CN110707745B - Multi-time scale economic dispatching method for electric heating comprehensive system based on improved VMD - Google Patents

Abstract

The invention discloses an improved VMD-based multi-time scale economic dispatching method of an electric heating comprehensive system, which comprises the steps of firstly, improving the VMD into a self-adaptive decomposition method capable of being based on a specified center frequency; then, a multi-time scale scheduling model of the electric heating integrated system containing hybrid energy storage is built, the center frequency is determined according to the wind power long-term trend, the energy storage and the thermal partial response speed, and the improved VMD is adopted to carry out 3-layer decomposition on wind power signals; the layer 1 is matched with wind power long-term trend and used for making a set start-stop, a set and preliminary power output of an electric boiler, the layer 2 frequency is adapted to the response speed of an energy type energy storage device and used for determining a charging and discharging plan of the energy storage device and the adjustment quantity of the set and the electric boiler, and the layer 3 frequency is adapted to a power type energy storage device and used for making a charging and discharging plan of the energy storage device and then corrected through a daily and real-time scheduling model. The improved VMD can effectively reduce the air discarding quantity and the load shedding quantity.

Description

Multi-time scale economic dispatching method for electric heating comprehensive system based on improved VMD

Technical Field

The invention relates to an improved VMD-based multi-time scale economic dispatching method for an electric heating comprehensive system, and belongs to the field of wind power uncertainty analysis and new energy grid-connected dispatching.

Background

In recent years, with the increasing serious problems of environmental pollution and energy crisis, the rapid development of renewable energy sources represented by wind power is attracting attention, but the wind abandoning phenomenon is serious due to the characteristics of wind power volatility and randomness, and huge loss is brought to economy.

In the multi-time scale optimized scheduling of the electric heating comprehensive system, energy storage devices with different response speeds and an electric boiler can play a role in absorbing and discarding wind, and the electric boiler can work when the discarding wind is serious, so that the output of a cogeneration unit is reduced, and the wind power absorption space is improved; the energy storage device can transfer energy in a space-time range, can be charged when the wind abandoning is serious, can be discharged when the wind power is low, and can well stabilize fluctuation, so that the device can be widely operated. The energy storage devices are mainly divided into two main categories according to different response speeds: energy and power types. The energy type energy storage device has high energy density, but has low response speed, can not be charged and discharged frequently, and is mainly represented by storage battery energy storage and pumped storage; the power type energy storage device has high response speed and can be charged and discharged frequently, but has low energy density, and is mainly represented by super capacitor and superconducting energy storage. The wind power output power can be divided into a low frequency band, a medium frequency band and a high frequency band according to the frequency, and because the response speeds of the energy storage device, the power type energy storage device and the electric boiler are different, the energy storage device or the electric boiler with slow response speed can not timely consume the wind power high frequency band power, so that the research on the multi-time scale optimized scheduling of the electric heating comprehensive system for determining the power of the energy storage device and the electric boiler which are suitable for the frequency according to the wind power different frequency band power has practical significance. Because the energy-type energy storage device and the power-type energy storage device have complementary performance advantages, the prior researches mostly combine the energy-type energy storage device and the power-type energy storage device to stabilize wind power fluctuation, wherein the hybrid energy storage mode of a storage battery and a super capacitor is the most typical. The above research mainly focuses on the establishment of the hybrid energy storage device and the optimization scheduling in the system, and different response speeds of the hybrid energy storage device need to decompose wind power signals at different frequencies, so that it is particularly important to select a reasonable power signal decomposition method.

Therefore, a scholars propose various signal decomposition methods, such as a hybrid energy storage control strategy based on wavelet packet decomposition, the power signals are divided into low frequency, medium frequency and high frequency by wavelet packet decomposition and reconstructed power signals and are further distributed according to different response speeds of an energy storage device, although wavelet analysis has good time-frequency localization characteristics, the decomposition effect depends on the selection of a base function and a threshold value, the self-adaptability is poor, the wind power signal characteristics are complex, strong uncertainty exists, the selection of the base function and the threshold value is difficult by utilizing the wavelet decomposition wind power signal, and therefore the wavelet analysis is not suitable for wind power signal decomposition; if a learner combines the wind power grid-connected fluctuation standard and the performance characteristics of the hybrid energy storage system, the self-adaptive wavelet packet decomposition method is provided, but the wind power grid-connected fluctuation standard is only used as a constraint condition of a first layer of wavelet decomposition, and for the rest intermediate frequency and high frequency parts, the wind power signals in two frequency ranges are simply divided by the response speed demarcation points of the energy type and power type energy storage devices, so that the wind power frequency band cannot be accurately determined around the response frequency of the energy storage devices. Due to the poor adaptive nature of wavelet decomposition, scholars have proposed Empirical Mode Decomposition (EMD) methods that can be used to process nonlinear and non-stationary signals with the advantage of complete adaptation. A learner proposes a smooth wind power fluctuation method adopting Empirical Mode Decomposition (EMD), but the requirement of intermittent energy power fluctuation change is not fully considered, and certain mode aliasing phenomenon exists in the EMD. The energy storage system control method of fuzzy clustering empirical mode decomposition (EEMD) is applied by a learner, but EEMD has the problems of overlarge data calculation amount and the like. Wavelet decomposition, EMD and EEMD methods have the problems of poor adaptivity, modal aliasing and large calculation amount respectively. The Variational Modal Decomposition (VMD) is a new signal adaptive decomposition estimation method, and the VMD converts the signal into a non-recursive variational modal decomposition mode, which is essentially a plurality of adaptive wiener filter groups, and shows better noise robustness. Compared with the three decomposition methods, the VMD has good robustness and high operation efficiency, has a solid theoretical basis, and has excellent performance in the aspects of modal separation and reconstruction of similar frequency signals. In the wind power signal, the VMD may adaptively decompose the wind power signal into K frequency band signals. So that the scholars apply it to decomposing the power of wind power and photovoltaic in order to stabilize the wind power and photovoltaic volatility with hybrid energy storage devices. The learner adaptively carries out variation modal decomposition on the original photovoltaic power by combining the photovoltaic power fluctuation standard and the characteristics of the energy storage element, so that the primary power distribution is realized, but the photovoltaic power fluctuation standard is only used as the constraint condition of the VMD decomposition first layer to carry out K value adaptive selection, but the rest intermediate frequency signals and high frequency signals of the photovoltaic are equally divided by different response speed demarcation points of the energy storage device, and cannot be accurately matched with the response speed of the energy storage device, and the phenomenon that the energy storage device with low response speed cannot stabilize the photovoltaic fluctuation in time may exist. When the above proposed decomposition method is suitable for the hybrid energy storage device, the decomposed signals are divided into ranges by the dividing points of different response speeds of the energy storage device, and the decomposed signals are not determined around the central frequency suitable for the energy storage device. In the electric heating integrated system, because of the characteristics of wind power fluctuation and randomness, wind power needs to be adaptively decomposed according to the response frequency of the energy storage device and the electric boiler, so that the advantages of stabilizing wind power fluctuation of the energy storage device and the electric boiler can be better exerted.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides the multi-time scale economic dispatching method of the electric heating comprehensive system based on the improved VMD, wind power is decomposed into frequency bands which are suitable for the hybrid energy storage devices and the electric boilers with different response speeds, so that the power distribution of the hybrid energy storage devices and the electric boilers is formulated, and the air discarding quantity and the load shedding quantity can be effectively reduced.

