CN116644866A - Comprehensive energy system robust optimization method and system considering wind-light uncertainty - Google Patents

Abstract

The application discloses a robust optimization method and a system for a comprehensive energy system considering wind-light uncertainty, wherein the method comprises the following steps: constructing a comprehensive energy system under a combined operation mode of a carbon-containing capturing, electricity-to-gas and cogeneration unit, and establishing an equipment model of the comprehensive energy system; according to the self-adaptive kernel density estimation, fitting a probability density function of the prediction errors in the wind power output prediction data before the day and the photovoltaic output prediction data, and integrating the probability density function to obtain a cumulative distribution function; constructing a fuzzy uncertainty set according to the cumulative distribution function; establishing a distributed robust optimization model of the comprehensive energy system based on the fuzzy uncertainty set and the affine adjustable strategy; and converting the distributed robust optimization model into a solvable model based on a dual theory and a convex optimization theory, and solving the solvable model. The economic optimization scheduling function of various terminal user equipment to the comprehensive energy system is realized.

Description

Robust optimization method and system for integrated energy system considering wind-solar uncertainty

technical field

The invention belongs to the technical field of comprehensive energy optimization, and in particular relates to a robust optimization method and system for an integrated energy system considering the uncertainty of scenery.

Background technique

An important feature of the integrated energy system (Integrated Energy System, IES) is to realize the connection and energy scheduling of multiple energy networks through various coupling devices. Combined Heat and Power unit (CHP) is an important energy device in the comprehensive energy system, which couples the power grid, heat network and gas network. Power-to-gas technology uses electricity to convert The conversion to natural gas has realized the coupling of the power grid and the gas grid and the consumption of new energy. Carbon capture technology can capture carbon from gas-fired power generation , is an important means to achieve carbon emission reduction. Existing studies rarely analyze the working characteristics of the three in the joint operation mode and their impact on new energy consumption and carbon emission reduction.

In addition, the optimal decision of IES is made based on the predicted power of wind power (WT) and photovoltaic (PV). In reality, wind power and photovoltaics have strong uncertainties, so how to eliminate the uncertainties of wind and solar power generation under the premise of reducing conservatism is very necessary.

Contents of the invention

The invention provides a robust optimization method and system for an integrated energy system considering the uncertainty of wind and solar energy, which is used to solve the technical problem of how to eliminate the influence of wind and wind power generation uncertainty under the premise of reducing conservatism.

In the first aspect, the present invention provides a robust optimization method for an integrated energy system considering the uncertainty of wind and light, including: constructing an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establishing the Describe the equipment model of the integrated energy system; obtain the historical wind power output data and photovoltaic historical output data, and cut the scene reduction of the wind power historical output data and photovoltaic historical output data, and obtain the current wind power output forecast data and photovoltaic output forecast data in typical scenarios; according to Adaptive kernel density estimation fits the probability density function of the forecast error in the wind power output forecast data and the photovoltaic output forecast data, and integrates the probability density function to obtain a cumulative distribution function; according to the cumulative distribution The function constructs a fuzzy uncertain set; based on the fuzzy uncertain set and the affine adjustable strategy, a distribution robust optimization model of the comprehensive energy system is established; based on the dual theory and convex optimization theory, the distribution robust optimization model is transformed into a solvable model, and solve the solution model.

In the second aspect, the present invention provides a robust optimization system for an integrated energy system considering the uncertainty of wind and light, including: a first building module configured to build a combined operation mode of carbon capture, power-to-gas and cogeneration units An integrated energy system, and an equipment model of the integrated energy system is established; the acquisition module is configured to acquire historical wind power output data and photovoltaic historical output data, and perform scene reduction on the wind power historical output data and photovoltaic historical output data, to obtain typical scenarios The day-ahead wind power output forecast data and the photovoltaic output forecast data; the fitting module is configured to fit the probability density function of the forecast error in the day-ahead wind power output forecast data and the photovoltaic output forecast data according to the adaptive kernel density estimation, and The probability density function is integrated to obtain a cumulative distribution function; the construction module is configured to construct a fuzzy uncertainty set according to the cumulative distribution function; the second establishment module is configured to be based on the fuzzy uncertainty set and an affine adjustable strategy A distribution robust optimization model of the integrated energy system is established; a solving module is configured to convert the distribution robust optimization model into a solvable model based on dual theory and convex optimization theory, and solve the solving model.

In a third aspect, an electronic device is provided, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, The instructions are executed by the at least one processor, so that the at least one processor executes the steps of the robust optimization method for an integrated energy system considering wind and solar uncertainties in any embodiment of the present invention.

In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program instructions are executed by a processor, the processor executes any embodiment of the present invention. Steps in a robust optimization method for comprehensive energy systems.

