CN116644866A - Comprehensive energy system robust optimization method and system considering wind-light uncertainty - Google Patents
Comprehensive energy system robust optimization method and system considering wind-light uncertainty Download PDFInfo
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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
Technical Field
The application belongs to the technical field of comprehensive energy optimization, and particularly relates to a robust optimization method and system for a comprehensive energy system considering wind-light uncertainty.
Background
An important feature of the integrated energy system (Integrated Energy System, IES) is the connection and energy scheduling of the various energy networks through various coupling devices. The cogeneration unit (Combined Heat and Power unit, CHP) serves as an important energy device in the integrated energy system, which couples the power grid, the heat grid and the gas grid. The electric energy is utilized by the electric energy conversion technologyThe natural gas is converted into the natural gas, so that the coupling of a power grid and a gas network and the consumption of new energy are realized. The carbon trapping technology can capture +.>Is an important means for realizing carbon emission reduction. The existing research is less in analyzing the working characteristics of the three in the combined operation mode and the influence on new energy consumption and carbon emission reduction.
Furthermore, the optimal decision of IES is made based on the predicted power of wind power (WT) and Photovoltaic (PV). In reality, wind power and photovoltaic have strong uncertainty, so that it is necessary to eliminate the influence of wind and light power generation uncertainty on the premise of reducing conservation.
Disclosure of Invention
The application provides a robust optimization method and a robust optimization system for a comprehensive energy system considering wind-light uncertainty, which are used for solving the technical problem of eliminating the influence of wind-light power generation uncertainty on the premise of reducing conservation.
In a first aspect, the present application provides a method for robust optimization of a comprehensive energy system, taking into account wind-solar uncertainty, comprising: 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; acquiring wind power historical output data and photovoltaic historical output data, and performing scene reduction on the wind power historical output data and the photovoltaic historical output data to obtain wind power output prediction data and photovoltaic output prediction data before the day under a typical scene; 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 an 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.
In a second aspect, the present application provides a robust optimization system for an integrated energy system taking into account wind-solar uncertainty, comprising: the first building module is configured to build a comprehensive energy system in a combined operation mode of the carbon-containing capturing, electricity-to-gas and cogeneration unit and build an equipment model of the comprehensive energy system; the acquisition module is configured to acquire wind power historical output data and photovoltaic historical output data, and perform scene reduction on the wind power historical output data and the photovoltaic historical output data to acquire wind power output prediction data and photovoltaic output prediction data before the day under a typical scene; the fitting module is configured to fit probability density functions of prediction errors in the wind power output prediction data before the day and the photovoltaic output prediction data according to the self-adaptive kernel density estimation, and integrate the probability density functions to obtain a cumulative distribution function; a construction module configured to construct a fuzzy uncertainty set from the cumulative distribution function; the second establishing module is configured to establish a distributed robust optimization model of the comprehensive energy system based on the fuzzy uncertainty set and an affine adjustable strategy; and the solving module is configured to convert the distributed robust optimization model into a solvable model based on a dual theory and a convex optimization theory and solve the solvable model.
In a third aspect, there is provided an electronic device, comprising: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the integrated energy system robust optimization method taking into account wind and solar uncertainty of any of the embodiments of the application.
In a fourth aspect, the present application also provides a computer readable storage medium, on which a computer program is stored, the program instructions, when executed by a processor, cause the processor to perform the steps of the method for robust optimization of an integrated energy system taking into account wind-solar uncertainty according to any of the embodiments of the present application.
The method and the system for optimizing the robustness of the comprehensive energy system by considering wind-light uncertainty have the following advantages: the probability density function of wind power and photovoltaic predicted power errors is fitted by the self-adaptive nuclear density estimation method, the subjective defect of unknown distribution by utilizing theoretical distribution assumption is overcome, a more compact and objective uncertainty set is built by fully utilizing the value of historical data, and the conservation is effectively reduced. Meanwhile, a day-ahead and real-time two-stage robust optimization model based on an affine adjustable strategy is established, the model overcomes the defects of the robust optimization model and a random optimization model, and the balance of robustness and efficiency can be realized on the premise of data driving. Finally, the optimization system established by combining the software and hardware technology and the network technology programs the optimization method, receives and processes the data from the comprehensive energy system in real time, makes decisions, and realizes the economic optimization scheduling function of various terminal user equipment on the comprehensive energy system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of an integrated energy system architecture according to an embodiment of the present application;
FIG. 2 is a flowchart of a robust optimization method for a comprehensive energy system, which is provided by an embodiment of the present application and takes into consideration wind-light uncertainty;
FIG. 3 is a block diagram of a robust optimization system for a comprehensive energy system, which is provided by an embodiment of the present application and takes into account wind-light uncertainty;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
A multi-energy coupled integrated energy system framework is shown in fig. 1. The system covers electric load, thermal load and cold load, wherein the electric load is powered by a cogeneration unit, a wind power unit, a photovoltaic unit, an upper power grid and a storage battery; the heat load is supplied by the cogeneration unit, the upper heat supply network, the gas boiler and the heat storage tank; the cold load is cooled by the electric refrigerating device and the absorption refrigerating equipment; and the gas net and the electric gas conversion equipment supply gas to the cogeneration unit and the gas boiler. The system combines the cogeneration unit, the carbon capture device and the electric gas conversion equipment into a whole, so that the thermoelectric decoupling of the unit is conveniently realized, the new energy consumption capability of the system is improved, and the carbon emission is reduced.
