CN118095796A - IES adjustable capability assessment method, device, equipment and medium based on conditional risk value - Google Patents
IES adjustable capability assessment method, device, equipment and medium based on conditional risk value Download PDFInfo
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Abstract
The invention relates to the technical field of optimal scheduling of power systems, in particular to an IES adjustable capability assessment method, device, equipment and medium based on conditional risk value, wherein the method comprises the following steps: establishing an adjustable capacity model comprising energy coupling equipment and energy storage equipment; in the day-ahead stage, a first objective function considering the maximum and minimum condition risk values of the exchange electric power of the IES and the upper-level power grid tie line at each moment and a second objective function taking the zero moment as a starting point, wherein the maximum and minimum condition risk values of the accumulated electric quantity of the IES and the upper-level power grid tie line are established; establishing and considering IES digestion constraints, energy storage device operation constraints, energy coupling device operation constraints, demand response constraints, power balance constraints and total cost constraints; and (3) considering scene generation and reduction of wind-light output uncertainty, solving an adjustable capacity model, drawing an upper limit and a lower limit of the IES on electric energy demand, and obtaining an adjustable capacity interval range of the IES.
Description
Technical Field
The invention relates to the technical field of optimal scheduling of power systems, in particular to an IES adjustable capability assessment method, device, equipment and medium based on conditional risk value.
Background
Along with the rapid increase of economy and the decline of reserves of non-renewable energy sources such as petroleum, coal and the like, the development strategy of replacing coal with electricity and gas is actively developed in China, the adjustment of energy structure is actively pushed, the electric energy has the characteristics of higher efficiency, safety and environmental protection, and becomes a core energy source of various secondary energy sources, and in a multi-energy coupling system, the electric energy plays an important role in coupling and interacting various energy sources. IES (INTEGRATED ENERGY SYSTEM ) is an important form of a multi-energy coupling system at the user side, and is an integrated system for integrated energy supply and sale, which is formed by organically coordinating and optimizing the processes of production, transmission, distribution, storage, conversion, consumption and the like of various forms of energy in the processes of planning, designing, building, running and the like.
IES can significantly improve the utilization efficiency of renewable energy sources and has been developed and applied to various types of parks. Since IES can effectively solve the problem of energy shortage, promote the utilization rate of renewable energy and reduce the emission of polluted gas, IES will become more and more important in energy supply systems and will become an important trend of future development.
In order to realize the transformation of the electric power system to low-carbon economy, the important way is to develop new energy power generation such as wind power, photovoltaic and the like, and the duty ratio of the new energy power generation in the coupling system is gradually increased. Because new energy output has volatility and uncertainty, large-scale grid connection can bring challenges to planning and operation of a power system. Therefore, how to calculate the adjustable capability range of a multi-energy coupling system would be a critical issue. Aiming at the problems, students at home and abroad conduct extensive researches, and in the aspect of energy acquisition, the method not only comprises electric energy supplied by an upper power grid and natural gas conveyed by a natural gas pipeline, but also effectively utilizes renewable energy sources, so that the optimal operation and scheduling of IES are realized. In the aspect of multi-energy coupling, the research on comprehensive energy systems at the present stage mainly relates to two energy forms, such as an electric-gas coupling system and an electric-thermal coupling system, and the research on more than three multi-energy coupling systems is relatively less. In the aspect of optimizing and scheduling the comprehensive energy system, the problem of the important research of most scholars is how to minimize the operation cost of the comprehensive energy system, maximize the income of the comprehensive energy system, reduce the carbon emission and the like, and few scholars research the adjustable capacity range of the comprehensive energy system. Therefore, how to evaluate the tunable capabilities of integrated energy systems is a problem that needs to be solved by those skilled in the art.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides an IES adjustable capability assessment method, device, equipment and medium based on conditional risk value, thereby effectively solving the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: an IES tunable ability assessment method based on conditional risk value includes the steps of:
Establishing an integrated energy system IES adjustable capacity model considering load demand response, wherein the adjustable capacity model comprises energy coupling equipment and energy storage equipment;
In the day-ahead stage, a first objective function considering the maximum and minimum condition risk values of the exchange electric power of the IES and the upper-level electric network connecting line at each moment and a second objective function taking the zero moment as a starting point and stopping the operation until each moment, wherein the maximum and minimum condition risk values of the accumulated electric quantity of the IES and the upper-level electric network connecting line are obtained;
Establishing a digestion constraint taking IES into account, an operation constraint of the energy storage device, an operation constraint of the energy coupling device, a demand response constraint, a power balance constraint and a total cost constraint;
And (3) generating and reducing scenes of wind-light output uncertainty, generating a typical scene based on a clustering method, solving the adjustable capacity model, drawing the upper limit and the lower limit of the IES on the electric energy requirement, and obtaining the adjustable capacity interval range of the IES.
Further, the first objective function includes:
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The second objective function includes:
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In the method, in the process of the invention, And/>Conditional risk value CVaR and risk value VaR,/>, respectively, for boundary exchange of electric power under IES tunable capabilityAnd/>CVaR and VaR values, respectively, for IES adjustable capability upper boundary switching electric power,/>To evaluate confidence of the lower boundary of the tunability,/>Is the probability of occurrence of the kth scene,/>In the kth scene, the interaction power between the IES and the upper power grid is obtained in each period; And/> The CVaR value and the VaR value of the accumulated exchange electric quantity of the lower boundary of the IES adjustable capacity are respectively,And/>CVaR value and VaR value of cumulative exchanged electric quantity of upper boundary of IES adjustable capacity of CVaR respectively,/>In the kth scenario, from time 0, the electricity flowing between IES and the upper grid is accumulated to each time.
Further, the constraints of the integrated energy system include:
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In the method, in the process of the invention, Wind power consumed by comprehensive energy system at time t,/>Is the maximum power of the wind driven generator,/>Photovoltaic power dissipated by comprehensive energy system at time t,/>Maximum output power at MPPT is tracked for photovoltaic operation at a maximum power point.
Further, the operational constraints of the energy coupling device include:
Electric heating boiler:
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In the method, in the process of the invention, For the heat generation power of the electric boiler in the t period,/>For the heat production efficiency of the electric boiler,/>AndThe electric power and the maximum electric power of the electric boiler in the t period are obtained;
Gas-fired boiler:
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In the method, in the process of the invention, For the heating power of the gas boiler in the period t,/>For the gas boiler to consume the power of natural gas in the period t,/>For heat supply efficiency,/>Is the maximum heat generation power of the boiler itself;
Electric gas conversion device:
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In the method, in the process of the invention, For the electric power consumed by the electric conversion device in the t period,/>Maximum electric power consumed by the electric conversion device;
Cogeneration unit:
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In the method, in the process of the invention, For the power generation of the cogeneration unit in the period t,/>Is the heating power in the period t,For the maximum power generation and the minimum power generation of the unit,/>For the gas consumption power of the unit in the period t,/>Respectively the power generation efficiency and the heating efficiency of the cogeneration unit,/>For the active output of the cogeneration unit in the t period,/>And/>The downhill speed and the uphill speed of the unit respectively.