The robust optimization method and system of the integrated energy system considering the uncertainty of wind and solar energy in this application have the following beneficial effects: the probability density function of wind power and photovoltaic prediction power error is proposed by using the adaptive kernel density estimation method, which overcomes the problem of using the theory The distribution assumes the subjective defect of unknown distribution, and makes full use of the value of historical data to construct a more compact and objective uncertainty set, which effectively reduces conservatism. At the same time, a day-ahead and real-time two-stage robust optimization model based on an affine adjustable strategy is established. This model overcomes the shortcomings of the robust optimization model and the stochastic optimization model, and can achieve robustness and efficiency under the premise of data-driven balance. Finally, the optimization system established by combining hardware and software technology and network technology programs the above-mentioned optimization method, accepts and processes data from the integrated energy system in real time, and makes decisions, and realizes the economic optimization of the integrated energy system by various end-user devices Scheduling function.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

Fig. 1 is a structural framework diagram of an integrated energy system provided by an embodiment of the present invention;

Fig. 2 is a flow chart of a method for robust optimization of an integrated energy system considering the uncertainty of scenery provided by an embodiment of the present invention;

Fig. 3 is a structural block diagram of a robust optimization system for an integrated energy system considering the uncertainty of scenery provided by an embodiment of the present invention;

Fig. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

The framework of the integrated energy system with multi-energy coupling is shown in Figure 1. The system covers electric loads, heat loads and cooling loads, in which electric loads are powered by cogeneration units, wind turbines, photovoltaic units, upper-level power grids and batteries; heat loads are powered by cogeneration units, upper-level heating networks, gas boilers and storage The heat is supplied by the hot tank; the cold load is supplied by the electric refrigeration device and the absorption refrigeration equipment; the gas is supplied to the combined heat and power unit and the gas boiler by the gas grid and the power-to-gas equipment. The system combines heat and power cogeneration units, carbon capture devices and power-to-gas equipment as a whole, which facilitates the thermoelectric decoupling of the units, improves the system's ability to absorb new energy and reduces carbon emissions.

Please refer to FIG. 2 , which shows a flowchart of a method for robust optimization of an integrated energy system considering wind and solar uncertainties in the present application.

As shown in Figure 2, a robust optimization method for an integrated energy system considering wind and solar uncertainties specifically includes the following steps:

Step S101, constructing an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establishing an equipment model of the integrated energy system.

In this step, the thermoelectric unit generates electric power and thermal power by burning natural gas, where the output model of the thermoelectric unit is:

,

In the formula, and /> are respectively the electric power generated by the thermoelectric unit at time t and the thermal power output by the thermoelectric unit at time t, /> is the low calorific value of natural gas, /> is the gas consumption of the thermoelectric unit at time t, /> and /> are the generating efficiency of the unit and the heat loss coefficient of the unit, respectively;

Carbon capture and storage (CCS) technology can capture CO ₂ and store the captured CO ₂ to reduce carbon emissions. The electricity consumed by carbon capture consists of basic energy consumption and operating energy consumption:

,

In the formula, is the electric energy consumed by the carbon capture equipment at time t, /> It is the 0-1 flag bit at time t, where 1 means capture, 0 means close, /> is the basic energy consumption of carbon capture at time t, /> is the operating energy consumption of carbon capture at time t, /> is the capture unit /> operating energy consumption, /> is the captured /> at time t quantity;

The amount of carbon dioxide used by power-to-gas is the same as the natural gas produced. The mathematical model of power-to-gas equipment is:

,

In the formula, is the amount of natural gas generated by the power-to-gas equipment at time t, /> is the power-to-gas conversion efficiency, is the electric energy consumed by the power-to-gas equipment at time t, /> is the low calorific value of natural gas;

The thermoelectric unit provides electric energy to the power-to-gas equipment and the carbon capture device, and the on-grid power provided by the thermoelectric unit is:

,

In the formula, is the on-grid power provided by the thermal power unit at time t, /> is the electric power generated by the thermoelectric unit at time t, /> is the electric energy consumed by the carbon capture equipment at time t, /> is the electric energy consumed by the power-to-gas equipment at time t;

In the joint operation mode, the electric-thermal output coupling characteristics of the thermoelectric unit are:

,

In the formula, , /> Respectively, the minimum and maximum value of the on-grid power provided by the thermal power unit, /> and Respectively, the minimum output electrothermal conversion coefficient of the thermoelectric unit and the maximum output electrothermal conversion coefficient of the thermoelectric unit, /> is the linear slope, /> is the thermal power output by the thermoelectric unit at time t, /> is the maximum power of the carbon capture device, /> is the maximum power of the power-to-gas equipment, /> is the minimum heat output of the thermoelectric unit, /> The on-grid power provided by the thermal power unit at time t;

Gas-fired boilers generate heat power by burning natural gas, where the expression for calculating the heat power is:

,

In the formula, is the thermal power generated by the gas boiler at time t, /> is the heat conversion efficiency of the gas boiler, /> is the low calorific value of natural gas, /> is the gas consumption of the gas-fired boiler at time t;

The energy conversion equipment model includes electric refrigerators and absorption refrigerators, and the models of electric refrigerators and absorption refrigerators are:

,

,

In the formula, and /> are respectively the cooling power output by the electric refrigerator at time t and the cooling power output by the absorption refrigerator at time t, /> and /> Respectively, the electric energy consumed by the electric refrigerator at time t and the heat energy consumed by the absorption refrigerator at time t, /> and /> are the cooling efficiency of the electric refrigerator and the cooling efficiency of the absorption refrigerator, respectively;

The energy storage equipment model of the system includes batteries and heat storage tanks, where the capacity models of batteries and heat storage tanks are:

,

In the formula, and /> Respectively, the capacity of electric energy storage at time t and the capacity of thermal energy storage at time t, /> , /> and , /> Respectively, the charging efficiency of electric energy storage at time t, the discharge efficiency of electric energy storage at time t, the heat storage efficiency of thermal energy storage at time t, and the heat release efficiency of thermal energy storage at time t, /> , /> and /> , /> Respectively, the charging power of electric energy storage at time t, the discharge power of electric energy storage at time t, the heat storage power of thermal energy storage at time t, and the heat release power of thermal energy storage at time t.

Step S102, obtaining historical wind power output data and photovoltaic historical output data, and performing scene reduction on the wind power historical output data and photovoltaic historical output data, to obtain the current wind power output forecast data and photovoltaic output forecast data in typical scenarios.

Step S103, fitting the probability density function of the prediction error in the day-ahead wind power output forecast data and the photovoltaic output forecast data according to the adaptive kernel density estimation, and integrating the probability density function to obtain a cumulative distribution function.

In this step, due to the uncertainty of wind power generation, there is a deviation between the predicted power of wind power and photovoltaic power and the actual power, which affects the execution of the dispatch plan.

The expressions of wind power forecast error and photovoltaic forecast error in IES are:

,

,

In the formula, and /> are respectively the forecast error of wind power at time t and the forecast error of photovoltaic power at time t, /> is the actual output of wind power at time t, /> is the output of wind power forecast at time t, /> is the actual PV output at time t, /> Output for photovoltaic prediction at time t;

Suppose there are n historical operating data in the integrated energy system , then the form of the kernel density estimate is:

,

In the formula, is the prediction error, /> is the number of samples, /> is the bandwidth, /> is the kernel function, /> is the probability density function, /> Indicates the kth historical forecast error.

bandwidth Determines the variance of the kernel function, which reflects the overall flatness of the kernel density estimation curve, and the estimation results of the kernel function under different bandwidths are significantly different. The bandwidth selection of traditional kernel density estimation depends on subjective judgment, which is not conducive to the simulation of kernel density estimation to obtain the real probability density function. In order to minimize the error, the mean square integral error is used to measure the bandwidth/> pros and cons, under weak assumptions,

,

In the formula, is the mean square integral error, /> is the asymptotic mean square integral error, /> is the decay rate of the error with time, /> is the number of samples, /> is the bandwidth;

,

In the formula, is the kernel function /> scale parameter, /> Generate the squared moments of the probability density function for the data, /> is the second derivative of the probability density function;

,

In the formula, is a random variable /> The kernel function.

minimize Equivalent to minimize /> , right /> Find the derivative, let the derivative be 0, and simplify to find the best bandwidth/> The expression is:

,

After selecting the kernel function and bandwidth, the adaptive kernel density estimation method simulates the real probability distribution curve, in which, the adaptive kernel density estimation method is used to fit the random variable The expression of the probability density function of is:

,

Integrate the probability density function to obtain a cumulative distribution function, wherein the expression of the cumulative distribution function is:

,

In the formula, is the cumulative distribution function, /> is a random variable /> The probability density function of .

Step S104, constructing a fuzzy uncertainty set according to the cumulative distribution function.

In this step, according to the above expression, a distributed fuzzy uncertainty set is constructed, which can be regarded as a is a circle point, a Wasserstein sphere with a certain distance as the radius. Wherein, the expression of the fuzzy uncertain set is:

,

In the formula, is the fuzzy uncertainty set, /> is the true distribution, /> For the estimated distribution, /> is the Wasserstein distance between the true distribution and the estimated distribution, /> is the total probability distribution;

,

In the formula, is the radius of the Wasserstein sphere, depending on the number of samples, /> is a constant, /> is the total number of samples, /> is the confidence level of the solution radius;

,

In the formula, is a real number, /> is the sample mean, /> Denotes the kth prediction error.

Step S105, establishing a distribution robust optimization model of the integrated energy system based on the fuzzy uncertain set and the affine adjustable strategy.