The invention is realized by the following technical scheme: the multi-time scale economic dispatching method of the electric heating integrated system based on the improved VMD is characterized by comprising the following steps of:

step 1: modifying the VMD into an adaptive decomposition method capable of being based on a specified center frequency;

step 2: determining a center frequency according to wind power long-term trend, energy storage and thermal partial response speed, and adopting the VMD after improvement in the step 1 to perform 3-layer decomposition on wind power signals;

step 3: based on the improved VMD method of the step 1 and the wind power signal 3-layer decomposition result of the step 2, a multi-time scale scheduling model of the electric heating integrated system containing hybrid energy storage is established, and the output of a unit, an energy storage device and an electric boiler which are suitable for different frequency bands of wind power are formulated.

The step 1 comprises the following specific steps:

Step 1.1: the central frequency omega is set at the response speed of the energy storage device or other coupling devices of the electric heating system _k Set to a known quantity with the aim of knowing the centre frequency ω of each mode _k In this case, the sum of the estimated bandwidths of each modality is minimized, that is, the sparsity is minimized, and the objective function is:

wherein: { u _k }＝{u ₁ ,…,u _k -all sub-mode sets; { omega _k }＝{ω ₁ ,…,ω _k And the central frequency omega is a set of corresponding central frequencies _k Is a constant;

step 1.2: using a quadratic penalty factor alpha and Lagrange multiplicators lambda ^v (t) changing the constraint variation problem into an unconstrained variation problem, wherein alpha ensures the reconstruction accuracy of the signal, lambda ^v (t) maintaining the stringency of the constraints, the extended Lagrangian expression is as follows:

step 1.3: solving the above variation problem by alternately updating u by using the alternate direction multiplier method _k ⁿ⁺¹ And lambda (lambda) ^v,n+1 Finding the optimal point of expanding the Lagrangian expression, where u _k ⁿ⁺¹ The fourier equidistant transformation can be utilized to transform into the frequency domain:

step 1.4: transforming the formula in the step 1.3 to a frequency domain by utilizing Fourier equidistant transformation, and solving a solution of a secondary optimization problem:

wherein:

corresponding to the current residual quantity->

Wiener filtering of (2); for->

Performing inverse Fourier transform with the real part of { u }, then _k (t) } center frequency ω _k Is constant.

The multi-time scale scheduling model of the electric heating integrated system containing hybrid energy storage in the step 3 comprises the following steps:

step 3.1 day-ahead scheduling model: the day-ahead schedule is a schedule plan 24h before the predicted time, and mainly comprises layer 1 data P of wind power day-ahead ^D,W0,U1 The start and stop of a conventional unit, the conventional unit, a cogeneration unit and an electric boiler are formulated,

the cogeneration unit is set to be in a normally open state, and only the running cost of the cogeneration unit is considered, and the running cost of the cogeneration unit, the starting-up cost of the conventional unit, the running cost of the electric boiler and the abandoned wind cost are taken as objective functions, wherein the formula is as follows:

min F ^D ＝min(C _NC ^D +C _NG，s ^D +C _NG，p ^D +C _EB ^D +C _loss ^D )

wherein the method comprises the steps of

Wherein: f (F) ^D Expressed as total cost of the system before day, C _NC ^D Expressed as a daily heat and power cogeneration unit operation cost function, C _NG,s ^D Represented as a conventional unit start-up cost function, C _NG,p ^D Expressed as a function of the running cost of the conventional unit in the future, C _EB ^D Expressed as a daily electric boiler operating cost function, C _loss ^D Expressed as a wind curtailment cost function; a, a _i ^chp ，b _i ^chp ，c _i ^chp Expressed as the i-th cogeneration unit cost coefficient; a, a _i ，b _i ，c _i Expressed as the cost coefficient of the ith conventional unit; CS (circuit switching) _i,t The method is represented as a starting cost coefficient of an ith thermal power generating unit at a moment t; u (U) _i,t ^D The method is characterized in that the method is represented as a start-stop state of an ith thermal power unit before the day at the time t, wherein 1 is started, and 0 is stopped; p (P) _i,t ^D,chp ，P _i,t ^D ，P _i,t ^D,EB ，P _i,t ^D,W0,U1 And P _i,t ^D,W,U1 Respectively representing the output of the ith cogeneration unit, the output of the conventional unit, the output of the electric boiler, the active output of the wind power prediction first layer and the actual dispatching active output of the wind power first layer at the time t before the day; NC, NG, EB and NW are the numbers of cogeneration units, conventional units, electric boilers and wind farms, T is a scheduling period,

the constraint conditions include:

1) Electric power balance constraint

Wherein: p (P) _t ^D,L Expressed as total electrical load power at time t before day;

2) Thermal power balance condition

Wherein: q (Q) _i,t ^D,chp ，Q _i,t ^D,EB Denoted as the heat power generated by the i-th cogeneration unit and the electric boiler, Q _t ^D,L Expressed as the total heat load power at time t before day,

3) Unit output upper and lower limits and climbing constraint

P _i,min ^chp ≤P _i,t ^chp ≤P _i,max ^chp

U _i,t P _i,min ≤P _i,t ≤U _i,t P _i,max

-R _D,i ^chp Δt≤P _i,t ^chp -P _i,t-1 ^chp ≤R _U,i ^chp Δt

-R _D,i Δt≤P _i,t -P _i,t-1 ≤R _U,i Δt

Wherein: p (P) _i,min ^chp And P _i,max ^chp The lower limit and the upper limit of the electric power of the i-th cogeneration unit are respectively P _i,min And P _i,max The lower limit and the upper limit of the electric power of the ith conventional unit are respectively R _U,i ^chp And R is _D,i ^chp Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith cogeneration unit, R _U,i And R is _D,i Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith conventional unit,

4) Wind power output constraint

0≤P _i,t ^D,W,U1 ≤P _i,t ^D,W0,U1

5) Cogeneration electrothermal coupling constraint

Q _i,t ^chp ＝η _chp P _i,t ^chp

Q _i,min ^chp ≤Q _i,t ^chp ≤Q _i,max ^chp

Wherein: η (eta) _chp Representing the thermoelectric ratio of the cogeneration unit, the invention takes eta _i,chp ＝0.75，Q _i,min ^chp And Q _i,max ^chp The lower limit and the upper limit of the heat power of the i-th cogeneration unit are respectively,

6) Electric boiler restraint

Q _i,t ^EB ＝η _EB P _i,t ^EB

P _i,min ^EB ≤p _i,t ^EB ≤P _i,max ^EB

-R _D,i ^EB Δt≤P _i,t ^EB -P _i,t-1 ^EB ≤R _U,i ^EB Δt

Wherein: η (eta) _EB Representing the heat efficiency of the electric boiler, the invention takes eta _EB ＝0.98，P _i,min ^EB And P _i,max ^EB The lower limit and the upper limit of the electric power of the ith electric boiler are respectively R _U,i ^EB And R is _D,i ^EB Respectively representing the upper limit of the ascending and descending slopes of the electric power of the ith electric boiler;

step 3.2, rolling the correction model within days: the intra-day correction is a plan 4 h-15 min before the predicted point, and is mainly based on the decision result before the day and the first layer data P in the wind power generation day ^S,W0,U1 Formulating the output adjustment quantity of the unit and the electric boiler, and generating the second layer of wind power daily data P ^S,W0,U2 The energy-type electricity storage device charge-discharge plan, the unit and the electric boiler output adjustment quantity are manufactured,

taking the total cost of the system as an objective function, the formula is as follows:

minF ^S ＝min(C _NC ^S +C _NG，s ^S +C _NG，p ^S +C _EB ^S +C _loss ^S )

wherein the method comprises the steps of

Wherein: ΔP _i,t ^chp 、ΔP _i,t And DeltaP _i,t ^EB Expressed as electric power adjustment amounts of cogeneration units, conventional units and electric boilers,

the constraint conditions include:

1) Electric power balance constraint

Wherein: p (P) _t ^S,L Expressed as total electric load power at time t in day, P _t ^S,W Expressed as actual dispatch power of wind power in the day, P _i,t ^ESS,n Denoted as i-th energy storage device charging and discharging power, P _i,t ^ESS,n >0 represents charging, P _i,t ^ESS,n <0 represents a discharge and,

2) Thermal power balance condition

Wherein: ΔQ _i,t ^chp And DeltaQ _i,t ^EB Thermal power regulating quantity expressed as cogeneration unit electric boiler, Q _t ^S,L Expressed as total heat load power at time t in the day,

3) Unit and electric boiler output correction quantity constraint

Comprehensively considering the maximum climbing of the single scheduling period delta t and the upper and lower limits of the unit and the electric boiler output to formulate constraint, and expressing as:

4) Power storage device restraint

Wherein: u (U) _i,t ^ch And U _i,t ^dc Respectively representing a charging state and a discharging state of an ith power storage device at a moment t, wherein 1 is an operating state, and 0 represents an inactive state; p (P) _i,t ^ch And P _i,t ^dc Respectively representing the charging power and the discharging power of the ith power storage device at the moment t; p (P) _i,min ^ch And P _i,max ^ch Respectively represent a lower limit and an upper limit of charging power, P _i,min ^dc And P _i,max ^dc Respectively represent lower and upper discharge power limits, E _i,t ^ESS Expressed as capacity of the ith power storage device at time t, E _i,min ^ESS And E is _i,max ^ESS Respectively representing the lower limit and the upper limit of the capacity of the ith power storage device, beta _ch And beta _dc Respectively represent a charging coefficient and a discharging coefficient,

the wind power output constraint, the cogeneration electric heat coupling constraint and the electric boiler constraint are consistent with the step 3.1;

step 3.3, real-time correction of the model: the real-time correction plan is formulated 15min before the predicted point, and the wind power real-time second layer data P is used for ^F,W0,U2 The energy-type electricity storage device charge and discharge adjustment quantity, the unit and the electric boiler output adjustment quantity are manufactured. Then wind power is used for real-time third layer data P ^F,W0,U3 And (5) making a charge and discharge plan of the power type electricity storage device.

Real-time second layer data P by wind power ^F,W0,U2 The objective functions of the charge and discharge adjustment quantity of the energy type electricity storage device, the unit and the output adjustment quantity of the electric boiler are similar to the scheduling model in 3.2 days, so that the total cost of the system is minimum, and the wind power is real-time third-layer data P ^F,W0,U3 When a charge and discharge plan of the power type electric storage device is formulated, the aim is to make the maximum charge and discharge capacity of the power type electric storage device the minimum air rejection quantity and cut load quantity, namely the maximum charge and discharge capacity of the power type electric storage device is used as an aim function, and the formula is as follows: minF (minF) ^F ＝min[P ^F,W0,U3 (P ^dc -P ^ch )]

The constraint conditions include:

wind power real-time third layer power P ^F,W0,U3 Balance constraint

Wherein: p (P) _i,t ^F,W,U3 Expressed as real-time third-layer actual scheduling power of wind power, P _i,t ^ESS,g Denoted as i-th energy storage device charging and discharging power, P _i,t ^ESS,g >0 represents charging, P _i,t ^ESS,g <0 represents a discharge and,

the wind power output constraint, the cogeneration electric heat coupling constraint and the electric boiler constraint are consistent with the step 3.1.

The beneficial effects of the invention are as follows: the invention relates to an improved VMD-based multi-time scale economic dispatching method of an electric heating comprehensive system, which comprises the steps of firstly, improving the VMD into a self-adaptive decomposition method capable of being based on a specified center frequency; then, a multi-time scale scheduling model of the electric heating integrated system containing hybrid energy storage is built, the center frequency is determined according to the wind power long-term trend, the energy storage and the thermal partial response speed, and the improved VMD is adopted to carry out 3-layer decomposition on wind power signals; the layer 1 decomposition result is matched with the wind power long-term trend and is used for making a machine set start-stop, a machine set and an electric boiler preliminary output, the layer 2 decomposition result frequency is adapted to the response speed of the energy-type energy storage device and is used for determining a charging and discharging plan of the energy storage device and the output adjustment quantity of the machine set and the electric boiler, and the layer 3 decomposition result frequency is adapted to the power-type energy storage device and is used for making a charging and discharging plan of the energy-type energy storage device and is corrected through a daily and real-time scheduling model. The method can decompose wind power into frequency bands suitable for the hybrid energy storage devices and the electric boilers with different response speeds, so that power distribution of the hybrid energy storage devices and the electric boilers is formulated, and the air discarding quantity and the load shedding quantity can be effectively reduced.

Description of the drawings:

FIG. 1 is a schematic diagram of an original wind power signal;

FIG. 2 is a schematic diagram of VMD decomposition signals before wind power 3-layer improvement;

FIG. 3 is a schematic diagram of VMD decomposition signals after wind power 3-layer modification;

FIG. 4 is a graph showing the relationship between wind power frequency bands and devices with different response speeds in an electrothermal integrated system;

FIG. 5 is a flow chart of a multi-time scale rolling schedule for an electrothermal synthesis system that improves VMD decomposition;

FIG. 6 is a diagram of an example structure;

FIG. 7 is a Case1 and Case2 air reject versus cut load comparison;

FIG. 8 is a graph showing wind power U2 and battery charge and discharge conditions after wind power real-time layer 2 data are subjected to unit adjustment before a Case1 improves VMD and after a Case2 improves VMD;

FIG. 9 is a graph showing wind power real-time layer 3 data U3 and super capacitor charge and discharge conditions before Case1 improves VMD and after Case2 improves VMD;

FIG. 10 is a Case2 and Case3 air reject versus cut load comparison;

FIG. 11 shows charge and discharge conditions of the Case2 wind power U2 and a storage battery, the Case2 wind power U3 and a super capacitor, and the Case3 wind power after the wind power U2 and the U3 and the storage battery are regulated by a set;

fig. 12 is a Case2 and Case4 air reject volume versus cut load comparison.

The specific embodiment is as follows:

the invention is further explained below with reference to the drawings.

An improved VMD-based multi-time scale economic scheduling method for an electrothermal integrated system comprises the following steps:

Step 1: the VMD is improved into an adaptive decomposition method capable of being based on a specified center frequency, and the method comprises the following specific steps:

in the electric heating integrated system, the response speeds of the energy type energy storage device, the power type energy storage device and the electric boiler are different, so that the wind power frequency ranges suitable for the energy type energy storage device, the power type energy storage device and the electric boiler are different. The VMD decomposition is completely self-adaptive decomposition according to signal characteristics, and the central frequency of the decomposed signal is possibly not adaptive to the response speed of the equipment, so that the energy storage device with low response speed cannot absorb high-frequency band waste wind. Therefore, the invention provides an improvement for VMD decomposition, and uses the response frequency of an energy storage device or other coupling devices in an electrothermal system as a center frequency to enable the response frequency to be adaptively decomposed according to the signal characteristics on the basis of the specified center frequency.

