CN112671028A - Comprehensive energy system wind power consumption method considering dynamic characteristics of heat supply network - Google Patents
Comprehensive energy system wind power consumption method considering dynamic characteristics of heat supply network Download PDFInfo
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Abstract
A wind power consumption method of a comprehensive energy system considering dynamic characteristics of a heat supply network comprises the steps of constructing a mathematical model of the comprehensive energy system; collecting historical data of a wind turbine generator, calculating and screening representative scenes; and constructing an optimized dispatching model of the comprehensive energy system, taking the mathematical model of the comprehensive energy system as constraint, utilizing a scene model, and utilizing a Monte Carlo method to solve to obtain a wind power consumption scheme of the comprehensive energy system. According to the method, the regional heat supply network is dynamically modeled, the dynamic characteristics of thermal delay and thermal storage of the regional heat supply network are fully excavated, the consumption space of wind power is improved, a Monte Carlo method is used for reflecting the generated energy of the wind generation set in the scheduling process, the uncertainty of the generated energy of the wind generation set is reflected, the waste of energy is reduced, the thermal demand and the electrical demand of a user are kept, and the economic benefit of an operator is increased while the safety of a power grid is ensured.
Description
Technical Field
The invention relates to the technical field of energy systems, in particular to a wind power consumption method of a comprehensive energy system considering the dynamic characteristics of a district heating network and a corresponding storage medium.
Background
In order to solve the environmental problem, sustainable development strategy is adhered to, and wind power is vigorously developed in recent years in China. Compared with the traditional thermal power, the wind power can not generate harmful emission such as greenhouse gas and the like, and meanwhile, fossil energy reserves are not consumed, so that the concept of sustainable development is met. However, the generated energy of the wind power has uncertainty, the generated energy is influenced by uncontrollable environmental factors such as wind speed and the like, the wind power is directly connected into a power grid, and the uncertainty possibly threatens the safe and stable operation of the power grid.
However, the generated energy of the wind turbine generator is uncertain by environmental factors (such as changes of wind speed), the existing prediction technology cannot complete absolute accurate prediction, a controllable power generation unit (such as a thermal power unit) is required to be coordinated and matched with the wind turbine generator, and adjustment and supplement are performed in time when the generated energy of the wind turbine generator deviates from a predicted value, so that the power output of the whole comprehensive energy system is kept stable, and the user load of the system can be stably met.
The running mode of a heat and power cogeneration unit for determining electricity by heat in winter exists in the north of China for a long time, and the heat and power cogeneration unit is used as an important component of a comprehensive energy system under the condition, the generated energy is limited by heat load for a long time, and the consumption space of wind power in the comprehensive energy system is further occupied.
Therefore, how to consider the dynamic characteristics of the heat supply network, realize the coordination of the power generation unit and the heat supply network, keep the stability of the operation of the power grid, and improve the wind power consumption capability becomes a technical problem to be solved urgently in the prior art.
Disclosure of Invention
The invention aims to provide a wind power consumption method of an integrated energy system considering dynamic characteristics of a heat supply network, which is characterized in that an integral model of the integrated energy system is established based on two dynamic characteristics of heat supply network heat delay and heat storage in the integrated energy system, uncertainty scheduling is carried out by a Monte Carlo method, a scheduling scheme of the integrated energy system is provided, and the wind power consumption capability in the integrated energy system is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a comprehensive energy system wind power consumption method considering dynamic characteristics of a heat supply network is characterized by comprising the following steps:
step S110 of establishing a mathematical model of the comprehensive energy system:
the method comprises the steps that a mathematical model of the comprehensive energy system is constructed based on a physical structure of the comprehensive energy system, the mathematical model of the comprehensive energy system comprises a power generation side unit model containing a cogeneration unit model, a thermoelectric conversion unit model of an electric heat pump and a heat storage device and a regional heat supply network model, the coupling complementary relation of electric energy and heat energy is considered, the electric heat pump, the heat storage device and the regional heat supply network are modeled by adopting a thermoelectric analogy method, thermal potential and thermal resistance are used for description, so that the electric potential and the resistance of the electric heat pump and the heat storage device are unified with the electric potential and the resistance of an electric energy part, and the influence of the dynamic characteristic of the heat network;
generating and screening a scene matrix based on the wind turbine generator set S120:
collecting historical data of a wind turbine generator, wherein the historical data comprises historical predicted values and historical measured values of wind power generation, conducting standardization processing on the historical data, calculating a historical prediction error rate by using the processed data, removing abnormal sample points in the historical error rate, conducting value domain transformation on the error rate by using logarithmic change, generating probability distribution of wind power generation errors by using a transformed mean value and variance, generating scenes from the probability distribution, screening the wind power generation error scenes by using a scene reduction method, and selecting representative scenes;
an optimization scheduling model construction and solving step S130 of the comprehensive energy system:
and (3) constructing an optimized dispatching model of the comprehensive energy system, wherein the optimized dispatching model takes the lowest energy consumption as an objective function, takes the mathematical models of all physical structure parts of the comprehensive energy system constructed in the step (S110) as constraint conditions, utilizes the representative scene model screened out in the step (S120), and utilizes a Monte Carlo method to solve the optimized dispatching model, so that the wind power consumption scheme of the comprehensive energy system is obtained.
Optionally, the step S110 includes:
the power generation side unit model construction substep S111 of the integrated energy system:
the power generation side unit model comprises one or more of a common thermal power unit model, a pumped condensing cogeneration unit model, a back pressure cogeneration unit model and a power storage unit model, the power generation side unit model is established according to unit parameter characteristics, and the power generation side unit model comprises one or more of start-stop state constraints of the units, climbing amplitude limitation constraints of the units, capacity size constraints of the units and charge-discharge rate constraints of the power storage units of the capacity constraints of the power storage units according to the unit working principle;
the electrical-thermal conversion unit model construction sub-step S112:
designing a heat storage device, wherein the heat storage device is arranged in a cogeneration power plant, the generated heat and the heat generated by an electric heat pump heat hot water in a water storage tank through an internal pipeline, the hot water is used for heating cold end backwater of a primary pipe network of a heat supply system, a temperature difference value between two objects in a heat transfer process is used as a potential energy source through a thermoelectric analogy method, and the potential energy source is divided by an equivalent thermal resistance between the two objects to obtain heat exchange power of the two objects for heat transfer;
the substep of constructing a district heating network model S113:
the regional heat supply network model comprises a primary pipeline model, a regional heat exchange station model, a secondary pipeline model and a heat user model, the regional heat supply network model consists of a heat medium momentum conservation equation, a heat medium energy conservation equation and a heat medium mass conservation equation, and a thermoelectric simulation model of the regional heat supply network can be obtained through deduction of the conservation equations.
