CN114519459A

CN114519459A - Scene analysis and hybrid energy storage based optimal scheduling method for thermoelectric combined system

Info

Publication number: CN114519459A
Application number: CN202210112632.1A
Authority: CN
Inventors: 时伟; 穆佩红; 刘成刚; 谢金芳
Original assignee: Zhejiang Yingji Power Technology Co ltd
Current assignee: Zhejiang Yingji Power Technology Co ltd
Priority date: 2022-01-29
Filing date: 2022-01-29
Publication date: 2022-05-20

Abstract

The invention belongs to the technical field of thermoelectric combination, and particularly relates to a thermoelectric combination system optimal scheduling method based on scene analysis and hybrid energy storage, which comprises the following steps: establishing a digital twin model of the combined heat and power system; setting a hybrid energy storage device in a combined heat and power system to absorb an operation strategy of abandoned wind; generating wind power output scenes corresponding to a plurality of wind power simulation errors and corresponding scene probabilities by adopting a scene analysis method, and then performing scene reduction to generate wind power output random scenes; establishing an upper-layer capacity optimization model: establishing an energy storage device capacity configuration optimization model by taking the minimum input cost and the minimum system operation cost of the hybrid energy storage device in all random scenes as an objective function; establishing a lower-layer scheduling optimization model: establishing a low-carbon economic operation model for the combined heat and power system to absorb the abandoned wind by taking the maximum income of the system under all random scenes as an objective function; and the upper layer model and the lower layer model are interactively solved to obtain the optimal operation scheme of the combined heat and power system and the optimal configuration capacity of the energy storage device.

Description

Scene analysis and hybrid energy storage based optimal scheduling method for thermoelectric combined system

Technical Field

The invention belongs to the technical field of thermoelectric combination, and particularly relates to a thermoelectric combination system optimal scheduling method based on scene analysis and hybrid energy storage.

Background

With the rapid development of wind power, the problem of wind abandon caused by wind-heat conflict in the three north areas of China is increasingly serious, large-scale wind abandon is normalized at present, the problem of wind abandon absorption becomes a bottleneck restricting the development and utilization of wind power in China, and the wide attention of the whole society is aroused. The main reason is that the minimum output of the cogeneration unit is greatly improved to meet the heat load in the heating period in winter, so that the space for wind power to surf the internet is insufficient.

At present, the scheme of wind absorption and wind abandonment mainly comprises the following steps: by improving the delivery capacity of the system, the power generation load of the system is improved, and the space for the system to accept wind power is increased; the operation constraint of 'fixing electricity with heat' of the thermoelectric unit is effectively decoupled by adopting a method of configuring the heat storage tank, and the abandoned wind is reduced. In addition, the combined heat and power system can realize the coordination and complementation among various energy sources, the energy utilization rate is improved, the energy storage device is arranged in the combined heat and power system, the energy utilization rate can be further improved, the wind power consumption is promoted, and the safe and reliable operation of the system is guaranteed.

Therefore, how to establish a combined heat and power system based on hybrid energy storage can effectively improve the effect of the system on the waste air, ensure the low-carbon economic operation of the system, and the optimal capacity configuration of the energy storage device is a problem which needs to be solved urgently at present.

Therefore, a new optimal scheduling method for the thermoelectric combination system based on scene analysis and hybrid energy storage needs to be designed based on the above technical problems.

Disclosure of Invention

The invention aims to provide a thermoelectric combination system optimal scheduling method based on scene analysis and hybrid energy storage.

In order to solve the technical problem, the invention provides a thermoelectric combination system optimal scheduling method based on scene analysis and hybrid energy storage, which comprises the following steps:

establishing a digital twin model of a combined heat and power system based on hybrid energy storage;

setting a hybrid energy storage device in a combined heat and power system to absorb an operation strategy of abandoned wind;

describing wind power generation uncertainty in the combined heat and power system by adopting a scene analysis method, generating a wind power output scene corresponding to a plurality of wind power simulation errors and corresponding scene probability, processing the wind power output scene by adopting a scene reduction technology to generate a wind power output random scene, and participating in optimal scheduling solution of the combined heat and power system;

establishing an upper-layer capacity optimization model: based on different capacity combinations of the hybrid energy storage devices, establishing a target function with the minimum input cost and the minimum system operation cost of the hybrid energy storage devices in all random scenes, setting related constraint conditions, and constructing an energy storage device capacity configuration optimization model;

