CN116109216A - Adjustability assessment method of source network storage system - Google Patents
Adjustability assessment method of source network storage system Download PDFInfo
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- 238000010521 absorption reaction Methods 0.000 claims description 9
- 239000003345 natural gas Substances 0.000 claims description 8
- 238000005057 refrigeration Methods 0.000 claims description 8
- 238000004146 energy storage Methods 0.000 claims description 7
- 238000010248 power generation Methods 0.000 claims description 7
- 230000009194 climbing Effects 0.000 claims description 6
- 238000005265 energy consumption Methods 0.000 claims description 6
- 230000003993 interaction Effects 0.000 claims description 5
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Abstract
The application relates to an adjustability evaluation method of a source network storage system, which comprises the following specific steps: s1: constructing a source network storage system structure model and establishing an energy balance relation; s2: establishing an objective function and constraint conditions according to the expected optimization targets, wherein the constraint conditions are required to meet the constraint conditions that a source network storage system purchases and sells power to a main power grid; s3: according to the system structure model, the objective function and the constraint condition, solving the adjustability potential and cost of the designated time period by using an MPC algorithm, and classifying the adjustability by considering the adjustability cost. According to the method, the actual characteristics of different flexible components are considered, the flexibility potential and the related cost of a designated time period are calculated, so that the source network storage system establishes sufficient flexibility reserve on the premise of meeting the use requirement of a user, and the power-assisted total power grid performs demand response scheduling.
Description
Technical Field
The application relates to the field of power grid planning, in particular to an adjustability evaluation method of a source network storage system.
Background
With the continuous increase of renewable energy share, the problem of adjustability of the power grid has attracted a great deal of attention in the research community. As the country gradually changes the energy structure, the adjustability of the power grid is gradually insufficient, and the demand side management becomes a promising adjustability option. This is because demand side management helps to achieve efficient energy utilization, mining user potential accurately, and distributed providing scalability. To provide scalability in the market, it is necessary to quantify it and to evaluate it with respect to costs.
Most of the existing adjustability researches are concentrated on a single system and a single building, interaction among a plurality of systems is lacked, the source of adjustability is single, and the adjustability is concentrated for a certain period of time, so that the achievement of quantification adjustability is difficult to put into application. There is no definition of adjustability or general method specified for different types of buildings and systems.
The source network storage system integrates renewable energy sources, energy storage facilities and a combined cooling, heating and power system, comprises a plurality of flexible components and is hopefully a stable adjustable source. However, the existing research on source network storage systems is mostly focused on energy consumption, economy and environment, and ignoring the potential of the source network storage systems in terms of adjustability.
Therefore, it is necessary to develop a research on the adjustability evaluation method of the source network storage system, and consider that a plurality of flexible components operate cooperatively in one source network storage system to obtain the maximum adjustability. The adjustability evaluation method for the source network storage system can be used for quantifying the adjustability and evaluating the cost, and can provide adjustability for the total power grid while considering the requirements of users.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method for evaluating adjustability of a source network storage system, by coordinating operation conditions of different components, so that the source network storage system has more adjustability reserves and lower adjustability cost on the premise of meeting the use requirement of a user, and both the user and the power grid requirement are considered.
In order to achieve the above purpose, the present application provides the following technical solutions:
the embodiment of the application provides an adjustability evaluation method of a source network storage system, which comprises the following specific steps:
s1: constructing a source network storage system structure model and establishing an energy balance relation;
s2: establishing an objective function and constraint conditions according to the expected optimization targets, wherein the constraint conditions are required to meet the constraint conditions that a source network storage system purchases and sells power to a main power grid;
s3: according to the system structure model, the objective function and the constraint condition, solving the adjustability potential and cost of the designated time period by using an MPC algorithm, and classifying the adjustability by considering the adjustability cost.
In the step S1, mathematical characteristics of the source network storage system model may be expressed as
in the formula 
Representation systemElectric output power of the component, ">
Representing consumption of grid power, < >>
Representing the power fed into the grid>
Representing the thermal output power of the system components.
