CN112561237A - Comprehensive energy system planning risk assessment method, application system and device - Google Patents
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Abstract
The invention provides a comprehensive energy system planning risk assessment method, an application system and a device. The method comprises the following steps: estimating a marginal distribution of natural gas prices and electricity prices; selecting a proper Copula function to depict the correlation between the natural gas price and the electric power price; estimating unknown parameters in the Copula function; constructing a comprehensive energy system planning model; and measuring the potential risk loss of the comprehensive energy system planning based on the CVaR index. The comprehensive energy system planning risk assessment method solves the problem that the comprehensive energy system planning risk assessment is carried out without considering the correlation and uncertainty between the prices of the natural gas and the electric energy in the prior art.
Description
Technical Field
The invention relates to the technical field of regional comprehensive energy system planning, in particular to a comprehensive energy system planning risk assessment method, an application system and a device.
Background
The comprehensive energy system is very sensitive to the energy price, and when the natural gas price or the electric power price suddenly fluctuates sharply, the comprehensive energy service provider can be prevented from entering and leaving, and can face the bankruptcy and the closure in severe cases, so that when the comprehensive energy system is planned by the energy service provider at first, the potential planning cost loss caused by accurately evaluating the natural gas price and the electric power price risk is crucial to the successful operation in later period.
At present, students at home and abroad carry out extensive and deep research on the risk assessment of the comprehensive energy system, and the research situation of the risk assessment of a new-generation energy system is combed from the aspect of the physical characteristic difference of different energy systems such as electricity, heat, gas and the like. Research is available to review risk assessment of the energy internet, except for research on risks at the physical information level. From the perspective of safety risk assessment of an electric power information physical system, a risk index calculation formula for quantitatively assessing information physical cooperative attack is researched, and risk defense measures are provided.
The research work makes very comprehensive risk assessment work with practical value from the aspects of planning and operation of the comprehensive energy system and operation among different main bodies, promotes the development of the research work in the aspect of comprehensive energy system assessment, and has great significance. But currently, in the aspect of comprehensive energy system planning, the research work related to economic loss risk caused by the correlation between uncertainty of energy prices is very little.
Disclosure of Invention
The invention aims to provide a method, an application system and a device for evaluating the planning risk of an integrated energy system, which can solve the problem that the planning risk evaluation of the integrated energy system is carried out without considering the correlation and uncertainty between the prices of natural gas and electric energy in the prior art.
In order to achieve the above purpose, the invention provides the following technical scheme:
a comprehensive energy system planning risk assessment method comprises the following steps:
s101, estimating edge distribution of natural gas price and electric power price; selecting market prices of natural gas and electric power within preset time, carrying out logarithmic differentiation on the prices of the natural gas and the electric power within the preset time, defining the fluctuation rate of the natural gas and the electric power price, and obtaining a first random variable of the daily fluctuation rate of the natural gas price and a second random variable of the daily fluctuation rate of the electric power price; determining edge distributions of the first random variable and the second random variable by adopting a non-parametric kernel density estimation method;
s102, selecting a proper Copula function to depict the correlation between the natural gas price and the electric power price; obtaining a secondary frequency histogram according to the natural gas price fluctuation rate and the nuclear density estimation of the electric power price fluctuation rate, and selecting a binary normal Copula function or a binary t-Copula function according to the shape of the secondary frequency histogram to describe a related structure between the natural gas price fluctuation rate and the electric power price fluctuation rate;
s103, estimating unknown parameters in the Copula function; estimating parameters of the binary normal Copula function and the binary t-Copula function by a maximum likelihood estimation method to obtain a density function graph of the normal Copula function and a density function graph of the t-Copula function;
comparing the binary normal Copula and the binary t-Copula with an empirical Copula function by Euclidean distance squared: determining the quality degrees of the binary normal Copula function and the binary t-Copula function; generating a natural gas price and electric power price joint scene according to the Copula model of the natural gas price fluctuation rate and the electric power price fluctuation rate;
s104, constructing a comprehensive energy system planning model;
and S105, measuring the potential risk loss of the comprehensive energy system planning based on the CVaR index.
