A park integrated energy system planning and operation optimization method and terminal

Abstract

The application discloses a park comprehensive energy system planning and operation optimization method and a terminal, comprising the following steps: a multi-energy flow and carbon trading volume coupling model is established; a park comprehensive energy system model based on the carbon energy coupling model is established; a scene generation model considering carbon trading price and wind and light uncertainty is established; and a park comprehensive energy system planning and operation double-layer optimization model based on conditional risk value is established. The application is used for depicting the actual situation that carbon trading volume depends on multi-energy flow, establishing a multi-energy flow and carbon trading volume coupling model to describe the coupling relationship between carbon trading volume and multi-energy flow, introducing carbon trading price uncertainty and wind and light power generation uncertainty considering time and space correlation into planning total cost fluctuation risk modeling, and putting forward a PIES planning and operation double-layer optimization model based on conditional risk value risk measurement, so that the problem that equipment capacity configuration is insufficient to cause planning total cost fluctuation risk caused by uncertain factors can be effectively avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy system planning and operation optimization, and particularly relates to a park integrated energy system planning and operation optimization method and terminal. BACKGROUND

[0002] Park integrated energy system (PIES) has the characteristics of large energy consumption and high energy density, and has the potential to integrate various renewable energies and utilize them, and is one of the important carriers to achieve the "double carbon" goal of China. The low-carbon development of PIES can be realized by participating in carbon trading and developing renewable clean energy. PIES participating in carbon trading refers to buying and selling carbon emission rights as a freely traded commodity on the carbon trading market, using market means to control the carbon emissions of PIES, and making profits therefrom. In addition, PIES can be configured with large-scale renewable clean energy to help achieve the "double carbon" goal. At the same time, the large-scale development of renewable clean energy also makes PIES have a good space for profit when participating in carbon trading.

[0003] However, the existing carbon trading market is still in its infancy, and the carbon trading price has strong uncertainty, combined with the strong power fluctuation characteristics of renewable energy generation itself in PIES, which directly affects the potential of PIES to participate in carbon trading stably. The total cost of PIES planning usually includes equipment investment cost, fuel cost, maintenance cost and carbon trading cost. Due to the influence of carbon trading price and renewable energy generation, two groups of uncertain factors, it is easy to cause the deviation of the total planning cost from the expected value, that is, the fluctuation risk of the total planning cost.

[0004] Therefore, the construction of a strong and reliable, low-carbon and economic PIES needs to avoid the occurrence of the total planning cost fluctuation risk, and for this purpose, it should be considered from the planning stage.

[0005] Due to the configuration of a high proportion of renewable energy in PIES, PIES can generate surplus carbon emission rights for sale in the carbon trading market to make a profit while meeting its own carbon emission demand when participating in carbon trading. However, PIES needs to be based on robust optimization configuration to avoid insufficient equipment capacity configuration and to be unable to cope with the planning total cost fluctuation risk caused by uncertain factors when participating in carbon trading for profit. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a park integrated energy system planning and operation optimization method and terminal, which effectively avoids the problem of insufficient equipment capacity configuration leading to the inability to cope with the planning total cost fluctuation risk caused by uncertain factors.

[0007] In order to solve the above technical problems, the technical scheme adopted by the present application is:

[0008] A park integrated energy system planning and operation optimization method, comprising the steps of:

[0009] S1, a multi-energy flow and carbon trading volume coupling model is established;

[0010] S2, according to the multi-energy flow and carbon trading volume coupling model, a park integrated energy system model based on carbon energy coupling model is established;

[0011] S3, according to the park integrated energy system model, a scenario generation model considering carbon trading price and wind and light uncertainty is established;

[0012] S4, according to the scenario generation model, a park integrated energy system planning and operation double-layer optimization model based on conditional risk value is established.

[0013] In order to solve the above technical problems, another technical solution adopted by the present application is:

[0014] A park integrated energy system planning and operation optimization terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0015] S1, a multi-energy flow and carbon trading volume coupling model is established;

[0016] S2, according to the multi-energy flow and carbon trading volume coupling model, a park integrated energy system model based on carbon energy coupling model is established;

[0017] S3, according to the park integrated energy system model, a scenario generation model considering carbon trading price and wind and light uncertainty is established;

[0018] S4, according to the scenario generation model, a park integrated energy system planning and operation double-layer optimization model based on conditional risk value is established.

[0019] The present application has the beneficial effect that: in order to depict the actual situation that carbon trading volume depends on multi-energy flow, a multi-energy flow and carbon trading volume coupling model is established to describe the coupling relationship between carbon trading volume and multi-energy flow, and then the carbon trading price uncertainty and the wind and light power generation uncertainty considering the time and space correlation are introduced into the planning total cost fluctuation risk modeling, and a park integrated energy system planning and operation double-layer optimization model based on conditional value-at-risk (CVaR) risk measurement is proposed to cope with the total cost fluctuation risk in the whole life cycle, that is, by focusing on analyzing the influence of the planning total cost fluctuation risk caused by carbon trading price and renewable energy output uncertainty on system configuration and operation optimization, the problem that the insufficient equipment capacity configuration leads to the planning total cost fluctuation risk caused by uncertain factors is effectively avoided. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The flow chart of a park integrated energy system planning and operation optimization method of the present application is shown in the figure;

[0021] Figure 2 The typical structure diagram of a PIES is shown in the figure;

[0022] Figure 3 The frequency histogram and kernel density estimation diagram of the carbon trading price prediction error of a typical PIES are shown in the figure;

[0023] Figure 4 The electric / thermal / cold load curve diagram of a typical PIES in summer, winter and spring and autumn typical days is shown in the figure;

[0024] Figure 5 The annual wind speed curve diagram of a typical PIES is shown in the figure;

[0025] Figure 6 The annual light intensity curve diagram of a typical PIES is shown in the figure;

[0026] Figure 7 The light intensity and wind speed average diagram of a typical PIES in different seasons typical days is shown in the figure;

[0027] Figure 8 The carbon trading price clustering scenario number and SSE relationship diagram is shown in the figure;

[0028] Figure 9 The light intensity clustering scenario number and SSE relationship diagram is shown in the figure;

[0029] Figure 10 The wind speed clustering scenario number and SSE relationship diagram is shown in the figure;

[0030] Figure 11 The clustered carbon trading price scenario is shown in the figure;

[0031] Figure 12 for the clustered light intensity scene;

[0032] Figure 13 for the clustered wind speed scene;

[0033] Figure 14 for the typical winter day gas and electricity purchase;

[0034] Figure 15 for the total cost and CVaR relationship curve under different confidence levels;

[0035] Figure 16 for the structure diagram of the park comprehensive energy system planning and operation optimization terminal of the application.