Referring to fig. 2, a flowchart of a robust optimization method for an integrated energy system that takes into account wind-solar uncertainty is shown.
As shown in fig. 2, the robust optimization method of the comprehensive energy system considering wind-light uncertainty specifically comprises the following steps:
and step S101, constructing a comprehensive energy system under a combined operation mode of the carbon-containing capturing, electricity-to-gas and heat-power cogeneration unit, and establishing an equipment model of the comprehensive energy system.
In this step, the thermoelectric unit generates electric power and thermal power by combusting natural gas, wherein an output model of the thermoelectric unit is:
，
in the method, in the process of the application,and->The electric power generated by the thermoelectric unit at the time t and the thermal power output by the thermoelectric unit at the time t are respectively +.>Is the low calorific value of natural gas, +.>For the air consumption of the thermoelectric unit at time t +.>And->The power generation efficiency of the unit and the heat loss coefficient of the unit are respectively;
carbon capture and sequestration (Carbon Capture and Storage, CCS) technology can capture CO 2 And will capture CO 2 The sealing is carried out, so that carbon emission is reduced, and the electric energy consumed by carbon capture consists of basic energy consumption and operation energy consumption:
，
in the method, in the process of the application,for the electrical energy consumed by the carbon capture device at time t, < >>A 0-1 flag bit at time t, wherein 1 indicates capture, 0 indicates off, ++>For the basic energy consumption of carbon capture at time t, < >>For the operating energy consumption of the carbon capture at time t, < >>Is a capturing unit->Operating energy consumption of->Captured for time t->An amount of;
the carbon dioxide amount used by the electric conversion gas is the same as that of the generated natural gas, and the mathematical model of the electric conversion gas equipment is as follows:
，
in the method, in the process of the application,for the natural gas quantity generated by the electric gas conversion device at the moment t, < >>For the conversion efficiency of the electric conversion gas,for the electric energy consumed by the electric conversion equipment at the moment t, < >>Is the low heating value of natural gas;
the thermoelectric unit provides electric energy for the electric conversion equipment and the carbon trapping device, and the internet power provided by the thermoelectric unit is as follows:
，
in the method, in the process of the application,internet power provided for thermoelectric unit at time t, < >>For the electric power generated by the thermoelectric unit at time t, < >>For the electrical energy consumed by the carbon capture device at time t, < >>The electric energy consumed by the electric conversion equipment at the moment t;
the thermoelectric unit has the following electric heating output coupling characteristics in the combined operation mode:
，
in the method, in the process of the application,、/>minimum and maximum internet power supplied to the thermoelectric units, respectively, < >>Andthe thermoelectric unit minimum output electrothermal conversion coefficient and the thermoelectric unit maximum output electrothermal conversion coefficient are respectively +.>Is a linear slope +.>For the thermal power output by the thermoelectric unit at time t, < >>Maximum power for carbon capture plant, +.>For maximum power of the electrical switching device, +.>For minimum heat output of thermoelectric unit, +.>The power for surfing the internet is provided for the thermoelectric unit at the moment t;
the gas boiler generates thermal power by burning natural gas, wherein an expression for calculating the thermal power is:
，
in the method, in the process of the application,for the heat 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, +.>The gas consumption of the gas boiler at the time t;
the energy conversion equipment model comprises an electric refrigerator and an absorption refrigerator, wherein the electric refrigerator and the absorption refrigerator are as follows:
，
，
in the method, in the process of the application,and->The cold power output by the electric refrigerator at the time t and the cold power output by the absorption refrigerator at the time t are respectively +.>And->The energy consumed by the electric refrigerator at the time t and the energy consumed by the absorption refrigerator at the time t are respectively +.>And->The refrigeration efficiency of the electric refrigerator and the refrigeration efficiency of the absorption refrigerator are respectively;
the energy storage equipment model of the system comprises a storage battery and a heat storage tank, wherein the capacity model of the storage battery and the heat storage tank is as follows:
，
in the method, in the process of the application,and->The capacity of the electric energy storage at the moment t and the capacity of the thermal energy storage at the moment t are respectively +.>、/>And、/>the charging efficiency of the electric energy storage at the time t, the discharging efficiency of the electric energy storage at the time t and the time t are respectivelyHeat storage efficiency of the thermal energy store, heat release efficiency of the thermal energy store at time t, +.>、/>And->、/>The charging power of the electric energy storage at the moment t, the discharging power of the electric energy storage at the moment t, the heat storage power of the thermal energy storage at the moment t and the heat release power of the thermal energy storage at the moment t are respectively adopted.