On the basis of the technical scheme, the invention can be further improved as follows:
further, defining the natural gas and electricity price fluctuation rates as:
in the formula, rgas,tIndicating the daily natural gas price at time t, relec,tRepresents the daily electricity price at time t; p is a radical ofgas,tIndicating daily fluctuation rate of natural gas price at time t, pe l ec,tRepresenting the daily fluctuation rate of the power price at the time t;
further, let pgasExpressing as a first random variable of daily fluctuation rate of natural gas price, let pelecA second random variable expressed as a daily fluctuation rate of the electricity price; obtaining a corresponding first frequency histogram through natural gas price fluctuation rate, and obtaining a corresponding second frequency histogram through nuclear density estimation of power price fluctuation rate;
obtaining a corresponding first function graph through natural gas price fluctuation rate, and obtaining a second function graph through kernel density estimation of electric power price fluctuation rate, wherein the first function graph is an empirical distribution function and a kernel density estimation distribution function graph of natural gas price fluctuation rate, and the second function graph is an empirical distribution function and a kernel density estimation distribution function graph of electric power price fluctuation rate;
and further, carrying out goodness-of-fit inspection on the natural gas price fluctuation rate and the power price fluctuation rate by using a K-S inspection method.
Further, the principle of kernel density estimation is as follows: is provided with Y1,Y2,…,YnIs a discrete sample from a univariate random variable, and the kernel density estimate of the density function f (y) at any point y is defined as:
in the formula, n is the number of samples, h is the window width, K (·) is a kernel function, the kernel function selects a Gaussian kernel function, and i is represented as the ith sample.
Further, the binary normal Copula function is:
in the formula phi-1An inverse function representing a standard normal distribution function; rho is a linear correlation system between the natural gas price fluctuation rate and the electric power price fluctuation rate; u represents a normal distribution expected value, and V represents a degree of freedom;
the binary t-Copula function is:
in the formula, upsilon represents a degree of freedom;
representing the inverse of a binary t-distribution function with degree of freedom v.
Further, let (a)i,bi) (i ═ 1,2, …, n) are samples taken from a two-dimensional population (a, B), let the empirical distribution functions of a and B be h (a) and h (B), respectively, defining the empirical Copula of the samples;
in the formula, u, v ∈[0,1]；I[·]Is an indicative function; when H (A)i) When u is less than or equal to u
Otherwise
An integrated energy system planning risk assessment application system, comprising:
the calculation module calculates the edge distribution of the natural gas price and the electric power price by a nonparametric kernel density estimation method, and can select a proper Copula function to depict the correlation between the natural gas price and the electric power price; the calculation module can calculate unknown parameters in the Copula function; the calculation module can generate a natural gas price and electric power price joint scene according to a Copula model of the natural gas price fluctuation rate and the electric power price fluctuation rate;
and the processing module can receive the natural gas price and electric power price combined scene and construct an integrated energy system planning model and a potential risk assessment model of the integrated energy system planning based on CVaR.
An integrated energy system planning risk assessment device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the integrated energy system planning risk assessment method according to any one of claims 1 to 7 when executing the computer program.
The invention has the following advantages:
according to the comprehensive energy system planning risk assessment method, the comprehensive energy system planning risk assessment application system and the comprehensive energy system planning risk assessment device, a joint distribution function between natural gas price and electric power price is constructed by applying a Copula theory and is used for sampling to generate a certain number of joint price scenes, and then the joint price scenes obtained by sampling are brought into a model with CVaR as a risk assessment index to carry out economic loss assessment. The problem that correlation and uncertainty between prices of natural gas and electric energy are not considered in the prior art to conduct comprehensive energy system planning risk assessment is solved.
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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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for integrated energy system planning risk assessment in accordance with the present invention;
FIG. 2 is a graph of a frequency histogram of natural gas price volatility versus kernel density estimation in accordance with the present invention;
FIG. 3 is a graph of a frequency histogram of power price volatility versus kernel density estimation in accordance with the present invention;
FIG. 4 is a graph of an empirical distribution function of natural gas price volatility versus an estimated distribution function of kernel density in accordance with the present invention;
FIG. 5 is a graph of an empirical distribution function of power price volatility versus an estimated distribution function of kernel density in accordance with the present invention;
FIG. 6 is a binary frequency histogram of natural gas price volatility and electricity price volatility according to the present invention;
FIG. 7 is a graph of the density function of a normal Copula function in the present invention;
FIG. 8 is a graph of the density function of the t-Copula function of the present invention;
FIG. 9 is a block diagram of a unified bus of the integrated energy system of the present invention;
fig. 10 is a flow chart of the risk assessment of the integrated energy system planning in the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. 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.