Optionally, the generating and screening step S120 of the scene matrix based on the wind turbine specifically includes:
historical data collection substep S121: historical prediction data and real-time measurement data of the wind turbine generator are obtained, and the data are processed. Calculating a predicted error rate, removing abnormal data, carrying out logarithm on the error rate, and solving the mean value and the variance of the error rate on the mapped value range scale;
the prediction error rate probability distribution function generating sub-step S122: according to the mean and variance of the pre-week prediction error rates obtained in the substep S121, assuming that the error rates follow normal distribution, performing data fitting to obtain a probability distribution function of the pre-week prediction error rates, and similarly, respectively obtaining a probability distribution function of the pre-day prediction error rates and a probability distribution function of the pre-hour prediction error rates;
scene matrix generation decomposition substep S123: dividing the probability distribution function obtained in the substep S122 into equal probability intervals, randomly extracting sampling points for any probability interval, solving sample values of the distribution function by using an inverse function, regenerating the sequence by using Cholesky decomposition, reducing the correlation among the sample values in a scene set matrix, and obtaining a relatively independent day-ahead prediction error rate scene set among scenes, wherein the scene set is in a matrix form;
the sub-step S124 is obtained by screening the representative error rate scene set: initializing the scene probability obtained in the substep S123, calculating distances between the scenes, calculating the probability distances between the scenes in the group according to the closest distance, finding out a group with the closest probability distance in the scene set generated in the substep S123 for merging, reserving one of the scenes corresponding to the probability and the total number of scenes in the scene set, and repeating the calculation and screening process until the remaining expected scenes are the representative error rate scene set.
Optionally, the substep S111 of building a power generation side unit model of the integrated energy system specifically includes:
constructing a model of a jth cogeneration unit, wherein the cogeneration unit is a condensing cogeneration unit, extracting steam from the final stage of a middle pressure cylinder in the power generation process, adjusting the heat supply proportion by changing the steam extraction amount, and constructing the following model according to the operation key parameters of the cogeneration unit:
in the formula (I), the compound is shown in the specification,
is the lower limit of the minimum power generation amount of the cogeneration unit in the running state,
is the maximum generating capacity upper limit in the running state of the cogeneration unit,
is the actual power generation amount of the cogeneration unit,
an identification bit for the running state of the cogeneration unit, 0 indicating that the cogeneration unit is in a shutdown state, 1 indicating that the cogeneration unit is in a startup state, and a lower subscript t identifying that the cogeneration unit runs or is shutdown for a time interval t, here one hour,
a set of cogeneration units is represented,
the scheduling time interval is expressed, and the formula constraint limits the generated energy of the cogeneration unit in the running stateLimiting;
in the formula (I), the compound is shown in the specification,
representing the variation of the actual power generation of the cogeneration unit in two adjacent hours,
in order to limit the power generation amount climbing amplitude of the cogeneration unit in the running state,
for the limitation of the variation amplitude of the power generation amount when the cogeneration unit is changed from the shutdown state to the startup state,
1 represents that the cogeneration unit is started from a shutdown state at the moment, and 0 represents that the cogeneration unit is started from a shutdown state at the moment, otherwise, the formula forms the climbing constraint of the cogeneration unit;
Considering that the cogeneration unit is a pumping condensing unit and uses the last stage of the intermediate pressure cylinder to pump air for supplying heat, the cogeneration unit has the following heat and power relationship:
in the formula (I), the compound is shown in the specification,
is the power generation amount of the cogeneration unit,
is the heat production quantity, alpha, of a cogeneration unitchp,maxIs the upper limit of the heat-electricity ratio of the cogeneration unit.
The capacity constraint of the electricity storage unit and the charge and discharge rate constraint of the electricity storage unit are as follows:
in the formula (I), the compound is shown in the specification,
for the size of the stored electricity of the electricity storage unit, SmaxIs the upper limit of the stored electricity quantity of the electricity storage unit, SminIs the stored electricity quantity of the electricity storage unitThe limit is that the temperature of the molten steel is limited,
is the rate of charge of the power storage unit,
is the maximum charge rate of the power storage unit,
is the rate of discharge of the power storage unit,
is the maximum discharge rate of the power storage unit.
The electrical-thermal conversion unit model construction substep S112 is specifically:
the heat energy source in the system is two parts, including the heat production of taking out congealing formula combined heat and power units and the heat production of electric heat pump, both heat productions all use the electric energy as the cost, for effectual stored energy and realize thermal continuation steady output, design a heat-retaining device, heat-retaining device establishes at combined heat and power plants, comprises a large-scale water storage tank and relevant parts, and the heat that takes out congealing formula combined heat and power units produced and the heat that the electric heat pump produced all passes through the hot water in the internal pipeline heating water storage tank earlier, utilizes the cold junction return water of this hot water heating area heating system primary pipe net again. Through the design of the water storage tank, the heat transfer process can be buffered, and the water supply temperature input of the primary pipe network is more stable. The thermoelectric simulation model of this section is as follows:
in the formula: mwIs the total mass of hot water in the water storage tank, cwIs the specific heat capacity of the hot water in the water storage tank, HlossFor dissipating heat, the temperature T of hot water in the water storage tankwAnd the ambient temperature ToTemperature difference and thermal dissipation resistance RoIs obtained by a ratio ofoutIs low grade heat source temperature, R is heat resistance of electric heat pump, DEHIs the overall heat capacity of the water storage tank, HHPFor thermal energy generated by electric heat pumps, PHPThe electric power consumed for heating the electric heat pump.