Step S102, wind power historical output data and photovoltaic historical output data are obtained, scene reduction is carried out on the wind power historical output data and the photovoltaic historical output data, and wind power output prediction data and photovoltaic output prediction data before the day in a typical scene are obtained.
And step S103, estimating and fitting a probability density function of the prediction error in the wind power output predicted data before the day and the photovoltaic output predicted data according to the self-adaptive kernel density, and integrating the probability density function to obtain a cumulative distribution function.
In the step, due to uncertainty of wind-light power generation, deviation exists between predicted power and actual power of wind power and photovoltaic, and execution of a scheduling plan is affected.
The expression of the wind power prediction error and the photovoltaic prediction error in IES is:
，
，
in the method, in the process of the application,and->The wind power prediction error at the moment t and the photovoltaic prediction error at the moment t are respectively +.>For the actual output of the wind power at the moment t, < >>Predicting the force for the wind power at the moment t, +.>For the actual output of the photovoltaic at time t, +.>Predicting the force for the photovoltaic at the moment t;
assume that there are n historical operating data in the integrated energy systemThe form of the kernel density estimate is:
，
in the method, in the process of the application,for prediction error +.>For the number of samples, +.>For bandwidth, & gt>As a kernel function->As a probability density function>Representing the kth historical prediction error.
Bandwidth of a communication deviceThe variance of the kernel function is determined, the flatness of the whole curve of kernel density estimation is reflected, and the difference of kernel function estimation results under different bandwidths is obvious. The bandwidth selection of the conventional kernel density estimation depends on subjective judgment, which is disadvantageous for the kernel density estimation simulation to obtain a true probability density function, and in order to minimize the error, the bandwidth is measured by the magnitude of the average square integral error>Is not limited to the above-mentioned method, under the condition of weak hypothesis,
，
in the method, in the process of the application,is the mean square integral error, +.>For progressive average squared integral error +.>For the decay rate of the error over time, +.>For the number of samples, +.>Is the bandwidth;
，
in the method, in the process of the application,is a kernel function->Scale parameter of->Generating a square moment of a probability density function for the data, < >>A quadratic derivative function of the probability density function;
，
in the method, in the process of the application,is a random variable +.>Is a kernel function of (a).
Minimization ofEquivalent to minimize +.>For->Deriving, setting the derivative to 0, and simplifying to obtain the optimal bandwidth->The expression of (2) is:
，
after the kernel function and the bandwidth are selected, the self-adaptive kernel density estimation method simulates a real probability distribution curve, wherein the self-adaptive kernel density estimation method is used for fitting a random variableThe probability density function of (c) is expressed as:
，
integrating the probability density function to obtain a cumulative distribution function, wherein the cumulative distribution function has the expression:
，
in the method, in the process of the application,for cumulative distribution function->Is a random variable +.>Probability density function of (a).
And step S104, constructing a fuzzy uncertainty set according to the cumulative distribution function.
In this step, a distributed set of fuzzy uncertainties is constructed from the above representations, which can be regarded as a set ofIs a dot, and is a Wasserstein sphere with a certain distance as a radius. Wherein the expression of the fuzzy uncertainty set is:
，
in the method, in the process of the application,for the fuzzy uncertainty set, +.>For true distribution +.>To estimate the distribution +.>Is trueWasserstein distance between real and estimated distribution, +.>Is the total probability distribution;
，
in the method, in the process of the application,radius of Wasserstein sphere, depending on number of samples,/o>Is constant (I)>For the total number of samples->Confidence level for the solution radius;
，
in the method, in the process of the application,is real number, < >>For the sample mean->Representing the kth prediction error.
Step S105, a distributed robust optimization model of the comprehensive energy system is established based on the fuzzy uncertainty set and the affine tunable strategy.