In this step, the first-stage optimization model and the second-stage optimization model of the integrated energy system are established based on the affine adjustable strategy. To minimize the operating cost of the integrated energy system, the second-stage optimization model considers the prediction error under the uncertainty of wind and rain, and formulates a real-time scheduling strategy by minimizing the expected value of the system adjustment cost under the worst distribution condition;

,

In the formula, min( ) represents the objective function of the first stage, is the electricity purchase cost of IES, /> Gas purchase cost for IES, /> is the start-stop cost of thermal power units and gas-fired boilers, /> for carbon storage costs, /> is the carbon trading cost, /> is the expected value of the adjustment cost caused by the forecast error, /> adjust the cost function for the second stage, /> Optimizing the objective function of the model for the second stage;

The adjustment cost of the second-stage optimization model adds the wind power and photovoltaic power abandonment penalty cost on the basis of the day-ahead cost, and deletes the start-up and stop cost of the equipment at the same time, wherein the The expression is:

,

In the formula, Indicates the scheduling period, /> To account for the electricity purchase cost of the forecast error, /> In order to consider the gas purchase cost of forecast error, /> Penalty cost for the system to abandon wind and light, /> To account for the carbon storage cost of forecast errors, /> Carbon trading costs to account for forecast errors;

,

In the formula, is the electricity price at time t, /> is the electric power purchased from the grid at time t in the real-time stage, /> is the electric power sold to the grid at time t in the real-time stage, /> is the electric power purchased from the grid at time t in the day-ahead period, /> is the electric power sold to the grid at time t in the day-ahead phase;

,

In the formula, is the unit penalty coefficient, /> is the curtailed wind power at time t in the real-time stage, /> is the optical power discarded at time t in the real-time stage.

The constraints of electrothermal energy storage are similar. Taking electric energy storage as an example, it needs to satisfy capacity constraints, power constraints, and state inequality constraints:

,

In the formula, and /> They are the 0-1 status flag bits of electric energy storage charging and discharging at time t respectively, /> and Respectively, the upper and lower limits of the battery energy storage power at time t, /> and /> Respectively are the upper and lower limits of the battery discharge power at time t, /> and /> Respectively are the upper and lower limits of the battery capacity at time t, /> is the energy storage capacity at the end of the dispatch period, is the energy storage capacity at the beginning of the dispatch period.

The power constraints of system power purchase and sale to the grid in the day-ahead stage are as follows:

,

In the formula, is the electric power purchased from the grid at time t in the day-ahead period, /> is the electric power sold to the grid at time t in the previous stage, /> and /> are the power constraints of purchasing and selling electricity from the grid, respectively, /> and /> is a 0-1 state variable used to represent electricity purchase and sale.

In the second stage, there is an error between the real-time power of wind power and photovoltaic power and the predicted power before the day, and the real-time variable must be changed on the basis of the day-ahead variable. In order to eliminate the impact of renewable energy output error, the balance can be re-realized by calling the flexible resources of IES and adjusting the power purchase and sale power. Based on the affine adjustable strategy, the real-time variables related to electric energy are associated with the day-ahead variables, and the real-time variable model related to the day-ahead variables is constructed, as follows:

,

In the formula, is the curtailed wind power at time t in the real-time stage, /> is the total prediction error of wind power generation at time t, /> is the abandoned light power at time t in the real-time stage, /> is the electric power purchased from the grid at time t in the real-time stage, /> is the electric power sold to the grid at time t in the real-time stage, /> is the charging power of the electric energy storage at time t in the real-time stage, /> is the charging power of electric energy storage at time t, /> is the discharge power of electric energy storage at time t in the real-time stage, /> is the discharge power of the electric energy storage at time t, , /> , /> , /> are the affine adjustable coefficients corresponding to wind power generation, photovoltaic power generation, energy storage charging and energy storage discharge in the real-time phase, and the values are between -1 and 1;

In the above equation, the first term on the left side of the equation represents the power variable in the real-time phase. The first term on the right side of the equation represents the power variable related to the day-ahead stage, and the second term represents the variable adjustment strategy, which is jointly determined by the affine adjustable coefficient and the predicted power error.

Finally, the distributed robust optimization scheduling model constructed includes the objective function of minimizing the operating cost of the two-stage system, a set of feasible domains formed by the constraints of the day-ahead phase, and another set of feasible domains formed by the constraints of the real-time phase through an affine adjustable strategy. area.

Step S106, transforming the distribution robust optimization model into a solvable model based on dual theory and convex optimization theory, and solving the solution model.

In this step, based on dual theory and convex optimization theory, the distribution robust optimization model is transformed into a solvable model, and the commercial solver CPLEX in MATLAB is called to solve it.

According to the dual theory, the upper bound problem of the worst prediction distribution of the second-stage optimization model is transformed into a lower bound problem, where the expression of the objective function of the transformed second-stage optimization model is:

,

In the formula, Indicates the kth historical forecast error, /> is the radius of the Wasserstein sphere, /> is the number of samples, adjust the cost function for the second stage, /> is the total forecast error of wind power and photovoltaic power, /> is a fuzzy uncertain set, /> is a dual variable, /> is the lower bound function, /> is the upper bound function of the adjusted cost under the worst condition;

Update the objective function and constraints of the distribution robust optimization model, where the objective function of the updated distribution robust optimization model is:

,

In the formula, is the transposed column vector corresponding to the optimization variable x, /> is the optimization variable;

The constraints of the updated distribution robust optimization model are:

,

In the formula, A is the coefficient matrix of the variables under the corresponding inequality constraints, is the constant column vector under the corresponding inequality constraint, G is the coefficient matrix of the corresponding constraint considering the prediction error, /> and /> yes /> the linear function of;

Transform the distribution robust optimization model into a solvable model based on convex optimization theory, wherein the expression of the objective function of the solution model is:

,

In the formula, is the auxiliary variable introduced;

The expression of the constraints of the solution model is:

,

In the formula, for /> The coefficient transpose matrix, /> for corresponding /> The optimization function, /> is the minimum value of prediction error, /> Indicates the kth historical forecast error, /> is the maximum value of prediction error, /> for corresponding /> The optimization function, /> for corresponding /> The optimization function, /> is the coefficient matrix of the corresponding constraint considering the prediction error, /> for corresponding /> A constant column vector of , /> for corresponding /> A constant column vector of .

The solution model in this embodiment eliminates random variables that are difficult to obtain , but using the lower bound value of the random variable that can be obtained /> , the upper bound value of the random variable /> and historical forecast error values/> , reducing the difficulty of solving. At the same time, the distributed robust optimization model established considering the worst conditions of the system combines the characteristics of stochastic optimization accuracy and robust optimization conservatism, so that the system can also meet the power constraints under the worst conditions and realize optimal scheduling of the system.

Please refer to FIG. 3 , which shows a structural block diagram of a robust optimization system for an integrated energy system considering wind and solar uncertainties in the present application.

As shown in FIG. 3 , the robust optimization system 200 includes a first establishment module 210 , an acquisition module 220 , a fitting module 230 , a construction module 240 , a second establishment module 250 and a solution module 260 .

Among them, the first establishment module 210 is configured to construct an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establishes an equipment model of the integrated energy system; the acquisition module 220 is configured to acquire The historical output data of wind power and the historical output data of photovoltaic power, and scene reduction is performed on the historical output data of wind power and historical photovoltaic power data, and the forecast data of wind power output and the forecast data of photovoltaic power in typical scenarios are obtained; the fitting module 230 is configured as Kernel density estimation fits the probability density function of the forecast error in the wind power output forecast data and the photovoltaic output forecast data, and integrates the probability density function to obtain a cumulative distribution function; the construction module 240 is configured to The cumulative distribution function constructs a fuzzy uncertainty set; the second establishment module 250 is configured to establish a distribution robust optimization model of the integrated energy system based on the fuzzy uncertainty set and an affine adjustable strategy; the solution module 260 is configured to be based on The dual theory and the convex optimization theory transform the distribution robust optimization model into a solvable model, and solve the solution model.

It should be understood that the modules described in FIG. 3 correspond to the steps in the method described with reference to FIG. 1 . Therefore, the operations and features and corresponding technical effects described above for the method are also applicable to the modules in FIG. 3 , and will not be repeated here.

In some other embodiments, the embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the program instructions are executed by a processor, the processor executes any of the above method embodiments A robust optimization method for integrated energy systems considering wind and solar uncertainties in ;

As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set to:

Construct an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establish the equipment model of the integrated energy system;

Obtain the historical wind power output data and photovoltaic historical output data, and cut the scene reduction of wind power historical output data and photovoltaic historical output data, and obtain the current wind power output forecast data and photovoltaic output forecast data in typical scenarios;

Fitting the probability density function of the forecast error in the wind power output forecast data and the photovoltaic output forecast data according to the adaptive kernel density estimation, and integrating the probability density function to obtain a cumulative distribution function;

Constructing a fuzzy uncertainty set according to the cumulative distribution function;

Establishing a distributed robust optimization model of an integrated energy system based on the fuzzy uncertain set and an affine adjustable strategy;

The distribution robust optimization model is transformed into a solvable model based on dual theory and convex optimization theory, and the solution model is solved.

The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; Stick optimization system uses the data created etc. In addition, a computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory located remotely from the processor, and these remote memories may be connected to the robust optimization system of the integrated energy system considering wind and solar uncertainties through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As shown in FIG. 4 , the device includes: a processor 310 and a memory 320 . The electronic device may further include: an input device 330 and an output device 340 . The processor 310, the memory 320, the input device 330, and the output device 340 may be connected via a bus or in other ways, and connection via a bus is taken as an example in FIG. 4 . The memory 320 is the computer-readable storage medium mentioned above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, realizes the comprehensive energy system robustness considering the uncertainty of wind and rain in the above method embodiments. Rod optimization method. The input device 330 can receive input numbers or character information, and generate key signal input related to user settings and function control of the robust optimization system for an integrated energy system considering wind and landscape uncertainties. The output device 340 may include a display device such as a display screen.

The above-mentioned electronic device can execute the method provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in this embodiment, refer to the method provided in the embodiment of the present invention.