[0036] Label explanation:

[0037] 1. A park comprehensive energy system planning and operation optimization terminal; 2. A memory; 3. A processor. DETAILED DESCRIPTION

[0038] To specifically explain the technical content, the achieved purposes and effects of the application, the following will be described in combination with the embodiments and the accompanying drawings.

[0039] The park comprehensive energy system planning and operation optimization method and terminal provided by the application are suitable for comprehensive energy system planning and operation optimization scenarios. The following will be specifically described in combination with the embodiments.

[0040] Please refer to Figure 1 , the embodiment one of the application is:

[0041] A park comprehensive energy system planning and operation optimization method, as Figure 1 shown, includes the following steps:

[0042] S1, a multi-energy flow and carbon trading volume coupling model is established.

[0043] S2, according to the multi-energy flow and carbon trading volume coupling model, a park comprehensive energy system model based on the carbon energy coupling model is established.

[0044] S3, according to the park comprehensive energy system model, a scene generation model considering carbon trading price and wind and light uncertainty is established.

[0045] S4, according to the scene generation model, a park comprehensive energy system planning and operation double-layer optimization model based on conditional value at risk is established.

[0046] That is, in the embodiment, by focusing on the analysis of the carbon trading price and the planning total cost fluctuation risk caused by the uncertainty of renewable energy output faced by the park comprehensive energy system, the influence of the system configuration and operation optimization is effectively avoided. The problem of insufficient device capacity configuration caused by the planning total cost fluctuation risk caused by uncertain factors.

[0047] Please refer to Figure 2 and Figure 3 , the second embodiment of the present application is:

[0048] A park comprehensive energy system planning and operation optimization method, based on the first embodiment, in the embodiment, the process of establishing a multi-energy flow and carbon trading volume coupling model in step S1 is as follows:

[0049] Among them, since the energy integrator model only reflects the transmission, transformation and storage process of energy, but cannot describe the coupling relationship between multi-energy flow and carbon trading volume of the system. The carbon trading volume refers to the difference between the carbon emissions and the free carbon quota of the system. In this embodiment, the carbon trading volume is introduced into the following formula (1) to establish a multi-energy flow and carbon trading volume coupling model, which is referred to as a carbon energy coupling model:

[0050]

[0051] It can be simplified as formula (2):

[0052]

[0053] In the above formula, α w (w=1,2,…,m) is the carbon emission coefficient of the wth input energy in the energy hub, β w (w=1,2,…,m) is the free carbon quota coefficient of the wth input energy in the energy hub, (w=1,2,…,u) is the carbon emission coefficient of the τth energy storage in the energy hub, ρ τ (w=1,2,…,u) is the free carbon quota coefficient of the τth energy storage in the energy hub, L is the output power vector, C is the conversion device coupling matrix, P is the input power vector, K is the storage device coupling matrix, H is the energy storage charging and discharging power vector, E is the carbon trading volume of the energy hub, A is the 1×m dimension energy supply side carbon trading volume coefficient matrix, A=[(α1-β1),(α2-β2),…,(α m -β m )], B is the 1×u dimension energy storage carbon trading volume coefficient matrix, M is the (n+1)×1 dimension carbon energy output matrix, D is the (n+1)×m dimension energy supply side carbon energy coupling coefficient combination matrix, U is the (n+1)×u dimension energy storage carbon energy coupling coefficient combination matrix.

[0054] As a supplement to the above embodiment one, in this embodiment, the park integrated energy system model based on carbon coupling model in step S2 is constructed as follows:

[0055] S21, a typical park integrated energy system PIES composed of combined heat and power (CHP), gas boiler (GB), electric chiller (EC), electric boiler (EB), battery energy storage system (BESS), photovoltaic (PV) and wind turbine (WT) and other equipment is adopted, that is, as shown in the figure. Figure 2 PIES purchases natural gas and electricity from the outside of the system, uses energy conversion equipment to cooperate with battery energy storage to meet the electricity / heat / cold load demand of users, at the same time, PIES can participate in carbon trading to sell surplus carbon emission rights to make profits.

[0056] In this embodiment, the carbon emissions of PIES come from the primary energy supply end and the carbon emissions generated by energy storage equipment, that is, the carbon emissions generated by external power grid purchase, natural gas consumption, PV and WT power generation and battery energy storage. Although PV, WT and battery energy storage have zero carbon emissions during operation, PV, WT and battery energy storage will produce a large amount of carbon emissions during production and transportation. Therefore, by referring to the life cycle analysis method, the carbon emissions of PV, WT and battery energy storage during operation can be obtained after normalization measurement.

[0057] S22, the PIES in formula (1) is modeled based on the coupling relationship between multi-energy flow and carbon trading volume, as follows: Figure 1

[0058]

[0059]

[0060]

[0061] In the above formula, P grid , P gas , P PV and P WT represent the power of external grid purchase, natural gas injection, photovoltaic injection and wind turbine injection, respectively. EL , P CL , P HL and E tr ​respectively represent the electricity / cooling / heating load and the carbon trading amount, c1 represents the electricity power distribution coefficient of the electricity supply load, c2 represents the electricity power distribution coefficient of the electricity boiler, c3 represents the natural gas power distribution coefficient of the heat and power cogeneration unit, and respectively represent the electricity efficiency of the heat and power cogeneration unit, the heat efficiency of the heat and power cogeneration unit, the heat efficiency of the gas boiler, the heat efficiency of the electricity boiler and the refrigeration efficiency of the electricity refrigerator, P ES represents the charge / discharge power of the electricity storage, when greater than 0, it represents the electricity storage charging, and when less than 0, it represents the electricity storage discharging, α grid , α PV , α WT , α gas and respectively represent the unit power carbon emission coefficient of the external grid electricity, the photovoltaic, the wind turbine, the natural gas and the electricity storage, β grid , β PV , β WT , β CHP , β GB and β ES respectively represent the unit power free carbon quota coefficient of the external grid electricity, the photovoltaic, the wind turbine, the heat and power cogeneration unit, the gas boiler and the electricity storage; in the above formula, the carbon trading amount of PIES is the sum of the carbon trading amounts of the five parts of the external grid electricity, the photovoltaic, the wind turbine, the natural gas and the electricity storage, if the carbon emission coefficient of one part is greater than the carbon quota coefficient, the carbon trading amount of the part is positive, if the carbon emission coefficient of one part is less than the carbon quota coefficient, the carbon trading amount of the part is negative, when the value is positive, PIES needs to purchase the carbon emission right from the carbon trading market, and when the value is negative, PIES can sell the excess carbon emission right in the carbon trading market.