The substep S113 of constructing the district heating network model specifically includes:
the constructed thermoelectric simulation model comprises the following concrete steps:
in the formula: x is a space coordinate with the unit of m; t is a time coordinate with the unit of s; rho is the fluid density in kg/m3(ii) a u is the velocity in the x direction in m/s. p is pressure in Pa; gxIs the acceleration of gravity, with the unit of m/s2；FwIs the wall friction in N. h is specific enthalpy of fluid, and the unit is kJ/kg; qwIs the wall surface heat flow with the unit of W/m2。
Optionally, the step S120 of generating and screening the scene matrix based on the wind turbine generator specifically includes:
the historical data collecting substep S121 is as follows: obtaining historical pre-week prediction data, pre-day prediction data, pre-hour prediction data and real-time measurement data of the wind turbine generator, processing the data, unifying dimensions and removing abnormal point data, respectively calculating pre-week prediction errors, pre-day prediction errors and pre-hour prediction error rates, screening again to remove abnormal error rate data, carrying out logarithmic transformation on the error rate data, mapping a value domain of the error rate data to [ infinity, + ∞ ] from [0,1], and respectively calculating the mean value and the variance of the pre-week prediction error rate, the pre-day prediction error rate and the pre-hour prediction error rate on the mapped value domain scale;
the prediction error rate probability distribution function generating sub-step S122 is: according to the mean and variance of the pre-week prediction error rates obtained in the substep S121, assuming that the error rates follow normal distribution, performing data fitting to obtain a probability distribution function of the pre-week prediction error rates, and similarly, respectively obtaining a probability distribution function of the pre-day prediction error rates and a probability distribution function of the pre-hour prediction error rates;
the scene matrix generation decomposition substep S123: dividing the probability distribution function F of the day-ahead prediction error rate obtained in the substep S122 into N equal probability intervals, randomly extracting a sampling point for any one probability interval, solving sample values of the day-ahead prediction error rate distribution function with the obeying probability interval by using an inverse function of F, regenerating the sequence of a day-ahead prediction error rate scene set by using a Cholesky decomposition method in the same way for the week-ahead prediction error rate probability distribution function and the hour-ahead prediction error rate probability distribution function, reducing the correlation among the sample values in a scene set matrix, and obtaining a day-ahead prediction error rate scene set which is relatively independent among scenes, wherein the scene set is in a matrix form.
The sub-step S124 is obtained by screening the representative error rate scene set: the probability of each scene obtained in the initialization substep S123 is the same value, and any two scenes C are calculatediAnd CjEuclidean distance between the two to determine scene CkAnd scene CiThe euclidean distance between them being closest, from which the probability distance between the set of scenes, i.e. the product of said euclidean distance and the probability, is calculated, the set of scenes for which the probability distance is closest, in the set of future predicted error rate scenes generated in sub-step S123, is found, the set of scenes for which the probability distance is closest being considered as having an approximationThe method comprises the steps of combining the characteristics of the current predicted error rate scene set, only reserving one scene, deleting the rest scenes, updating the corresponding probability of the reserved scene and the total number of scenes in the scene set, repeating the calculation and screening process until k expected scenes remain and are representative k scene sets of the predicted error rate in the day ahead, and calculating and screening the scene sets of the predicted error rate in the week ahead and the scene sets of the predicted error rate in the hour ahead in the same mode.
Optionally, in the step S130 of constructing and solving an optimized scheduling model of the integrated energy system:
performing optimized scheduling by using a Monte Carlo method, taking the minimum energy consumption as a target function, taking the comprehensive energy system model constructed in S110 as a constraint condition, taking the day-ahead prediction error rate, week-ahead prediction error rate and hour-ahead prediction error rate of the generated power of the independent wind power unit generated in S120 as input scene sets, and constructing a scene-based uncertainty optimized scheduling problem, wherein the target function of the problem is as follows:
in the formula: κ is the scene set generated in step S125,
the amount of fuel consumed for the start-up of the unit,
the amount of fuel consumed for the unit shut down,
and for the minimum fuel consumption of the unit in the running state, Q (y, xi) is a fuel consumption function corresponding to the generating capacity of the unit, and the wind power consumption scheme of the comprehensive energy system is obtained by solving.
Optionally, the historical data of the wind turbine generator specifically includes: the data includes week-ahead forecast data, day-ahead forecast data, hour-ahead forecast data, and real-time measurement data of the three.
The invention further discloses a storage medium for storing computer executable instructions, which is characterized in that:
the computer executable instructions, when executed by the processor, perform the above-described theme data-based wind power consumption method for an integrated energy system that takes into account thermal grid dynamics.
The invention utilizes a thermoelectric analogy method to construct a model capable of reflecting two dynamic characteristics of the heat delay and the heat storage of a regional heat supply network, utilizes a Monte Carlo method to analyze a scene of a wind power error rate, and provides a method for utilizing the dynamic characteristics of the heat supply network to absorb wind power in a comprehensive energy system.
According to the invention, on the premise of not changing the structure of the original comprehensive energy system as much as possible, the dynamic modeling is carried out on the regional heat supply network, the dynamic characteristics of heat delay and heat storage are fully excavated, the consumption space of wind power can be improved in the scheduling process of the comprehensive energy system by utilizing the characteristics, the generated energy of the wind power generator set is reflected by using a Monte Carlo method in the scheduling process, the uncertainty of the generated energy of the wind power generator set is reflected, the waste of energy is reduced, the heat demand and the electricity demand of a user are kept, and the economic benefit of an operator is increased while the safety of a power grid is ensured.