establishing a lower-layer scheduling optimization model: establishing a low-carbon economic operation model of the combined heat and power system for wind curtailment absorption and abandonment by setting related constraint conditions by taking the maximum income of the system under all random scenes as a target function based on the operation income and the cost of the combined heat and power system;

and (3) interactive solving of upper and lower layer models: and carrying out bidirectional interactive solution on the energy storage device capacity configuration optimization model and the combined heat and power system wind-absorption and wind-abandoning low-carbon economic operation model to obtain the optimal operation scheme of the combined heat and power system and the optimal configuration capacity of the energy storage device.

Further, the method for establishing the digital twin model of the combined heat and power system based on the hybrid energy storage comprises the following steps:

a mechanism modeling and data identification method is adopted to establish a mixed energy storage-based digital twin model of the combined heat and power system, namely:

establishing physical models of a hybrid energy storage device, cogeneration, a wind turbine generator and an electric boiler entity;

the hybrid energy storage device includes: a heat storage device and an electricity storage device;

establishing a controllable closed-loop logic model according to a logic mechanism relation among all physical entities of the combined heat and power system based on hybrid energy storage, and mapping the physical model to the logic model;

building a simulation model of the thermoelectric combined system based on the collected operation data, state data and physical attribute data of the thermoelectric combined system with hybrid energy storage, and adjusting and optimizing parameters of the simulation model according to the error of a predicted value and an actual value output by the simulation model;

carrying out virtual-real fusion on the physical model, the logic model and the simulation model, and constructing a system-level digital twin model of a physical entity of the hybrid energy storage-based thermoelectric combination system in a virtual space;

and accessing multi-working-condition real-time operation data of the hybrid energy storage-based thermoelectric combined system into the system-level digital twin model, and performing self-adaptive identification correction on a simulation result of the system-level digital twin model by adopting a reverse identification method to obtain the identified and corrected digital twin model of the hybrid energy storage-based thermoelectric combined system.

Further, when the electric boiler simulation model is constructed, the electric boiler device converts electric energy into heat energy, optimization of a heat load curve is realized under the guidance of time-of-use electricity price, and an output heat power model is expressed as follows:

Q_eb(t)＝η_ebP_eb(t)；

wherein Q is_eb(t) outputting thermal power of the electric boiler at the moment t; eta_ebThe heating efficiency is improved; p_eb(t) inputting electric power into the electric boiler;

the electric boiler output cost is expressed as:

C_eb(t)＝k_ebP_eb(t)；

wherein, C_eb(t) the output cost of the electric boiler at the moment t; k is a radical of_ebIs the unit output cost coefficient of the electric boiler;

when the simulation model of the wind turbine generator is constructed, the output power of the wind turbine generator is expressed as follows:

wherein, P_w,tThe output is wind power; v is the actual wind speed; v. of_ci、v_coRespectively cut-in and cut-out wind speeds; p_weThe installed capacity of wind power; v. of_rRated wind speed;

when the simulation model of the cogeneration unit is constructed,

wherein the content of the first and second substances,

outputting electric power for the cogeneration unit at the time t; eta_GTThe generating efficiency of the cogeneration unit;

the thermal power output by the cogeneration unit at the moment t is provided; eta_HEThe heat conversion efficiency of the cogeneration unit;

the natural gas consumption of the cogeneration unit at the time t is shown; beta is the low calorific value of natural gas;

when the simulation model of the energy storage device is built,

wherein x is an energy type, x is e representing electricity, and x is h representing heat; e_x,t+1The energy is the energy after charging or discharging; e_x,tEnergy before charging or discharging; delta_xThe energy loss rate of the energy storage system; p_x,c,t、P_x,d,tRespectively charge and discharge energy power; mu.s_xIs a variable of 0,1, mu_x0 represents energy release, mu_x1 represents charging; e_x,min、E_x,maxRespectively, minimum and maximum stored energy.