[0062] S23, c1, c2 and c3 are intermediate variables introduced to meet the requirements of the modeling form of the energy hub, which can be represented by the output power of each device unit and the efficiency of the device, that is, the following formulas (6)-(8):

[0063]

[0064]

[0065]

[0066] In the above formula, G CHP represents the natural gas power consumed by the heat and power cogeneration unit, represents the output power of the electricity refrigerator, represents the heat output power of the electricity boiler, represents the discharging power of the storage, represents the charging power of the storage.

[0067] As a supplement to the above embodiment one, in this embodiment, the scene generation model construction process considering carbon trading price and wind light uncertainty in step S3 is specifically as follows:

[0068] S31, model the carbon trading price uncertainty, specifically as follows:

[0069] According to the historical data of carbon trading provided by the official website of China carbon emission trading (take 2017-2021 for example), the neural network technology is used to train and predict the carbon trading price to obtain the prediction error, and further adopt the kernel density estimation method to obtain the frequency histogram of prediction error, as shown in Figure 3 .

[0070] According to the frequency histogram shown in Figure 3 , it can be determined that the prediction error obeys the normal distribution with zero mean, so the prediction error is superimposed with the predicted value of carbon trading price to obtain the actual carbon trading probability distribution, that is, the following formula (9):

[0071]

[0072] x e represents the carbon trading price random variable, μ e and σ e respectively represent the mean and standard deviation of carbon trading price.

[0073] Since photovoltaic and wind turbine power generation are related to light intensity and wind speed respectively and show linear relationship, the spatio-temporal correlation and uncertainty of light intensity and wind speed are modeled. In the same region and adjacent time period, light intensity and wind speed have certain spatio-temporal correlation due to factors such as climate conditions, so the wind light spatio-temporal correlation modeling is described from two aspects of wind light spatial correlation modeling and wind light time series correlation modeling.

[0074] S32, model the wind light uncertainty considering spatio-temporal correlation, including wind light spatial correlation modeling and wind light time series correlation modeling.

[0075] S32.A, the Copula function is used to establish the joint probability distribution of wind light to describe the wind light spatial correlation, and the specific steps of wind light spatial correlation modeling are as follows:

[0076] Based on the historical data of light intensity and wind speed, the probability distribution model of light intensity and wind speed is established respectively, and the probability density function of light intensity is shown in the following formulas (10)-(12):

[0077]

[0078]

[0079]

[0080] Γ(·) represents the gamma function, represents the cumulative probability distribution of the solar irradiance at time t, represents the solar irradiance at time t, and respectively represent the shape parameter of the solar irradiance at time t, and respectively represent the mean and the standard deviation of the solar irradiance at time t. For the uncertainty of the wind speed, the Weibull distribution is used to describe it, and the probability density function of the wind speed is shown in the following formulas (13)-(15):

[0081]

[0082]

[0083]

[0084] WT a WT (ω,t) and b WT (ω,t) respectively represent the scale parameter and the shape parameter of the wind speed at time t in ω season, σ PV (ω,t) represents the standard deviation of the wind speed at time t in ω season, and v(ω,t) represents the wind speed value at time t in ω season.

[0085]

[0086] At the same time, since there is often a negative correlation between wind and light, the Frank-Copula function can well describe the non-negative and negative correlation between variables, so the Frank-Copula function is selected to model the joint probability distribution of the solar irradiance and the wind speed, and the expression of the joint probability distribution of the wind and light is shown in the following formula (16):

[0087]

[0088] In the above formula, ρ represents the correlation coefficient, u PV = F(G T (ω,t)), F(·) represents the cumulative probability distribution of the solar irradiance, and u WT = G(v(ω,t)), G(·) represents the cumulative probability distribution of the wind speed.

[0089] ​​​​In addition, since the light intensity and wind speed at a current time are related to the light intensity and wind speed at the migration time or the previous time, the time sequence correlation exists. Since the integrated autoregressive moving average method can well capture the time sequence fluctuation characteristics of the variable, the integrated autoregressive moving average method is used to model the time sequence correlation of the light intensity and wind speed.

[0090] S32. The integrated autoregressive moving average method is used to model the time sequence correlation of the light intensity and wind speed, and the wind light time sequence correlation modeling is as shown in the following formulas (17)-(20):

[0091]

[0092]

[0093] ▽·G T,t =G T,t-1 (19);

[0094] ▽·v t =v t-1 (20)。

[0095] In the above formula, ▽ represents the back shift operator of the light intensity and wind speed, d PV and d WT represent the order of the difference operation of the light intensity and wind speed, ε PV,t and ε WT,t represent the Gaussian white noise of the light intensity and wind speed, φ PV,υ and φ WT,υ represent the υth autoregressive term coefficient of the light intensity and wind speed, θ PV,υ and θ WT,υ represent the υth moving average term coefficient of the light intensity and wind speed, p PV and q PV represent the order of the autoregressive term and the order of the moving average term of the light intensity, p WT and q WT represent the order of the autoregressive term and the order of the moving average term of the wind speed. According to the historical data of the light intensity and wind speed, the maximum likelihood estimation method can be used to estimate the autoregressive term coefficient and the moving average term coefficient and the corresponding order.

[0096] At the same time, since the sampling method can obtain discrete scenarios to describe the possible scenarios of carbon trading price, light intensity and wind speed in the future, and the importance sampling method can concentrate more samples in the tail of the probability distribution when sampling, which is more in line with CVaR, that is, the concept of conditional value at risk for measuring the tail risk of economic loss. Therefore, the importance sampling method is used to obtain the carbon trading price, light intensity and wind speed scenarios.

[0097] The specific steps are as follows:

[0098] S33, according to the established wind and light joint probability distribution, the joint scene of light intensity and wind speed at a certain time section is obtained by importance sampling method, specifically:

[0099] S33.a, random numbers a1 and a2 are generated in the interval [0, 1];

[0100] S33.b, let the probability distribution function value u PV of light intensity be a1, and the probability distribution function value u WT of wind speed be a2, that is, the solution of formula (21) is obtained:

[0101]

[0102] S33.c, repeat steps S33.a-S33.b to obtain a certain number of probability distribution function values of light intensity and wind speed;

[0103] S33.d, since u WT = G(v(ω, t)), the inverse function operation is used, that is, G T (ω, t) = F -1 (u PV ) and v(ω, t) = G -1 (u WT ) are solved respectively, the random number samples obtained in step S33.c are converted into joint scenes X = {x PV , x WT} of light intensity and wind speed, the generated scenes consider the spatial correlation between light intensity and wind speed, then the importance sampling method is used to obtain light intensity scene y PV and wind speed scene y WT with time sequence correlation.