As shown in fig. 1 to 10, the present invention provides a method for evaluating a risk of planning an integrated energy system, including:
s101, estimating edge distribution of natural gas price and electric power price;
in the step, market prices of natural gas and electric power in the preset time are selected, logarithmic differentiation is carried out on the prices of the natural gas and the electric power in the preset time, the united distribution function of the natural gas price and the electric power price is constructed by taking the historical daily price data of the American PJM electric power market and the Henry natural gas market as an example, and the data time span is selected from 3 and 17 days in 2014 to 12 and 28 days in 2017. Because the natural gas price and electric power price value and fluctuation are all great, and after taking the logarithm difference to its price, can reduce the numerical value and make things convenient for statistical analysis, define the price fluctuation rate and be:
in the formula, rgas,tIndicating the daily natural gas price at time t, relec,tRepresents the daily electricity price at time t; p is a radical ofgas,tIndicating daily fluctuation rate of natural gas price at time t, pelec,tRepresenting the daily fluctuation rate of the power price at the time t;
the Copula theory can separate the edge distribution of random variables and the related structures between the random variables to research when the random variable joint distribution is constructed, and can capture the advantages of nonlinear, asymmetric and tail related relations among the variables and the like, and the Copula theory is widely applied to the generation of the wind power plant output scene. According to the method, the correlation between the natural gas price and the electric power price is considered, and a Copula function is utilized to generate a natural gas price and electric power price combined price scene for the comprehensive energy system planning risk assessment.
Let p begasTo representFor the first random variable of daily fluctuation rate of natural gas price, let pelecA second random variable expressed as a daily fluctuation rate of the electricity price;
the nuclear density estimation method is widely used because it does not need to know the distribution model of the sample in advance, and it only uses the sample data as the basis to research the distribution characteristics of the data. Determining a first random variable p using a non-parametric kernel density estimation methodgasAnd a second random variable pelecThe edge distribution of (2);
as shown in fig. 2, a frequency histogram of natural gas price fluctuation rate and a kernel density estimation graph are obtained, and a corresponding first frequency histogram is obtained by the natural gas price fluctuation rate;
as shown in fig. 3, the frequency histogram of the power price fluctuation rate and the kernel density estimation map are used to obtain a corresponding second frequency histogram through the kernel density estimation of the power price fluctuation rate;
as shown in fig. 4, the empirical distribution function of the natural gas price fluctuation rate and the kernel density estimation distribution function map are used to obtain a corresponding first function map of the natural gas price fluctuation rate, where the first function map is the empirical distribution function of the natural gas price fluctuation rate and the kernel density estimation distribution function map.
As shown in fig. 5, the second function map is obtained by estimating the kernel density of the electric power price fluctuation rate, and is an empirical distribution function and a kernel density estimation distribution function map of the electric power price fluctuation rate.
The principle of the kernel density estimation is as follows: is provided with Y1,Y2,…,YnIs a discrete sample from a univariate random variable, and the kernel density estimate of the density function f (y) at any point y is defined as:
in the formula, n is the number of samples, h is the window width, K (·) is a kernel function, the kernel function selects a Gaussian kernel function, and i is represented as the ith sample.
S102, selecting a proper Copula function to depict the correlation between the natural gas price and the electric power price;
in this step, a binary frequency histogram of natural gas price fluctuation rate and electric power price fluctuation rate as shown in fig. 6; selecting a proper Copula function to depict the correlation between the natural gas price and the electric power price; in determining the natural gas price fluctuation rate pgasIs distributed at the edge of Ugas＝F(pgas) And power price fluctuation rate pelecEdge distribution V ofelec＝F(pelec) Then, a secondary frequency histogram is obtained according to the kernel density estimation of the natural gas price fluctuation rate and the electric power price fluctuation rate, an appropriate Copula function is selected according to the shape of the secondary frequency histogram, and the binary frequency histogram shows that the secondary frequency histogram has a basically symmetrical tail part, namely (U)gas,Velec) Has symmetrical tails.
A density function graph of a normal Copula function as shown in fig. 7 and a density function graph of a t-Copula function as shown in fig. 8; therefore, a binary normal Copula function or a binary t-Copula function is selected to describe a related structure between the natural gas price fluctuation rate and the electric power price fluctuation rate;
the binary normal Copula function is:
in the formula phi-1An inverse function representing a standard normal distribution function; rho is a linear correlation system between the natural gas price fluctuation rate and the electric power price fluctuation rate; u represents a normal distribution expected value, and V represents a degree of freedom;
the binary t-Copula function is:
in the formula, upsilon represents a degree of freedom;
representing the inverse of a binary t-distribution function with degree of freedom v.