Further, the method for setting the operation strategy of the hybrid energy storage device in the combined heat and power system comprises the following steps:

in the wind abandoning period, the power storage device is started to store electric energy at low price, and when the power storage device reaches the maximum power and still cannot consume all the abandoned wind, the electric boiler is started to increase the valley value of the electric load, reduce the lower limit of the electric power of the thermoelectric unit and increase the wind power online space; if the electric boiler still cannot absorb all the abandoned wind, starting the heat storage device again to improve the wind power grid space; in the peak period of the electric load and the low-power period of the wind power, the electric energy is discharged by the electric storage device, the peak value of the electric load is cut, and the income is increased by utilizing the peak-valley price difference; meanwhile, the electric boiler stops heating, and the heat storage device releases heat for heat supply load.

Further, a scene analysis method is adopted to describe wind power generation uncertainty in the combined heat and power system, wind power output scenes corresponding to a plurality of wind power simulation errors and corresponding scene probabilities are generated, a scene reduction technology is adopted to process the wind power output scenes to generate wind power output random scenes, and the wind power output random scenes participate in optimal scheduling solving of the combined heat and power system, and the method specifically comprises the following steps:

the method for generating the wind power output scene by adopting the quasi-Monte Carlo method comprises the following steps:

the wind speed is used as a random variable, uniformly distributed quasi-random sequences are generated in a [0,1] interval according to the distribution characteristics of the wind speed, then the uniformly distributed quasi-random sequences are converted into normally distributed random numbers by adopting inverse transformation, the probability distribution in an actual wind power output sample is simulated, a probability density model is established, and the probability of a corresponding scene is obtained; the pseudo-random sequence comprises a halton sequence and a sobol sequence; the inverse transformation of the normal distribution comprises a Box-Muller algorithm and a Moro algorithm;

the method for reducing the wind power output scene by adopting a K-means clustering algorithm comprises the following steps:

random selection of M_εThe individual scene is taken as a cluster center, and a cluster center scene set is represented as: c ═ η_ε ^c}(ε＝1,2,...,M_s)；

And determining the rest scene sets as:

calculating the scene distance from the residual scene to the cluster center scene:

according to the distance matrix D_ε,ε′Classifying the rest scenes into cluster centers with the nearest distance; the clustered set is: q ═ C_j}(i＝1,2,...,M_s) In which C is_jRepresenting a homogeneous set of scenes;

the clustering calculation method comprises the following steps: hypothesis clustering C_jIn which is L_εCalculating the sum of Euclidean distances between each scene and other scenes, and selecting the scene with the minimum distance as a new clustering center;

and repeating the calculation of the scene distance and the cluster center until the cluster center and the clustering result are not changed any more, and ending the scene reduction.

Further, establishing an upper-layer capacity optimization model: based on different capacity combinations of the hybrid energy storage devices, establishing a target function which takes the minimum input cost and the minimum system operation cost of the hybrid energy storage devices under all random scenes as well as setting related constraint conditions and constructing an energy storage device capacity configuration optimization model, wherein the method comprises the following steps:

establishing a target function of minimum input cost and system operation cost of the hybrid energy storage device under all random scenes:

wherein L is_εFor random number of scenes, pi_εIs the probability of occurrence of scene epsilon; combined heat and power system operating cost C_rdlh,εObtaining the data from a lower scheduling optimization model;

investment cost: c_cn,tz,ε＝C_glP_gl+C_tesS_tes+C_dlP_dl+C_swS_sw；C_glCost per unit electric boiler capacity; p_glThe construction capacity of the electric boiler; c_tesIs the price per unit capacity of the heat storage device; s_tesThe construction capacity of the heat storage device; c_dlCost per unit of stored power; p is_dlThe capacity is the construction capacity of the stored power; c_swA price per capacity that is the capacity of the electricity storage device; s_swIs the construction capacity of the electricity storage device;

the operation and maintenance cost is as follows: c_cn,yx,ε＝C_batP_bat,ε+C_tsP_ts,ε；C_batConverting the cost coefficient for depreciation of the power storage device; p is_bat,εPower for charging or discharging; c_tsA depreciation conversion cost coefficient of the heat storage device; p is_ts,εThe thermal power of the heat storage device under the scene epsilon;

transaction cost: c_cn,jy＝C_biW_bi-C_siW_si；C_bi、W_bi、C_si、W_siRespectively is the purchase electricity price, the purchase electricity quantity, the selling electricity price and the selling electricity quantity in one time period of a day;

setting a constraint condition:

remaining power constraint conditions of the power storage device:

SOC_min≤SOC_i≤SOC_max；

P_bat,ε≤P_bat,ε.max；

wherein, SOC_iFor the remaining capacity of the storage means at any point in time, SOC_i-1The residual capacity at the previous time point; eta_bc,ε、η_bd,εThe charging and discharging efficiencies of the electric storage device under the scene epsilon are respectively; SOC_min、SOC_maxRespectively the minimum and maximum residual electric quantity of the electric storage device under the scene epsilon; p_bat.ε，maxThe rated power of the power storage device under the scene epsilon;

constraint conditions of residual electric quantity of the heat storage device are as follows:

Q_i,ε≤Q_ε,max；

P_ts,ε≤P_ts,ε.max；

wherein Q is_i,εThe residual heat of the heat storage device at any time point under the scene epsilon; q_i-1,εThe residual heat of the previous time point under the scene epsilon; eta_tc,ε、η_td,εThe heat storage efficiency and the heat release efficiency of the heat storage device under the scene epsilon are respectively; q_ε,maxThe maximum residual heat of the heat storage device under the scene epsilon; p is_ts,ε.maxThe rated power of the heat storage device under the scene epsilon;

thermal load demand constraints: p_t,load,ε′≤η_tdQ_t,out,ε+P_t,gl,ε；P_t,load,ε' is the total heat load demand under the scene epsilon; p_t,gl,εThe power of the electric boiler at the moment t under the scene epsilon; q_t,out,εFor the heat-releasing power of the heat-storage device under the scene epsilon

Electric power and electric quantity balance constraint: p_t,w,ε＝P_t,gl,ε+P_ess,t,ε；P_t,w,εPlanning output of wind power at t moment under a scene epsilon; p_ess,tAnd the abandoned wind power is consumed by the power storage device at the moment t under the scene epsilon.

Further, establishing a lower-layer scheduling optimization model: based on the operating income and the cost of the combined heat and power system, the method establishes a target function which maximizes the system benefits under all random scenes, sets related constraint conditions, and constructs a combined heat and power system wind-abandoning low-carbon economic operation model, which comprises the following steps:

establishing an objective function which maximizes the system income under all random scenes:

wherein L is_εFor random number of scenes, pi_εIs the probability of occurrence of scene epsilon;

cogeneration power revenue:

wherein, c_eThe day-ahead electricity price of the cogeneration unit; p_t,s,εGenerating output power for a correction plan of a cogeneration unit under a scene epsilon after considering abandoned wind peak regulation; t is an operation scheduling period; Δ t is a unit time interval;

cogeneration heat recovery:

c_hsupplying heat unit price for the cogeneration unit; q_t,s,εThe total heat output power of the cogeneration unit and the heat storage under the scene epsilon,

the thermal output Q generated by the nth cogeneration unit at the moment t under the scene epsilon_t,in,εFor the heat storage power, Q, of the heat storage unit under the scene epsilon_t,out,εThe heat release power of the heat storage device under the scene epsilon;

electric power assisted peak shaving revenue:

wherein, c_peakPeak-shaving electricity price for the cogeneration unit to participate in wind abandoning and consumption; p_t,s,εThe method comprises the following steps that' planned power generation output of a cogeneration unit under a scene epsilon is not considered when abandoned wind peak shaving is not considered; p_t,c,εAnd P_t,d,εRespectively charging power and discharging power of the power storage device under the scene epsilon;

heat storage and income: i is_tes,ε＝c_peakβQ_t,tes,ε；

Wherein Q is_t,tes,εThe heat storage device finally stores heat every day under the scene epsilon; beta is the thermoelectric ratio of the cogeneration unit;

and (3) electricity selling income: i is_dl，ε＝c_dlW_sz,ε；c_dlThe price of electricity sold to the power grid for the system; w_sz,εAbandoning the total electric quantity of wind for consumption;

the running cost of the cogeneration unit is as follows:

wherein the content of the first and second substances,

the electric output of the nth cogeneration unit in the scene epsilon at the moment t is obtained;

the thermal output of the nth cogeneration unit in the scene epsilon at the moment t is shown; n is the total number of the cogeneration units; epsilon_CHPThe fuel cost coefficient of the cogeneration unit; gamma ray_PAnd gamma_HRespectively representing the unit electric output of the cogeneration unit and the fuel consumed by the unit electric output;

the wind power operation and maintenance cost is as follows:

t is the tth period; k is a radical of_wThe cost coefficient is the wind power operation maintenance cost coefficient; p_t,w,εPlanned wind power output at the moment t under the scene epsilon;

the system carbon emission trading cost is as follows:

calculating the carbon emission transaction cost based on the actual carbon emission amount and the carbon emission right quota amount of the wind turbine generator and the cogeneration generator, and expressing as follows:

C^carbontrading total costs for carbon emissions; p is a radical of^carbonA carbon transaction price;

the actual carbon emission of the unit;

allocating amount for unit carbon emission right;

σ_jthe carbon emission intensity of the wind turbine generator is shown;

the electric output of a jth wind turbine generator set under a scene epsilon; sigma_nThe electric power carbon emission intensity of the cogeneration unit; s is the total number of the wind turbine generators;

q is the electric power carbon emission quota;

the wind abandon penalty cost is:

and when the output of the fan reaches the upper limit of the system, abandoned wind occurs, and the punished cost of the abandoned wind is expressed as:

υ_wpunishment coefficient for abandoned wind; p_t,w,ε' is the actual wind power output at the moment t under the scene epsilon;

setting the constraint conditions of the cogeneration system:

electric power balance constraint:

P_t,gl,εthe power of the electric boiler at the moment t under the scene epsilon; p is_t,load,εThe electric load demand is at the moment t under the scene epsilon;

and thermal power balance constraint:

η_chthe efficiency of the electric boiler; p_t,load,ε' is the total heat load demand under the scene epsilon;

cogeneration units associated constraints: the method comprises the following steps of thermoelectric unit output restraint:

the maximum heat output of the cogeneration unit under the scene epsilon; thermoelectric unit climbing restraint: -RD_n,ε≤P_t,s,ε-P_t-1,s,ε≤RU_n,ε；-RD_n,ε、RU_n,εRespectively the up-down climbing rate of the cogeneration unit n under the scene epsilon;

and (3) operation restraint of the power storage device: e_t,es≤E₀；E_min≤E_t,es≤E_max；E_t,esThe electricity storage capacity at the end of each day; e₀A desired initial power storage device capacity; e_max、E_minThe upper limit and the lower limit of the capacity of the power storage device are respectively set;

and (3) operation constraint of the heat storage device: q is not less than 0_t,tes,ε≤Q_tes,ε,max；Q_t,in≤Q_{in,ε_max}；Q_t,out≤Q_{out,ε_max}；Q_tes,ε,maxThe maximum value of the heat storage amount under the scene epsilon; q_{in,ε_max}The maximum heat storage power of the heat storage device under the scene epsilon; q_{out,ε_max}The maximum heat release power of the heat storage device under the scene epsilon.

Further, the upper and lower layer models are solved interactively: performing bidirectional interactive solution on the energy storage device capacity configuration optimization model and the combined heat and power system wind-curtailment low-carbon economic operation model to obtain an optimal operation scheme of the combined heat and power system and an optimal configuration capacity of the energy storage device, wherein the method comprises the following steps:

solving an energy storage device capacity configuration optimization model to obtain energy storage device capacity configuration information;

taking the capacity configuration information of the corresponding energy storage device as a decision variable of a lower-layer scheduling optimization model, solving a wind-curtailed low-carbon economic operation model of the combined heat and power system to obtain an optimal operation scheme, and returning to an upper-layer capacity optimization model;

and the upper-layer capacity optimization model recalculates the system operation cost in the objective function according to the fed-back optimal operation scheme, updates the fitness function, performs iterative optimization again, and obtains the optimal configuration capacity of the energy storage device according to the optimal operation scheme and the capacity configuration information of the energy storage device.

Further, the algorithm adopted by the solution of the upper-layer capacity optimization model and the lower-layer scheduling optimization model comprises the following steps: particle swarm optimization algorithm, whale algorithm, genetic algorithm and mixed integer programming method.