[0104] S34, combine the generated wind and light time sequence scenes y PV and y WT , and correct the generated scenes x PV and x WT with spatial correlation, specifically:

[0105] S34.a, sort the wind speed scene x WT in the wind and light spatial correlation scene X from small to large, and the sorted wind speed scene is denoted as x WT , in order to maintain the consistency of the correlation, the light intensity scene x PV corresponding to x WT changes with x PV , and the changed light intensity scene is denoted as x PV .;

[0106] S34.b, regarding the time sequence scenario y PV and y WT Transpose the matrix and reconstruct it using column vectors to obtain the square matrix y'. PV and y' WT Take x WT Wind speed scenarios y' at the same time section and with temporal correlation WT , according to x WT Arrange the scenes with the same size in order, and denote the resulting scene as y. WT The scenes in the remaining time segments follow y WT Changes, denoted as y WT,t The spatial correlation scenario is corrected for each time section according to the following formula (22):

[0107]

[0108] In the above formula, Y' WT,t Similarly, for the corrected spatiotemporally correlated wind speed scene, we can obtain the corrected spatiotemporally correlated lighting scene X'. WT,t .

[0109] S35. To accelerate the calculation speed, the generated similar scenes are clustered using the K-means clustering method to reduce the number of scenes. Then, the number of clustered scenes for carbon trading price, light intensity and wind speed is determined using the sum of squared errors index. The expression for the sum of squared errors is shown in the following formula (23):

[0110]

[0111] In the above formula, Let γ represent the a-th class of the γ-th random variable. express The sample data in Let the centroid of the a-th class of the γ-th random variable be represented. Let γ represent the total number of clusters for the γth random variable. According to formula (23), the number of cluster scenarios for carbon trading price, light intensity and wind speed can be determined respectively.

[0112] This allows us to obtain typical scenarios for carbon trading prices, light intensity, and wind speed.

[0113] S36, respectively using vector S e S PV and S WT The typical scenarios representing carbon trading prices, solar radiation intensity, and wind speed are denoted as S = {S_{t}}. e ,S PV ,S WT}N where N denotes the total number of scenarios.

[0114] As a further supplement to the above embodiment one, in this embodiment, the process of constructing the CVaR-based park integrated energy system planning and operation bi-level optimization model in step S4 is specifically as follows:

[0115] S41, establish the objective function of the CVaR-based park integrated energy system planning and operation bi-level optimization model:

[0116] According to the risk measurement method of conditional value at risk, the CVaR-based park integrated energy system planning and operation bi-level optimization model takes the minimum conditional value at risk of the total life cycle planning and operation cost as the goal, and establishes the objective function of the following formulas (24)-(30):

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] In the above formula, represents the risk value of the total planning and operation cost under the confidence level θ, which is the threshold of the maximum total planning cost that does not exceed the given confidence level, represents the total planning and operation cost under scenario s, represents the equal annual investment cost under scenario s, represents the fuel cost under scenario s, represents the maintenance cost under scenario s, represents the carbon trading cost under scenario s, when is negative, indicating selling, and the park integrated energy system obtains income, when is positive, indicating buying, and the park integrated energy system pays cost, represents the carbon trading price under scenario s, r represents the discount rate, J represents the equipment candidate set, and l j represents the life of the jth equipment, represents the capacity of the jth equipment under scenario s, c inv,j represents the unit capacity price of the jth equipment, cmain,j represents the maintenance cost per unit power of the jth device, represents the output power of the jth device at time t under the s scenario, T represents the total annual operating time, which is 8760 hours, and respectively represent the natural gas purchase amount and the electricity purchase amount from the external network of the system at time t under the s scenario, λ gas and λ elec respectively represent the natural gas price and the electricity price, Δt represents the system operating time interval, which is 1 hour, CVaR θ represents the conditional value at risk at a confidence level, θ is the confidence level, reflecting the aversion level of the system to the fluctuation risk of the total cost, and in this paper, the risk aversion level is represented by the confidence level.

[0125] S42, establish the constraint conditions of the integrated energy system planning and operation bi-level optimization model based on the conditional value at risk:

[0126] The constraint conditions include equality constraints and inequality constraints. The equality constraint is the system power balance constraint established by using the carbon energy coupling model, and the inequality constraint is the upper and lower limits of the output power during device operation and the installation capacity constraint of the device:

[0127] 0≤P j,t ≤Q j (31);

[0128]

[0129]

[0130] In the above formula, P grid represents the maximum transmission power of the tie line, Q j represents the maximum installation capacity of the device j.

[0131] In addition, the electric energy storage also needs to satisfy the energy storage relationship before and after charging and discharging, as well as the maximum charging and discharging power and energy storage capacity constraint, then by introducing an auxiliary solving variable z s , the above constraint formulas (31) to (33) are converted into the following formula (34) and two linear inequalities (35) to (36):

[0132]

[0133] z s ≥0 (35);

[0134]

[0135] S43, a branch and bound algorithm is used to solve the park comprehensive energy system planning and operation double-layer optimization model, and the planning and operation double-layer optimization scheme under different confidence levels is obtained.

[0136] Please refer to Figures 2 to 15 , embodiment three of the present application is:

[0137] A park comprehensive energy system planning and operation optimization method, based on the above embodiment two, in this embodiment, taking the typical PIES structure as shown in the figure as a test example, all devices in the figure constitute a device candidate set. The PIES adopts the "grid-connected and off-grid" mode. In order to truly reflect the actual operation of the system, three typical day data of summer, winter and spring and autumn are selected to represent the annual operation. The economic and technical parameters of each device are shown in the following table 1: Figure 2 Figure 2 Table 1 Economic and technical parameters of devices

[0138]

[0139]

[0140]

[0141] The electric / thermal / cold load curves of PIES in summer, winter and spring and autumn typical days are shown in the figure. Among them, the discount rate is 6%, the maximum transmission power of the tie line is 6MW, the fixed gas price is 2.55 yuan / m 3 , the low heat value of natural gas combustion is 9.7kWh / m 3 , and the converted natural gas price is 0.26 yuan / kWh. Assuming that the park adopts time-of-use electricity price, one day is divided into valley period (0:00-8:00), peak period (8:00-12:00, 16:00-20:00) and flat period (12:00-16:00, 20:00-24:00), then the flat period electricity price is 0.8 yuan / kWh, the peak period electricity price is 1.6 yuan / kWh, and the valley period electricity price is 0.4 yuan / kWh. Figure 4

[0142] The unit active power carbon emission coefficients of different energy types and the free carbon quota coefficients allocated by the government are shown in table 2:

[0143] Table 2 Carbon emission coefficients and carbon quota coefficients of different energy types

[0144]

[0145] ​​​As can be seen from Table 2, the carbon emission coefficient per unit power of wind turbines and photovoltaic power generation is less than the carbon quota coefficient. This means that when PIES participates in carbon trading, it can offset the carbon emissions of the system itself through wind turbines and photovoltaic power generation, and then sell the excess carbon emission rights in the carbon trading market to make a profit.