Further, the operational constraints of the energy storage device include:
An electricity storage device:
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In the method, in the process of the invention, The electric quantity stored by the electric storage equipment in the t period, the maximum electric storage capacity and the minimum electric storage capacity are respectively; /(I)The charging efficiency and the discharging efficiency of the electricity storage equipment are respectively; /(I)Respectively charging power and discharging power of the electricity storage equipment in a t period; /(I)Respectively the maximum charge power and the maximum discharge power allowed by the storage battery; /(I)Respectively representing two working states of the electricity storage equipment in working, namely charging and discharging states, respectivelyIndicating that the electricity storage device is in a charged state,/>Indicating that the energy storage device is operating in a discharge state and that the energy storage device is only operating in one state; /(I)The storage capacity at the beginning and the end of the scheduling period respectively;
and (3) gas storage equipment:
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In the method, in the process of the invention, For the stored natural gas quantity at the time of the gas storage device t,/>Indicates the maximum natural gas amount which can be stored by the gas storage device,/>And/>Respectively the power of gas storage and gas injection of the gas storage device at the time t,/>And/>Respectively indicate the efficiency of gas storage and gas injection,/>And/>Respectively show two working states of the gas storage device at the time t, namely gas storage and gas injection states, and the gas storage device is in a gas storage state and a gas injection stateIndicating that the gas storage device works in a gas storage state,/>Indicating that the gas storage device works in a gas injection state,/>And/>Respectively representing the maximum gas storage and gas injection power of the gas storage device,/>And/>Respectively representing the stored natural gas quantity of the gas storage device at the beginning and the end of a dispatching cycle;
Heat storage device:
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In the method, in the process of the invention, Representing the heat storage capacity of the heat storage device at the t period,/>Representing the maximum heat storage capacity of the heat storage device,/>And/>The heat storage power and the heat release power of the heat storage device at the t period are respectively/>Respectively representing the heat storage efficiency and the heat release efficiency of the heat storage device,/>And/>Maximum heat storage power and heat release power of the heat storage device respectively,/>And/>Respectively representing two working states of the heat storage device, namely a heat storage state and a heat release state,/>, respectivelyIndicating that the heat storage device works in a heat storage state,/>Indicating that the heat storage device is working in a heat release state,/>Respectively representing the heat storage capacity of the heat reservoir at the beginning and end of the scheduling period.
Further, the demand response constraint includes:
translatable load:
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In the method, in the process of the invention, Representing the translational state of the translatable load during the t period,/>Time represents load translation,/>Time indicates that the load is not translated,/>Indicating the start time,/>Representing a continuous run time of the translatable load;
load can be transferred:
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In the method, in the process of the invention, Representing the transition amount of the transferable load at time t,/>And/>Representing the minimum and maximum values of transferable load power, respectively;
Load can be reduced:
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In the method, in the process of the invention, Is a 0/1 variable, and represents a load shedding state in a certain period t of time,/>Indicating that the load can be cut down,/>Representation is not clipped,/>For load shedding factor at t period,/>，/>To cut down the power before load participation in scheduling,/>Is the maximum reduction number.
Further, the power balancing constraint includes:
the electrical bus power balance constraint expression:
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The air bus power balance constraint expression:
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Thermal bus power balance constraint expression:
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In the method, in the process of the invention, For the electrical load at time t,/>For the gas load at time t,/>Is the thermal load at time t.
Further, the total cost constraint includes:
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In the method, in the process of the invention, For the total cost,/>Is a preset cost,/>Is the total cost of purchasing IES,/>And/>Cost of wind and light abandon respectively,/>Is the total cost of the energy storage device,/>Is the carbon-emission cost,/>Responding to costs for demand.
Further, the scene generation and reduction considering the uncertainty of the wind-light output comprises:
based on historical wind power and photovoltaic output in n days, selecting a Gaussian kernel function to generate a probability density function of the wind power and photovoltaic output in each period of 24 hours based on a kernel density estimation method every other hour;
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wherein: t represents 24 time periods; And/> Representing the output of wind power and photovoltaic in a t period; /(I)And/>Representing wind power and photovoltaic output at the time t of the day d; h represents bandwidth; /(I)Representing a gaussian kernel function;
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solving the cumulative distribution function of wind power and photovoltaic according to probability density functions of wind power and photovoltaic And/>Establishing a combined distribution function of wind power and photovoltaic output in each period based on a Frank-Copula function;
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Wherein: Is a two-dimensional Frank-Copula function, namely:
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Wherein: ，/>，/> is a related parameter,/> And/>In/>Representation ofPositive correlation,/>Representation/>Negative correlation,/>Representation/>Tend to be independent;
Sampling the joint distribution function of each time period, and obtaining wind power and photovoltaic output of each time period corresponding to the accumulated probability by using a cubic spline interpolation method;
In the cumulative probability interval In, divide it into/>Between cells, and in any one of the intervalsAnd/>Above, the cumulative probability/>, respectivelyAnd/>Is an independent variable, in/>And/>As a dependent variable, a cubic spline polynomial over the interval is obtained by a cubic spline interpolation method as follows:
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Wherein: ，/> Coefficients during fitting;
Accumulating probability values for arbitrary samples And/>In the above, the ratio of/>，/>For the size of the samples, it will fall between cells/>And/>In, will/>And/>Substituting the data into the above data, the sampled wind power and photovoltaic output data of each period can be obtained;
By using Clustering pairs/>Clustering the group sampling results to generate/>And the wind-light output is typical, and the probability of each scene is calculated.
Further, the solving the adjustable capacity model includes:
And solving the comprehensive energy system adjustable capacity range evaluation model based on a MATLAB+ Yalmip + Gurobi solver by using a mixed integer linear programming method, and drawing upper and lower limits of the comprehensive energy system on electric energy requirements at all moments to obtain a comprehensive energy system adjustable capacity range result.
The invention also comprises an IES adjustable capability assessment device based on conditional risk value, which comprises the following steps of:
The modeling unit is used for establishing an integrated energy system IES adjustable capacity model considering load demand response, and the adjustable capacity model comprises energy coupling equipment and energy storage equipment;
The objective function unit is used for establishing a first objective function considering the maximum and minimum condition risk values of the exchange electric power of the IES and the upper-level power grid interconnection line at each moment and a second objective function taking the zero moment as a starting point, stopping the operation until each moment, and accumulating the maximum and minimum condition risk values of the electric quantity of the communication between the IES and the upper-level power grid interconnection line;
a constraint unit for establishing a digestion constraint taking IES into account, an operation constraint of the energy storage device, an operation constraint of the energy coupling device, a demand response constraint, a power balance constraint, and a total cost constraint;
the solving unit is used for solving the adjustable capacity model by considering scene generation and reduction of wind-light output uncertainty and generating a typical scene based on a clustering method, drawing the upper limit and the lower limit of the IES on the electric energy requirement and obtaining the adjustable capacity interval range of the IES.