Step 1.1: the central frequency omega is set at the response speed of the energy storage device or other coupling devices of the electric heating system _k Set to a known quantity with the aim of knowing the centre frequency ω of each mode _k In this case, the sum of the estimated bandwidths of each modality is minimized, that is, the sparsity is minimized, and the objective function is:

wherein: { u _k }＝{u ₁ ,…,u _k -all sub-mode sets; { omega _k }＝{ω ₁ ,…,ω _k And the central frequency omega is a set of corresponding central frequencies _k Is a constant;

step 1.2: using a quadratic penalty factor alpha and Lagrange multiplicators lambda ^v (t) changing the constraint variation problem into an unconstrained variation problem, wherein alpha ensures the reconstruction accuracy of the signal, lambda ^v (t) maintaining the stringency of the constraints, the extended Lagrangian expression is as follows:

step 1.3: solving the above variation problem by alternately updating u by using the alternate direction multiplier method _k ⁿ⁺¹ And lambda (lambda) ^v,n+1 Finding the optimal point of expanding the Lagrangian expression, where u _k ⁿ⁺¹ The fourier equidistant transformation can be utilized to transform into the frequency domain:

step 1.4: transforming the formula in the step 1.3 to a frequency domain by utilizing Fourier equidistant transformation, and solving a solution of a secondary optimization problem:

wherein:

corresponding to the current residual quantity->

Wiener filtering of (2); for->

Performing inverse Fourier transform with the real part of { u }, then _k (t) } center frequency ω _k Is constant.

Step 2: determining a center frequency according to wind power long-term trend, energy storage and thermal partial response speed, and adopting the VMD after improvement in the step 1 to carry out 3-layer decomposition on wind power signals, wherein the specific steps are as follows:

in the electric heating integrated system, the response frequency is determined according to the long-term trend of wind power, the difference of the response speed of the energy storage and the electric boiler, and is used as the center frequency, the improved VMD is adopted to carry out 3-layer decomposition on the wind power signal, and meanwhile, the improved VMD also corresponds to the low frequency band, the medium frequency band and the high frequency band of the wind power signal. The wind power signals are decomposed from the layer 1 and are mainly used for starting, stopping and outputting of the machine set, the layer 2 wind power signals are used for planning an energy storage device with slow response time and an electric boiler, and the third wind power signals are used for planning an energy storage device (super capacitor) with fast response time.

And decomposing the wind power signals by utilizing a VMD before improvement method and a VMD after improvement method respectively, wherein the data source is operation data disclosed by an Elia belgium electric operator, and the sampling resolution is 15min. The wind power data curve for the first 500 samples starting at 7.5.2019 is shown in fig. 1. The 3-layer decomposed signal obtained by VMD decomposition before modification is shown in fig. 2, and the 3-layer decomposed signal obtained by VMD decomposition after modification is shown in fig. 3.

The energy storage device response characteristics are compared as shown in table 1. The three-layer center frequencies were 4.1221e-4,0.0114 and 0.0416, respectively, from the pre-improvement VMD decomposition results of FIG. 2. The time constant reflected by the first layer center frequency 4.1221e-4 of the wind power signal and the second layer center frequency 0.0114 of the wind power signal before improvement is smaller than 60min, and the time constant reflected by the second layer center frequency is smaller than 10min, so that the wind power signal is decomposed, although the wind power signal reflects the minimum estimated bandwidth of all modes, the wind power signal cannot be well adapted to the time constant of the energy storage device, and the wind power fluctuation cannot be stabilized by the energy storage device in time.

Table 1 analysis and comparison of energy storage device characteristics

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In FIG. 3, the wind power center frequencies of the first layer, the second layer and the third layer of wind power are respectively set to be 2.78e-6,5e-4 and 0.05 through VMD decomposition after modification. The wind power signal decomposed in this way is suitable for the long-term trend of wind power, energy type energy storage and power type energy storage response time constants. The improved VMD method is not only suitable for the response speed of the energy storage device and the electric boiler in the electric heating integrated system, but also suitable for other response speed equipment in the energy internet, the response frequency of the energy storage device can be determined according to the response speed of other types of energy storage devices, and the wind power is decomposed into frequency bands corresponding to the center frequency as the center frequency so as to adapt to the response speed of the equipment, so that the improved VMD method has universality and adaptability. FIG. 4 shows the relationship between the wind power frequency band and the devices with different response speeds in the electrothermal integrated system.

Different devices in the electric heating system have different response speeds, the response speeds of the super capacitor, the superconducting energy storage and the flywheel energy storage are high, the storage battery, the pumped storage and the electric boiler are secondary, the conventional unit and the thermoelectric unit are secondary, the devices with different response speeds form a system, and wind power change frequency ranges capable of following are different, so that wind power is required to be decomposed at a specified center frequency by utilizing the improved VMD according to the response speed. As shown in FIG. 4, when the wind power frequency is between (0, 6.94 e-4), the frequency range corresponds to wind power long-term trend components and is used for determining the center frequency of the improved VMD decomposition layer 1, and the frequency range can be used for making a start-stop plan and a preliminary output of a conventional unit and a thermal motor unit; when the wind power frequency is between (6.94 e-4,4.16 e-4), the frequency range corresponds to wind power fluctuation components and is used for determining the center frequency of the layer 2 of the VMD decomposition after improvement, and a storage battery, a pumped storage and an electric boiler with low response speed stabilize wind power fluctuation in the frequency range; when the wind power frequency is between (4.16 e-4, ++), the frequency band corresponds to wind power random components and is used for determining the improved VMD decomposition layer 3 center frequency, the frequency band is a high frequency band, and the influence of wind power randomness is reduced by the super capacitor, the superconducting energy storage and the flywheel energy storage with high response speed.

Step 3: based on the improved VMD method of the step 1 and the wind power signal 3-layer decomposition result of the step 2, a multi-time scale scheduling model of the electric heating integrated system containing hybrid energy storage is established, and the output of a unit, an energy storage device and an electric boiler which are suitable for different frequency bands of wind power are formulated. The method comprises the following specific steps:

the scheduling model of the invention is divided into 3 time scales: day-ahead scheduling model ([ T, t+T)]) Intra-day rolling correction model ([ t.t +16)]) And real-time correction model ([ t, t+1)]) 1 scheduling period in 15 min. The first part firstly uses the improved VMD decomposition provided by the invention to predict the power data P of wind power day ahead ^D,W0 Divided into 3 layers, and the wind power day-ahead data P of 1 layer ^D,W0,U1 Making a unit start-stop, a unit and an electric boiler preliminary output; second part predicts the wind power within the day power data P by improving VMD decomposition ^{S ,W0} Divided into 3 layers, and data P of 1 layer in wind power day is used ^S,W0,U1 Formulating the output adjustment quantity of a unit and an electric boiler, and generating layer 2 data P in the wind power day ^S,W0,U2 The method comprises the steps of preparing a charge-discharge plan of an energy type electricity storage device, a unit and an electric boiler output adjustment quantity; third part predicts the wind power in real time by improving VMD decomposition ^F,W0 Divided into 3 layers, and wind power is used for generating real-time layer 2 data P ^F,W0,U2 The charge and discharge regulating variable of the energy type electricity storage device, the set and the output regulating variable of the electric boiler are prepared, and the wind power is used for generating real-time layer 3 data P ^F,W0,U3 And (5) making a charge and discharge plan of the power type electricity storage device. Fig. 5 shows the overall flow of a multi-time scale rolling schedule.