(ai,bi) (i ═ 1,2, …, n) are samples taken from a two-dimensional population (a, B), let the empirical distribution functions of a and B be h (a) and h (B), respectively, defining the empirical Copula of the samples;
in the formula, u, v is belonged to [0,1]]；I[·]Is an indicative function; when H (A)i) When u is less than or equal to u
Otherwise
S103, estimating unknown parameters in the Copula function;
in the step, parameters of the binary normal Copula function and the binary t-Copula function are estimated through a maximum likelihood estimation method to obtain a density function graph of the normal Copula function and a density function graph of the t-Copula function;
estimating parameters of the binary normal Copula function and the binary t-Copula function by a maximum likelihood estimation method according to the daily fluctuation rate of the natural gas price and the daily fluctuation rate of the electric power price calculated by the formula (1) and the formula (2) to obtain a density function graph of the normal Copula function and a density function graph of the t-Copula function; table 1 shows the Copula function estimation parameters in the present invention; the model estimation results are shown in table 1:
TABLE 1
Comparing the binary normal Copula and the binary t-Copula with an empirical Copula function by Euclidean distance squared: determining the quality degrees of the binary normal Copula function and the binary t-Copula function; generating a natural gas price and electric power price joint scene according to the Copula model of the natural gas price fluctuation rate and the electric power price fluctuation rate;
comparison of squared euclidean distances:
as can be seen from the calculation and the euclidean distance square comparison, the euclidean distance square between the binary normal Copula and the empirical Copula is 0.0170, and the euclidean distance square between the binary t-Copula and the empirical Copula is 0.0165. 0.0170<0.0165, therefore, under the guidance of the Euclidean distance square index, the linear correlation parameter is 0.9738, and the correlation between the natural gas price fluctuation rate and the electric power price fluctuation rate can be better fitted by the binary t-Copula with the degree of freedom of 13 and the empirical Copula model.
S104, constructing a comprehensive energy system planning model;
in the step, a comprehensive energy system planning model is constructed; a typical unified bus structure of an integrated energy system is shown in fig. 9, where the integrated energy system includes a Combined heat and power unit (CHP), a Gas Boiler (GB), an Electric refrigerator (EC), an Absorption refrigerator (AC), a Photovoltaic system (PV), an Electric bus, a hot bus, and a cold bus, where the Electric bus needs to satisfy a power balance constraint of the Electric bus, the hot bus needs to satisfy a power balance constraint of the hot bus, and the cold bus needs to satisfy a power balance constraint of the cold bus;
1) combined heat and power unit
The cogeneration unit produces electrical energy and heat energy by consuming natural gas; the operation model of the cogeneration unit is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively representing the electric output power and the heat output power of the cogeneration unit at the moment t; gCHP(t) the natural gas power consumed by the cogeneration unit at the moment t is represented;
and
respectively representing the electric output power efficiency and the thermal output power efficiency of the cogeneration unit;
and
respectively representing the upper limit and the lower limit of the electric output power of the cogeneration unit.
2) Gas boiler
The gas boiler is another device for providing heat energy by burning natural gas, and the operation model is as follows:
in the formula (I), the compound is shown in the specification,
representing the heat output power of the gas boiler at the time t; gGB(t) represents the natural gas power consumed by the gas boiler at time t;
representing the heat output power efficiency of the gas boiler;
represents the upper limit of the heat output power of the gas boiler.
3) Electric refrigerator
The electric refrigerator refrigerates by consuming electric energy, and the operation model is as follows:
in the formula (I), the compound is shown in the specification,
representing the cold output power of the electric refrigerator at the time t;
represents the electric power consumed by the electric refrigerator at the time t;
representing the cold output power efficiency of the electric refrigerator;
represents the upper limit of the cold output power of the electric refrigerator.
4) Absorption refrigerator
The absorption refrigerator is another refrigeration device which refrigerates by consuming heat power, and the operation model is as follows:
in the formula (I), the compound is shown in the specification,
represents the cold output power of the absorption refrigerator at the time t;
represents the electric power consumed by the absorption chiller at time t;
representing the cold output power efficiency of the absorption chiller;
represents the upper limit of the cold output power of the absorption chiller.