The invention also includes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the method as described above when executing the computer program.
The invention also includes a storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
The beneficial effects of the invention are as follows: the invention considers the risk value brought by wind and light uncertainty based on CVaR method, takes the maximum value and the minimum value of the exchange power on the tie line of the comprehensive energy system and the upper power grid at each moment as an objective function, takes the zero moment as a starting point, cuts off the maximum value and the minimum value of the accumulated circulation electric quantity of the tie line of the comprehensive energy system and the upper power grid at each moment as an objective function, establishes the evaluation model of the adjustable capacity range of the comprehensive energy system based on CVaR method based on the above, solves and draws the upper limit and the lower limit of the comprehensive energy system on the electric energy requirement, and further describes the adjustable capacity range of the comprehensive energy system. The method can enable the superior system to better schedule the comprehensive energy system, is favorable for complete and stable operation of the power grid, improves the operation stability of the comprehensive energy system, and also ensures the reliability of power supply.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of the method of example 1;
FIG. 2 is a schematic view of the structure of the device in example 1;
FIG. 3 is a flow chart of a method for evaluating the adjustable capacity range of the integrated energy system based on CVaR in example 2;
FIG. 4 is a system configuration diagram of the integrated energy system in example 2;
FIG. 5 is a graph showing predicted power of wind power and photovoltaic in different scenarios in the integrated energy system of example 2;
FIG. 6 is a graph showing the electrical load, thermal load and air load power predictions for the integrated energy system of example 2;
FIG. 7 is a schematic diagram of the upper and lower limits of the exchange electric power and the exchange electric power of the integrated energy system and the upper grid in embodiment 2;
FIG. 8 is a schematic diagram of the upper and lower limits of the total power exchanged between the integrated energy system and the upper grid and the total natural gas exchanged in embodiment 2;
fig. 9 is a schematic structural diagram of a computer device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
As shown in fig. 1: an IES tunable ability assessment method based on conditional risk value includes the steps of:
establishing an integrated energy system IES adjustable capacity model considering load demand response, wherein the adjustable capacity model comprises energy coupling equipment and energy storage equipment;
In the day-ahead stage, a first objective function considering the maximum and minimum condition risk values of the exchange electric power of the IES and the upper-level electric network connecting line at each moment and a second objective function taking the zero moment as a starting point and stopping the operation until each moment, wherein the maximum and minimum condition risk values of the accumulated electric quantity of the IES and the upper-level electric network connecting line are obtained;
Establishing a digestion constraint taking IES into account, an operation constraint of the energy storage device, an operation constraint of the energy coupling device, a demand response constraint, a power balance constraint and a total cost constraint;
And (3) generating and reducing scenes of wind-light output uncertainty, generating a typical scene based on a clustering method, solving an adjustable capacity model, drawing an upper limit and a lower limit of the IES on electric energy requirements, and obtaining an adjustable capacity interval range of the IES.
In this embodiment, the first objective function includes:
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the second objective function includes:
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In the method, in the process of the invention, And/>Conditional risk value CVaR and risk value VaR,/>, respectively, for boundary exchange of electric power under IES tunable capabilityAnd/>CVaR and VaR values, respectively, for IES adjustable capability upper boundary switching electric power,/>To evaluate confidence of the lower boundary of the tunability,/>Is the probability of occurrence of the kth scene,/>In the kth scene, the interaction power between the IES and the upper power grid is obtained in each period; And/> The CVaR value and the VaR value of the accumulated exchange electric quantity of the lower boundary of the IES adjustable capacity are respectively,And/>CVaR value and VaR value of cumulative exchanged electric quantity of upper boundary of IES adjustable capacity of CVaR respectively,/>In the kth scenario, from time 0, the electricity flowing between IES and the upper grid is accumulated to each time.
Wherein, the consumption constraint of the comprehensive energy system comprises:
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In the method, in the process of the invention, Wind power consumed by comprehensive energy system at time t,/>Is the maximum power of the wind driven generator,/>Photovoltaic power dissipated by comprehensive energy system at time t,/>Maximum output power at MPPT is tracked for photovoltaic operation at a maximum power point.
The operational constraints of the energy coupling device include:
Electric heating boiler:
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In the method, in the process of the invention, For the heat generation power of the electric boiler in the t period,/>For the heat production efficiency of the electric boiler,/>AndThe electric power and the maximum electric power of the electric boiler in the t period are obtained;
Gas-fired boiler:
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In the method, in the process of the invention, For the heating power of the gas boiler in the period t,/>For the gas boiler to consume the power of natural gas in the period t,/>For heat supply efficiency,/>Is the maximum heat generation power of the boiler itself;
Electric gas conversion device:
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In the method, in the process of the invention, For the electric power consumed by the electric conversion device in the t period,/>Maximum electric power consumed by the electric conversion device;
Cogeneration unit:
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In the method, in the process of the invention, For the power generation of the cogeneration unit in the period t,/>Is the heating power in the period t,For the maximum power generation and the minimum power generation of the unit,/>For the gas consumption power of the unit in the period t,/>Respectively the power generation efficiency and the heating efficiency of the cogeneration unit,/>For the active output of the cogeneration unit in the t period,/>And/>The downhill speed and the uphill speed of the unit respectively.
The operational constraints of the energy storage device include:
An electricity storage device:
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In the method, in the process of the invention, The electric quantity stored by the electric storage equipment in the t period, the maximum electric storage capacity and the minimum electric storage capacity are respectively; /(I)The charging efficiency and the discharging efficiency of the electricity storage equipment are respectively; /(I)Respectively charging power and discharging power of the electricity storage equipment in a t period; /(I)Respectively the maximum charge power and the maximum discharge power allowed by the storage battery; /(I)Respectively representing two working states of the electricity storage equipment in working, namely charging and discharging states, respectivelyIndicating that the electricity storage device is in a charged state,/>Indicating that the energy storage device is operating in a discharge state and that the energy storage device is only operating in one state; /(I)The storage capacity at the beginning and the end of the scheduling period respectively;
and (3) gas storage equipment:
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In the method, in the process of the invention, For the stored natural gas quantity at the time of the gas storage device t,/>Indicates the maximum natural gas amount which can be stored by the gas storage device,/>And/>Respectively the power of gas storage and gas injection of the gas storage device at the time t,/>And/>Respectively indicate the efficiency of gas storage and gas injection,/>And/>Respectively show two working states of the gas storage device at the time t, namely gas storage and gas injection states, and the gas storage device is in a gas storage state and a gas injection stateIndicating that the gas storage device works in a gas storage state,/>Indicating that the gas storage device works in a gas injection state,/>And/>Respectively representing the maximum gas storage and gas injection power of the gas storage device,/>And/>Respectively representing the stored natural gas quantity of the gas storage device at the beginning and the end of a dispatching cycle;
Heat storage device:
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In the method, in the process of the invention, Representing the heat storage capacity of the heat storage device at the t period,/>Representing the maximum heat storage capacity of the heat storage device,/>And/>The heat storage power and the heat release power of the heat storage device at the t period are respectively/>Respectively representing the heat storage efficiency and the heat release efficiency of the heat storage device,/>And/>Maximum heat storage power and heat release power of the heat storage device respectively,/>And/>Respectively representing two working states of the heat storage device, namely a heat storage state and a heat release state,/>, respectivelyIndicating that the heat storage device works in a heat storage state,/>Indicating that the heat storage device is working in a heat release state,/>Respectively representing the heat storage capacity of the heat reservoir at the beginning and end of the scheduling period.