The energy balance of the source network storage system in the step S1 includes electric energy balance and thermal energy balance, specifically:
the electric energy balance is as follows:
wherein ,
is->
Electric energy load of period, ">
Output power of gas turbine, photovoltaic power supply and wind turbine, respectively, +.>
Respectively, battery charge and discharge power,/->
For the purpose of heat pump energy consumption power,
for decision variables, representing the charge and discharge states,
the heat energy balance is as follows:
wherein the two formulas respectively represent heat energy balance constraint during refrigeration and heating,
is->
Thermal energy load of time period heating, +.>
Is->
Thermal energy load of time period refrigeration, +.>
Heat output during heating modes of heat pump, gas turbine and gas boiler respectively, +.>
Indicating the heat output and heat absorption during heating mode of the heat storage device, < >>
Heat output in cooling mode of heat pump and cooled absorber, respectively, < >>
Representing heat output and heat absorption in the cooling mode of the heat storage device>
For decision variables, the storage and output energy situation of the heat storage device is represented.
In the step S2, the source network storage system uses the minimized cost as a reference objective function:
wherein 
Consumption of grid power for reference mode,/->
Feeding grid power for reference mode,/->
And
natural gas consumption of gas turbine and gas boiler, respectively,/->
，/>
Electricity costs and supply benefits in units of yuan/kWh, respectively, +.>
Is the use cost of natural gas, and the unit is Yuan/mMiao>
For the number of steps in the prediction horizon, +.>
For the time step +.>
Is the total cost of the reference pattern.
The penalty term is added to the reference objective function in the step S2, and the adjustable objective function is obtained as follows
wherein ,
consumption of grid power for adjustable mode, +.>
Feeding grid power for reference mode,/->
Is the target power consumed or fed during the elastic interval, < >>
Is the total cost of the adjustable mode +.>
Representing the running cost of the battery,/->
and />
Respectively charging and discharging power.
An adjustment target of the adjustable target
The method has the following characteristics:
in the formula 
The maximum power of the heat pump and the gas turbine are respectively indicated.
The constraint conditions of the source network storage system in the step S2 are as follows:
the source network storage system-power network interaction constraint is to avoid the condition that the system purchases and sells power at the same time, and the specific constraint is that
in the formula 
Wind turbine, photovoltaic power supply, gas turbine and battery energy storage, respectively->
The unit of energy output in time interval is kWh,>
represents a suitable upper bound, +.>
As a binary variable, 1 when the source network storage system feeds power to the grid, the others are 0; />
Is->
Electric energy purchased from the grid by the time interval system in kWh @>
Is->
The electrical load of the time period system, with the unit of kWh,
the combined cooling, heating and power system is constrained as follows:
the switch and the start-stop of the combined heat and power system can meet the following constraint:
wherein 
，/>
For binary variables, the system is indicated separately +.>
Period and->
The time period running state is 1 when running, and the other conditions are 0; />
1 when the system is closed, and 0 in other cases; />
1 at system start-up, 0 in other cases,
to avoid frequent start/stop, the start/stop time constraint is
in the formula 
Indicating the time period of stay of the device in the run mode, start-up mode and shut-down mode, respectively, +.>
A 1 when the device is in the start-up mode, and other cases 0,
climbing constraint of combined heat and power generation system is as follows
in the formula 
Is->
Time period cooling and heating cogeneration system power, +.>
Are respectively->
And the power of the period of climbing up and down the cold and heat cogeneration system.
The specific steps of the step S3 for solving the adjustability potential and the related cost of the elastic time period by using the MPC algorithm are as follows:
solving an optimal control problem by using a reference objective function to obtain power consumption of the power grid in a reference mode
And feed-in power grid->
And the use of various flexible components, the solution representing the optimal load distribution of the system;
specifying elastic interval
Solving an optimal control problem by using an adjustable objective function to obtain power consumption of the power grid in an adjustable mode>
And feed-inPower grid->
And the use cases of various flexible components;
the computing system is in elastic interval
Quantifying the extent to which the period deviates from its reference operating state; the specific adjustability calculating method comprises the following steps: />
in the formula 
Indicating the number of adjustability in the elastic interval in kWh,/o>
Representing an adjustable cost; negative and positive tunability are defined as the ability of the system to increase/decrease power consumption/generation, respectively, compared to a reference case;
the adjustability provided by different adjustability components has different cost, and different flexibility targets can be set
Repeating the three steps for multiple times, and comparing the flexibility component used each time with the corresponding cost.