Drawings
FIG. 1 is a flow chart of a method for wind power consumption by an integrated energy system that takes into account thermal grid dynamics, according to an embodiment of the present invention;
FIG. 2 is a detailed sub-step of the integrated energy system mathematical model building step according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the detailed sub-steps of the generating and screening step of the wind turbine-based scene matrix according to the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The invention is characterized in that: in order to keep the operation stability of a power grid, a comprehensive energy system can be constructed, an uncontrollable wind turbine generator and a controllable thermal power generator including a cogeneration generator are integrated into a whole system to participate in energy scheduling, and two types of energy, namely electric energy and heat energy, existing in the system are considered at the same time. The heat energy and the electric energy have different energy properties, the electric energy is easy to transmit but not easy to store, the heat energy is easy to store but not easy to transmit, and the wind power consumption in the system can be promoted by utilizing the characteristic of cooperative complementation of the heat energy and the electric energy, so that the phenomena of wind abandon and the like are reduced.
The comprehensive energy system is characterized in that advanced physical information technology and innovative management modes are utilized in a certain area, multiple energy sources such as electric energy and heat energy in the area are integrated, and coordinated planning, optimized operation, cooperative management, interactive response and complementary mutual assistance among multiple heterogeneous energy subsystems are achieved. The energy utilization efficiency is effectively improved and the sustainable development of energy is promoted while the diversified energy utilization requirements in the system are met.
Specifically, referring to fig. 1, a flowchart of a wind power consumption method of an integrated energy system considering dynamic characteristics of a heat supply network is shown, which specifically includes the following steps:
step S110 of establishing a mathematical model of the comprehensive energy system:
the method comprises the steps of constructing a mathematical model of the comprehensive energy system based on a physical structure of the comprehensive energy system, wherein the mathematical model of the comprehensive energy system comprises a power generation side unit model containing a cogeneration unit model, a thermoelectric conversion unit model of an electric heating pump and a heat storage device and a regional heat supply network model, considering the coupling complementary relation of electric energy and heat energy, modeling the electric heating pump, the heat storage device and the regional heat supply network by adopting a thermoelectric analogy method, describing by adopting thermal potential and thermal resistance, unifying the electric potential and the resistance of the electric energy part, and reflecting the influence of the dynamic characteristic of the heat network on the comprehensive energy system.
Generating and screening a scene matrix based on the wind turbine generator set S120:
the method comprises the steps of collecting historical data of the wind turbine generator, wherein the historical data comprises historical predicted values and historical measured values of wind power generation, conducting standardization processing on the historical data, calculating historical prediction error rates by using the processed data, removing abnormal sample points in the historical error rates, conducting value domain transformation on the error rates by using logarithmic changes, generating probability distribution of wind power generation errors by using transformed mean values and variances, generating scenes from the probability distribution, screening the wind power generation error scenes by using a scene reduction method, and selecting representative scenes.
An optimization scheduling model construction and solving step S130 of the comprehensive energy system:
and (3) constructing an optimized dispatching model of the comprehensive energy system, wherein the optimized dispatching model takes the lowest energy consumption as an objective function, takes the mathematical models of all physical structure parts of the comprehensive energy system constructed in the step (S110) as constraint conditions, utilizes the representative scene model screened out in the step (S120), and utilizes a Monte Carlo method to solve the optimized dispatching model, so that the wind power consumption scheme of the comprehensive energy system is obtained.
In a specific embodiment, the historical data of the wind turbine generator specifically includes: the data includes week-ahead forecast data, day-ahead forecast data, hour-ahead forecast data, and real-time measurement data of the three.
Specifically, the step S110 includes:
the power generation side unit model construction substep S111 of the integrated energy system:
the power generation side unit model comprises one or more of a common thermal power unit model, a pumped condensing cogeneration unit model, a back pressure cogeneration unit model and a power storage unit model, the power generation side unit model is established according to unit parameter characteristics, and the power generation side unit model comprises one or more of start-stop state constraint of the unit, climbing amplitude limitation constraint of the unit, capacity size constraint of the unit, capacity constraint of the power storage unit and charge-discharge rate constraint of the power storage unit according to the unit working principle.
The electrical-thermal conversion unit model construction sub-step S112:
the heat storage device is designed for effectively storing energy and realizing continuous and stable output of heat, the heat storage device is arranged in a cogeneration power plant and consists of a large water storage tank and related components, the generated heat and the heat generated by an electric heating pump are used for heating hot water in the water storage tank through an internal pipeline, and then cold end return water of a primary pipe network of the hot water heating area heating system is utilized.
Through the design of the water storage tank, the heat transfer process can be buffered, and the water supply temperature input of the primary pipe network is more stable. The thermoelectric analogy method utilizes the temperature difference between two objects in the heat transfer process as a potential energy source, and the potential energy source is divided by the equivalent thermal resistance between the two objects to obtain the heat exchange power of the heat transfer of the two objects.
The substep of constructing a district heating network model S113:
the regional heat supply network model comprises a primary pipeline model, a regional heat exchange station model, a secondary pipeline model and a heat user model, the regional heat supply network model consists of a heat medium momentum conservation equation, a heat medium energy conservation equation and a heat medium mass conservation equation, and a thermoelectric simulation model of the regional heat supply network can be obtained through deduction of the conservation equations.
In one particular embodiment:
the sub-step S111 of building the power generation side unit model of the comprehensive energy system is specifically as follows:
constructing a model of a jth cogeneration unit, wherein the cogeneration unit is a condensing cogeneration unit, extracting steam from the final stage of a middle pressure cylinder in the power generation process, adjusting the heat supply proportion by changing the steam extraction amount, and constructing the following model according to the operation key parameters of the cogeneration unit:
in the formula (I), the compound is shown in the specification,
is the lower limit of the minimum power generation amount of the cogeneration unit in the running state,
is the maximum generating capacity upper limit in the running state of the cogeneration unit,
is the actual power generation amount of the cogeneration unit,
an identification bit for the running state of the cogeneration unit, 0 indicating that the cogeneration unit is in a shutdown state, 1 indicating that the cogeneration unit is in a startup state, and a lower subscript t identifying that the cogeneration unit runs or is shutdown for a time interval t, here one hour,
a set of cogeneration units is represented,
representing a scheduling time interval, wherein the formula constraint limits the upper and lower limits of the generated energy of the cogeneration unit in the running state;
in the formula (I), the compound is shown in the specification,
representing the variation of the actual power generation of the cogeneration unit in two adjacent hours,
in order to limit the power generation amount climbing amplitude of the cogeneration unit in the running state,
for the limitation of the variation amplitude of the power generation amount when the cogeneration unit is changed from the shutdown state to the startup state,
1 represents that the cogeneration unit is started from a shutdown state at the moment, and 0 represents that the cogeneration unit is started from a shutdown state at the moment, otherwise, the formula forms the climbing constraint of the cogeneration unit;
Considering that the cogeneration unit is a pumping condensing unit and uses the last stage of the intermediate pressure cylinder to pump air for supplying heat, the cogeneration unit has the following heat and power relationship:
in the formula (I), the compound is shown in the specification,
is the power generation amount of the cogeneration unit,
is the heat production quantity, alpha, of a cogeneration unitchp,maxIs the upper limit of the heat-electricity ratio of the cogeneration unit.