The demand response constraints include:
translatable load:
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In the method, in the process of the invention, Representing the translational state of the translatable load during the t period,/>Time represents load translation,/>Time indicates that the load is not translated,/>Indicating the start time,/>Representing a continuous run time of the translatable load;
load can be transferred:
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In the method, in the process of the invention, Representing the transition amount of the transferable load at time t,/>And/>Representing the minimum and maximum values of transferable load power, respectively;
Load can be reduced:
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In the method, in the process of the invention, Is a 0/1 variable, and represents a load shedding state in a certain period t of time,/>Indicating that the load can be cut down,/>Representation is not clipped,/>For load shedding factor at t period,/>，/>To cut down the power before load participation in scheduling,/>Is the maximum reduction number.
The power balancing constraints include:
the electrical bus power balance constraint expression:
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The air bus power balance constraint expression:
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Thermal bus power balance constraint expression:
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In the method, in the process of the invention, For the electrical load at time t,/>For the gas load at time t,/>Is the thermal load at time t.
The total cost constraints include:
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In the method, in the process of the invention, For the total cost,/>Is a preset cost,/>Is the total cost of purchasing IES,/>And/>Cost of wind and light abandon respectively,/>Is the total cost of the energy storage device,/>Is the carbon-emission cost,/>Responding to costs for demand.
In this embodiment, scene generation and curtailment taking into account wind-light output uncertainty includes:
based on historical wind power and photovoltaic output in n days, selecting a Gaussian kernel function to generate a probability density function of the wind power and photovoltaic output in each period of 24 hours based on a kernel density estimation method every other hour;
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wherein: t represents 24 time periods; And/> Representing the output of wind power and photovoltaic in a t period; /(I)And/>Representing wind power and photovoltaic output at the time t of the day d; h represents bandwidth; /(I)Representing a gaussian kernel function;
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solving the cumulative distribution function of wind power and photovoltaic according to probability density functions of wind power and photovoltaic And/>Establishing a combined distribution function of wind power and photovoltaic output in each period based on a Frank-Copula function;
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Wherein: Is a two-dimensional Frank-Copula function, namely:
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Wherein: ，/>，/> is a related parameter,/> And/>In/>Representation ofPositive correlation,/>Representation/>Negative correlation,/>Representation tends to/>Independent;
Sampling the joint distribution function of each time period, and obtaining wind power and photovoltaic output of each time period corresponding to the accumulated probability by using a cubic spline interpolation method;
In the cumulative probability interval In, divide it into/>Between cells, and in any one interval/>And/>Above, the cumulative probability/>, respectivelyAnd/>Is an independent variable, in/>And/>As a dependent variable, a cubic spline polynomial over the interval is obtained by a cubic spline interpolation method as follows:
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Wherein: ，/> Coefficients during fitting;
Accumulating probability values for arbitrary samples And/>In the above, the ratio of/>，/>For the size of the samples, it will fall between cells/>And/>In, will/>And/>Substituting the data into the above data, the sampled wind power and photovoltaic output data of each period can be obtained;
By using Clustering pairs/>Clustering the group sampling results to generate/>And the wind-light output is typical, and the probability of each scene is calculated.
Wherein solving the tunable capability model includes:
and drawing upper and lower limits of the comprehensive energy system on the electric energy demand at each moment based on the comprehensive energy system adjustable capacity range evaluation model solved by the MATLAB+ Yalmip + Gurobi solver by using a mixed integer linear programming method, and obtaining a comprehensive energy system adjustable capacity range result.
According to the embodiment, a model of a comprehensive energy system is built, the model comprises three energy forms of electricity, gas and heat, internal equipment comprises but is not limited to wind power and photovoltaic new energy power generation equipment, energy coupling equipment such as an electric heating boiler, a gas burning boiler and electricity-to-gas conversion equipment, energy storage equipment for electricity storage, heat storage and gas storage comprises electric load, thermal load and gas load on a demand side, and meanwhile, the demand response and economic constraint of the load are considered, and an external energy supply system of the comprehensive energy system comprises an upper power grid and a natural gas supply system; the method is based on CVaR, risk value brought by wind-solar uncertainty is considered, maximum and minimum values of exchange power on a comprehensive energy system and an upper-level power grid tie line at each moment are taken as an objective function, zero moment is taken as a starting point, the maximum and minimum values of accumulated circulation electric quantity of the comprehensive energy system and the upper-level power grid tie line are cut off to each moment, constraint conditions comprise operation constraint and upper and lower limit constraint of each device in the comprehensive energy system, power balance constraint and economic constraint of an electric bus, an air bus and a thermal bus are taken into consideration, an evaluation model of the adjustable capacity range of the comprehensive energy system based on the CVaR method is established based on the above, the upper limit and the lower limit of the electric energy demand of the comprehensive energy system are solved and drawn through a MATLAB/Yalmip/gurobi solver, and the adjustable capacity range of the comprehensive energy system is further described. The method can enable the superior system to better schedule the comprehensive energy system, is favorable for complete and stable operation of the power grid, improves the operation stability of the comprehensive energy system, and also ensures the reliability of power supply.
As shown in fig. 2, an IES tunable ability assessment apparatus based on conditional risk value, using the method as described above, includes:
the modeling unit is used for establishing an integrated energy system IES adjustable capacity model considering load demand response, wherein the adjustable capacity model comprises energy coupling equipment and energy storage equipment;
The objective function unit is used for establishing a first objective function considering the maximum and minimum condition risk values of the exchange electric power of the IES and the upper power grid tie line at each moment and a second objective function taking the zero moment as a starting point, stopping the operation until each moment, and accumulating the maximum and minimum condition risk values of the electric quantity of the communication between the IES and the upper power grid tie line;
The constraint unit is used for establishing a digestion constraint taking IES into consideration, an operation constraint of the energy storage device, an operation constraint of the energy coupling device, a demand response constraint, a power balance constraint and a total cost constraint;
The solving unit is used for solving the adjustable capacity model by considering scene generation and reduction of wind-light output uncertainty and generating a typical scene based on a clustering method, drawing the upper limit and the lower limit of the IES on the electric energy demand and obtaining the adjustable capacity interval range of the IES.