5) Photovoltaic system
The electric output power of the photovoltaic is mainly comprehensively determined by the illumination intensity irradiated on the surface of the photovoltaic, the physical parameters of the photovoltaic and the like; the output characteristic of the photovoltaic can be described by a P-G curve, wherein P represents the output power of the photovoltaic, and G represents the illumination intensity; the photovoltaic operation model is as follows:
in the formula, PPV(t) represents the output power of the photovoltaic at time t; qPVRepresents the peak capacity (kWp) of the photovoltaic; f. ofPVThe power derating factor of the photovoltaic is expressed to represent that the output power is reduced due to dust, dirt, aging and the like on the surface of the photovoltaic, and the power derating factor is generally 0.9; gT(t) represents the actual light intensity (kW/m2) at time t; gT,STC(t) represents the illumination intensity under the standard test condition at the time t;
representing the upper limit of the photovoltaic output power.
The electrical bus power balance constraint:
in the formula, Pgrid(t) power is purchased from the power grid at the moment t; l iselec(t) represents the electrical load at time t;
the thermal bus power balance constraint:
in the formula, Lheat(t) represents the thermal load at time t;
the cold bus power balance constraint:
in the formula, Lcold(t) represents the cooling load at time t.
And S105, measuring the potential risk loss of the comprehensive energy system planning based on the CVaR index.
Introducing a conditional risk value index to measure potential risk loss faced by the integrated energy system planning; the potential risk loss is the loss of planning total cost caused by uncertainty of natural gas price and electric power price;
the total cost of the integrated energy system plan, including the annual value costs such as initial investment, annual maintenance costs, and annual operating fuel costs, may be expressed as:
Ctotal＝Cinvest+Cmain+Cfuel (22)
in the formula, CtotalEqual annual total cost ($), C for integrated energy system planninginvestAnd CfuelRespectively representing the annual value cost ($) of initial investment of equipment, the annual maintenance cost ($) and the annual fuel consumption cost ($);
the total cost loss function for the integrated energy system plan based on conditional risk values may be expressed as:
in the formula, Ctotal,βRepresenting the total cost of the integrated energy system planning at a confidence level beta; beta is a confidence level, and reflects the aversion level of the comprehensive energy service provider to the risk;
representing the total planned cost of the integrated energy system under the s-th scene; s is a natural gas price and electric power price combined scene set obtained through a Monte Carlo sampling technology; rhorsThe probability of the occurrence of the s-th natural gas price and power price combined scene is shown; and s represents the s-th natural gas price and electric power price joint scene.
Generating data samples among NxM dimensions [0,1] meeting t-Copula function distribution by using a copularand function in an MATLAB toolbox, wherein N is the total number of the samples, and M is a random variable dimension;
by adopting an inverse transformation method, the generated random number sample can obtain a natural gas price fluctuation rate scene and an electric power price fluctuation rate scene corresponding to the original joint distribution function through the inverse operation of the determined respective edge distribution functions of the natural gas price fluctuation rate and the electric power price fluctuation rate; and further obtaining a natural gas price scene and an electric power price scene according to the inverse operation of the formula (1) and the formula (2).
An integrated energy system planning risk assessment application system, comprising:
the calculation module calculates the edge distribution of the natural gas price and the electric power price by a nonparametric kernel density estimation method, and can select a proper Copula function to depict the correlation between the natural gas price and the electric power price; the calculation module can calculate unknown parameters in the Copula function; the calculation module can generate a natural gas price and electric power price joint scene according to a Copula model of the natural gas price fluctuation rate and the electric power price fluctuation rate;
and the processing module can receive the natural gas price and electric power price combined scene and construct an integrated energy system planning model and a potential risk assessment model of the integrated energy system planning based on CVaR.
An integrated energy system planning risk assessment device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the integrated energy system planning risk assessment method according to any one of claims 1 to 7 when executing the computer program.
As shown in fig. 10, a natural gas price and electric power price joint distribution is constructed based on a Copula theory, a certain number of joint price scenes are extracted by using a monte carlo sampling technology according to the joint distribution, and a CVaR-based comprehensive energy system planning risk assessment index model is constructed; planning a risk assessment index model according to the CVaR-based comprehensive energy system; calculating the planning total cost under each joint price scene, judging whether all the scenes are completely calculated, if so, outputting a risk loss value, and ending; if not, returning to the step of calculating the total planning cost in each joint price scene when K is K + 1;
as shown in fig. 10, taking a certain integrated energy system plan as an example, the following six sets of planning schemes are subjected to economic risk loss assessment:
(1)CHP,GB,EC,AC,PV；
(2)CHP,GB,EC,PV；
(3)CHP,GB,AC,PV；
(4)CHP,GB,EC,AC；
(5)GB,EC,AC,PV；
(6)CHP,EC,AC,PV.