In the step, a first-stage optimization model and a second-stage optimization model of the comprehensive energy system are established based on an affine adjustable strategy, wherein the first-stage optimization model makes a day-ahead scheduling plan according to the predicted power of wind power and photovoltaic, the running cost of the comprehensive energy system is minimized, the second-stage optimization model takes the predicted error under wind and light uncertainty into consideration, and the real-time scheduling strategy is made by minimizing the expected value of the system adjustment cost under the worst distribution condition;
，
where min (. Cndot.) represents the objective function of the first stage,for the cost of electricity purchase of IES +.>For IES gas purchase cost, +.>For the start-stop cost of the thermoelectric unit and the gas boiler, < >>For carbon storage costs>For carbon trade cost, < >>For the desired value of the adjustment costs resulting from the prediction error, < >>Adjusting the cost function for the second phase,/->Optimizing an objective function of the model for the second stage;
the adjustment cost of the second-stage optimization model increases the electricity discarding penalty cost of wind power and photovoltaic on the basis of the day-ahead cost, and simultaneously eliminates the start-stop cost of equipment, whereinThe expression of (2) is:
，
in the method, in the process of the application,indicates the scheduling period,/->To take account of the prediction error, purchase costs, +.>To take account of the prediction error, the cost of purchasing gas, +.>Penalty cost for system wind and light abandon, < ->Carbon storage costs for consideration of prediction errors +.>Carbon trade costs for consideration of prediction errors;
，
in the method, in the process of the application,for the electricity price at time t->Electric power purchased from the grid for the real-time phase t moment, < >>Electric power sold to the grid for the real-time phase t moment,/->Electric power purchased from the grid for the moment t of the day-ahead phase, +.>The electric power sold to the power grid at the moment t in the day-ahead stage;
，
in the method, in the process of the application,is a unit penalty coefficient, +>For the wind-discarding power at the moment t of the real-time phase, < + >>The optical power is discarded at the moment of the real-time phase t.
Constraint conditions of electric heat energy storage are similar, taking electric energy storage as an example, the constraint conditions of capacity constraint, power constraint and state inequality constraint are required to be met:
，
in the method, in the process of the application,and->The bit is a 0-1 state bit for charging and discharging the electric energy storage at the moment t, and the bit is a +.>Andthe upper limit and the lower limit of the energy storage power of the battery at the moment t are respectively +.>And->Respectively, the upper and lower of the discharge power of the battery at the moment tLimited (I)>And->The upper and lower limits of the battery capacity at time t are respectively +.>For the energy storage capacity at the end of the scheduling period,for the energy storage capacity at the beginning of the scheduling period.
The constraints of the system electricity purchase and the electric power sold to the power grid in the day-ahead stage are as follows:
，
in the method, in the process of the application,electric power purchased from the grid for the moment t of the day-ahead phase, +.>Electric power sold to the grid for the time t of the day-ahead phase,/->And->The power constraint of purchasing and selling electricity from the power grid is +.>And->Is a 0-1 state variable for characterizing electricity purchases and electricity sales.
In the second stage, the real-time power of wind power and photovoltaic is in error with the predicted power before the day, and the real-time variable must be changed on the basis of the predicted power before the day. In order to eliminate the influence of the renewable energy output error, the balance can be realized again by calling the flexible resource of the IES and adjusting the power purchase and sale power. And (3) correlating the real-time variable related to the electric energy with the day-ahead variable based on an affine adjustable strategy, and constructing a real-time variable model related to the day-ahead variable, wherein the real-time variable model is as follows:
，
in the method, in the process of the application,for the wind-discarding power at the moment t of the real-time phase, < + >>For the total prediction error of the wind-solar power generation at the moment t, < + >>For the optical power of the drop at the moment t of the real-time phase, < >>Electric power purchased from the grid for the real-time phase t moment, < >>Electric power sold to the grid for the real-time phase t moment,/->Charging power for the electric energy storage at time t in real time>Charging power for the electrical energy storage at time t +.>For the discharge power of the electric energy store at the time t of the real-time phase, < >>The discharge power of the electric energy storage at the time t,、/>、/>、/>affine adjustable coefficients corresponding to wind power generation, photovoltaic power generation, energy storage charging and energy storage discharging in real time are respectively taken as values 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 associated with the day-ahead phase and the second term represents the adjustment strategy for the variable, which is determined by the affine tunable coefficients together with the predicted power error.
Finally, the constructed distributed robust optimization scheduling model comprises a two-stage system running cost minimization objective function, a set of feasible domains formed by day-ahead stage constraints and another set of feasible domains formed by real-time stage constraints through affine tunable strategies.
And step S106, 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.
In the step, based on the dual theory and the convex optimization theory, the distributed robust optimization model is converted into a solvable model, and a commercial solver CPLEX in MATLAB is called for solving.