The capacity constraint of the electricity storage unit and the charge and discharge rate constraint of the electricity storage unit are as follows:
in the formula (I), the compound is shown in the specification,
for the size of the stored electricity of the electricity storage unit, SmaxIs the upper limit of the stored electricity quantity of the electricity storage unit, SminIs the lower limit of the stored electricity quantity of the electricity storage unit,
is the rate of charge of the power storage unit,
is the maximum charge rate of the power storage unit,
is the rate of discharge of the power storage unit,
is the maximum discharge rate of the power storage unit.
The electrical-thermal conversion unit model construction substep S112 is specifically:
the heat energy source in the system is two parts, including the heat production of taking out congealing formula combined heat and power units and the heat production of electric heat pump, both heat productions all use the electric energy as the cost, for effectual stored energy and realize thermal continuation steady output, design a heat-retaining device, heat-retaining device establishes at combined heat and power plants, comprises a large-scale water storage tank and relevant parts, and the heat that takes out congealing formula combined heat and power units produced and the heat that the electric heat pump produced all passes through the hot water in the internal pipeline heating water storage tank earlier, utilizes the cold junction return water of this hot water heating area heating system primary pipe net again. Through the design of the water storage tank, the heat transfer process can be buffered, and the water supply temperature input of the primary pipe network is more stable. The thermoelectric simulation model of this section is as follows:
in the formula: mwIs the total mass of hot water in the water storage tank, cwIs the specific heat capacity of the hot water in the water storage tank, HlossFor dissipating heat, the temperature T of hot water in the water storage tankwAnd the ambient temperature ToTemperature difference and thermal dissipation resistance RoIs obtained by a ratio ofoutIs low grade heat source temperature, R is heat resistance of electric heat pump, DEHIs the overall heat capacity of the water storage tank, HHPFor thermal energy generated by electric heat pumps, PHPThe electric power consumed for heating the electric heat pump.
The substep S113 of constructing the district heating network model specifically includes:
the constructed thermoelectric simulation model comprises the following concrete steps:
in the formula: x is a space coordinate with the unit of m; t is a time coordinate with the unit of s; rho is the fluid density in kg/m3(ii) a u is the velocity in the x direction in m/s. p is pressure in Pa; gxIs the acceleration of gravity, with the unit of m/s2；FwIs the wall friction in N. h is specific enthalpy of fluid, and the unit is kJ/kg; qwIs the wall surface heat flow with the unit of W/m2。
Referring to fig. 3, the generating and screening step S120 of the scene matrix based on the wind turbine specifically includes:
historical data collection substep S121: historical prediction data and real-time measurement data of the wind turbine generator are obtained, and the data are processed. And calculating a predicted error rate, removing abnormal data, carrying out logarithm on the error rate, and solving the mean value and the variance of the error rate on the mapped value range scale.
The prediction error rate probability distribution function generating sub-step S122: according to the mean and variance of the past prediction error rates obtained in the substep S121, assuming that the error rates follow normal distribution, data fitting is performed to obtain a probability distribution function of the past prediction error rates, and similarly, a probability distribution function of the past prediction error rates and a probability distribution function of the past hour prediction error rates are obtained respectively.
Scene matrix generation decomposition substep S123: dividing the probability distribution function obtained in the substep S122 into equal probability intervals, randomly extracting sampling points for any probability interval, solving sample values of the distribution function by using an inverse function, regenerating the sequence by using Cholesky decomposition, reducing the correlation among the sample values in a scene set matrix, and obtaining a relatively independent day-ahead prediction error rate scene set among scenes, wherein the scene set is in a matrix form.
The sub-step S124 is obtained by screening the representative error rate scene set: initializing the scene probability obtained in the substep S123, calculating distances between the scenes, calculating the probability distances between the scenes in the group according to the closest distance, finding out a group with the closest probability distance in the scene set generated in the substep S123 for merging, reserving one of the scenes corresponding to the probability and the total number of scenes in the scene set, and repeating the calculation and screening process until the remaining expected scenes are the representative error rate scene set.
Further, the step S120 of generating and screening the scene matrix based on the wind turbine generator specifically includes:
the historical data collecting substep S121 is as follows: obtaining historical pre-week prediction data, pre-day prediction data, pre-hour prediction data and real-time measurement data of the wind turbine generator, processing the data, unifying dimensions and removing abnormal point data, respectively calculating pre-week prediction errors, pre-day prediction errors and pre-hour prediction error rates, screening again to remove abnormal error rate data, carrying out logarithmic transformation on the error rate data, mapping a value domain of the error rate data to [ infinity, + ∞ ] from [0,1], and respectively calculating the mean value and the variance of the pre-week prediction error rate, the pre-day prediction error rate and the pre-hour prediction error rate on the mapped value domain scale;
the prediction error rate probability distribution function generating sub-step S122 is: according to the mean and variance of the pre-week prediction error rates obtained in the substep S121, assuming that the error rates follow normal distribution, performing data fitting to obtain a probability distribution function of the pre-week prediction error rates, and similarly, respectively obtaining a probability distribution function of the pre-day prediction error rates and a probability distribution function of the pre-hour prediction error rates;
the scene matrix generation decomposition substep S123: dividing the probability distribution function F of the day-ahead prediction error rate obtained in the substep S122 into N equal probability intervals, randomly extracting a sampling point for any one probability interval, solving sample values of the day-ahead prediction error rate distribution function with the obeying probability interval by using an inverse function of F, regenerating the sequence of a day-ahead prediction error rate scene set by using a Cholesky decomposition method in the same way for the week-ahead prediction error rate probability distribution function and the hour-ahead prediction error rate probability distribution function, reducing the correlation among the sample values in a scene set matrix, and obtaining a day-ahead prediction error rate scene set which is relatively independent among scenes, wherein the scene set is in a matrix form.