Step 3.1 day-ahead scheduling model: the day-ahead schedule is a schedule plan 24h before the predicted time, and mainly comprises layer 1 data P of wind power day-ahead ^D,W0,U1 The start and stop of a conventional unit, the conventional unit, a cogeneration unit and an electric boiler are formulated,

the cogeneration unit is set to be in a normally open state, only the running cost of the cogeneration unit is considered, the running cost of the cogeneration unit, the starting cost of a conventional unit, the running cost of the conventional unit, the running cost of an electric boiler and the abandoned wind cost are taken as objective functions, and the formula is as follows:

min F ^D ＝min(C _NC ^D +C _NG，s ^D +C _NG，p ^D +C _EB ^D +C _loss ^D )

wherein the method comprises the steps of

Wherein: f (F) ^D Expressed as total cost of the system before day, C _NC ^D Expressed as a daily heat and power cogeneration unit operation cost function, C _NG,s ^D Represented as a conventional unit start-up cost function, C _NG,p ^D Expressed as a function of the running cost of the conventional unit in the future, C _EB ^D Expressed as a daily electric boiler operating cost function, C _loss ^D Expressed as a wind curtailment cost function; a, a _i ^chp ，b _i ^chp ，c _i ^chp Expressed as the i-th cogeneration unit cost coefficient; a, a _i ，b _i ，c _i Expressed as the cost coefficient of the ith conventional unit; CS (circuit switching) _i,t The method is represented as a starting cost coefficient of an ith thermal power generating unit at a moment t; u (U) _i,t ^D The method is characterized in that the method is represented as a start-stop state of an ith thermal power unit before the day at the time t, wherein 1 is started, and 0 is stopped; p (P) _i,t ^D,chp ，P _i,t ^D ，P _i,t ^D,EB ，P _i,t ^D,W0,U1 And P _i,t ^D,W,U1 Respectively representing the output of the ith cogeneration unit, the output of the conventional unit, the output of the electric boiler, the active output of the wind power prediction first layer and the actual dispatching active output of the wind power first layer at the time t before the day; NC, NG, EB and NW are the numbers of cogeneration units, conventional units, electric boilers and wind farms, and T is a scheduling period.

The constraint conditions include:

1) Electric power balance constraint

Wherein: p (P) _t ^D,L Expressed as total electronegativity at time t before dayLoad power.

2) Thermal power balance condition

Wherein: q (Q) _i,t ^D,chp ，Q _i,t ^D,EB Denoted as the heat power generated by the i-th cogeneration unit and the electric boiler, Q _t ^D,L Expressed as total heat load power at time t before day.

3) Unit output upper and lower limits and climbing constraint

P _i,min ^chp ≤P _i,t ^chp ≤P _i,max ^chp

U _i,t P _i,min ≤P _i,t ≤U _i,t P _i,max

-R _D,i ^chp Δt≤P _i,t ^chp -P _i,t-1 ^chp ≤R _U,i ^chp Δt

-R _D,i Δt≤P _i,t -P _i,t-1 ≤R _U,i Δt

Wherein: p (P) _i,min ^chp And P _i,max ^chp The lower limit and the upper limit of the electric power of the i-th cogeneration unit are respectively P _i,min And P _i,max The lower limit and the upper limit of the electric power of the ith conventional unit are respectively R _U,i ^chp And R is _D,i ^chp Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith cogeneration unit, R _U,i And R is _D,i Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith conventional unit.

4) Wind power output constraint

0≤P _i,t ^D,W,U1 ≤P _i,t ^D,W0,U1

5) Cogeneration electrothermal coupling constraint

Q _i,t ^chp ＝η _chp P _i,t ^chp

Q _i,min ^chp ≤Q _i,t ^chp ≤Q _i,max ^chp

Wherein: η (eta) _chp Representing the thermoelectric ratio of the cogeneration unit, the invention takes eta _i,chp ＝0.75，Q _i,min ^chp And Q _i,max ^chp The lower limit and the upper limit of the thermal power of the i-th cogeneration unit are respectively.

6) Electric boiler restraint

Q _i,t ^EB ＝η _EB P _i,t ^EB

P _i,min ^EB ≤p _i,t ^EB ≤P _i,max ^EB

-R _D,i ^EB Δt≤P _i,t ^EB -P _i,t-1 ^EB ≤R _U,i ^EB Δt

Wherein: η (eta) _EB Representing the heat efficiency of the electric boiler, the invention takes eta _EB ＝0.98，P _i,min ^EB And P _i,max ^EB The lower limit and the upper limit of the electric power of the ith electric boiler are respectively R _U,i ^EB And R is _D,i ^EB Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith electric boiler.

Step 3.2, rolling the correction model within days: the intra-day correction is a plan 4 h-15 min before the predicted point, and is mainly based on the decision result before the day and the first layer data P in the wind power generation day ^S,W0,U1 Formulating the output adjustment quantity of the unit and the electric boiler, and generating the second layer of wind power daily data P ^S,W0,U2 The energy-type electricity storage device charge-discharge plan, the unit and the electric boiler output adjustment quantity are manufactured,

taking the total cost of the system as an objective function, the formula is as follows:

min F ^S ＝min(C _NC ^S +C _NG，s ^S +C _NG，p ^S +C _EB ^S +C _loss ^S )

wherein the method comprises the steps of

Wherein: ΔP _i,t ^chp 、ΔP _i,t And DeltaP _i,t ^EB The electric power adjustment amounts expressed as a cogeneration unit, a conventional unit, and an electric boiler, and the constraint conditions include:

1) Electric power balance constraint

Wherein: p (P) _t ^S,L Expressed as total electric load power at time t in day, P _t ^S,W Expressed as actual dispatch power of wind power in the day, P _i,t ^ESS,n Denoted as i-th energy storage device charging and discharging power, P _i,t ^ESS,n >0 represents charging, P _i,t ^ESS,n <0 represents discharge.

2) Thermal power balance condition

/>

Wherein: ΔQ _i,t ^chp And DeltaQ _i,t ^EB Thermal power regulating quantity expressed as cogeneration unit electric boiler, Q _t ^S,L Expressed as total heat load power at time t in day

3) Unit and electric boiler output correction quantity constraint

Comprehensively considering the maximum climbing of the single scheduling period delta t and the upper and lower limits of the unit and the electric boiler output to formulate constraint, and expressing as:

4) Power storage device restraint

Wherein: u (U) _i,t ^ch And U _i,t ^dc Respectively representing a charging state and a discharging state of an ith power storage device at a moment t, wherein 1 is an operating state, and 0 represents an inactive state; p (P) _i,t ^ch And P _i,t ^dc Respectively representing the charging power and the discharging power of the ith power storage device at the moment t; p (P) _i,min ^ch And P _i,max ^ch Respectively represent a lower limit and an upper limit of charging power, P _i,min ^dc And P _i,max ^dc Respectively represent lower and upper discharge power limits, E _i,t ^ESS Expressed as capacity of the ith power storage device at time t, E _i,min ^ESS And E is _i,max ^ESS Respectively representing the lower limit and the upper limit of the capacity of the ith power storage device, beta _ch And beta _dc Respectively representing the charge coefficient and the discharge coefficient.

The wind power output constraint, the cogeneration electric heat coupling constraint and the electric boiler constraint are consistent with the step 3.1;

step 3.3, real-time correction of the model: the real-time correction plan is formulated 15min before the predicted point, and the wind power real-time second layer data P is used for ^F,W0,U2 The energy-type electricity storage device charge and discharge adjustment quantity, the unit and the electric boiler output adjustment quantity are manufactured. Then wind power is used for real-time third layer data P ^F,W0,U3 And (5) making a charge and discharge plan of the power type electricity storage device.