According to the dual theory, converting the upper bound problem of the worst prediction distribution of the second-stage optimization model into the lower bound problem, wherein the expression of the objective function of the converted second-stage optimization model is as follows:
，
in the method, in the process of the application,represents the kth historical prediction error, +.>Radius of Wasserstein sphere, < >>In order to obtain the number of samples,adjusting the cost function for the second phase,/->For the total prediction error of wind power and photovoltaic, < >>To blur the uncertainty set +.>As dual variables +.>For the lower bound function>An upper bound function for adjusting the cost under the worst condition;
updating an objective function and constraint conditions of the distributed robust optimization model, wherein the objective function of the updated distributed robust optimization model is as follows:
，
in the method, in the process of the application,for optimizing the transposed column vector corresponding to variable x, < >>Is an optimization variable;
constraint conditions of the updated distributed robust optimization model are as follows:
，
where A is a coefficient matrix corresponding to a variable under the inequality constraint,for the constant column vector under the constraint of the corresponding inequality, G is the coefficient matrix taking the corresponding constraint under the prediction error into account, < ->And->Is->Is a linear function of (2);
converting the distributed robust optimization model into a solveable model based on a convex optimization theory, wherein the expression of an objective function of the solveable model is as follows:
，
in the method, in the process of the application,as an introduced auxiliary variable;
the expression of the constraint condition of the solving model is as follows:
，
in the method, in the process of the application,is->Transposed matrix of coefficients,/>For corresponding->Is>For the minimum of prediction error, +.>Represents the kth historical prediction error, +.>For maximum prediction error, +_>For corresponding->Is>For corresponding->Is>For a coefficient matrix taking into account the corresponding constraint under prediction error, < >>For corresponding->Constant column vector, ">For corresponding->Is a constant column vector of (c).
The solution model in this embodiment eliminates the difficult-to-solve random variablesBut utilizesLower bound of random variable which can be determined +.>Random variable upper bound->And historical prediction error value +.>The solving difficulty is reduced. Meanwhile, the distribution robust optimization model established under the worst condition of the system is considered, and the characteristics of random optimization accuracy and robust optimization conservation are combined, so that the system can meet the power constraint under the worst condition, and the optimal scheduling of the system is realized.
Referring to fig. 3, a block diagram of a robust optimization system for an integrated energy system that accounts for wind-solar uncertainty according to the present application is shown.
As shown in fig. 3, the robust optimization system 200 includes a first setup module 210, an acquisition module 220, a fitting module 230, a construction module 240, a second setup module 250, and a solution module 260.
The first building module 210 is configured to build a comprehensive energy system in a combined operation mode of the carbon-containing capturing, electricity-to-gas and cogeneration unit, and build an equipment model of the comprehensive energy system; the obtaining module 220 is configured to obtain wind power historical output data and photovoltaic historical output data, and perform scene reduction on the wind power historical output data and the photovoltaic historical output data to obtain wind power output prediction data and photovoltaic output prediction data before the day under a typical scene; the fitting module 230 is configured to fit a probability density function of the prediction error in the wind power output prediction data before day and the photovoltaic output prediction data according to the adaptive kernel density estimation, and integrate the probability density function to obtain a cumulative distribution function; a construction module 240 configured to construct a fuzzy uncertainty set from the cumulative distribution function; a second building module 250 configured to build a distributed robust optimization model of the integrated energy system based on the fuzzy uncertainty set and affine tunable strategy; a solution module 260 configured to transform the distributed robust optimization model into a solveable model based on dual theory and convex optimization theory and solve the solution model.
It should be understood that the modules depicted in fig. 3 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 3, and are not described here again.
In other embodiments, embodiments of the present application further provide a computer readable storage medium having stored thereon a computer program, where the program instructions, when executed by a processor, cause the processor to perform the integrated energy system robust optimization method taking into account wind-solar uncertainty in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present application stores computer-executable instructions configured to:
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;
acquiring wind power historical output data and photovoltaic historical output data, and performing scene reduction on the wind power historical output data and the photovoltaic historical output data to obtain wind power output prediction data and photovoltaic output prediction data before the day under a typical scene;
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 an 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 computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of a robust optimization system of the integrated energy system that accounts for wind-solar uncertainty, etc. In addition, the 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 optionally includes memory remotely located with respect to the processor, which may be connected via a network to the integrated energy system robust optimization system that accounts for wind-solar uncertainty. Examples of such 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 according to an embodiment of the present application, as shown in fig. 4, where 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, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 4. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory 320, i.e. implements the integrated energy system robust optimization method described above in the method embodiments that take into account wind-solar uncertainty. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the integrated energy system robust optimization system that take into account wind and solar uncertainty. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present application.
As an embodiment, the electronic device is applied to a robust optimization system of an integrated energy system considering wind-light uncertainty, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
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;
acquiring wind power historical output data and photovoltaic historical output data, and performing scene reduction on the wind power historical output data and the photovoltaic historical output data to obtain wind power output prediction data and photovoltaic output prediction data before the day under a typical scene;
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 an 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.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A robust optimization method of a comprehensive energy system considering wind-light uncertainty is characterized by comprising 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;
acquiring wind power historical output data and photovoltaic historical output data, and performing scene reduction on the wind power historical output data and the photovoltaic historical output data to obtain wind power output prediction data and photovoltaic output prediction data before the day under a typical scene;
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 an 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.