Example 2:
The embodiment of the invention provides an IES adjustable capability range evaluation method based on CVaR. Referring to fig. 3, fig. 3 is a flowchart illustrating a method for evaluating the IES adjustable capability range based on CVaR according to an embodiment of the present invention. The optimized scheduling method may include:
S110, building models of wind power and photovoltaic new energy power generation equipment, electric heating boilers, electric conversion equipment and other coupling energy equipment, electricity storage, heat storage and gas storage energy storage equipment and load side demand response.
S120, scene generation and reduction considering wind-light output uncertainty: and generating a wind-light output scene based on the kernel density estimation and the Copula function, and obtaining a typical scene through K-means cluster reduction.
S130, based on CVaR method, considering the risk value of wind-light output uncertainty, determining an objective function, wherein the objective function is CVaR values of maximum value and minimum value of exchange electric power on the tie line of the comprehensive energy system and the upper power grid at each moment. 2. And starting from the zero moment, stopping the integrated energy system and the upper power grid tie line to accumulate the maximum value and the minimum value CVaR of the circulated electric quantity at each moment.
S140, determining constraint conditions of the model, including: 1. and (3) establishing a model of an adjustable capacity assessment method of the comprehensive energy system by integrating power balance constraint and upper and lower limit constraint of power when each device in the energy system operates, such as output constraint of wind power and photovoltaic, constraint of electricity storage, heat storage and gas storage devices, power constraint and upper and lower limit constraint of output when coupling devices such as an electricity-to-gas unit and a cogeneration unit operate, and power balance constraint of 2, an electricity bus, a gas bus and a heat bus, and economic constraint including energy purchasing cost, abandoned wind and abandoned light punishment, carbon emission and demand response compensation cost.
And S150, solving a model for evaluating the adjustable capacity of the comprehensive energy system to obtain an optimization result, and drawing the adjustable capacity range of the comprehensive energy system.
In the embodiment of the invention, on the premise of meeting the constraint conditions established in the steps, the objective function of the comprehensive energy system adjustable capacity range evaluation model is solved, and finally, the result of the comprehensive energy system adjustable capacity range evaluation can be obtained.
It can be seen from the foregoing that the embodiment of the present invention provides an evaluation method based on CVaR of the adjustable capability range of a comprehensive energy system, by establishing a model of the comprehensive energy system, including coupling of three energy forms of electricity, gas and heat, internal devices include, but are not limited to, wind power and photovoltaic new energy power generation devices, electric heating boilers, gas boilers, electric conversion gas and other energy coupling devices, electricity storage, heat storage and gas storage energy storage devices, including electric load, thermal load and gas load on a demand side, while considering the demand response and economic constraint of the load, and an external energy supply system of the comprehensive energy system includes an upper grid and a natural gas supply system; the method is based on CVaR, risk value brought by wind-solar uncertainty is considered, maximum and minimum values of exchange power on a comprehensive energy system and an upper-level power grid tie line at each moment are taken as an objective function, zero moment is taken as a starting point, the maximum and minimum values of accumulated circulation electric quantity of the comprehensive energy system and the upper-level power grid tie line are cut off to each moment, constraint conditions comprise operation constraint and upper and lower limit constraint of each device in the comprehensive energy system, power balance constraint and economic constraint of an electric bus, an air bus and a thermal bus are taken into consideration, an evaluation model of the adjustable capacity range of the comprehensive energy system based on the CVaR method is established based on the above, the upper limit and the lower limit of the electric energy demand of the comprehensive energy system are solved and drawn through a MATLAB/Yalmip/gurobi solver, and the adjustable capacity range of the comprehensive energy system is further described. The method can enable the superior system to better schedule the comprehensive energy system, is favorable for complete and stable operation of the power grid, improves the operation stability of the comprehensive energy system, and also ensures the reliability of power supply.
Specifically, in the above embodiment, the expression of the objective function is:
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In the method, in the process of the invention, And/>CVaR and VaR values, respectively, for boundary-switched electric power at IES tunable capability,/>And/>CVaR and VaR values, respectively, for IES adjustable capability upper boundary switching electric power,/>To evaluate confidence of the lower boundary of the tunability,/>For the probability of occurrence of the kth scene,In the kth scene, the interaction power between the IES and the upper power grid is obtained in each period; /(I)AndCVaR and VaR values, respectively, for the cumulative exchange capacity at the lower boundary of the IES capability of tuning,/>AndCVaR value and VaR value of cumulative exchanged electric quantity of upper boundary of IES adjustable capacity of CVaR respectively,/>In the kth scenario, from time 0, the electricity flowing between IES and the upper grid is accumulated to each time.
In the embodiment of the invention, the constraint conditions of the wind power and photovoltaic power consumed by the comprehensive energy system, the operation constraint of the electricity storage equipment, the operation constraint of the heat storage equipment and the operation constraint of the gas storage equipment are used as the constraint conditions of the model, and the adjustable capacity assessment model of the comprehensive energy system is built by using the constraint conditions of the upper limit and lower limit constraint of the output force of the electric heating boiler, the gas boiler and the electric conversion device, the constraint of the upper limit and lower limit of the heat generation and heat generation of the cogeneration device, the climbing constraint, the constraint of the demand response model and the total cost constraint.
Further, in the above embodiment, the scene generation and reduction considering the uncertainty of the wind-light output is as follows:
(1) Based on historical wind power and photovoltaic output in n days, selecting a Gaussian kernel function to generate a probability density function of the wind power and photovoltaic output in each period of 24 hours based on a kernel density estimation method every other hour;
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Wherein t represents 24 time periods; And/> Representing the output of wind power and photovoltaic in a t period; /(I)And/>Representing wind power and photovoltaic output at the time t of the day d; h represents bandwidth; /(I)Representing a gaussian kernel function;
。
(2) Solving the cumulative distribution function of wind power and photovoltaic according to probability density functions of wind power and photovoltaic And/>Establishing a combined distribution function of wind power and photovoltaic output in each period based on a Frank-Copula function;
/>
Wherein: As a two-dimensional Frank-Copula function, i.e.
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Wherein:，/>，/> is a related parameter,/> And/>In/>Representation ofPositive correlation,/>Representation/>Negative correlation,/>Representation/>Tend to be independent.
(3) Sampling the joint distribution function of each time period, and obtaining wind power and photovoltaic output of each time period corresponding to the cumulative probability by using a cubic spline interpolation method.