As an implementation, the above-mentioned electronic device is applied to a robust optimization system of an integrated energy system considering uncertainties of scenery and scenery, and is used for a client, including: at least one processor; and a memory communicatively connected to at least one processor; Wherein, the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor, so that the at least one processor can:

Construct an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establish the equipment model of the integrated energy system;

Obtain the historical wind power output data and photovoltaic historical output data, and cut the scene reduction of wind power historical output data and photovoltaic historical output data, and obtain the current wind power output forecast data and photovoltaic output forecast data in typical scenarios;

Fitting the probability density function of the forecast error in the wind power output forecast data and the photovoltaic output forecast data according to the adaptive kernel density estimation, and integrating the probability density function to obtain a cumulative distribution function;

Constructing a fuzzy uncertainty set according to the cumulative distribution function;

Establishing a distributed robust optimization model of an integrated energy system based on the fuzzy uncertain set and an affine adjustable strategy;

The distribution robust optimization model is transformed into a solvable model based on dual theory and convex optimization theory, and the solution model is solved.

Through the above description of the implementations, those skilled in the art can clearly understand that each implementation can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods of various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims (10)

1. A method for robust optimization of integrated energy systems considering uncertainties in scenery, characterized in that it comprises: Construct an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establish the equipment model of the integrated energy system; Obtain the historical wind power output data and photovoltaic historical output data, and cut the scene reduction of wind power historical output data and photovoltaic historical output data, and obtain the current wind power output forecast data and photovoltaic output forecast data in typical scenarios; Fitting the probability density function of the forecast error in the wind power output forecast data and the photovoltaic output forecast data according to the adaptive kernel density estimation, and integrating the probability density function to obtain a cumulative distribution function; Constructing a fuzzy uncertainty set according to the cumulative distribution function; Establishing a distributed robust optimization model of an integrated energy system based on the fuzzy uncertain set and an affine adjustable strategy; The distribution robust optimization model is transformed into a solvable model based on dual theory and convex optimization theory, and the solution model is solved. 2. A robust optimization method for an integrated energy system considering wind and solar uncertainties according to claim 1, characterized in that, said construction of an integrated energy system under the joint operation mode of carbon capture, power-to-gas, and cogeneration units energy system, and establish the equipment model of the comprehensive energy system including: The thermoelectric unit generates electric power and thermal power by burning natural gas, where the output model of the thermoelectric unit is: , In the formula, and /> are respectively the electric power generated by the thermoelectric unit at time t and the thermal power output by the thermoelectric unit at time t, /> is the low calorific value of natural gas, /> is the gas consumption of the thermoelectric unit at time t, /> and /> are the generating efficiency of the unit and the heat loss coefficient of the unit, respectively; The electrical energy consumed by carbon capture consists of basic energy consumption and operating energy consumption: , In the formula, is the electric energy consumed by the carbon capture equipment at time t, /> It is the 0-1 flag bit at time t, where 1 means capture, 0 means close, /> is the basic energy consumption of carbon capture at time t, /> is the operating energy consumption of carbon capture at time t, /> is the capture unit /> operating energy consumption, /> is the captured /> at time t quantity; The amount of carbon dioxide used by the power-to-gas equipment is the same as the natural gas produced. The mathematical model of the power-to-gas equipment is: , In the formula, is the amount of natural gas generated by the power-to-gas equipment at time t, /> is the conversion efficiency of power to gas, /> is the electric energy consumed by the power-to-gas equipment at time t, /> is the low calorific value of natural gas; The thermoelectric unit provides electric energy to the power-to-gas equipment and the carbon capture device, and the on-grid power provided by the thermoelectric unit is: , In the formula, is the on-grid power provided by the thermal power unit at time t, /> is the electric power generated by the thermoelectric unit at time t, is the electric energy consumed by the carbon capture equipment at time t, /> is the electric energy consumed by the power-to-gas equipment at time t; In the joint operation mode, the electric-thermal output coupling characteristics of the thermoelectric unit are: , In the formula, , /> Respectively, the minimum and maximum value of the on-grid power provided by the thermal power unit, /> and /> Respectively, the minimum output electrothermal conversion coefficient of the thermoelectric unit and the maximum output electrothermal conversion coefficient of the thermoelectric unit, /> is the linear slope, is the thermal power output by the thermoelectric unit at time t, /> is the maximum power of the carbon capture device, /> is the maximum power of the power-to-gas equipment, /> is the minimum heat output of the thermoelectric unit, /> The on-grid power provided by the thermal power unit at time t; Gas-fired boilers generate heat power by burning natural gas, where the expression for calculating the heat power is: , In the formula, is the thermal power generated by the gas boiler at time t, /> is the heat conversion efficiency of the gas boiler, /> is the low calorific value of natural gas, /> is the gas consumption of the gas-fired boiler at time t; The energy conversion equipment model includes electric refrigerators and absorption refrigerators, and the models of electric refrigerators and absorption refrigerators are: , , In the formula, and /> are respectively the