2. The method for robust optimization of a comprehensive energy system considering wind-solar uncertainty according to claim 1, wherein the steps of constructing the comprehensive energy system in a combined operation mode of a carbon-containing capture, electric conversion gas and cogeneration unit, and establishing an equipment model of the comprehensive energy system include:
the thermoelectric unit generates electric power and thermal power by combusting natural gas, wherein an output model of the thermoelectric unit is as follows:
，
in the method, in the process of the application,and->The electric power generated by the thermoelectric unit at the time t and the thermal power output by the thermoelectric unit at the time t are respectively +.>Is the low calorific value of natural gas, +.>For the air consumption of the thermoelectric unit at time t +.>And->The power generation efficiency of the unit and the heat loss coefficient of the unit are respectively;
the electrical energy consumed by carbon capture consists of basic energy consumption and operation energy consumption:
，
in the method, in the process of the application,for the electrical energy consumed by the carbon capture device at time t, < >>A 0-1 flag bit at time t, wherein 1 indicates capture, 0 indicates off, ++>For the basic energy consumption of carbon capture at time t, < >>For the operating energy consumption of the carbon capture at time t, < >>Is a capturing unit->Operating energy consumption of->Captured for time t->An amount of;
the carbon dioxide amount used by the electric gas conversion equipment is the same as that of the generated natural gas, and the mathematical model of the electric gas conversion equipment is as follows:
，
in the method, in the process of the application,for the natural gas quantity generated by the electric gas conversion device at the moment t, < >>For the conversion efficiency of electricity to gas, +.>For the electric energy consumed by the electric conversion equipment at the moment t, < >>Is the low heating value of natural gas;
the thermoelectric unit provides electric energy for the electric conversion equipment and the carbon trapping device, and the internet power provided by the thermoelectric unit is as follows:
，
in the method, in the process of the application,internet power provided for thermoelectric unit at time t, < >>For the electric power generated by the thermoelectric unit at the time t,for the electrical energy consumed by the carbon capture device at time t, < >>The electric energy consumed by the electric conversion equipment at the moment t;
the thermoelectric unit has the following electric heating output coupling characteristics in the combined operation mode:
，
in the method, in the process of the application,、/>minimum and maximum internet power supplied to the thermoelectric units, respectively, < >>And->The thermoelectric unit minimum output electrothermal conversion coefficient and the thermoelectric unit maximum output electrothermal conversion coefficient are respectively +.>Is a linear slope of the slope,for the thermal power output by the thermoelectric unit at time t, < >>Maximum power for carbon capture plant, +.>For maximum power of the electrical switching device, +.>For minimum heat output of thermoelectric unit, +.>The power for surfing the internet is provided for the thermoelectric unit at the moment t;
the gas boiler generates thermal power by burning natural gas, wherein an expression for calculating the thermal power is:
，
in the method, in the process of the application,for the heat power generated by the gas boiler at time t, < >>Is the heat conversion efficiency of the gas boiler +.>Is natural gasLow calorific value of->The gas consumption of the gas boiler at the time t;
the energy conversion equipment model comprises an electric refrigerator and an absorption refrigerator, wherein the electric refrigerator and the absorption refrigerator are as follows:
，
，
in the method, in the process of the application,and->The cold power output by the electric refrigerator at the time t and the cold power output by the absorption refrigerator at the time t are respectively +.>And->The energy consumed by the electric refrigerator at the time t and the energy consumed by the absorption refrigerator at the time t are respectively +.>And->The refrigeration efficiency of the electric refrigerator and the refrigeration efficiency of the absorption refrigerator are respectively;
the energy storage equipment model of the system comprises a storage battery and a heat storage tank, wherein the capacity model of the storage battery and the heat storage tank is as follows:
，
in the method, in the process of the application,and->The capacity of the electric energy storage at the moment t and the capacity of the thermal energy storage at the moment t are respectively +.>、/>And->、The charging efficiency of the electric energy storage at the time t, the discharging efficiency of the electric energy storage at the time t, the heat storage efficiency of the thermal energy storage at the time t and the heat release efficiency of the thermal energy storage at the time t are respectively +.>、/>And->、/>The charging power of the electric energy storage at the moment t, the discharging power of the electric energy storage at the moment t, the heat storage power of the thermal energy storage at the moment t and the heat release power of the thermal energy storage at the moment t are respectively adopted.