Compared with the prior art, the invention has the beneficial effects that: the system is suitable for a source network storage system and a micro-grid with various flexible components; the source network storage system with various flexible components can be subjected to adjustability and related cost evaluation, and an additional benefit evaluation method reference is provided for integrally developing source network storage integrated service in a park.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a source network storage system adjustability assessment method;
FIG. 2 is a diagram of a source network storage system energy framework;
fig. 3 is a source network storage system adjustability cost assessment curve.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, the source network storage system adjustability evaluation method provided by the invention comprises the following steps:
s1: constructing a source network storage system structure model and establishing an energy balance relation;
s2: establishing an objective function and constraint conditions according to the expected optimization targets, wherein the constraint conditions are required to meet the constraint conditions that the comprehensive system purchases and sells power to the total power grid;
s3: according to the system structure model, the objective function and the constraint condition, solving the adjustability potential and cost of the designated time period by using an MPC algorithm, and classifying the adjustability by considering the adjustability cost.
The energy framework in the source network storage system is shown in fig. 2. Including energy supply device photovoltaic Power (PV), wind Turbine (WT), hybrid Gas Turbine (GT), gas Boiler (GB), energy conversion device is Heat Pump (HP), absorption Chiller (AC), and energy storage device includes Battery (Battery), and heat storage device (TES).
Some preparation work is needed to be carried out in the construction of the source network storage system structure model, and the specific contents are as follows:
s10: and acquiring load parameters, equipment parameters and system parameters of the source network storage system.
S11: and constructing the system structure model according to the load parameters, the equipment parameters and the system parameters of the source network storage system.
The system model to be constructed for step S1 can be divided into a device model and a system structure model.
The mathematical characteristics of the system structure model are that
in the formula 
Representing the electrical output power of a system component +.>
Representing consumption of grid power, < >>
Representing the power fed into the grid>
Representing the thermal output power of the system components.
The device models can be divided into three types, namely an energy supply device model, an energy conversion device model and an energy storage device model.
The energy supply equipment comprises a photovoltaic power source (PV), a Wind Turbine (WT), a mixed Gas Turbine (GT) and a Gas Boiler (GB), which are greatly influenced by solar radiation, wind speed and load factors respectively, and the concrete model is as follows:
the photovoltaic power model is as follows:
in the formula 
For the photovoltaic installed capacity (kW), G is the solar radiation intensity (kW/m 2),>
is the solar radiation intensity under standard test conditions (+.>
），/>
Is the temperature coefficient (%/° C), -/-, etc.>
Is the photovoltaic surface temperature (°c) and +.>
PV temperature under standard test conditions 25 ℃, -A.sub.f>
Representing the power factor E [0,1 ]]，/>
Represents photovoltaic power generation, the unit is kW, +.>
Representation->
And the photovoltaic power of the source network storage system is accessed in a period of time, and the unit is kW.
Wind turbine model
in the formula 
Rated power (kW),>
indicating wind power efficiency->
Respectively represent wind speed, rated wind speed, cut-in wind speed and cut-out wind speed, < >>
Representing the power factor E [0,1 ]]，/>
Representation->
The wind power of the combined heat and power generation system is connected in time period, and the unit is kW +>
The wind power generation is expressed in kW.
The gas turbine model is
in the formula 
Is->
Time period gas turbine power generation,/->
Indicating the natural gas rate of combustion of the gas turbine, +.>
For the electrical efficiency factor>
Is->
Waste heat generated by the gas turbine during time period +.>
Is the gas turbine thermal efficiency factor.
The gas boiler model is
in the formula 
Representation->
Heat generation capacity of gas boiler in time period ∈>
Indicating the natural gas rate of combustion in the gas turbine,
is the heat efficiency factor of the gas boiler
The energy conversion device comprises a Heat Pump (HP) and an Absorption Chiller (AC), and the specific model is as follows:
the heat pump model is
in the formula 
Representation->
Heat generated by heat pump during time period refrigeration +.>
Is->
Heat pump energy consumption of period->
Is the electrothermal conversion coefficient in the refrigeration mode, +.>
Representation->
Heat generated by the heat pump during heating in time period +.>
Is the electrothermal conversion coefficient under the heating mode.