cooling power output by the electric refrigerator at time t and the cooling power output by the absorption refrigerator at time t, /> and /> Respectively, the electric energy consumed by the electric refrigerator at time t and the heat energy consumed by the absorption refrigerator at time t, /> and /> are the cooling efficiency of the electric refrigerator and the cooling efficiency of the absorption refrigerator, respectively; The energy storage equipment model of the system includes batteries and heat storage tanks, where the capacity models of batteries and heat storage tanks are: , In the formula, and /> Respectively, the capacity of electric energy storage at time t and the capacity of thermal energy storage at time t, /> , /> and /> , Respectively, the charging efficiency of electric energy storage at time t, the discharge efficiency of electric energy storage at time t, the heat storage efficiency of thermal energy storage at time t, and the heat release efficiency of thermal energy storage at time t, /> , /> and /> , /> Respectively, the charging power of electric energy storage at time t, the discharge power of electric energy storage at time t, the heat storage power of thermal energy storage at time t, and the heat release power of thermal energy storage at time t. 3. A method for robust optimization of an integrated energy system considering uncertainties in scenery and scenery according to claim 1, wherein said fitting said day-ahead wind power output prediction data and said photovoltaic Output the probability density function of the prediction error in the forecast data, and integrate the probability density function to obtain the cumulative distribution function including: The expressions of wind power forecast error and photovoltaic forecast error in IES are: , , In the formula, and /> are respectively the forecast error of wind power at time t and the forecast error of photovoltaic power at time t, /> is the actual output of wind power at time t, /> is the output of wind power forecast at time t, /> is the actual PV output at time t, /> Output for photovoltaic prediction at time t; Suppose there are n historical operating data in the integrated energy system , then the form of the kernel density estimate is: , In the formula, is the prediction error, /> is the number of samples, /> is the bandwidth, /> is the kernel function, /> is the probability density function, /> Indicates the kth historical forecast error; Bandwidth is measured by the magnitude of the mean squared integral error pros and cons, under weak assumptions, , In the formula, is the mean square integral error, /> is the asymptotic mean square integral error, /> is the decay rate of the error with time, /> is the number of samples, /> is the bandwidth; , In the formula, is the kernel function /> scale parameter, /> Generate the squared moments of the probability density function for the data, /> is the second derivative of the probability density function; , In the formula, is a random variable /> The kernel function; minimize Equivalent to minimize /> , right /> Find the derivative, let the derivative be 0, and simplify to find the best bandwidth/> The expression is: , After selecting the kernel function and bandwidth, the adaptive kernel density estimation method simulates the real probability distribution curve, in which, the adaptive kernel density estimation method is used to fit the random variable The expression of the probability density function of is: , Integrate the probability density function to obtain a cumulative distribution function, wherein the expression of the cumulative distribution function is: , In the formula, is the cumulative distribution function, /> is a random variable /> The probability density function of . 4. A method for robust optimization of integrated energy systems considering the uncertainty of scenery and scenery according to claim 1, wherein the expression of the fuzzy uncertainty set is: , In the formula, is the fuzzy uncertainty set, /> is the true distribution, /> For the estimated distribution, /> is the Wasserstein distance between the true distribution and the estimated distribution, /> is the total probability distribution; , In the formula, is the radius of the Wasserstein sphere, depending on the number of samples, /> is a constant, /> is the total number of samples, /> is the confidence level of the solution radius; , In the formula, is a real number, /> is the sample mean, /> Denotes the kth prediction error. 5. A method for robust optimization of an integrated energy system considering uncertainties in scenery and scenery according to claim 1, wherein said establishment of a distributed robust optimization model of an integrated energy system based on an affine adjustable strategy comprises: Based on the affine adjustable strategy, the first-stage optimization model and the second-stage optimization model of the integrated energy system are established, wherein the first-stage optimization model formulates a day-ahead scheduling plan based on the predicted power of wind power and photovoltaics, and minimizes the Operating cost, the second-stage optimization model considers the prediction error under the uncertainty of the scenery, and minimizes the expected value of the system adjustment cost under the worst distribution condition to formulate a real-time scheduling strategy; The objective function is to minimize the total operating cost of the two stages, wherein the expression of the objective function is: , In the formula, min( ) represents the objective function of the first stage, is the electricity purchase cost of IES, /> is the gas purchase cost of the IES, is the start-stop cost of thermal power units and gas-fired boilers, /> for carbon storage costs, /> is the carbon trading cost, /> is the expected value of the adjustment cost caused by the forecast error, /> adjust the cost function for the second stage, /> Optimizing the objective function of the model for the second stage; The adjustment cost of the second-stage optimization model adds the wind power and photovoltaic power abandonment penalty cost on the basis of the day-ahead cost, and deletes the start-stop cost of the equipment at the same time, wherein the The expression is: , In the formula, Indicates the scheduling period, /> To account for the electricity purchase cost of the forecast error, /> In order to consider the gas purchase cost of forecast error, /> Penalty cost for the system to abandon wind and light, /> To account for the carbon storage cost of forecast errors, /> Carbon trading costs to account for forecast errors; , In the formula, is the electricity price at time t, /> is the electric power purchased from the grid at time t in the real-time stage, /> is the electric power sold to the grid at time t in the real-time stage, /> is the electric power purchased from the grid at time t in the day-ahead period, /> is the electric power sold to the grid at time t in the day-ahead phase; , In the formula, is the unit penalty coefficient, /> is the curtailed wind power at time t in the real-time stage, /> is the optical power discarded at time t in the real-time stage. 