3. The method of claim 1, wherein the fitting the probability density function of the prediction errors in the wind power output prediction data before day and the photovoltaic output prediction data according to the adaptive kernel density estimation, and integrating the probability density function, and obtaining the cumulative distribution function comprises:
the expression of the wind power prediction error and the photovoltaic prediction error in IES is:
，
，
in the method, in the process of the application,and->The wind power prediction error at the moment t and the photovoltaic prediction error at the moment t are respectively +.>For the actual output of the wind power at the moment t, < >>Predicting the force for the wind power at the moment t, +.>For the actual output of the photovoltaic at time t, +.>Predicting the force for the photovoltaic at the moment t;
assume that there are n historical operating data in the integrated energy systemThe form of the kernel density estimate is:
，
in the method, in the process of the application,for prediction error +.>For the number of samples, +.>For bandwidth, & gt>As a kernel function->As a probability density function>Representing a kth historical prediction error;
the bandwidth is measured by the magnitude of the average square integral errorIs not limited to the above-mentioned method, under the condition of weak hypothesis,
，
in the method, in the process of the application,is the mean square integral error, +.>For progressive average squared integral error +.>For the decay rate of the error over time, +.>For the number of samples, +.>Is the bandwidth;
，
in the method, in the process of the application,is a kernel function->Scale parameter of->Generating a square moment of a probability density function for the data, < >>A quadratic derivative function of the probability density function;
，
in the method, in the process of the application,is a random variable +.>Is a kernel function of (a);
minimization ofEquivalent to minimize +.>For->Deriving, setting the derivative to 0, and simplifying to obtain the optimal bandwidth->The expression of (2) is:
，
after the kernel function and the bandwidth are selected, the self-adaptive kernel density estimation method simulates a real probability distribution curve, wherein the self-adaptive kernel density estimation method is used for fitting a random variableThe probability density function of (c) is expressed as:
，
integrating the probability density function to obtain a cumulative distribution function, wherein the cumulative distribution function has the expression:
，
in the method, in the process of the application,for cumulative distribution function->Is a random variable +.>Probability density function of (a).
4. The method for robust optimization of a comprehensive energy system taking into account wind-solar uncertainty as claimed in claim 1, wherein the expression of the fuzzy uncertainty set is:
，
in the method, in the process of the application,for the fuzzy uncertainty set, +.>For true distribution +.>To estimate the distribution +.>For the Wasserstein distance between the true and estimated distribution, +.>Is the total probability distribution;
，
in the method, in the process of the application,radius of Wasserstein sphere, depending on number of samples,/o>Is constant (I)>For the total number of samples->To solve forConfidence level of radius;
，
in the method, in the process of the application,is real number, < >>For the sample mean->Representing the kth prediction error.
5. The method for robust optimization of a comprehensive energy system considering wind-solar uncertainty according to claim 1, wherein the establishing a distributed robust optimization model of the comprehensive energy system based on affine tunable strategy comprises:
establishing a first-stage optimization model and a second-stage optimization model of the comprehensive energy system based on an affine adjustable strategy, wherein the first-stage optimization model makes a day-ahead scheduling plan according to the predicted power of wind power and photovoltaic to minimize the running cost of the comprehensive energy system, and the second-stage optimization model makes a real-time scheduling strategy by considering the predicted error under the condition of uncertain wind power and the predicted value of the minimum system adjustment cost under the worst distribution condition;
taking the minimum total running cost of two stages as an objective function, wherein the expression of the objective function is as follows:
，
where min (. Cndot.) represents the objective function of the first stage,for the cost of electricity purchase of IES +.>For the cost of gas purchase of IES,for the start-stop cost of the thermoelectric unit and the gas boiler, < >>For carbon storage costs>For carbon trade cost, < >>For the desired value of the adjustment costs resulting from the prediction error, < >>Adjusting the cost function for the second phase,/->Optimizing an objective function of the model for the second stage;
the adjustment cost of the second-stage optimization model increases the electricity discarding penalty cost of wind power and photovoltaic on the basis of the day-ahead cost, and simultaneously eliminates the start-stop cost of equipment, whereinThe expression of (2) is:
，
in the method, in the process of the application,indicates the scheduling period,/->To take into account pre-heatingCost of purchase of measurement error, +.>To take account of the prediction error, the cost of purchasing gas, +.>Penalty cost for system wind and light abandon, < ->Carbon storage costs for consideration of prediction errors +.>Carbon trade costs for consideration of prediction errors;
，
in the method, in the process of the application,for the electricity price at time t->Electric power purchased from the grid for the real-time phase t moment, < >>Electric power sold to the grid for the real-time phase t moment,/->Electric power purchased from the grid for the moment t of the day-ahead phase, +.>The electric power sold to the power grid at the moment t in the day-ahead stage;
，
in the method, in the process of the application,is a unit penalty coefficient, +>For the wind-discarding power at the moment t of the real-time phase, < + >>The optical power is discarded at the moment of the real-time phase t.
