, the probability constraint condition is specifically represented by the following formula:
in the above formula, the first and second carbon atoms are,the predicted value of the airflow probability in the comprehensive energy system is less than or equal to the maximum airflow limit value corresponding to the natural gas pipeline in the period tProbability of (α)plIs the probability that the allowed flow rate is out of limit.
6. The probabilistic constraint modeling method for multi-energy flow supply and demand balance of an integrated energy system according to claim 5, wherein the determining the probabilistic constraint condition in the integrated energy system using the probabilistic predictive value further comprises:
acquiring the probability of out-of-limit total emission, maximum emission and allowable emission of traditional system equipment, combined heat and power generation equipment and gas boiler equipment in the comprehensive energy system;
correspondingly, the probability constraint condition is specifically represented by the following formula:
wherein,for the t period of time total emission in the integrated energy systemLess than or equal to the maximum discharge amountProbability of (β)tIs the probability that the allowable emission amount is out of limit.
7. The probabilistic constraint modeling method for multi-energy supply and demand balance of the integrated energy system according to claim 1, wherein the objective function of multi-energy supply and demand balance of the integrated energy system is specifically represented by the following formula:
wherein t is a time interval sequence number and gammatEnergy value cost data for time period t, GtEnergy amount of energy source for t period, ag,t、bg,tAnd cg,tGenerating cost coefficient for system equipment, EG for set of system equipment group, Eg,tThe generated power of the system equipment in the time period t is shown, and H is the number of the time periods.
8. The probabilistic constraint modeling method for multi-energy flow supply and demand balance of the integrated energy system according to claim 5, wherein the constraint condition specifically comprises:
a multi-energy flow supply and demand balance constraint of the integrated energy system and a safe operation constraint of system equipment of the integrated energy system;
the multi-energy flow supply and demand balance constraint of the comprehensive energy system specifically comprises an electric power balance constraint and a natural gas balance constraint, and the safe operation constraint specifically comprises a heat and power cogeneration equipment safe operation constraint;
the power balance constraint is specifically represented by the following formula:
wherein l is the serial number of the power line in the integrated energy system, EL is the set of the power line in the integrated energy system, PLl,tIs the power of the power line l, θ, for a period of tlf,tIs at t timePhase angle of starting end of segment power line l, thetale,tIs the terminal phase angle, x, of the power line l during a period tlIs the reactance of the power line l;
the natural gas balance constraint is specifically represented by the following formula:
wherein Q isi,tThe net injection amount and f of natural gas of a node i in the t period in the comprehensive energy systemim,tAnd fnm,tRespectively injecting natural gas into the node i at the t period in the comprehensive energy system, flowing out the natural gas, and Fj.tGas, G consumed by compressor in t period branch j in the comprehensive energy systemijThe values of gas taking coefficients of the compressors in the branch i and the branch j of the integrated energy system are set to be 1 when gas is taken from the node i;
the safe operation constraint of the cogeneration equipment specifically comprises an electric output operation constraint of the cogeneration equipment of an energy hub node i where the energy hub model is located and a thermal output operation constraint of the cogeneration equipment;
the electrical output operation constraint is specifically represented by the following formula:
wherein N is the number of nodes, H is the number of time periods,The electric energy output of the thermoelectric cogeneration equipment in the node i in the time period t,For the electric energy output of the cogeneration equipment in the node i in the time period tThe upper limit of the force,Efficiency of converting natural gas into electrical energy for a cogeneration unit, kappa the distribution coefficient of natural gas between a gas boiler plant and a cogeneration plant, Pgas,i,tNatural gas input to an energy hub node i in the comprehensive energy system at the time period t;
the thermodynamic operating constraint is specifically represented by the following formula:
wherein,the heat energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The upper limit of the thermal energy output of the thermoelectric cogeneration equipment in the node i in the time period t,The efficiency of converting natural gas into heat energy in the thermoelectric cogeneration equipment in the node i in the time period t is shown.
9. A probabilistic constraint modeling device for multi-energy flow supply and demand balance of an integrated energy system is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring constraint conditions corresponding to a plurality of pre-established energy hub models in the comprehensive energy system;
the second acquisition module is used for acquiring uncertainty factors in the integrated energy system and predicting probability predicted values corresponding to the uncertainty factors by using sample data corresponding to the uncertainty factors according to a predefined rule;
the first determination module is used for determining probability constraint conditions in the comprehensive energy system by using the probability predicted value;
the third acquisition module is used for acquiring power generation cost data and energy value cost data of system equipment in the comprehensive energy system;
the second determination module is used for determining a target function of multi-energy supply and demand balance of the comprehensive energy system according to the power generation cost data and the energy value data;
and the establishing module is used for establishing a multi-energy flow supply and demand balance model of the comprehensive energy system by utilizing the constraint conditions, the probability constraint conditions and the objective function.
10. A probabilistic constraint modeling device for multi-energy flow supply and demand balance of an integrated energy system is characterized by comprising:
a memory for storing a computer program;
a processor for executing a computer program stored in the memory to implement the steps of the probabilistic constraint modeling method of integrated energy system multi-energy flow supply and demand balance of any of claims 1 to 8.
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