6. A method for robust optimization of integrated energy systems considering uncertainties in scenery and scenery according to claim 5, wherein the constraints of the first-stage optimization model include energy balance constraints, gas power balance constraints , thermal energy storage constraints and electric energy storage constraints; The expression of the energy balance constraint is: , In the formula, is the purchased electricity power at time t, /> is the wind power at time t, /> is the photovoltaic power at time t, is the on-grid power provided by the thermal power unit at time t, /> is the electric load at time t, /> is the discharge power of electric energy storage at time t, /> is the charging power of electric energy storage at time t, /> is the electric energy consumed by the electric refrigerator at time t; , In the formula, is the heat load at time t, /> is the heat storage power of thermal energy storage at time t, /> is the heat energy consumed by the absorption refrigerator at time t, /> is the thermal power output by the thermoelectric unit at time t, /> is the thermal power generated by the gas boiler at time t, /> is the heat release power of thermal energy storage at time t; , In the formula, is the cooling load at time t, /> is the cooling power output by the absorption refrigerator at time t, /> is the cooling power output by the electric refrigerator at time t; The expression of the gas power balance constraint is: , In the formula, is the gas consumption of the gas-fired boiler at time t, /> is the gas consumption of the thermoelectric unit at time t, /> is the amount of natural gas generated by the power-to-gas equipment at time t, /> is the gas volume purchased from the gas network at time t. 7. A method for robust optimization of integrated energy systems considering uncertainties in scenery and scenery according to claim 5, characterized in that, said distribution robust optimization model is transformed into a solvable method based on dual theory and convex optimization theory model, and solving the solution model includes: According to the dual theory, the upper bound problem of the worst prediction distribution of the second-stage optimization model is transformed into a lower bound problem, where the expression of the objective function of the transformed second-stage optimization model is: , In the formula, Indicates the kth historical forecast error, /> is the radius of the Wasserstein sphere, /> is the number of samples, /> adjust the cost function for the second stage, /> is the prediction error, /> is a fuzzy uncertain set, /> is a dual variable, /> is the lower bound function, /> is the upper bound function of the adjusted cost under the worst condition; Update the objective function and constraints of the distribution robust optimization model, where the objective function of the updated distribution robust optimization model is: , In the formula, denoted in the optimization variable /> The minimum running cost under, /> for the optimization variable /> The corresponding transposed column vector, is the optimization variable; The constraints of the updated distribution robust optimization model are: , In the formula, A is the variable under the constraint of the corresponding inequality coefficient matrix, /> is a constant column vector under the corresponding inequality constraints, is the coefficient matrix of the corresponding constraint considering the prediction error, /> and /> are forecast errors/> the linear function of; Transform the distribution robust optimization model into a solvable model based on convex optimization theory, wherein the expression of the objective function of the solution model is: , In the formula, is the auxiliary variable introduced; The expression of the constraints of the solution model is: , In the formula, for /> The coefficient transpose matrix, /> for corresponding /> The optimization function, /> is the minimum value of prediction error, /> Indicates the kth historical forecast error, /> is the maximum value of prediction error, /> for corresponding /> The optimization function of for corresponding /> The optimization function, /> is the coefficient matrix of the corresponding constraint considering the prediction error, /> for corresponding /> A constant column vector of , /> for corresponding /> A constant column vector of . 8. A robust optimization system for an integrated energy system considering the uncertainty of scenery, characterized in that it includes: The first building module is configured to build an integrated energy system under the joint operation mode of carbon capture, power-to-gas and cogeneration units, and establish an equipment model of the integrated energy system; The acquisition module is configured to obtain historical wind power output data and photovoltaic historical output data, and perform scene reduction on the wind power historical output data and photovoltaic historical output data, and obtain the current wind power output forecast data and photovoltaic output forecast data in typical scenarios; The fitting module is configured to fit the probability density function of the prediction error in the wind power output forecast data and the photovoltaic output forecast data according to the adaptive kernel density estimation, and integrate the probability density function to obtain the cumulative distribution function; A construction module configured to construct a fuzzy uncertainty set according to the cumulative distribution function; The second establishment module is configured to establish a distributed robust optimization model of the integrated energy system based on the fuzzy uncertain set and the affine adjustable strategy; The solution module is configured to convert the distribution robust optimization model into a solvable model based on dual theory and convex optimization theory, and solve the solution model. 9. An electronic device, characterized in that it comprises: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, The instructions are executed by the at least one processor, so that the at least one processor performs the method of any one of claims 1-7. 10. A computer-readable storage medium, on which a computer program is stored, wherein, when the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.