The absorption cooler model is
in the formula 
Representation->
Heat of cooling in time period->
Representation->
Heat of time period heating, ++>
Is the cold-hot conversion coefficient.
The energy storage device comprises a Battery (Battery), and a heat storage device (TES), and the specific model is as follows:
the battery model is
in the formula 
Is->
Energy in a time period battery->
Is->
Energy in the battery during time, +.>
，/>
Indicating battery charge-discharge power, < >>
Indicating charge and discharge efficiencyThe rate.
The model of the heat storage equipment is
in the formula 
Is->
Energy in a time-period heat storage device, +.>
Is->
Energy in a time-period heat storage device, +.>
、/>
Respectively indicate->
Heat stored and released by the thermal storage device during time periods +.>
Is the ratio of the energy loss and the energy consumption,
、/>
is the charge and discharge efficiency.
The energy balance may be classified into electric energy balance and thermal energy balance.
The electric energy balance is as follows:
wherein ,
is->
Electric energy load of period, ">
Output power of gas turbine, photovoltaic power supply and wind turbine, respectively, +.>
Respectively, battery charge and discharge power,/->
For the purpose of heat pump energy consumption power,
for decision variables, representing the charge and discharge states,
the heat energy balance is as follows:
wherein the two formulas respectively represent heat energy balance constraint during refrigeration and heating,
is->
Thermal energy load of time period heating, +.>
Is->
Thermal energy load of time period refrigeration, +.>
Heat output during heating modes of heat pump, gas turbine and gas boiler respectively, +.>
Indicating the heat output and heat absorption during heating mode of the heat storage device, < >>
Heat output in cooling mode of heat pump and cooled absorber, respectively, < >>
Representing heat output and heat absorption in the cooling mode of the heat storage device>
For decision variables, the storage and output energy situation of the heat storage device is represented.
In the step S2, the source network storage system uses the minimized cost as a reference objective function:
wherein 
Consumption of grid power for reference mode,/->
Feeding grid power for reference mode,/->
And
natural gas consumption of gas turbine and gas boiler, respectively,/->
，/>
Electricity costs and supply benefits in units of yuan/kWh, respectively, +.>
Is the use cost of natural gas, and the unit is Yuan/mMiao>
For the number of steps in the prediction horizon, +.>
For the time step +.>
Is the total cost of the reference pattern.
The penalty term is added to the reference objective function in the step S2, and the adjustable objective function is obtained as follows
wherein ,
consumption of grid power for adjustable mode, +.>
The grid power is fed for the reference mode,
is the target power consumed or fed during the elastic interval, < >>
Is the total cost of the adjustable mode +.>
Representing the running cost of the battery,/->
and />
Respectively charging and discharging power.
An adjustment target of the adjustable target
The method has the following characteristics:
in the formula 
The maximum power of the heat pump and the gas turbine are respectively indicated.
The constraint conditions in the step S2 comprise the interaction constraint of a source network storage system and a power network and the constraint of a combined cooling, heating and power system.
The source network storage system-power network interaction constraint is to avoid the condition that the system purchases and sells power at the same time, and the specific constraint is that
in the formula 
Wind turbine, photovoltaic power supply, gas turbine and battery energy storage, respectively->
The unit of energy output in time interval is kWh,>
represents a suitable upper bound, +.>
As a binary variable, 1 when the source network storage system feeds power to the grid, the others are 0; />
Is->
Electric energy purchased from the grid by the time interval system in kWh @>
Is->
The electrical load of the time period system, with the unit of kWh,
the combined cooling, heating and power system is constrained as follows:
the switch and the start-stop of the combined heat and power system can meet the following constraint:
wherein 
，/>
For binary variables, the system is indicated separately +.>
Period and->
The time period running state is 1 when running, and the other conditions are 0; />
1 when the system is closed, and 0 in other cases; />
1 at system start-up, 0 in other cases,
to avoid frequent start/stop, the start/stop time constraint is
in the formula 
Indicating the time period of stay of the device in the run mode, start-up mode and shut-down mode, respectively, +.>
A 1 when the device is in the start-up mode, and other cases 0,
climbing constraint of combined heat and power generation system is as follows
in the formula 
Is->
Time period cooling and heating cogeneration system power, +.>
Are respectively->
And the power of the period of climbing up and down the cold and heat cogeneration system.