In the cumulative probability intervalIn, divide it into/>Between cells, and in any one of the intervalsAnd/>Above, the cumulative probability/>, respectivelyAnd/>Is an independent variable, in/>And/>As a dependent variable, a cubic spline polynomial over the interval is obtained by a cubic spline interpolation method as follows
；
Wherein:，/> Is the coefficient in the fitting process.
Accumulating probability values for arbitrary samplesAnd/>In the above, the ratio of/>，/>For the size of the samples, it will fall between cells/>And/>In, will/>And/>Substituting the above formula, the sampled wind power and photovoltaic output data of each period can be obtained.
(4) By usingClustering pairs/>Clustering the group sampling results to generate/>And the wind-light output is typical, and the probability of each scene is calculated.
The expression of the power constraint of the wind power and photovoltaic new energy power generation device is as follows:
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In the method, in the process of the invention, Wind power consumed by comprehensive energy system at time t,/>Maximum power generated by wind driven generator,/>Photovoltaic power dissipated by comprehensive energy system at time t,/>Maximum output power at MPPT for photovoltaic operation.
The operation constraint expression of the coupling device is as follows:
(1) Electric heating boiler:
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In the method, in the process of the invention, For the heat generation power of the electric boiler in the t period,/>For the heat production efficiency of the electric boiler,/>AndThe electric power and the maximum electric power of the electric boiler in the t period.
(2) Gas-fired boiler:
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In the method, in the process of the invention, For the heating power of the gas boiler in the period t,/>For the gas boiler to consume the power of natural gas in the period t,/>For heat supply efficiency,/>Is the maximum heat production power of the boiler itself.
(3) Electric gas conversion device:
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In the method, in the process of the invention, For the electric power consumed by the electric conversion device in the t period,/>The maximum electric power consumed by the electric conversion device.
(4) Cogeneration unit:
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In the method, in the process of the invention, For the power generation of the cogeneration unit in the period t,/>Is the heating power in the period t,For the maximum power generation and the minimum power generation of the unit,/>For the gas consumption and the gas quantity of the unit in the period t,/>Respectively the power generation efficiency and the heating efficiency of the unit,/>For the active output of the cogeneration unit in the t period,/>And/>The downhill speed and the uphill speed of the unit respectively.
The operation constraint expression of the electricity storage equipment is as follows:
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In the method, in the process of the invention, The electric quantity stored by the electric storage equipment in the t period, the maximum electric storage capacity and the minimum electric storage capacity are respectively; /(I)The charging efficiency and the discharging efficiency of the electricity storage equipment are respectively; /(I)Respectively charging power and discharging power of the electricity storage equipment in a t period; /(I)Respectively the maximum charge power and the maximum discharge power allowed by the storage battery; /(I)Respectively representing two working states of the electricity storage equipment in working, namely charging and discharging states, respectivelyIndicating that the electricity storage device is in a charged state,/>Indicating that the energy storage device is operating in a discharge state and that the energy storage device is only operating in one state; /(I)The storage capacities at the beginning and end of the scheduling period respectively.
The operation constraint expression of the gas storage device is as follows:
；/>
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In the method, in the process of the invention, For the stored natural gas quantity at the time of the gas storage device t,/>Indicates the maximum natural gas amount which can be stored by the gas storage device,/>And/>Respectively the power of gas storage and gas injection of the gas storage device at the time t,/>And/>Respectively indicate the efficiency of gas storage and gas injection,/>And/>Respectively show two working states of the gas storage device at the time t, namely gas storage and gas injection states, and the gas storage device is in a gas storage state and a gas injection stateIndicating that the gas storage device works in a gas storage state,/>Indicating that the gas storage device works in a gas injection state,/>And/>Respectively representing the maximum gas storage and gas injection power of the gas storage device,/>And/>Respectively representing the stored natural gas quantity of the gas storage device at the beginning and the end of the dispatching cycle.
The operation constraint expression of the heat storage equipment is as follows:
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In the method, in the process of the invention, Representing the heat storage capacity of the heat storage device at the t period,/>Representing the maximum heat storage capacity of the heat storage device,/>And/>The heat storage power and the heat release power of the heat storage device at the t period are respectively/>Respectively representing the heat storage efficiency and the heat release efficiency of the heat storage device,/>And/>Maximum heat storage power and heat release power of the heat storage device respectively,/>And/>Respectively representing two working states of the heat storage device, namely a heat storage state and a heat release state,/>, respectivelyIndicating that the heat storage device works in a heat storage state,/>Indicating that the heat storage device is working in a heat release state,/>Respectively representing the heat storage capacity of the heat reservoir at the beginning and end of the scheduling period.
The expression of the demand response model and the constraint is as follows:
(1) Translatable load:
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In the method, in the process of the invention, Representing the translational state of a translatable load over a certain period t,/>The time of this indicates the load translation,Time indicates that the load is not translated,/>Indicating the start time,/>Representing the duration of the translatable load. /(I)
(2) Load can be transferred:
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In the method, in the process of the invention, Representing the transition amount of the transferable load at time t,/>And/>Representing the minimum and maximum values of transferable load power, respectively.
(3) Load can be reduced:
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In the method, in the process of the invention, Is a 0/1 variable, and represents a load shedding state in a certain period t of time,/>Indicating that the load can be cut down,/>Representation is not clipped,/>For load shedding factor at t period,/>，/>To cut down the power before load participation in scheduling,/>Is the maximum reduction number.
The power balance constraint expression of the electric bus is as follows:
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The air bus power balance constraint expression is:
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The thermal bus power balance constraint expression is:
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the economic constraints are as follows:
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In the method, in the process of the invention, For the electrical load at time t,/>For the gas load at time t,/>For the thermal load at the moment t,For the total cost,/>Is a preset cost,/>Is the total cost of purchasing IES,/>And/>Cost of wind and light abandon respectively,/>Is the total cost of the energy storage device,/>Is the carbon-emission cost,/>Responding to costs for demand.
Optionally, in the foregoing embodiment, solving the evaluation model of the adjustable capability of the integrated energy system to obtain the adjustable capability range of the integrated energy system based on CVaR includes:
In the embodiment of the invention, a mixed integer linear programming method is utilized to solve the problem based on a MATLAB+ Yalmip + Gurobi solver.
In order to demonstrate the effectiveness of the evaluation method based on CVaR comprehensive energy system adjustable capacity range provided by the invention, the following description is made with reference to a specific application scenario.
Referring to fig. 4, fig. 5 and fig. 6, fig. 4 is a schematic diagram of a model for evaluating the adjustable capacity of the integrated energy system according to an embodiment of the present invention, fig. 5 is a wind power and photovoltaic power prediction curve of the integrated energy system according to an embodiment of the present invention, and fig. 6 is a prediction curve of electric, gas and heat loads of the integrated energy system according to an embodiment of the present invention.