[0146] In this embodiment, based on historical carbon trading price data (2017-2021), neural network technology is used to train and predict carbon trading prices, and the predicted average carbon trading price μ is obtained. e Approximately 50 yuan / ton, standard deviation σ e The price is 15 yuan / ton. Taking the annual wind speed and solar radiation intensity of a certain region in North China in 2020 as an example (data sampling interval is 1 hour), the standardized data is as follows: Figure 5 and Figure 6 As shown.

[0147] Based on the actual data throughout the year, the value of parameter ρ in equation (16) can be estimated to be 0.212, and the average per-unit light intensity μ of typical days in different seasons at different times can be estimated. PV (ω,t) and mean wind speed μ WT (ω,t) such as Figure 7 As shown.

[0148] The standard deviation of light intensity is approximately σ. PV (ω,t)=0.25μ PV (ω,t), the standard deviation of wind speed is approximately σ WT (ω,t)=0.25μ WT (ω,t). The parameters to be estimated in equations and can be estimated using the maximum likelihood estimation method. The estimation results are shown in Table 3.

[0149] Table 3 Parameters of the Integrated Autoregressive Moving Average Model

[0150]

[0151] Then, using importance sampling, 500 scenarios for carbon trading prices, light intensity, and wind speed can be obtained respectively. Using K-means clustering, the relationship between the SSE (Symptom Sequence) of carbon trading prices, light intensity, and wind speed and the number of clustered scenarios can be obtained, as follows: Figure 8 , Figure 9 and Figure 10 As shown in the figure, the SSE (Solution-Oriented Score) for carbon trading price, light intensity, and wind speed decreases with the increase of the number of clustered scenarios. When the number of clustered scenarios for carbon trading price, light intensity, and wind speed is greater than 12, 14, and 14 respectively, the rate of SSE decrease becomes very slow. Therefore, the final number of typical scenarios for carbon trading price, light intensity, and wind speed selected are 12, 14, and 14 respectively. The typical scenarios for carbon trading price, light intensity, and wind speed after clustering are shown in the figure. Figure 11 ,Figure 12 and Figure 13 The total number of scenarios N composed of typical scenarios of carbon trading price, light intensity and wind speed is (12x14x14) = 2352, and the probability of each scenario is 1 / 2352.

[0152] To illustrate the effectiveness of the optimization configuration method proposed in this embodiment in coping with the total cost fluctuation risk of planning and operation optimization, three cases are set up for comparative analysis in this example.

[0153] Case I: without considering electric energy storage, the determined carbon trading price and renewable energy output;

[0154] Case II: without considering electric energy storage, the carbon trading price and renewable energy output are uncertain and time-space related, and θ is set to 90%;

[0155] Case III: considering electric energy storage, the carbon trading price and renewable energy output are uncertain and time-space related, and θ is set to 90%.

[0156] The planning and operation comparison results of the three cases are shown in Tables 4 and 5, respectively:

[0157] Table 4 Comparison results of planning schemes of the three cases

[0158]

[0159]

[0160] As can be seen from Table 4, the capacities of CHP, EB, PV and WT in Case II are 294 kW, 271 kW, 1424 kWp and 599 kW more than those in Case I, while the capacity of GB is 613 kW less than that in Case I. The capacities of WT, EB and GB in Case III are 584 kW, 249 kW and 56 kW more than those in Case II, while the capacities of CHP and PV are 674 kW and 5972 kWp less than those in Case II. The comparison results of the planning schemes will be analyzed from the economic point of view first.

[0161] As can be seen from Table 5, the total cost of Case II is 1.5378 million yuan higher than that of Case I. The following analyzes the investment cost, operation and maintenance cost and carbon trading cost. The gas and electricity purchase quantities of the winter typical day under the three cases are shown in Figure 14 (a)-(b).

[0162] From Figure 14As shown in (a)-(b), in scenarios I and II, since the system lacks energy storage, scenario II requires a larger renewable energy capacity to reduce system carbon emissions due to the uncertainty of carbon trading prices and renewable energy output. This necessitates increasing gas purchases to boost CHP output and increasing external grid purchases to address renewable energy fluctuations. Consequently, the operation and maintenance costs and carbon trading costs in scenario II increase by RMB 359,600 and RMB 44,800, respectively. The increased capacity of CHP, PV, and WT in scenario II leads to an investment cost increase of RMB 1,133,400 compared to scenario I, ultimately resulting in a higher total cost for scenario II. However, scenario II considers the risk of planned total cost fluctuations during optimized configuration, resulting in a CVaR reduction of RMB 1,351,200 compared to scenario I. The CVaR value reflects the level of planned total cost fluctuation risk faced by the system; therefore, compared to scenario I, scenario II reduces the risk of planned and operating total cost fluctuations. By comparing scenarios I and II, it is shown that when the system is not equipped with energy storage, the planning and operation optimization method proposed in this invention can reduce the risk of fluctuation in the total planning and operation cost of the system, but this comes at the cost of increased carbon emissions and total cost.

[0163] On the other hand, compared to Scenario II, Scenario III reduces the total cost by RMB 1.6293 million. This is because Scenario III incorporates energy storage to address the uncertainty of renewable energy output, eliminating the need for CHP (Consumer Gas Storage) to reduce gas purchases and eliminating electricity purchases from the external grid. Therefore, compared to Scenario II, Scenario III relatively reduces both gas and electricity purchases from the external grid. Figure 14 As shown in (a)-(b), this significantly reduces the operation and maintenance costs of Scenario III by approximately RMB 1.2064 million, far exceeding the increase in investment costs of RMB 290,600. Furthermore, the RMB 62,900 profit generated from negative carbon emissions in Scenario III helps reduce the total system cost. Therefore, the total cost of Scenario III is lower than that of Scenario II. Simultaneously, since configuring energy storage can address the uncertainty of renewable energy output, the CVaR of Scenario III is reduced by RMB 625,700 compared to Scenario II, thus mitigating the risk of fluctuations in total planning and operating costs. The comparison between Scenario II and Scenario III demonstrates that when the system is equipped with energy storage, it not only reduces the risk of fluctuations in the total planning and operating costs but also enables the system to achieve negative carbon emissions, i.e., the system generates equivalent negative carbon emissions and obtains carbon trading revenue.