6. The method for robust optimization of integrated energy systems with consideration of wind-solar uncertainty as claimed in claim 5, wherein the constraint conditions of the first-stage optimization model include energy balance constraint, air power balance constraint, thermal energy storage constraint and electric energy storage constraint;
the expression of the energy balance constraint is:
，
in the method, in the process of the application,for the power purchase at time t +.>For the wind power at time t +.>Is the photovoltaic power at the time t,internet power provided for thermoelectric unit at time t, < >>As the electrical load at the time t,/>for the discharge power of the electrical energy storage at time t, < >>Charging power for the electrical energy storage at time t +.>The electric energy consumed by the electric refrigerator at the time t;
，
in the method, in the process of the application,for the thermal load at time t +.>Heat storage power for thermal energy storage at time t +.>For the heat energy consumed by the absorption refrigerator at time t, < >>For the thermal power output by the thermoelectric unit at time t, < >>For the heat power generated by the gas boiler at time t, < >>The exothermic power of the heat energy storage at the moment t;
，
in the method, in the process of the application,for the cold load at time t +.>For the cold power output by the absorption refrigerator at time t, < >>The cold power output by the electric refrigerator at the time t;
the expression of the air power balance constraint is as follows:
，
in the method, in the process of the application,is the gas consumption of the gas boiler at the moment t +.>For the air consumption of the thermoelectric unit at time t +.>For the natural gas quantity generated by the electric gas conversion device at the moment t, < >>The amount of gas purchased from the gas network at time t.
7. The method for robust optimization of a comprehensive energy system considering wind-solar uncertainty according to claim 5, wherein the transforming the distributed robust optimization model into a solvable model based on dual theory and convex optimization theory and solving the solution model comprises:
according to the dual theory, converting the upper bound problem of the worst prediction distribution of the second-stage optimization model into the lower bound problem, wherein the expression of the objective function of the converted second-stage optimization model is as follows:
，
in the method, in the process of the application,represents the kth historical prediction error, +.>Radius of Wasserstein sphere, < >>For the number of samples, +.>Adjusting the cost function for the second phase,/->For prediction error +.>To blur the uncertainty set +.>As dual variables +.>For the lower bound function>An upper bound function for adjusting the cost under the worst condition;
updating an objective function and constraint conditions of the distributed robust optimization model, wherein the objective function of the updated distributed robust optimization model is as follows:
，
in the method, in the process of the application,expressed in optimization variables->Minimum operating costs->For optimizing variables->The corresponding transposed column vector is used to determine,is an optimization variable;
constraint conditions of the updated distributed robust optimization model are as follows:
，
wherein A is a variable under the constraint of the corresponding inequalityCoefficient matrix of>To correspond to a constant column vector under the inequality constraint,for a coefficient matrix taking into account the corresponding constraint under prediction error, < >>And->All are prediction error->Is a linear function of (2);
converting the distributed robust optimization model into a solveable model based on a convex optimization theory, wherein the expression of an objective function of the solveable model is as follows:
，
in the method, in the process of the application,as an introduced auxiliary variable;
the expression of the constraint condition of the solving model is as follows:
，
in the method, in the process of the application,is->Transposed matrix of coefficients,/>For corresponding->Is>For the minimum of prediction error, +.>Represents the kth historical prediction error, +.>Maximum of prediction errorValue of->For corresponding->Is used to determine the optimum function of (1),for corresponding->Is>For a coefficient matrix taking into account the corresponding constraint under prediction error, < >>For corresponding->Constant column vector, ">For corresponding->Is a constant column vector of (c).
8. A robust optimization system for a comprehensive energy system that accounts for wind-solar uncertainty, comprising:
the first building module is configured to build a comprehensive energy system in a combined operation mode of the carbon-containing capturing, electricity-to-gas and cogeneration unit and build an equipment model of the comprehensive energy system;
the acquisition module is configured to acquire wind power historical output data and photovoltaic historical output data, and perform scene reduction on the wind power historical output data and the photovoltaic historical output data to acquire wind power output prediction data and photovoltaic output prediction data before the day under a typical scene;
the fitting module is configured to fit probability density functions of prediction errors in the wind power output prediction data before the day and the photovoltaic output prediction data according to the self-adaptive kernel density estimation, and integrate the probability density functions to obtain a cumulative distribution function;
a construction module configured to construct a fuzzy uncertainty set from the cumulative distribution function;
the second establishing module is configured to establish a distributed robust optimization model of the comprehensive energy system based on the fuzzy uncertainty set and an affine adjustable strategy;
and the solving module is configured to convert the distributed robust optimization model into a solvable model based on a dual theory and a convex optimization theory and solve the solvable model.
9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any of claims 1 to 7.
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