Firstly, establishing an objective function for evaluating the adjustable capacity range of the comprehensive energy system, wherein the objective function is as follows:
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Then, acquiring output data, electric, thermal and gas load data and specific information of energy storage equipment and coupling equipment of wind power and photovoltaic, and establishing a model for evaluating the adjustable capacity of the comprehensive energy system, wherein the model comprises the following steps:
1) Parameters of wind power and photovoltaic output are shown in table 1:
TABLE 1
2) The electrical, thermal, and gas load data are shown in table 2:
TABLE 2
3) The parameters of the energy storage equipment are shown in Table 3
TABLE 3 Table 3
4) The coupling device parameters are as follows
Cogeneration unit:
TABLE 4 Table 4
Electric heating boiler: the maximum power consumption is 500kW, and the heat generating efficiency is 0.85;
Gas-fired boiler: the maximum heating power is 1000kW, and the heat generating efficiency is 0.9;
and solving a mixed integer linear model based on the evaluation of the adjustable capacity range of the CVaR integrated energy system by adopting a MATLAB+ Yalmip + Gurobi solver to obtain the maximum and minimum CVaR values of the exchange electric power on the integrated energy system and the upper power grid connecting line and the maximum and minimum CVaR values of the accumulated circulated electric quantity of the integrated energy system and the upper power grid connecting line from zero time to each time.
FIG. 7 is a schematic diagram of maximum and minimum CVaR values of the electric power exchanged between the integrated energy system and the upper grid in an embodiment of the present invention; fig. 8 is a schematic diagram of maximum and minimum CVaR values of the exchange accumulated electric quantity between the integrated energy system and the upper grid according to an embodiment of the present invention.
Please refer to fig. 9, which illustrates a schematic structure of a computer device according to an embodiment of the present application. The computer device 400 provided in the embodiment of the present application includes: a processor 410 and a memory 420, the memory 420 storing a computer program executable by the processor 410, which when executed by the processor 410 performs the method as described above.
The embodiment of the present application also provides a storage medium 430, on which storage medium 430 a computer program is stored which, when executed by the processor 410, performs a method as above.
The storage medium 430 may be implemented by any type of volatile or nonvolatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM), erasable Programmable Read-Only Memory (ErasableProgrammable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The meaning of "a plurality of" is two or more, unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily for the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (13)
1. An IES tunable ability assessment method based on conditional risk value, comprising:
Establishing an integrated energy system IES adjustable capacity model considering load demand response, wherein the adjustable capacity model comprises energy coupling equipment and energy storage equipment;
In the day-ahead stage, a first objective function considering the maximum and minimum condition risk values CVaR of the exchange electric power of the IES and the upper-level electric network connecting line at each moment and a second objective function considering the maximum and minimum condition risk values CVaR of the accumulated electric quantity of the IES and the upper-level electric network connecting line at each moment are established by taking the zero moment as a starting point and stopping the zero moment;
Establishing a digestion constraint taking IES into account, an operation constraint of the energy storage device, an operation constraint of the energy coupling device, a demand response constraint, a power balance constraint and a total cost constraint;
And (3) generating and reducing scenes of wind-light output uncertainty, generating a typical scene based on a clustering method, solving the adjustable capacity model, drawing the upper limit and the lower limit of the IES on the electric energy requirement, and obtaining the adjustable capacity interval range of the IES.
2. The conditional risk value-based IES tunable ability assessment method according to claim 1, wherein the first objective function comprises:
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The second objective function includes:
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In the method, in the process of the invention, And/>Conditional risk value CVaR and risk value VaR,/>, respectively, for boundary exchange of electric power under IES tunable capabilityAnd/>CVaR and VaR values, respectively, for IES adjustable capability upper boundary switching electric power,/>To evaluate confidence of the lower boundary of the tunability,/>Is the probability of occurrence of the kth scene,/>In the kth scene, the interaction power between the IES and the upper power grid is obtained in each period; /(I)AndCVaR and VaR values, respectively, for the cumulative exchange capacity at the lower boundary of the IES capability of tuning,/>AndCVaR value and VaR value of cumulative exchanged electric quantity of upper boundary of IES adjustable capacity of CVaR respectively,/>In the kth scenario, from time 0, the electricity flowing between IES and the upper grid is accumulated to each time.
3. The conditional risk value-based IES tunable ability assessment method according to claim 2, wherein the consumption constraint of the integrated energy system comprises:
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In the method, in the process of the invention, Wind power consumed by comprehensive energy system at time t,/>Is the maximum power of the wind driven generator,Photovoltaic power dissipated by comprehensive energy system at time t,/>Maximum output power at MPPT is tracked for photovoltaic operation at a maximum power point.
4. The conditional risk value-based IES tunable capability assessment method according to claim 3, wherein the operational constraints of the energy coupling device include:
Electric heating boiler:
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In the method, in the process of the invention, For the heat generation power of the electric boiler in the t period,/>For the heat production efficiency of the electric boiler,/>And/>The electric power and the maximum electric power of the electric boiler in the t period are obtained;
Gas-fired boiler:
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In the method, in the process of the invention, For the heating power of the gas boiler in the period t,/>For the gas boiler to consume the power of natural gas in the period t,/>For heat supply efficiency,/>Is the maximum heat generation power of the boiler itself;
Electric gas conversion device:
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In the method, in the process of the invention, For the electric power consumed by the electric conversion device in the t period,/>Maximum electric power consumed by the electric conversion device;
Cogeneration unit:
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In the method, in the process of the invention, For the power generation of the cogeneration unit in the period t,/>Is the heating power in the period t,For the maximum power generation and the minimum power generation of the unit,/>For the gas consumption power of the unit in the period t,/>Respectively the power generation efficiency and the heating efficiency of the cogeneration unit,/>For the active output of the cogeneration unit in the t period,/>And/>The downhill speed and the uphill speed of the unit respectively.