[0164] Further, taking Case III as an example, the influence of different confidence levels on the planning and operation optimization results is analyzed, and the influence of whether considering the space-time correlation of wind and light on the planning results is compared. The comparison results of the planning schemes under different confidence levels are shown in Tables 6 and 7, wherein W1 is the planning result without considering the space-time correlation of wind and light, and W2 is the planning result considering the space-time correlation of wind and light.

[0165] Table 6 Comparison results of planning schemes under different confidence levels

[0166]

[0167] Table 7 Comparison results of economic performance and carbon trading volume under different confidence levels (unit: ten thousand yuan, ton)

[0168]

[0169] It can be seen that, whether considering the space-time correlation of wind and light or not, with the increase of the confidence level, the system tends to avoid the total cost fluctuation risk of the planning more and more, the electric energy storage capacity of the system is increased to cope with the renewable energy output uncertainty, so that the total cost of system planning and operation is increased, but the planning and operation total cost fluctuation risk measured by CVaR is reduced. In addition, when the system is configured with a high proportion of renewable energy and a certain capacity of electric energy storage, the system produces negative carbon emissions, and with the increase of the confidence level, the carbon trading income of the system is increased, the system actively utilizes the uncertainty of the carbon trading price to make a profit, but needs to increase a certain investment cost as a price. By comparing the planning results considering the space-time correlation of wind and light or not, it can be seen that under the same confidence level, the electric energy storage capacity configuration considering the space-time correlation of wind and light is greater than that without considering the space-time correlation of wind and light. In addition, it can be seen that under the same confidence level, the total planning cost considering the space-time correlation of wind and light is higher than that without considering the space-time correlation of wind and light, and the CVaR is lower than that without considering the space-time correlation of wind and light. The main reason is that when the system considers the space-time correlation of wind and light, a larger electric energy storage needs to be configured to cope with the cost fluctuation risk, so that the investment cost of the system is increased, and the total cost of planning and operation is increased, but the system can avoid high cost fluctuation risk.

[0170] The comparison results of the total cost and CVaR of the system under different confidence levels are shown in Table 8. With the increase of the confidence level, the total cost of the system is increased and the CVaR is reduced, and the rate of the CVaR value reduction is increased. There is a contradictory relationship between the total cost and the CVaR of the system, and PIES needs to select the corresponding planning and operation optimization scheme according to the risk preference of itself. Figure 15

[0171] Please refer to Figure 16 , the fourth embodiment of the present application is:

[0172] ​The park integrated energy system planning and operation optimization terminal 1 comprises a memory 2, a processor 3 and a computer program stored in the memory 2 and executable on the processor 3. In the embodiment, the processor 3 implements the steps of any one of the above embodiments one to three when executing the computer program.

[0173] To sum up, the park integrated energy system planning and operation optimization method and terminal provided by the application establish a multi-energy flow and carbon trading volume coupling model based on an energy concentrator to depict the coupling relationship between carbon trading volume and multi-energy flow. A Copula function and an integrated autoregressive moving average method are used to establish a wind and light output spatio-temporal correlation model for wind and light scene generation. In addition, a PIES planning model of CVaR risk measurement is constructed to cope with the planning total cost fluctuation risk. Through an example verification, the application can effectively reduce the planning and operation total cost fluctuation risk faced by the system.

[0174] The above description is only an embodiment of the application, and does not limit the patent range of the application. Any equivalent transformation or direct or indirect application in the related technical field based on the content of the specification and drawings is also included in the patent protection range of the application.

Claims

1. A method for park integrated energy system planning and operation optimization, characterized in that, The method comprises the steps of: S1, establishing a multi-energy flow and carbon trading volume coupling model; S2, establishing a park integrated energy system model based on the carbon energy coupling model according to the multi-energy flow and carbon trading volume coupling model; S3, establishing a scenario generation model considering carbon trading price and wind and light uncertainty according to the park integrated energy system model; S4, establishing a park integrated energy system planning and operation bi-level optimization model based on conditional value at risk according to the scenario generation model; The step S1 is specifically: The carbon trading volume is introduced into the following formula (1) to establish a multi-energy flow and carbon trading volume coupling model: which can be simplified as formula (2): wherein, α w (w = 1, 2, ···, m) is the carbon emission coefficient of the wth input energy in the energy hub, β w (w = 1, 2, ···, m) is the free carbon quota coefficient of the wth input energy in the energy hub, is the carbon emission coefficient of the τth energy storage in the energy hub, ρ τ (w = 1, 2, ···, u) is the free carbon quota coefficient of the τth energy storage in the energy hub, L is the output power vector, C is the conversion device coupling matrix, P is the input power vector, K is the storage device coupling matrix, H is the energy storage charging and discharging power vector, E is the carbon transaction amount of the energy hub, A is the 1 × m dimension energy supply side carbon transaction amount coefficient matrix, B is the 1 × u dimension energy storage carbon transaction amount coefficient matrix, M is the (n + 1) × 1 dimension carbon energy output matrix, D is the (n + 1) × m dimension energy supply side carbon energy coupling coefficient combination matrix, and U is the (n + 1) × u dimension energy storage carbon energy coupling coefficient combination matrix. where A = [(α1-β1), (α2-β2), ···, (αn-βn)], m -β m )], The step S3 is specifically: S31, modeling the carbon trading price uncertainty; S32, modeling the wind and light uncertainty considering the space-time correlation, including wind and light space correlation modeling and wind and light time sequence correlation modeling; S32.A, using a Copula function to establish a wind and light joint probability distribution to describe the wind and light space correlation; S32.B, using an integrated autoregressive moving average method to model the time sequence correlation of light intensity and wind speed; S33, obtaining the joint scenarios of light intensity and wind speed at a certain time section by importance sampling method based on the established wind and light joint probability distribution; S34, combine the generated landscape timing scene y PV and y WT , correct the generated spatially dependent scene x PV and x WT ​ S35, clustering the generated similar scenarios by K-means clustering method, and using the error sum of squares index to determine the number of clustering scenarios of carbon trading price, light intensity and wind speed; S36, respectively, vector S e , S PV and S WT represent typical scenarios of carbon trading price, light intensity and wind speed, and the typical scenario set constructed by the scenario vectors of the three is S = {S e ,S PV ,S WT} N , wherein N represents the total number of scenarios; The step S4 is specifically: S41, establishing an objective function of the park integrated energy system planning and operation bi-level optimization model based on conditional value at risk: the park integrated energy system planning and operation bi-level optimization model based on conditional value at risk takes the conditional value at risk of the total cost of the whole life cycle planning and operation as the target; S42, establishing the constraint conditions of the park integrated energy system planning and operation bi-level optimization model based on conditional value at risk: the constraint conditions include equality constraints and inequality constraints, the equality constraints are the system power balance constraints established by the carbon energy coupling model, and the inequality constraints are the upper and lower limits of the output of the device operation and the installation capacity constraints of the device.