The sub-step S124 is obtained by screening the representative error rate scene set: the probability of each scene obtained in the initialization substep S123 is the same value, and any two scenes C are calculatediAnd CjEuclidean distance between the two to determine scene CkAnd scene CiThe euclidean distance between them is the closest, and based on this closest distance, the probability distance, i.e. the product of said euclidean distance and the probability, is calculated, in the set of the future predicted error rate scenes generated in sub-step S123, a set of scenes with the closest probability distance is found, the set of scenes is considered to have similar characteristics for merging, only one of the scenes is retained, and the remaining scenes are deleted, and the corresponding probability of the retained scenes and the total number of scenes in the set of scenes are updated, and the above calculation and screening process is repeated until there remain k desired scenes, which are representative sets of k future predicted error rate scenes, and calculation and screening are performed in the same manner for the set of the future predicted error rate scenes and the set of the hour-ahead predicted error rate scenes.
In the step S130 of constructing and solving the optimal scheduling model of the integrated energy system:
and (3) performing optimized scheduling by using a Monte Carlo method, and constructing a scene-based uncertainty optimized scheduling problem by using the minimum energy consumption as an objective function, the comprehensive energy system model constructed in the S110 as a constraint condition, and the generated energy prediction error rate of the independent wind power unit generated in the S120 as an input scene set.
In particular, the method comprises the following steps of,
performing optimized scheduling by using a Monte Carlo method, taking the minimum energy consumption as a target function, taking the comprehensive energy system model constructed in S110 as a constraint condition, taking the day-ahead prediction error rate, week-ahead prediction error rate and hour-ahead prediction error rate of the generated power of the independent wind power unit generated in S120 as input scene sets, and constructing a scene-based uncertainty optimized scheduling problem, wherein the target function of the problem is as follows:
in the formula: κ is the scene set generated in step S125,
the amount of fuel consumed for the start-up of the unit,
the amount of fuel consumed for the unit shut down,
for minimum fuel consumption in the unit running state, Q (y, ξ) is a fuel consumption function corresponding to the unit power generation amount.
In the invention, the uncertainty optimization scheduling problem can be solved through the existing solving tool, and because the model constructed in S110 reflects the dynamic characteristics of the heat supply network, the solving result considers the influence of the dynamic characteristics of heat supply network heat delay and heat storage on the scheduling process of the comprehensive energy system, and the dynamic characteristics of the heat supply network are utilized to reduce the energy consumption of the comprehensive energy system, improve the wind power utilization rate in the comprehensive energy system and bring environmental protection benefit and economic value.
The invention further discloses a storage medium for storing computer executable instructions, which is characterized in that:
the computer executable instructions, when executed by the processor, perform the above-described theme data-based wind power consumption method for an integrated energy system that takes into account thermal grid dynamics.
Therefore, the invention constructs a model capable of reflecting two dynamic characteristics of the heat delay and the heat storage of the regional heat supply network by using a thermoelectric comparison method, analyzes the scene of the wind power error rate by using a Monte Carlo method, and provides a method for absorbing the wind power in the comprehensive energy system by using the dynamic characteristics of the heat supply network.
According to the invention, on the premise of not changing the structure of the original comprehensive energy system as much as possible, the dynamic modeling is carried out on the regional heat supply network, the dynamic characteristics of heat delay and heat storage are fully excavated, the consumption space of wind power can be improved in the scheduling process of the comprehensive energy system by utilizing the characteristics, the generated energy of the wind power generator set is reflected by using a Monte Carlo method in the scheduling process, the uncertainty of the generated energy of the wind power generator set is reflected, the waste of energy is reduced, the heat demand and the electricity demand of a user are kept, and the economic benefit of an operator is increased while the safety of a power grid is ensured.
While the invention has been described in further detail with reference to specific preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A comprehensive energy system wind power consumption method considering dynamic characteristics of a heat supply network is characterized by comprising the following steps:
step S110 of establishing a mathematical model of the comprehensive energy system:
the method comprises the steps that a mathematical model of the comprehensive energy system is constructed based on a physical structure of the comprehensive energy system, the mathematical model of the comprehensive energy system comprises a power generation side unit model containing a cogeneration unit model, a thermoelectric conversion unit model of an electric heat pump and a heat storage device and a regional heat supply network model, the coupling complementary relation of electric energy and heat energy is considered, the electric heat pump, the heat storage device and the regional heat supply network are modeled by adopting a thermoelectric analogy method, thermal potential and thermal resistance are used for description, so that the electric potential and the resistance of the electric heat pump and the heat storage device are unified with the electric potential and the resistance of an electric energy part, and the influence of the dynamic characteristic of the heat network;
generating and screening a scene matrix based on the wind turbine generator set S120:
collecting historical data of a wind turbine generator, wherein the historical data comprises historical predicted values and historical measured values of wind power generation, conducting standardization processing on the historical data, calculating a historical prediction error rate by using the processed data, removing abnormal sample points in the historical error rate, conducting value domain transformation on the error rate by using logarithmic change, generating probability distribution of wind power generation errors by using a transformed mean value and variance, generating scenes from the probability distribution, screening the wind power generation error scenes by using a scene reduction method, and selecting representative scenes;
an optimization scheduling model construction and solving step S130 of the comprehensive energy system:
and (3) constructing an optimized dispatching model of the comprehensive energy system, wherein the optimized dispatching model takes the lowest energy consumption as an objective function, takes the mathematical models of all physical structure parts of the comprehensive energy system constructed in the step (S110) as constraint conditions, utilizes the representative scene model screened out in the step (S120), and utilizes a Monte Carlo method to solve the optimized dispatching model, so that the wind power consumption scheme of the comprehensive energy system is obtained.