5. The conditional risk value-based IES tunable capability assessment method according to claim 4, wherein the operating constraints of the energy storage device include:
An electricity storage device:
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In the method, in the process of the invention, The electric quantity stored by the electric storage equipment in the t period, the maximum electric storage capacity and the minimum electric storage capacity are respectively; /(I)The charging efficiency and the discharging efficiency of the electricity storage equipment are respectively; /(I)Respectively charging power and discharging power of the electricity storage equipment in a t period; /(I)Respectively the maximum charge power and the maximum discharge power allowed by the storage battery; /(I)Respectively represent two working states of the electricity storage equipment when in work, namely a charging state and a discharging state,Indicating that the electricity storage device is in a charged state,/>Indicating that the energy storage device is operating in a discharge state and that the energy storage device is only operating in one state; /(I)The storage capacity at the beginning and the end of the scheduling period respectively;
and (3) gas storage equipment:
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In the method, in the process of the invention, For the stored natural gas quantity at the time of the gas storage device t,/>Indicates the maximum natural gas amount which can be stored by the gas storage device,/>And/>Respectively the power of gas storage and gas injection of the gas storage device at the time t,/>And/>Respectively indicate the efficiency of gas storage and gas injection,/>And/>Respectively show two working states of the gas storage device at the time t, namely gas storage and gas injection states, and the gas storage device is in a gas storage state and a gas injection stateIndicating that the gas storage device works in a gas storage state,/>Indicating that the gas storage device works in a gas injection state,/>And/>Respectively representing the maximum gas storage and gas injection power of the gas storage device,/>And/>Respectively representing the stored natural gas quantity of the gas storage device at the beginning and the end of a dispatching cycle;
Heat storage device:
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；
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In the method, in the process of the invention, Representing the heat storage capacity of the heat storage device at the t period,/>Indicating the maximum heat storage capacity of the heat storage device,And/>The heat storage power and the heat release power of the heat storage device at the t period are respectively/>Respectively representing the heat storage efficiency and the heat release efficiency of the heat storage device,/>And/>Maximum heat storage power and heat release power of the heat storage device respectively,/>And/>Respectively representing two working states of the heat storage device, namely a heat storage state and a heat release state,/>, respectivelyIndicating that the heat storage device works in a heat storage state,/>Indicating that the heat storage device is working in a heat release state,/>Respectively representing the heat storage capacity of the heat reservoir at the beginning and end of the scheduling period.
6. The conditional risk value-based IES tunable ability assessment method according to claim 5, wherein the demand response constraints include:
translatable load:
；
；
In the method, in the process of the invention, Representing the translational state of the translatable load during the t period,/>Time represents load translation,/>Time indicates that the load is not translated,/>Indicating the start time,/>Representing a continuous run time of the translatable load;
load can be transferred:
；
；
In the method, in the process of the invention, Representing the transition amount of the transferable load at time t,/>And/>Representing the minimum and maximum values of transferable load power, respectively;
Load can be reduced:
；
；
In the method, in the process of the invention, Is a 0/1 variable, and represents a load shedding state in a certain period t of time,/>Indicating that the load can be cut down,/>Representation is not clipped,/>For load shedding factor at t period,/>，/>To cut down the power before load participation in scheduling,/>Is the maximum reduction number.
7. The conditional risk value-based IES tunable capability assessment method according to claim 6, wherein the power balancing constraints include:
the electrical bus power balance constraint expression:
；
The air bus power balance constraint expression:
；
Thermal bus power balance constraint expression:
；
In the method, in the process of the invention, For the electrical load at time t,/>For the gas load at time t,/>Is the thermal load at time t.
8. The conditional risk value-based IES tunable ability assessment method according to claim 1, wherein the total cost constraint comprises:
；
；
In the method, in the process of the invention, For the total cost,/>Is a preset cost,/>Is the total cost of purchasing IES,/>And/>Cost of wind and light abandon respectively,/>Is the total cost of the energy storage device,/>Is the carbon-emission cost,/>Responding to costs for demand.
9. The method for estimating the capability of IES adjustability based on conditional risk value according to claim 1, wherein the scene generation and reduction considering the uncertainty of wind-light output comprises:
based on historical wind power and photovoltaic output in n days, selecting a Gaussian kernel function to generate a probability density function of the wind power and photovoltaic output in each period of 24 hours based on a kernel density estimation method every other hour;
；
wherein: t represents 24 time periods; And/> Representing the output of wind power and photovoltaic in a t period; /(I)And/>Representing wind power and photovoltaic output at the time t of the day d; h represents bandwidth; /(I)Representing a gaussian kernel function;
；
solving the cumulative distribution function of wind power and photovoltaic according to probability density functions of wind power and photovoltaic And/>Establishing a combined distribution function of wind power and photovoltaic output in each period based on a Frank-Copula function;
；
Wherein: Is a two-dimensional Frank-Copula function, namely:
；
Wherein: ，/>，/> is a related parameter,/> And/>In/>Representation ofPositive correlation,/>Representation/>Negative correlation,/>Representation/>Tend to be independent;
Sampling the joint distribution function of each time period, and obtaining wind power and photovoltaic output of each time period corresponding to the accumulated probability by using a cubic spline interpolation method;
In the cumulative probability interval In, divide it into/>Between cells, and in any one of the intervalsAnd/>The cubic spline polynomial in this section is obtained by the cubic spline interpolation method using the cumulative probabilities u and v as independent variables and x and y as dependent variables, respectively, as follows:
；
Wherein: ，/> Coefficients during fitting;
Accumulating probability values for arbitrary samples And/>Will fall between certain cells/>AndIn the interior,/>，/>For the scale of the sampling, will/>And/>Substituting the data into the above data, the sampled wind power and photovoltaic output data of each period can be obtained;
By using Clustering pairs/>Clustering the group sampling results to generate/>And the wind-light output is typical, and the probability of each scene is calculated.
10. The conditional risk value-based IES tunable capability assessment method according to claim 1, wherein the solving the tunable capability model includes:
And solving the comprehensive energy system adjustable capacity range evaluation model based on a MATLAB+ Yalmip + Gurobi solver by using a mixed integer linear programming method, and drawing upper and lower limits of the comprehensive energy system on electric energy requirements at all moments to obtain a comprehensive energy system adjustable capacity range result.
11. An IES tunable ability assessment device based on conditional risk value, characterized by using the method according to any one of claims 1 to 10, comprising:
The modeling unit is used for establishing an integrated energy system IES adjustable capacity model considering load demand response, and the adjustable capacity model comprises energy coupling equipment and energy storage equipment;
The objective function unit is used for establishing a first objective function considering the maximum and minimum condition risk values of the exchange electric power of the IES and the upper-level power grid interconnection line at each moment and a second objective function taking the zero moment as a starting point, stopping the operation until each moment, and accumulating the maximum and minimum condition risk values of the electric quantity of the communication between the IES and the upper-level power grid interconnection line;
a constraint unit for establishing a digestion constraint taking IES into account, an operation constraint of the energy storage device, an operation constraint of the energy coupling device, a demand response constraint, a power balance constraint, and a total cost constraint;
the solving unit is used for solving the adjustable capacity model by considering scene generation and reduction of wind-light output uncertainty and generating a typical scene based on a clustering method, drawing the upper limit and the lower limit of the IES on the electric energy requirement and obtaining the adjustable capacity interval range of the IES.
12. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-10 when executing the computer program.
13. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-10.
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| CN116681294A (en) * | 2023-03-21 | 2023-09-01 | 长沙理工大学 | Comprehensive energy system capacity control method adopting improved WCVaR and adjustable risk preference |
| CN117910776A (en) * | 2024-02-06 | 2024-04-19 | 河海大学 | Comprehensive energy system adjustable capability assessment method, device, equipment and storage medium |
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| CN116681294A (en) * | 2023-03-21 | 2023-09-01 | 长沙理工大学 | Comprehensive energy system capacity control method adopting improved WCVaR and adjustable risk preference |
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