2. The park integrated energy system planning and operation optimization method according to claim 1, characterized in that, The step S2 is specifically: S21, using a typical park integrated energy system composed of combined heat and power units, gas boilers, electric chillers, electric boilers, electric energy storage, photovoltaic and wind turbine devices, and purchasing natural gas and electricity from the outside of the system, and using energy conversion devices to cooperate with electric energy storage to meet the electric / thermal / cold load demand of users; The typical park integrated energy system participates in carbon trading to sell surplus carbon emission rights to make profits; The carbon emissions of the typical park integrated energy system come from the primary energy supply end and the carbon emissions generated by energy storage devices, i.e. the carbon emissions generated by external power grid electricity purchase, natural gas consumption, photovoltaic and wind turbine power generation and electric energy storage, and the carbon emissions of photovoltaic, wind turbine and electric energy storage during operation are obtained by normalizing the carbon emissions by referring to the life cycle analysis method; S22, modeling the multi-energy flow and carbon trading volume coupling relationship of the typical park integrated energy system, as shown in the following formulas (3)-(5): wherein P grid , P gas , P PV and P WT represent the external grid electricity purchasing power, the natural gas injection power, the photovoltaic injection power and the wind turbine injection power, respectively, P EL , P CL , P HL and E tr represent the electricity / cooling / heating load and the carbon trading amount, respectively, c1 represents the electricity power distribution coefficient of the electricity supply load, c2 represents the electricity power distribution coefficient of the electricity boiler, c3 represents the natural gas power distribution coefficient of the combined heat and power unit, and represent the electricity efficiency of the combined heat and power unit, the thermal efficiency of the combined heat and power unit, the thermal efficiency of the gas boiler, the thermal efficiency of the electricity boiler and the refrigeration efficiency of the electric refrigerator, respectively, P ES represents the charge / discharge power of the electricity storage, when greater than 0, it represents the electricity storage charging, when less than 0, it represents the electricity storage discharging, a grid , a PV , a WT , a gas and represent the unit power carbon emission coefficient of the external grid electricity, the photovoltaic, the wind turbine, the natural gas and the electricity storage, respectively, b grid , b PV , b WT , b CHP , b GB and b ES represent the unit power free carbon quota coefficient of the external grid electricity, the photovoltaic, the wind turbine, the combined heat and power unit, the gas boiler and the electricity storage, respectively. The carbon trading amount of the typical park comprehensive energy system is the sum of the carbon trading amounts of the five parts of external network electricity purchase, photovoltaic, fan, natural gas and electric energy storage, if the carbon emission coefficient of a part is greater than the carbon quota coefficient, the carbon trading amount of the part is positive, if the carbon emission coefficient of a part is less than the carbon quota coefficient, the carbon trading amount of the part is negative, when the value is positive, the typical park comprehensive energy system needs to purchase carbon emission rights from the carbon trading market, when the value is negative, the typical park comprehensive energy system can sell the excess carbon emission rights in the carbon trading market; S23, c1, c2 and c3 are intermediate variables introduced to meet the requirements of energy concentrator modeling form, which can be expressed by the output power of each device unit and the efficiency of the device, that is, the following formulas (6)-(8): where G CHP represents the natural gas power consumed by the cogeneration unit, represents the output power of the electric chiller, represents the thermal output power of the electric boiler, represents the energy storage discharging power, represents the energy storage charging power.