Real-time second layer data P by wind power ^F,W0,U2 And the objective functions of the charge and discharge adjustment quantity of the energy type electricity storage device, the unit and the output adjustment quantity of the electric boiler are similar to a scheduling model in 3.2 days, so that the total cost of the system is minimum. Wind power real-time third layer data P ^F,W0,U3 When a charge and discharge plan of the power type electric storage device is formulated, the aim is to make the maximum charge and discharge capacity of the power type electric storage device the minimum air rejection quantity and cut load quantity, namely the maximum charge and discharge capacity of the power type electric storage device is used as an aim function, and the formula is as follows: min F ^F ＝min[P ^F,W0,U3 (P ^dc -P ^ch )]

The constraint conditions include:

wind power real-time third layer power P ^F,W0,U3 Balance constraint

Wherein: p (P) _i,t ^F,W,U3 Expressed as real-time third-layer actual scheduling power of wind power, P _i,t ^ESS,g Denoted as i-th energy storage device charging and discharging power, P _i,t ^ESS,g >0 represents charging, P _i,t ^ESS,g <0 represents discharge. .

The wind power output constraint, the cogeneration electric heat coupling constraint and the electric boiler constraint are consistent with the step 3.1.

The invention selects and verifies the modified IEEE-39 node system, and comprises 2 cogeneration units, 8 thermal power units, 1 electric boiler, 1 wind power plant, 1 energy type electricity storage device and 1 power type electricity storage device. The energy type electricity storage device adopts a storage battery, and the power type electricity storage device adopts a super capacitor. Wind farm data was from the 2019 8-7 operating data published by Elia wind farm in belgium and normalized to 2000MW in installed capacity. The electrical storage device parameters are shown in table 2. The energy storage device is connected to the node 38, and the power storage device is connected to the node 39. The example structure is shown in fig. 6.

Table 2 parameters of the power store

In order to illustrate the influence of the matched operation of the VMD decomposition method and the response speed of the hybrid energy storage device on the air discarding quantity and the cut load quantity, the influence of the single energy storage device and the hybrid energy storage device on the air discarding quantity and the cut load quantity and the influence of the electric boiler on the energy storage capacity in the electric heating integrated system are improved, the following 4 cases are set:

1) Case1: the scheduling model consists of a day-ahead, day-in and real-time scheduling model by adopting a VMD decomposition method before improvement. The electric storage device is a storage battery and a super capacitor, and the system is not provided with an electric boiler. And (3) making a set start-stop and set and electric boiler preliminary output by the first layer of data of the wind power, making a storage battery charge-discharge plan and set and electric boiler adjustment quantity by the second layer of data of the wind power, making a super capacitor charge-discharge plan by the third layer of data of the wind power, and correcting by a daily and real-time scheduling model.

2) Case2: the scheduling model is composed of a day-ahead, day-in and real-time scheduling model by adopting an improved VMD decomposition method. The electricity storage device consists of a storage battery and a super capacitor, and the system is not provided with an electric boiler. The scheduling plan is the same as Case1.

3) Case3: the scheduling model consists of a day-ahead scheduling model and a day-ahead scheduling model by adopting an improved VMD decomposition method. The electric storage device is a single energy type electric storage device (storage battery), and the system is not provided with an electric boiler. And (3) making a set start-stop and set and electric boiler preliminary output by the first layer of wind power, making a storage battery charge-discharge plan and a set and electric boiler adjustment by the second layer of wind power data and the third layer of wind power data, and correcting by a daily model.

4) Case4: the scheduling model is composed of a day-ahead, day-in and real-time scheduling model by adopting an improved VMD decomposition method. The electricity storage device consists of a storage battery and a super capacitor, and the system is provided with an electric boiler. The scheduling plan is the same as Case1.

Case1 and Case2 analysis:

and comparing Case1 with Case2, and comparing the influence of wind power decomposition power and energy storage devices with different response speeds on the air discarding quantity and the cut load quantity before and after the electric heating system improves the VMD decomposition method. The Case1 and Case2 air reject volumes and cut loads are shown in fig. 7. Where positive values represent the amount of air rejected and negative values represent the amount of cut load. The wind power U2 and the charge and discharge conditions of the storage battery after the wind power real-time layer 2 data are regulated by the machine set before the VMD is improved by the Case1 and after the VMD is improved by the Case2 are shown in fig. 8. The real-time layer 3 data U3 of wind power and the charging and discharging conditions of the super capacitor before improving the VMD by Case1 and after improving the VMD by Case2 are shown in figure 9.

As shown in fig. 7, the sum of the system air rejection and cut load was reduced from 5525.5MW to 448.3MW by 91.8% using the modified VMD method. The air discarding quantity is reduced to 214.1MW from 5512.7MW, and the cut load quantity is increased to 234.1MW from 1.81 MW. When the VMD before improvement is adopted to decompose wind power signals, the central frequencies of three layers are 3.1685e-4, 0.0118 and 0.0314 respectively. The time constant reflected by the first layer center frequency 3.1685e-4 of the wind power signal decomposed by the VMD before improvement is smaller than 60min, and the time constant reflected by the second layer center frequency 0.0118 is smaller than 10min. And the explanation decomposes the wind power of the medium frequency band contained in the layer 1 data, and the wind power of the high frequency band contained in the second layer data. From fig. 8, it can be seen that, before improvement, the VMD decomposes the wind power real-time layer 2 data, the wind discarding phenomenon of the wind power U2 after the set adjustment occurs in the periods of 1-6h and 12-24h, the wind discarding power exceeds 500MW in most periods, and the load shedding situation occurs only once. The VMD decomposes the wind power high-frequency band signal contained in the wind power second layer data before improvement, the wind power medium-frequency band power contained in the wind power 1 layer data before improvement, the VMD decomposes the wind power fluctuation which is not suitable for the response speed of the storage battery and cannot be accurately reflected by the layer 2 data before improvement, the wind power fluctuation is not suitable for the response speed of the storage battery, and the storage battery can only consume a small part of waste wind charge and lack of load shedding period discharge, so that the storage battery cannot consume the waste wind of the frequency band. The VMD decomposes wind power real-time layer 2 data after improvement, the wind abandon exists in 1-5h, the cut load exists in 7-14h, the decomposed wind power can be adapted to the response speed of the storage battery, the storage battery is charged during wind abandon, and the discharge can reduce the wind abandon quantity and the cut load quantity to the maximum extent during the cut load. As can be seen from the graph 9, the wind power signals of the 3 rd layer of wind power decomposed by the two methods are suitable for the response speed of the super capacitor, so that the super capacitor can well reduce the wind discarding quantity and the cut load quantity of the frequency band. Therefore, in the electric heating system, the wind power frequency band decomposed by the VMD after improvement can be adapted to the response speed of the storage battery and the super capacitor, and the air discarding quantity and the load shedding quantity are reduced to the greatest extent.

Case2 and Case3 analysis:

and comparing Case2 with Case3, decomposing wind power by the VMD after improvement, and comparing the influence of the electric heating system on the air discarding quantity and the load shedding quantity under the single power storage device and the mixed power storage device. The Case2 and Case3 reject volumes and reject loads are shown in fig. 10. Where positive values represent the amount of air rejected and negative values represent the amount of cut load. The wind power U2 and the storage battery charge and discharge after the real-time layer 2 data of the Case2 wind power are adjusted by the machine set, the wind power U3 and the super capacitor charge and discharge after the real-time layer 3 data of the Case2 wind power are adjusted by the machine set, and the wind power U2, the wind power U3 and the storage battery charge and discharge after the Case3 wind power is adjusted by the machine set are shown in fig. 11.