in order to illustrate the effect of the correlation between the natural gas price and the electric power price on the comprehensive energy system planning economic risk loss measurement, two groups of comparative analysis scenes are set:
scene I: the correlation between the natural gas price and the electric power price is not considered, and the risk loss value of each planning scheme under different confidence levels is calculated;
scene II: and calculating the risk loss value of each planning scheme under different confidence levels by considering the correlation between the natural gas price and the power price.
The natural gas price of the integrated energy system adopts a fixed gas price of 0.018$/kW, the average value of the natural gas price is 0.018$/kW, and the variance is 0.1 times of the average value. The comprehensive energy system adopts time-of-use electricity price, one day is divided into peak time periods (8:00-12:00 and 16:00-20:22), ordinary time periods (12:00-16:00 and 20:00-24:00) and valley time periods (0:00-8:00), electricity price 0.12$/kW in the time-of-use electricity price valley time period is taken as a mean value, 0.05-time mean value is taken as a variance, 2000 natural gas price and electric power price combined probability distribution scenes are extracted by using Monte Carlo sampling technology according to a natural gas price and electric power price combined probability distribution model constructed according to the content of the part 1, and the probability of each combined scene is 0.0005; the risk loss for each set of plans at 80%, 85%, 90%, 95%, and 99% confidence levels can be calculated according to equation (16), as shown in tables 2-7:
TABLE 2
TABLE 3
TABLE 4
TABLE 5
TABLE 6
TABLE 7
Table 2 is the risk loss for scheme 1 of the present invention at different confidence levels; table 3 is the risk loss for scheme 2 of the present invention at different confidence levels; table 4 is the risk loss for scheme 3 of the present invention at different confidence levels; table 5 is the risk loss for scheme 4 of the present invention at different confidence levels; table 6 is the risk loss for scheme 5 of the present invention at different confidence levels; table 7 is the risk loss for scheme 6 of the present invention at different confidence levels.
As can be seen from tables 2-7, for each set of planning scenarios, the risk loss measured in Case II is greater than the risk loss measured in Case I at the same confidence level, i.e., the loss due to risk is underestimated without regard to the correlation between the gas price and the electricity price. The main reason is that when the joint distribution of the natural gas price and the electric power price is constructed for sampling, the tail correlation of the natural gas price and the electric power price is considered, namely when an extreme event occurs, unrelated risks start to be gradually correlated, correlated risks exist, and the correlation is amplified. The drastic fluctuation of the natural gas price can cause the fluctuation of the electric power price, further cause the calculated risk loss to be larger, and better accord with the actual situation.
In the following, under Case I, the analysis is performed with the risk loss of each planning scenario at a 90% confidence level as an example.
It can be seen that the risk loss value of the scheme 5 is the largest, that is, the planning scheme 5 is selected, and the risk loss faced by the integrated energy service provider is the largest, mainly because the scheme 5 is not provided with a CHP cogeneration device, the system is inefficient to operate, the heat load is supplied only by a gas boiler by burning natural gas, and no other complementary heating device is used for dispersing the risk loss, so that the risk loss caused to the scheme 5 is the largest when the natural gas price and the electric power price fluctuate.
Comparing the scheme 1 with the scheme 3, it can be seen that the scheme 1 is provided with more electric refrigerators than the scheme 3, but the risk loss measured by the two schemes is almost the same, which indicates that the risk loss of the comprehensive energy system is not greatly affected by the more-configured electric refrigerators.
Comparing the scheme 1 with the scheme 6, it can be seen that although the scheme 1 is more equipped with a gas boiler than the scheme 6, the risk loss values measured under the two planning schemes are the same, which indicates that the gas boiler is not the main equipment causing the risk loss of the integrated energy system.
Comparing scheme 5 with scheme 6, it can be seen that scheme 6 is provided with a CHP device, and the CHP device is replaced by a gas boiler in scheme 5, so that the risk loss measured in scheme 5 is greater than that measured in scheme 6, indicating that the CHP device has the function of dispersing the risk loss.