2. The integrated energy system wind power consumption method according to claim 1, characterized in that:
specifically, the step S110 includes:
the power generation side unit model construction substep S111 of the integrated energy system:
the power generation side unit model comprises one or more of a common thermal power unit model, a pumped condensing cogeneration unit model, a back pressure cogeneration unit model and an electricity storage unit model, the power generation side unit model is established according to unit parameter characteristics, and the power generation side unit model comprises one or more of start-stop state constraint of the unit, climbing amplitude limitation constraint of the unit, capacity size constraint of the unit, capacity constraint of the electricity storage unit and charge-discharge rate constraint of the electricity storage unit according to the unit working principle;
the electrical-thermal conversion unit model construction sub-step S112:
designing a heat storage device, wherein the heat storage device is arranged in a cogeneration power plant, the generated heat and the heat generated by an electric heat pump heat hot water in a water storage tank through an internal pipeline, the hot water is used for heating cold end backwater of a primary pipe network of a heat supply system, a temperature difference value between two objects in a heat transfer process is used as a potential energy source through a thermoelectric analogy method, and the potential energy source is divided by an equivalent thermal resistance between the two objects to obtain heat exchange power of the two objects for heat transfer;
the substep of constructing a district heating network model S113:
the regional heat supply network model comprises a primary pipeline model, a regional heat exchange station model, a secondary pipeline model and a heat user model, the regional heat supply network model consists of a heat medium momentum conservation equation, a heat medium energy conservation equation and a heat medium mass conservation equation, and a thermoelectric simulation model of the regional heat supply network can be obtained through deduction of the conservation equations.
3. The integrated energy system wind power consumption method according to claim 2, characterized in that:
the generating and screening step S120 of the scene matrix based on the wind turbine specifically includes:
historical data collection substep S121: historical prediction data and real-time measurement data of the wind turbine generator are obtained, and the data are processed. Calculating a predicted error rate, removing abnormal data, carrying out logarithm on the error rate, and solving the mean value and the variance of the error rate on the mapped value range scale;
the prediction error rate probability distribution function generating sub-step S122: according to the mean and variance of the pre-week prediction error rates obtained in the substep S121, assuming that the error rates follow normal distribution, performing data fitting to obtain a probability distribution function of the pre-week prediction error rates, and similarly, respectively obtaining a probability distribution function of the pre-day prediction error rates and a probability distribution function of the pre-hour prediction error rates;
scene matrix generation decomposition substep S123: dividing the probability distribution function obtained in the substep S122 into equal probability intervals, randomly extracting sampling points for any probability interval, solving sample values of the distribution function by using an inverse function, regenerating the sequence by using Cholesky decomposition, reducing the correlation among the sample values in a scene set matrix, and obtaining a relatively independent day-ahead prediction error rate scene set among scenes, wherein the scene set is in a matrix form;
the sub-step S124 is obtained by screening the representative error rate scene set: initializing the scene probability obtained in the substep S123, calculating distances between the scenes, calculating the probability distances between the scenes in the group according to the closest distance, finding out a group with the closest probability distance in the scene set generated in the substep S123 for merging, reserving one of the scenes corresponding to the probability and the total number of scenes in the scene set, and repeating the calculation and screening process until the remaining expected scenes are the representative error rate scene set.
4. The integrated energy system wind power consumption method of claim 3, wherein:
the sub-step S111 of building the power generation side unit model of the comprehensive energy system is specifically as follows:
constructing a model of a jth cogeneration unit, wherein the cogeneration unit is a condensing cogeneration unit, extracting steam from the final stage of a middle pressure cylinder in the power generation process, adjusting the heat supply proportion by changing the steam extraction amount, and constructing the following model according to the operation key parameters of the cogeneration unit:
in the formula (I), the compound is shown in the specification,
is the lower limit of the minimum power generation amount of the cogeneration unit in the running state,
is the maximum generating capacity upper limit in the running state of the cogeneration unit,
is the actual power generation amount of the cogeneration unit,
an identification bit for the running state of the cogeneration unit, 0 indicating that the cogeneration unit is in a shutdown state, 1 indicating that the cogeneration unit is in a startup state, and a lower subscript t identifying that the cogeneration unit runs or is shutdown for a time interval t, here one hour,
a set of cogeneration units is represented,
the scheduling time interval is expressed, and the upper limit and the lower limit of the generated energy of the cogeneration unit in the running state are limited by the formula;
in the formula (I), the compound is shown in the specification,
representing the variation of the actual power generation of the cogeneration unit in two adjacent hours,
in order to limit the power generation amount climbing amplitude of the cogeneration unit in the running state,
for the limitation of the variation amplitude of the power generation amount when the cogeneration unit is changed from the shutdown state to the startup state,
1 represents that the cogeneration unit is started from a shutdown state at the moment, and 0 represents that the cogeneration unit is started from a shutdown state at the moment, otherwise, the formula forms the climbing constraint of the cogeneration unit;
considering that the cogeneration unit is a pumping condensing unit and uses the last stage of the intermediate pressure cylinder to pump air for supplying heat, the cogeneration unit has the following heat and power relationship:
in the formula (I), the compound is shown in the specification,
is the power generation amount of the cogeneration unit,
is the heat production quantity, alpha, of a cogeneration unitchp,maxIs the upper limit of the heat-electricity ratio of the cogeneration unit.
The capacity constraint of the electricity storage unit and the charge and discharge rate constraint of the electricity storage unit are as follows:
in the formula (I), the compound is shown in the specification,
for the size of the stored electricity of the electricity storage unit, SmaxIs the upper limit of the stored electricity quantity of the electricity storage unit, SminIs the lower limit of the stored electricity quantity of the electricity storage unit,
is the rate of charge of the power storage unit,
is the maximum charge rate of the power storage unit,
is the rate of discharge of the power storage unit,
is the maximum discharge rate of the power storage unit.