As shown in fig. 10, after the hybrid power storage device is utilized, the sum of the system air rejection and the cut load is reduced from 1229.5MW to 448.3MW, which is reduced by 63.5%. The air discarding quantity is reduced from 635.2MW to 214.1MW, and 66.2%. The reject load was reduced from 594.2MW to 234.1MW, by 60.6%. The air discarding quantity and the load cutting quantity are greatly reduced. In fig. 11, when t=3, after charging by the hybrid power storage device, the air discarding amount is reduced from 140.4MW to 0MW, so that it can be seen that the single power storage device is insufficient to dissipate the air discarding amount, and after the wind power is divided into the intermediate frequency and the high frequency by the hybrid power storage device, the wind power with different frequencies can be timely consumed by the power storage devices with different response speeds, and the super capacitor in the figure can consume the wind power with the high frequency band at this time when t=3. As another example, at t=10, the cut load was reduced from 165.8MW to 71MW after discharging through the hybrid power storage device. Therefore, the single energy type electricity storage device is insufficient to compensate wind power fluctuation in discharging, the mixed electricity storage device is in a wind power medium frequency band, the storage battery completely compensates wind power fluctuation amount, and the super capacitor can be charged and discharged in time to compensate wind power shortage when in a wind power high frequency band. Therefore, in the electric heating system, when wind power is decomposed into frequency bands suitable for the power storage devices with different response speeds, the hybrid power storage device can reduce the system air discarding quantity and the cut load quantity more than the single power storage device.

Case2 and Case4 analysis:

comparison of Case2 and Case4 compares the effect on the capacity of the hybrid electric storage device after the electric boiler is added to the electric heating system. The Case2 and Case4 air reject volumes and cut loads are shown in fig. 12. The capacity of the storage battery in Case2 is 2000MW, and the capacity of the super capacitor is 500MW; the capacity of the storage battery in Case4 is 1800MW, and the capacity of the super capacitor is 500MW.

As shown in FIG. 12, after the electric boiler is added, the total sum of the system air discarding quantity and the cut load quantity is reduced from 448.3MW to 145.2MW, and the total sum is reduced by 67.6%. As can be seen from FIG. 12, after the electric boiler is added, the original 1-3h of abandoned wind can be consumed, so that the heat load is provided for the system, the output of the cogeneration unit is reduced, and the wind power consumption space is increased.

And due to the addition of the electric boiler, the capacity of the energy type electricity storage device is reduced from 2000MW to 1800MW. Therefore, in the electric heating system, after the electric boiler is added, not only the air discarding quantity, the load cutting quantity and the total running cost of the system can be reduced, but also the capacity of the electric storage device can be reduced.

To analyze the differences between the total cost of the system and the air volume and the cut load in cases 1 to 4, the total cost of the system and the sum of the air volume and the cut load in cases 1 to 4 are shown in table 3.

TABLE 3 Total cost of Case1 to Case4 systems

From Case1 and Case2, it can be seen that the wind power signal decomposed by wind power after VMD improvement can be adapted to the response speed of the energy storage device, so that the wind-discarding capacity of the energy storage device can be brought into play to a greater extent, and the system cost is reduced. As can be seen from Case2 and Case3, the hybrid energy storage not only reduces the amount of waste air and cut load, but also reduces the cost of system operation compared to a single energy storage device. It can be seen from Case3 and Case4 that after the electric boiler is introduced into the electric heating system, the output of the cogeneration unit can be reduced, the wind power absorption space is increased, the air discarding quantity and the load shedding quantity are reduced, and the system cost and the capacity of the energy storage device can be reduced.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (2)

1. The multi-time scale economic dispatching method of the electric heating integrated system based on the improved VMD is characterized by comprising the following steps of:

step 1: modifying the VMD into an adaptive decomposition method capable of being based on a specified center frequency;

Step 2: determining a center frequency according to wind power long-term trend, energy storage and thermal partial response speed, and adopting the VMD after improvement in the step 1 to perform 3-layer decomposition on wind power signals;

step 3: based on the improved VMD method of the step 1 and the wind power signal 3-layer decomposition result of the step 2, a multi-time scale scheduling model of an electric heating integrated system containing hybrid energy storage is established, and the output of a unit, an energy storage device and an electric boiler which are suitable for different frequency bands of wind power are formulated;

the step 1 comprises the following specific steps:

step 1.1: the central frequency omega is set according to the response speed of an energy storage device, a conventional unit, a cogeneration unit or an electric boiler _k Set to a known quantity with the aim of knowing the centre frequency ω of each mode _k In this case, the sum of the estimated bandwidths of each modality is minimized, that is, the sparsity is minimized, and the objective function is:

wherein: { u _k }＝{u ₁ ,…,u _k -all sub-mode sets; { omega _k }＝{ω ₁ ,…,ω _k And the central frequency omega is a set of corresponding central frequencies _k Is a constant;

step 1.2: using a quadratic penalty factor alpha and Lagrange multiplicators lambda ^v (t) changing the constraint variation problem into an unconstrained variation problem, wherein alpha ensures the reconstruction accuracy of the signal, lambda ^v (t) maintaining the stringency of the constraints, the extended Lagrangian expression is as follows:

Step 1.3: solving the above variation problem by alternately updating u by using the alternate direction multiplier method _k ⁿ⁺¹ And lambda (lambda) ^v,n+1 Finding the optimal point of expanding the Lagrangian expression, where u _k ⁿ⁺¹ The fourier equidistant transformation can be utilized to transform into the frequency domain:

step 1.4: transforming the formula in the step 1.3 to a frequency domain by utilizing Fourier equidistant transformation, and solving a solution of a secondary optimization problem:

wherein:

corresponding to the current residual quantity->

Wiener filtering of (2); for->

Performing inverse Fourier transform with the real part of { u }, then _k (t) } center frequency ω _k Is constant.

2. The improved VMD-based multi-time scale economic dispatch method of the electric heating integrated system of claim 1, wherein the multi-time scale dispatch model of the electric heating integrated system with hybrid energy storage in step 3 comprises the following steps:

step 3.1 day-ahead scheduling model: the day-ahead schedule is a schedule plan 24h before the predicted time, and mainly comprises layer 1 data P of wind power day-ahead ^D,W0,U1 The start and stop of a conventional unit, the conventional unit, a cogeneration unit and an electric boiler are formulated,

the cogeneration unit is set to be in a normally open state, and only the running cost of the cogeneration unit is considered, and the running cost of the cogeneration unit, the starting-up cost of the conventional unit, the running cost of the electric boiler and the abandoned wind cost are taken as objective functions, wherein the formula is as follows:

minF ^D ＝min(C _NC ^D +C _NG，s ^D +C _NG，p ^D +C _EB ^D +C _loss ^D )

Wherein the method comprises the steps of

Wherein: f (F) ^D Expressed as total cost of the system before day, C _NC ^D Expressed as a daily heat and power cogeneration unit operation cost function, C _NG,s ^D Represented as a conventional unit start-up cost function, C _NG,p ^D Expressed as a function of the running cost of the conventional unit in the future, C _EB ^D Expressed as a daily electric boiler operating cost function, C _loss ^D Expressed as a wind curtailment cost function; a, a _i ^chp ，b _i ^chp ，c _i ^chp Expressed as the i-th cogeneration unit cost coefficient; a, a _i ，b _i ，c _i Expressed as the cost coefficient of the ith conventional unit; CS (circuit switching) _i,t The method is represented as a starting cost coefficient of an ith thermal power generating unit at a moment t; u (U) _i,t ^D The method is characterized in that the method is represented as a start-stop state of an ith thermal power unit before the day at the time t, wherein 1 is started, and 0 is stopped; p (P) _i,t ^D,chp ，P _i,t ^D ，P _i,t ^D,EB ，P _i,t ^D,W0,U1 And P _i,t ^D,W,U1 Respectively representing the output of the ith cogeneration unit, the output of the conventional unit, the output of the electric boiler, the active output of the wind power prediction first layer and the actual dispatching active output of the wind power first layer at the time t before the day; NC, NG, EB and NW are the numbers of cogeneration units, conventional units, electric boilers and wind farms, T is a scheduling period,

the constraint conditions include:

1) Electric power balance constraint

Wherein: p (P) _t ^D,L Expressed as total electrical load power at time t before day;

2) Thermal power balance condition

Wherein: q (Q) _i,t ^D,chp ，Q _i,t ^D,EB Denoted as the heat power generated by the i-th cogeneration unit and the electric boiler, Q _t ^D,L Expressed as the total heat load power at time t before day,

3) Unit output upper and lower limits and climbing constraint

P _i,min ^chp ≤P _i,t ^chp ≤P _i,max ^chp

U _i,t P _i,min ≤P _i,t ≤U _i,t P _i,max

-R _D,i ^chp Δt≤P _i,t ^chp -P _i,t-1 ^chp ≤R _U,i ^chp Δt

-R _D,i Δt≤P _i,t -P _i,t-1 ≤R _U,i Δt

Wherein: p (P) _i,min ^chp And P _i,max ^chp The lower limit and the upper limit of the electric power of the i-th cogeneration unit are respectively P _i,min And P _i,max The lower limit and the upper limit of the electric power of the ith conventional unit are respectively R _U,i ^chp And R is _D,i ^chp Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith cogeneration unit, R _U,i And R is _D,i Respectively represents the upper limit of the ascending and descending slopes of the electric power of the ith conventional unit,

4) Wind power output constraint

0≤P _i,t ^D,W,U1 ≤P _i,t ^D,W0,U1

5) Cogeneration electrothermal coupling constraint

Q _i,t ^chp ＝η _chp P _i,t ^chp

Q _i,min ^chp ≤Q _i,t ^chp ≤Q _i,max ^chp

Wherein: η (eta) _chp Represents the thermoelectric ratio of the cogeneration unit and eta is taken _i,chp ＝0.75，Q _i,min ^chp And Q _i,max ^chp The lower limit and the upper limit of the heat power of the i-th cogeneration unit are respectively,

6) Electric boiler restraint

Q _i,t ^EB ＝η _EB P _i,t ^EB

P _i,min ^EB ≤p _i,t ^EB ≤P _i,max ^EB

-R _D,i ^EB Δt≤P _i,t ^EB -P _i,t-1 ^EB ≤R _U,i ^EB Δt

Wherein: η (eta) _EB Indicating the thermal efficiency of the electric boiler, taking eta _EB ＝0.98，P _i,min ^EB And P _i,max ^EB The lower limit and the upper limit of the electric power of the ith electric boiler are respectively R _U,i ^EB And R is _D,i ^EB Respectively representing the upper limit of the ascending and descending slopes of the electric power of the ith electric boiler;

step 3.2, rolling the correction model within days: the intra-day correction is a plan 4 h-15 min before the predicted point, and is mainly based on the decision result before the day and the first layer data P in the wind power generation day ^S,W0,U1 Formulating the output adjustment quantity of the unit and the electric boiler, and generating the second layer of wind power daily data P ^S,W0,U2 The energy-type electricity storage device charge-discharge plan, the unit and the electric boiler output adjustment quantity are manufactured,

taking the total cost of the system as an objective function, the formula is as follows:

minF ^S ＝min(C _NC ^S +C _NG，s ^S +C _NG，p ^S +C _EB ^S +C _loss ^S )

Wherein the method comprises the steps of

Wherein: ΔP _i,t ^chp 、ΔP _i,t And DeltaP _i,t ^EB Expressed as electric power adjustment amounts of cogeneration units, conventional units and electric boilers,

the constraint conditions include:

1) Electric power balance constraint

Wherein: p (P) _t ^S,L Expressed as total electric load power at time t in day, P _t ^S,W Expressed as actual dispatch power of wind power in the day, P _i,t ^ESS,n Denoted as i-th energy storage device charging and discharging power, P _i,t ^ESS,n >0 represents charging, P _i,t ^ESS,n <0 represents a discharge and,

2) Thermal power balance condition

Wherein: ΔQ _i,t ^chp And DeltaQ _i,t ^EB Thermal power regulating quantity expressed as cogeneration unit electric boiler, Q _t ^S,L Expressed as total heat load power at time t in the day,

3) Unit and electric boiler output correction quantity constraint

Comprehensively considering the maximum climbing of the single scheduling period delta t and the upper and lower limits of the unit and the electric boiler output to formulate constraint, and expressing as:

4) Power storage device restraint

Wherein: u (U) _i,t ^ch And U _i,t ^dc Respectively representing a charging state and a discharging state of an ith power storage device at a moment t, wherein 1 is an operating state, and 0 represents an inactive state; p (P) _i,t ^ch And P _i,t ^dc Respectively representing the charging power and the discharging power of the ith power storage device at the moment t; p (P) _i,min ^ch And P _i,max ^ch Respectively represent a lower limit and an upper limit of charging power, P _i,min ^dc And P _i,max ^dc Respectively represent lower and upper discharge power limits, E _i,t ^ESS Expressed as capacity of the ith power storage device at time t, E _i,min ^ESS And E is _i,max ^ESS Respectively representing the lower limit and the upper limit of the capacity of the ith power storage device, beta _ch And beta _dc Respectively represent a charging coefficient and a discharging coefficient,

the wind power output constraint, the cogeneration electric heat coupling constraint and the electric boiler constraint are consistent with the step 3.1;

step 3.3, real-time correction of the model: the real-time correction plan is formulated 15min before the predicted point, and the wind power real-time second layer data P is used for ^F,W0,U2 The charge and discharge regulating variable of the energy type electricity storage device, the set and the output regulating variable of the electric boiler are manufactured, and then the wind power is used for real-time third layer data P ^F,W0,U3 Making a charge-discharge plan of the power type electricity storage device,

real-time second layer data P by wind power ^F,W0,U2 The objective functions of the charge and discharge adjustment quantity of the energy type electricity storage device, the unit and the output adjustment quantity of the electric boiler are similar to the scheduling model in 3.2 days, so that the total cost of the system is minimum, and the wind power is real-time third-layer data P ^F,W0,U3 When a charge and discharge plan of the power type electric storage device is formulated, the aim is to make the maximum charge and discharge capacity of the power type electric storage device the minimum air rejection quantity and cut load quantity, namely the maximum charge and discharge capacity of the power type electric storage device is used as an aim function, and the formula is as follows:

minF ^F ＝min[P ^F,W0,U3 (P ^dc -P ^ch )]

the constraint conditions include:

wind power real-time third layer power P ^F,W0,U3 Balance constraint

Wherein: p (P) _i,t ^F,W,U3 Expressed as real-time third-layer actual scheduling power of wind power, P _i,t ^ESS,g Denoted as i-th energy storage device charging and discharging power, P _i,t ^ESS,g >0 represents charging, P _i,t ^ESS,g <0 represents a discharge and,

the wind power output constraint, the cogeneration electric heat coupling constraint and the electric boiler constraint are consistent with the step 3.1.

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