Comparing scheme 1 with scheme 4, it can be seen that scheme 1 configures a photovoltaic system more than scheme 4, and the risk loss value measured by scheme 1 is smaller than scheme 4, which indicates that the photovoltaic system has a function of dispersing risks. In addition, comparison of scheme 3 and scheme 4 also shows that the photovoltaic system has the effect of dissipating risk losses.
Comparing scheme 2 and scheme 3, it can be seen that scheme 2 is provided with an electric refrigerator for cooling, and scheme 3 is provided with an absorption refrigerator instead of the electric refrigerator for cooling, resulting in that the risk loss measured by scheme 3 is smaller than that measured by scheme 2, which indicates that the absorption refrigerator has a risk dispersion effect compared with the electric refrigerator. It can also be seen from a comparison of scheme 1 and scheme 2 that absorption chillers have the effect of dispersion risk.
Comparing scheme 2 with scheme 4, it can be seen that scheme 2 is not equipped with a photovoltaic absorption chiller to supply cold, and scheme 4 is not equipped with a photovoltaic system to supply cold, so that the risk loss measured by scheme 4 is smaller than that of scheme 2, indicating that the photovoltaic system is stronger than the absorption chiller in terms of risk dispersing capability.
Comparing scheme 2 with scheme 6, it can be seen that scheme 2 is not equipped with an absorption chiller, and scheme 6 is not equipped with a gas boiler, resulting in that the risk loss measured by scheme 6 is less than scheme 2, indicating that the absorption chiller has the function of dispersing risks, while the gas boiler is slightly less in the aspect of dispersing risk. It can also be seen by comparing scheme 4 with scheme 6 that the photovoltaic system has a certain effect of dispersing risk, while the gas boiler is less effective in dispersing risk.
Comparing scheme 4 and scheme 5, it can be seen that scheme 4 is not configured with a photovoltaic system, and scheme 5 is not configured with CHP, resulting in a lower risk loss for scheme 4 than for scheme 5, indicating that CHP is stronger than a photovoltaic system in reducing risk loss.
Through the analysis, the absorption refrigerator and the photovoltaic system have the function of risk dispersion, the risk loss of planning can be reduced, and the risk dispersion capability of the photovoltaic system is stronger than that of the absorption refrigerator; in addition, CHP also has the function of dispersing risk loss, and the CHP has stronger capability of reducing risk loss than a photovoltaic system. And the gas boiler and the electric boiler play little role in reducing risk loss.
At the 90% confidence level, the difference in risk loss measured for each set of planning plans in the Case I and Case II scenarios is calculated as: 56.11M $, 43.6M $, 61.02M $, 53.19M $, 34.82M $, and 56.96M $. The comparative analysis shows that the difference of the risk losses measured by the scheme 5 under the scenes of Case I and Case II is minimum, and the difference of the risk losses measured by the scheme 3 is maximum, which indicates that the scheme 3 has the strongest risk resistance, but underestimates the most serious risk loss; while scenario 5 is the weakest resistant to risk, scenario 5 underestimates the least risk loss.
Furthermore, it can be seen that as the confidence level increases, the risk loss for each set of planning scenarios in the Case I and Case II scenarios decreases, primarily because the increased level of aversion to risk loss by the integrated energy service provider is traded for a decrease in risk loss at the expense of an increase in its overall planning cost.
The method provided by the invention constructs a joint distribution function between the natural gas price and the electric power price by applying a Copula theory for sampling to generate a certain number of joint price scenes, and then brings the sampled joint price scenes into a model taking CVaR as a risk assessment index for economic loss assessment. By way of example analysis, the following conclusions are reached:
1) ignoring the correlation between natural gas prices and electricity prices underestimates the economic risk loss caused by uncertainty in natural gas prices and electricity prices, resulting in energy service providers not being able to leave enough funds to resist the risk.