The electrical-thermal conversion unit model construction substep S112 is specifically:
the heat energy source in the system is two parts, including the heat production of taking out congealing formula combined heat and power units and the heat production of electric heat pump, both heat productions all use the electric energy as the cost, for effectual stored energy and realize thermal continuation steady output, design a heat-retaining device, heat-retaining device establishes at combined heat and power plants, comprises a large-scale water storage tank and relevant parts, and the heat that takes out congealing formula combined heat and power units produced and the heat that the electric heat pump produced all passes through the hot water in the internal pipeline heating water storage tank earlier, utilizes the cold junction return water of this hot water heating area heating system primary pipe net again. Through the design of the water storage tank, the heat transfer process can be buffered, and the water supply temperature input of the primary pipe network is more stable. The thermoelectric simulation model of this section is as follows:
in the formula: mwIs the total mass of hot water in the water storage tank, cwIs the specific heat capacity of the hot water in the water storage tank, HlossFor dissipating heat, the temperature T of hot water in the water storage tankwAnd the ambient temperature ToTemperature difference and thermal dissipation resistance RoIs obtained by a ratio ofoutIs low grade heat source temperature, R is heat resistance of electric heat pump, DEHIs the overall heat capacity of the water storage tank, HHPFor thermal energy generated by electric heat pumps, PHPThe electric power consumed for heating the electric heat pump.
The substep S113 of constructing the district heating network model specifically includes:
the constructed thermoelectric simulation model comprises the following concrete steps:
in the formula: x is a space coordinate with the unit of m; t is a time coordinate with the unit of s; rho is the fluid density in kg/m3(ii) a u is the velocity in the x direction in m/s. p is pressure in Pa; gxIs the acceleration of gravity, with the unit of m/s2；FwIs the wall friction in N. h is specific enthalpy of fluid, and the unit is kJ/kg; qwIs the wall surface heat flow with the unit of W/m2。
5. The integrated energy system wind power consumption method of claim 4, wherein:
the generating and screening step S120 of the scene matrix based on the wind turbine generator set specifically includes:
the historical data collecting substep S121 is as follows: obtaining historical pre-week prediction data, pre-day prediction data, pre-hour prediction data and real-time measurement data of the wind turbine generator, processing the data, unifying dimensions and removing abnormal point data, respectively calculating pre-week prediction errors, pre-day prediction errors and pre-hour prediction error rates, screening again to remove abnormal error rate data, carrying out logarithmic transformation on the error rate data, mapping a value domain of the error rate data to [ infinity, + ∞ ] from [0,1], and respectively calculating the mean value and the variance of the pre-week prediction error rate, the pre-day prediction error rate and the pre-hour prediction error rate on the mapped value domain scale;
the prediction error rate probability distribution function generating sub-step S122 is: according to the mean and variance of the pre-week prediction error rates obtained in the substep S121, assuming that the error rates follow normal distribution, performing data fitting to obtain a probability distribution function of the pre-week prediction error rates, and similarly, respectively obtaining a probability distribution function of the pre-day prediction error rates and a probability distribution function of the pre-hour prediction error rates;
the scene matrix generation decomposition substep S123: dividing the probability distribution function F of the day-ahead prediction error rate obtained in the substep S122 into N equal probability intervals, randomly extracting a sampling point for any one probability interval, solving sample values of the day-ahead prediction error rate distribution function with the obeying probability interval by using an inverse function of F, regenerating the sequence of a day-ahead prediction error rate scene set by using a Cholesky decomposition method in the same way for the week-ahead prediction error rate probability distribution function and the hour-ahead prediction error rate probability distribution function, reducing the correlation among the sample values in a scene set matrix, and obtaining a day-ahead prediction error rate scene set which is relatively independent among scenes, wherein the scene set is in a matrix form.
The sub-step S124 is obtained by screening the representative error rate scene set: the probability of each scene obtained in the initialization substep S123 is the same value, and any two scenes C are calculatediAnd CjEuclidean distance between the two to determine scene CkAnd scene CiThe euclidean distance between them is the closest, and based on this closest distance, the probability distance, i.e. the product of said euclidean distance and the probability, is calculated, in the set of the future predicted error rate scenes generated in sub-step S123, a set of scenes with the closest probability distance is found, the set of scenes is considered to have similar characteristics for merging, only one of the scenes is retained, and the remaining scenes are deleted, and the corresponding probability of the retained scenes and the total number of scenes in the set of scenes are updated, and the above calculation and screening process is repeated until there remain k desired scenes, which are representative sets of k future predicted error rate scenes, and calculation and screening are performed in the same manner for the set of the future predicted error rate scenes and the set of the hour-ahead predicted error rate scenes.
6. The integrated energy system wind power consumption method of claim 5, wherein:
in the step S130 of constructing and solving the optimal scheduling model of the integrated energy system:
performing optimized scheduling by using a Monte Carlo method, taking the minimum energy consumption as a target function, taking the comprehensive energy system model constructed in S110 as a constraint condition, taking the day-ahead prediction error rate, week-ahead prediction error rate and hour-ahead prediction error rate of the generated power of the independent wind power unit generated in S120 as input scene sets, and constructing a scene-based uncertainty optimized scheduling problem, wherein the target function of the problem is as follows:
in the formula: κ is the scene set generated in step S125,
the amount of fuel consumed for the start-up of the unit,
the amount of fuel consumed for the unit shut down,
and for the minimum fuel consumption of the unit in the running state, Q (y, xi) is a fuel consumption function corresponding to the generating capacity of the unit, and the wind power consumption scheme of the comprehensive energy system is obtained by solving.
7. The integrated energy system wind power consumption method according to any one of claims 1 to 6, characterized in that:
the historical data of the wind turbine generator specifically comprises: the data includes week-ahead forecast data, day-ahead forecast data, hour-ahead forecast data, and real-time measurement data of the three.
8. A storage medium for storing computer-executable instructions, characterized in that:
the computer-executable instructions, when executed by a processor, perform the method for integrated energy system wind power consumption accounting for thermal grid dynamics based on the subject data of any one of claims 1-7.
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