3. The park integrated energy system planning and operation optimization method according to claim 2, characterized in that, The step S3 is specifically: S31, the carbon trading price uncertainty is modeled, specifically: The neural network technology is used to train and predict the carbon trading price to obtain a prediction error, the kernel density estimation method is used to obtain a frequency histogram of the prediction error, the frequency histogram is used to determine that the prediction error obeys a normal distribution with zero mean, the prediction error is superimposed with the predicted value of the carbon trading price to obtain an actual carbon trading probability distribution, that is, the following formula (9): where x e represents the carbon trading price random variable, μ e and σ e represent the carbon trading price mean and standard deviation, respectively; S32, the wind and light uncertainty considering the space-time correlation is modeled, including wind and light space correlation modeling and wind and light time sequence correlation modeling; S32.A, the Copula function is used to establish a wind and light joint probability distribution to describe the wind and light space correlation, and the specific steps of the wind and light space correlation modeling are as follows: Based on the historical data of the light intensity and the wind speed, the probability distribution models of the light intensity and the wind speed are respectively established, the probability density function of the light intensity is shown in the following formulas (10)-(12): where Γ(·) denotes the gamma function, denotes the light intensity at season t, denotes the light intensity at season t, denotes the shape parameter of the light intensity at season t, denotes the mean and the standard deviation of the light intensity at season t, respectively. For the uncertainty of the wind speed, the Weibull distribution is used to describe, and the probability density function of the wind speed is shown in the following formulas (13)-(15): wherein, and respectively represent the scale parameter and the shape parameter of the wind speed at the season t, a scale parameter and a shape parameter of the wind speed at the season t, a standard deviation of the wind speed at the season t, a scale parameter and a shape parameter of the wind speed at the season t, a wind speed value at the season t, a scale parameter and a shape parameter of the wind speed at the season t, a wind speed value at the season t, a mean value of the wind speed at the season t. The Frank-Copula function is used to model the joint probability distribution of the light intensity and the wind speed, and the expression of the wind and light joint probability distribution is shown in the following formula (16): wherein p denotes a correlation coefficient, F(·) denotes a cumulative probability distribution of the light intensity, G(·) denotes a cumulative probability distribution of the wind speed; S32.B, the integrated autoregressive moving average method is used to model the time sequence correlation of the light intensity and the wind speed, and the wind and light time sequence correlation modeling is shown in the following formulas (17)-(20): wherein, denote the back-shift operator of the irradiance intensity and the wind speed, d PV and d WT denote the order of the difference operation of the irradiance intensity and the wind speed, respectively, ε PV,t and ε WT,t denote the Gaussian white noise of the irradiance intensity and the wind speed, respectively, φ PV,υ and φ WT,υ denote the υth autoregressive term coefficient of the irradiance intensity and the wind speed, respectively, θ PV,υ and θ WT,υ denote the υth moving average term coefficient of the irradiance intensity and the wind speed, respectively, p PV and q PV denote the order of the autoregressive term and the order of the moving average term of the irradiance intensity, respectively, p WT and q WT denote the order of the autoregressive term and the order of the moving average term of the wind speed, respectively, according to the historical data of the irradiance intensity and the wind speed, the autoregressive term coefficient and the moving average term coefficient and the corresponding orders can be estimated by the maximum likelihood estimation method; S33, according to the established wind and light joint probability distribution, the joint scene of the light intensity and the wind speed at a certain time section is obtained by the importance sampling method, specifically: S33.a, random numbers a1 and a2 are generated in the interval [0, 1]; S33.b, the probability distribution function value u of the light intensity PV = a1, the probability distribution function value u of the wind speed is obtained from the constructed joint probability distribution of the wind and light WT = a2, that is, the solution of the following equation (21) is obtained S33.c, steps S33.a-S33.b are repeated to obtain a certain number of light intensity and wind speed probability distribution function values; S33.d, since Using the inverse function operation, i.e. solving and The random number sample obtained in step S33.c is converted into a joint scene X = {x PV ,x WT} of the light intensity and the wind speed, and the light intensity scene y PV and the wind speed scene y WT with time correlation are obtained by the importance sampling method; S34, combine the generated landscape timing scene y PV and y WT , correct the generated scene x with spatial correlation PV and x WT , in particular S34.a. wind speed scenario x in the wind-light space correlation scenario X WT From small to large, the sorted wind speed scenario is denoted as To keep the consistency of the correlation, the scenarios x WT The corresponding light intensity scenario x PV Follow x WT Changes, the changed light intensity scenario is denoted as S34.b, regarding the time sequence scenario y PV and y WT Transpose the matrix and reconstruct it using column vectors to obtain the square matrix y'. PV and y' WT Take x WT Wind speed scenarios y' at the same time section and with temporal correlation WT , according to x WT Arrange the scenes with the same size in order, and denote the resulting scene as: The remaining time segments follow Changes are denoted as The spatial correlation scenario is corrected according to the following formula (22): Among them, Y' WT,t Similarly, for the corrected spatiotemporally correlated wind speed scene, we can obtain the corrected spatiotemporally correlated lighting scene X'. WT,t ; S35, the generated similar scenes are clustered by the K-means clustering method, and the cluster scene number of the carbon trading price, the light intensity and the wind speed is determined by using the error sum of squares index, and the expression of the error sum of squares is shown in the following formula (23): wherein, represents the a-th class of the γ-th random variable, represents sample data in the, represents the centroid of the a-th class of the γ-th random variable, represents the total number of clusters of the γ-th random variable, according to formula (23), the number of clustered scenarios of carbon trading price, light intensity and wind speed can be determined respectively; S36, respectively, with vector S e , S PV and S WT represent typical scenarios of carbon trading price, light intensity and wind speed, and the typical scenario set constructed by the scenario vectors of the three is S = {S e ,S PV ,S WT} N , wherein N represents the total number of scenarios.

4. The park integrated energy system planning and operation optimization method of claim 3, wherein, Step S4 is specifically: S41, the objective function of the park comprehensive energy system planning and operation double-layer optimization model based on the conditional value at risk is established: According to the risk measurement method of conditional value at risk, a park comprehensive energy system planning and operation bi-level optimization model based on conditional value at risk is established, and the conditional value at risk of the total cost of the whole life cycle planning and operation is taken as the target, and the following formulas (24)-(30) are established: wherein, represents the risk value of the planning and operation total cost at the confidence level θ, which is a threshold of the maximum planning total cost not exceeding the given confidence level, represents the planning and operation total cost under the s scenario, represents the equal annual investment cost under the s scenario, represents the fuel cost under the s scenario, represents the maintenance cost under the s scenario, represents the carbon trading cost under the s scenario, when is negative, it represents selling, and the park integrated energy system obtains income, when is positive, it represents buying, and the park integrated energy system pays cost, represents the carbon trading price under the s scenario, r represents the discount rate, J represents the equipment candidate set, l j represents the life of the jth equipment, represents the capacity of the jth equipment under the s scenario, c inv,j represents the unit capacity price of the jth equipment, c main,j represents the maintenance cost per unit power of the jth equipment, represents the output power of the jth equipment at t time under the s scenario, T represents the total annual operation time, which is 8760 hours, and respectively represent the natural gas purchase amount and the power purchase amount from the external network of the system at t time under the s scenario, λ gas and λ elec respectively represent the natural gas price and the power price, Δt represents the system operation time interval, which is 1 hour, CVaR θ represents the conditional risk value at the confidence level θ, which reflects the aversion level of the system to the planning total cost fluctuation risk. S42, constraint conditions of the park comprehensive energy system planning and operation bi-level optimization model based on conditional value at risk are established. The constraint conditions include equality constraints and inequality constraints, the equality constraints are system power balance constraints established by using the carbon energy coupling model, and the inequality constraints are output upper and lower limit constraints and equipment installation capacity constraints during equipment operation: 0 < P j,t ≤ Q j (31); wherein, denotes the maximum transmission power of the tie line, denotes the maximum installed capacity of the plant j; where the energy storage of the electric energy storage also needs to satisfy the energy storage relationship before and after charging and discharging, and the maximum charging and discharging power and energy storage constraints, and then auxiliary solving variables z s The above constraint formulas (31) to (33) are converted into the following formula (34) and two linear inequalities (35) to (36): z s ≥0 (35)； S43, a branch and bound algorithm is used to solve the park comprehensive energy system planning and operation bi-level optimization model, and planning and operation bi-level optimization schemes under different confidence levels are obtained.

5. A park integrated energy system planning and operation optimization terminal, characterized in that, A computer program product comprising a memory, a processor and a computer program stored on the memory and loadable on the processor, the processor implementing the steps of the method according to any one of claims 1-4 when executing the computer program.

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