According to the step S3, according to the system structure model, the objective function and the constraint condition, solving the adjustability potential and the cost of the designated time period by using an MPC algorithm, and classifying the adjustability by considering the adjustability cost. The method comprises the following specific steps:
s30: solving an optimal control problem by using a reference objective function to obtain power consumption of the power grid in a reference mode
And feed-in power grid->
And the use of various flexible components, the solution represents the optimal load distribution of the system.
S31: specifying elastic interval
Solving an optimal control problem by using an adjustable objective function to obtain power consumption of the power grid in an adjustable mode>
And feed-in power grid->
And the use of various flexible components.
S32: the computing system is in elastic interval
The degree to which the period deviates from its reference operating state. The specific adjustability calculating method comprises the following steps:
in the formula 
Indicating the number of adjustability in the elastic interval in kWh,/o>
Representing an adjustable cost; negative and positive tunability are defined as the ability of the system to increase/decrease power consumption/generation, respectively, compared to a reference case.
S33: the adjustability provided by different adjustability components has different cost, and different flexibility targets can be set
Repeating the three steps for multiple times, and comparing the flexibility component used each time with the corresponding cost.
The present application is further described below by reference to the accompanying drawings and Matlab simulation examples.
In Matlab simulation examples, the electrical load, thermal load and renewable energy influencing factors of the source network storage system can be generated by a load generator, and the relevant parameters of various devices in the source network storage system are shown in table 1:
table 1: source network storage system component parameter table
The prices of the consumed energy sources of the source network storage system are shown in table 2:
table 2: energy price of source network storage system
The calculation can be done for the same flexibility interval using different adjustability targets, since the system consists of different flexible components and their characteristics, the adjustable cost of each of these components is different, resulting in a piecewise linear adjustability function. This function can be expressed as a flexible cost curve, and fig. 3 shows an example of such an expression, where there are two adjustable cost curves, day and night, respectively.
In summary, it is shown that: according to the source network storage system adjustability assessment method, the source network storage system model can be established, and all system components can be coordinated and controlled to provide more adjustability for a power grid while considering user demands, so that the source network storage system adjustability and related cost can be accurately assessed.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (8)
1. The adjustability evaluation method of the source network storage system is characterized by comprising the following specific steps of:
s1: constructing a source network storage system structure model and establishing an energy balance relation;
s2: establishing an objective function and constraint conditions according to the expected optimization targets, wherein the constraint conditions are required to meet the constraint conditions that a source network storage system purchases and sells power to a main power grid;
s3: according to the system structure model, the objective function and the constraint condition, solving the adjustability potential and cost of the designated time period by using an MPC algorithm, and classifying the adjustability by considering the adjustability cost.
2. The method for evaluating the scalability of a source network storage system according to claim 1, wherein in said step S1, mathematical characteristics of a source network storage system model can be expressed as
in the formula 
Representing the electrical output power of a system component +.>
Representing consumption of grid power, < >>
Representing the power fed into the grid>
Representing the thermal output power of the system components.
3. The method for evaluating the adjustability of a source network storage system according to claim 1, wherein the energy balance of the source network storage system in step S1 includes electric energy balance and thermal energy balance, specifically:
the electric energy balance is as follows:
wherein ,
is->
Electric energy load of period, ">
Output power of gas turbine, photovoltaic power supply and wind turbine, respectively, +.>
Respectively, battery charge and discharge power,/->
For heat pump energy consumption, < >>
For decision variables, representing the charge and discharge states,
the heat energy balance is as follows:
wherein the two formulas respectively represent heat energy balance constraint during refrigeration and heating,
is->
The thermal energy load of the time period heating,
is->
Thermal energy load of time period refrigeration, +.>
Heat output during heating modes of heat pump, gas turbine and gas boiler respectively, +.>
Indicating the heat output and heat absorption during heating mode of the heat storage device, < >>
Heat output in cooling mode of heat pump and cooled absorber, respectively, < >>
Representing heat output and heat absorption in the cooling mode of the heat storage device>
For decision variables, the storage and output energy situation of the heat storage device is represented.