2) Aiming at the typical comprehensive energy system architecture diagram provided by the invention, the CHP, the absorption refrigerator and the photovoltaic system can be induced to have the functions of dispersing risks and reducing risk loss through example analysis.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include more than one of the feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A comprehensive energy system planning risk assessment method is characterized by specifically comprising the following steps:
s101, estimating edge distribution of natural gas price and electric power price; selecting market prices of natural gas and electric power within preset time, defining the price of the natural gas and the fluctuation rate of the electric power price, and obtaining a first random variable of the daily fluctuation rate of the natural gas price and a second random variable of the daily fluctuation rate of the electric power price; determining edge distributions of the first random variable and the second random variable by adopting a non-parametric kernel density estimation method;
s102, selecting a proper Copula function to depict the correlation between the natural gas price and the electric power price; obtaining a secondary frequency histogram according to the natural gas price fluctuation rate and the nuclear density estimation of the electric power price fluctuation rate, and selecting a binary normal Copula function or a binary t-Copula function according to the shape of the secondary frequency histogram to describe a related structure between the natural gas price fluctuation rate and the electric power price fluctuation rate;
s103, estimating unknown parameters in the Copula function; estimating parameters of the binary normal Copula function and the binary t-Copula function by a maximum likelihood estimation method; comparing the binary normal Copula and the binary t-Copula with an empirical Copula function by Euclidean distance squared: determining the quality degrees of the binary normal Copula function and the binary t-Copula function; generating a natural gas price and electric power price joint scene according to the Copula model of the natural gas price fluctuation rate and the electric power price fluctuation rate;
s104, constructing a comprehensive energy system planning model;
and S105, constructing a potential risk assessment model of the CVaR-based comprehensive energy system planning.
2. The integrated energy system planning risk assessment method according to claim 1, wherein the natural gas and electricity price fluctuation rates are defined as:
in the formula, rgas,tIndicating the daily natural gas price at time t, relec,tRepresents the daily electricity price at time t; p is a radical ofgas,tIndicating daily fluctuation rate of natural gas price at time t, pelec,tIndicating the daily fluctuation rate of the power price at time t.
3. The integrated energy system planning risk assessment method according to claim 2, wherein let p begasExpressing as a first random variable of daily fluctuation rate of natural gas price, let pelecA second random variable expressed as a daily fluctuation rate of the electricity price; by corresponding to the natural gas price fluctuation rateThe corresponding second frequency histogram is obtained through the kernel density estimation of the power price fluctuation rate;
the natural gas price fluctuation rate calculation method based on the kernel density estimation includes the steps that a corresponding first function graph is obtained through the natural gas price fluctuation rate, a second function graph is obtained through the kernel density estimation of the power price fluctuation rate, the first function graph is an empirical distribution function and a kernel density estimation distribution function graph of the natural gas price fluctuation rate, and the second function graph is an empirical distribution function and a kernel density estimation distribution function graph of the power price fluctuation rate.
4. The integrated energy system planning risk assessment method according to claim 3, wherein the goodness-of-fit test is performed on the nuclear density estimation results of the natural gas price fluctuation rate and the electric power price fluctuation rate by a K-S test method.
5. The integrated energy system planning risk assessment method according to claim 1, wherein the kernel density estimation principle is: is provided with Y1,Y2,…,YnIs a discrete sample from a univariate random variable, and the kernel density estimate of the density function f (y) at any point y is defined as:
in the formula, n is the number of samples, h is the window width, K (·) is a kernel function, the kernel function selects a Gaussian kernel function, and i is represented as the ith sample.
6. The integrated energy system planning risk assessment method according to claim 1, wherein the binary normal Copula function is:
in the formula phi-1Represents a standard normal distribution functionAn inverse function of the number; rho is a linear correlation system between the natural gas price fluctuation rate and the electric power price fluctuation rate; u represents a normal distribution expected value, and V represents a degree of freedom;
the binary t-Copula function is:
in the formula, upsilon represents a degree of freedom;
representing the inverse of a binary t-distribution function with degree of freedom v.
7. The integrated energy system planning risk assessment method according to claim 1, wherein (a) isi,bi) (i ═ 1,2, …, n) are samples taken from a two-dimensional population (a, B), let the empirical distribution functions of a and B be h (a) and h (B), respectively, defining the empirical Copula of the samples;
in the formula, u, v is belonged to [0,1]]；I[·]Is an indicative function; when H (A)i) When u is less than or equal to u
Otherwise
8. An integrated energy system planning risk assessment application system, comprising:
the calculation module calculates the edge distribution of the natural gas price and the electric power price by a nonparametric kernel density estimation method, and can select a proper Copula function to depict the correlation between the natural gas price and the electric power price; the calculation module can calculate unknown parameters in the Copula function; the calculation module can generate a natural gas price and electric power price joint scene according to a Copula model of the natural gas price fluctuation rate and the electric power price fluctuation rate;
and the processing module can receive the natural gas price and electric power price combined scene and construct an integrated energy system planning model and a potential risk assessment model of the integrated energy system planning based on CVaR.
9. An integrated energy system planning risk assessment device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the integrated energy system planning risk assessment method according to any one of claims 1 to 7 when executing the computer program.
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