4. The method for evaluating the scalability of a source network storage system according to claim 1, wherein in step S2, the source network storage system uses a minimized cost as a reference objective function:
wherein 
Consumption of grid power for reference mode,/->
Feeding grid power for reference mode,/->
and />
Natural gas consumption of gas turbine and gas boiler, respectively,/->
，/>
Electricity costs and supply benefits in units of yuan/kWh, respectively, +.>
Is the use cost of natural gas, and the unit isMeta/m pattern, meta/m pattern>
For the number of steps in the prediction horizon, +.>
For the time step +.>
Is the total cost of the reference pattern.
5. The method for evaluating the adjustability of a source network storage system according to claim 4, wherein the penalty term is added to the reference objective function in step S2, and the obtained adjustability objective function is
wherein ,
consumption of grid power for adjustable mode, +.>
Feeding grid power for reference mode,/->
Is the target power consumed or fed during the elastic interval, < >>
Is the total cost of the adjustable mode +.>
Indicating the running cost of the battery,/>
and />
Respectively charging and discharging power.
6. The method for evaluating the adjustability of a source network storage system according to claim 5, wherein the adjustment target of the adjustability target is
The method has the following characteristics:
in the formula 
Respectively represents the maximum power of the heat pump and the gas turbine。
7. The method for evaluating the adjustability of a source network storage system according to claim 1, wherein the constraint conditions of the source network storage system in step S2 are as follows:
the source network storage system-power network interaction constraint is to avoid the condition that the system purchases and sells power at the same time, and the specific constraint is that
in the formula 
Energy storage for wind turbine, photovoltaic power supply, gas turbine and battery respectively
The unit of energy output in time interval is kWh,>
represents a suitable upper bound, +.>
As a binary variable, 1 when the source network storage system feeds power to the grid, the others are 0; />
Is->
Electric energy purchased from the grid by the time interval system in kWh @>
Is->
The electrical load of the time period system, with the unit of kWh,
the combined cooling, heating and power system is constrained as follows:
the switch and the start-stop of the combined heat and power system can meet the following constraint:
wherein 
，/>
For binary variables, the system is indicated separately +.>
Period and->
The time period running state is 1 when running, and the other conditions are 0; />
1 when the system is closed, and 0 in other cases; />
1 at system start-up, 0 in other cases,
to avoid frequent start/stop, the start/stop time constraint is
in the formula 
Indicating the time period of stay of the device in the run mode, start-up mode and shut-down mode, respectively, +.>
A 1 when the device is in the start-up mode, and other cases 0,
climbing constraint of combined heat and power generation system is as follows
in the formula 
Is->
Time period cooling and heating cogeneration system power, +.>
Are respectively->
And the power of the period of climbing up and down the cold and heat cogeneration system.
8. The method for evaluating the scalability of a source network storage system according to claim 1, wherein the specific steps of solving the scalability potential and the related costs of the elastic time period by using the MPC algorithm in step S3 are as follows:
solving an optimal control problem by using a reference objective function to obtain power consumption of the power grid in a reference mode
And feed-in power grid->
And the use of various flexible components, the solution representing the optimal load distribution of the system;
specifying elastic interval
Solving the optimal control problem by using an adjustable objective function to obtain an adjustable modelGrid consumption power under ∈ ->
And feed-in power grid->
And the use cases of various flexible components;
the computing system is in elastic interval
Quantifying the extent to which the period deviates from its reference operating state; the specific adjustability calculating method comprises the following steps:
in the formula 
Indicating the number of adjustability in the elastic interval in kWh,/o>
Representing an adjustable cost; negative and positive tunability are defined as the ability of the system to increase/decrease power consumption/generation, respectively, compared to a reference case;
the adjustability provided by different adjustability components has different cost, and different flexibility targets can be set
Repeating the three steps for multiple times, and comparing the flexibility component used each time with the corresponding cost. />
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