The method has the advantages that a digital twin model of the combined heat and power system based on hybrid energy storage is established; setting an operation strategy of a hybrid energy storage device in the combined heat and power system; describing uncertainty of wind power output by adopting a scene analysis method, and generating a random scene of the wind power output; constructing an energy storage device capacity configuration optimization model; constructing a low-carbon economic operation model for the heat and power combined system for absorbing abandoned wind; the method comprises the steps of obtaining the optimal operation scheme of the system and the optimal capacity configuration of the energy storage device by double-layer model interactive solution, realizing prediction based on a digital twin model, making decisions based on the prediction result, establishing an intelligent combined heat and power system, weakening the fluctuation influence of wind power output by adopting a scene analysis method, generating a wind power output scene by a quasi-Monte Carlo method, ensuring scene diversity, reducing the generated scene by a clustering algorithm, reducing the solution complexity, reducing the calculation amount, considering the solution efficiency of the double-layer model, and ensuring the low carbon, the economy and the consumption of renewable energy in system operation.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a flow chart of an optimal scheduling method of a combined thermal power system based on scene analysis and hybrid energy storage according to the present invention;

FIG. 2 is a schematic diagram of an optimized scheduling model according to the present invention.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1 and 2, the embodiment provides a thermoelectric combination system optimal scheduling method based on scene analysis and hybrid energy storage, including:

Establishing a digital twin model of a combined heat and power system based on hybrid energy storage; setting an operation strategy of a hybrid energy storage device in the combined heat and power system; describing uncertainty of wind power output by adopting a scene analysis method, and generating a random scene of the wind power output; constructing an energy storage device capacity configuration optimization model; constructing a heat and power combined system consumption abandoned wind low-carbon economic operation model; the method comprises the steps of obtaining the optimal operation scheme of the system and the optimal capacity configuration of the energy storage device by double-layer model interactive solution, realizing prediction based on a digital twin model, making decisions based on the prediction result, establishing an intelligent combined heat and power system, weakening the fluctuation influence of wind power output by adopting a scene analysis method, simulating and generating a wind power output scene by a quasi-Monte Carlo method, ensuring scene diversity, reducing the generated scene by a clustering algorithm, reducing the solution complexity, reducing the calculation amount, considering the solution efficiency of the double-layer model, and ensuring the low carbon, the economy and the consumption of renewable energy in system operation.

In this embodiment, the establishing a hybrid energy storage based combined heat and power system digital twin model by using a mechanism modeling and data identification method specifically includes: constructing a physical model, a logic model and a simulation model of the hybrid energy storage-based thermoelectric combined system; wherein, the establishment of the physical model comprises the following steps: establishing physical models of a hybrid energy storage device, cogeneration, a wind turbine generator and an electric boiler entity; the hybrid energy storage device at least comprises a heat storage device and an electric storage device; the establishment of the logic model comprises the following steps: establishing a controllable closed-loop logic model according to a logic mechanism relation among all physical entities of the combined heat and power system based on hybrid energy storage, and mapping the physical model to the logic model; the establishment of the simulation model comprises the following steps: building a simulation model of the thermoelectric combined system based on the collected operation data, state data and physical attribute data of the thermoelectric combined system with hybrid energy storage, and adjusting and optimizing parameters of the simulation model according to the error of a predicted value and an actual value output by the simulation model; carrying out virtual-real fusion on the physical model, the logic model and the simulation model, and constructing a system-level digital twin model of a physical entity of the hybrid energy storage-based thermoelectric combined system in a virtual space; accessing multi-working-condition real-time operation data of the hybrid energy storage-based thermoelectric combined system into the system-level digital twin model, and performing self-adaptive identification correction on a simulation result of the system-level digital twin model by adopting a reverse identification method to obtain a digital twin model of the hybrid energy storage-based thermoelectric combined system after identification correction; the effect of the wind absorption and abandoning of the combined heat and power system is ensured by setting different types of energy storage devices and corresponding operation strategies;

in this embodiment, when the electric boiler simulation model is constructed, the electric boiler device can convert electric energy into heat energy, the optimization of a heat load curve is realized under the guidance of time-of-use electricity price, and the output heat power model is expressed as:

Q_eb(t)＝η_ebP_eb(t)；

the electric boiler output cost is expressed as:

C_eb(t)＝k_ebP_eb(t)；

wherein, C_eb(t) the output cost of the electric boiler at the moment t; k is a radical of_ebUnit output cost for electric boilerA coefficient;

when the simulation model of the cogeneration unit is constructed, the cogeneration unit comprises a gas turbine and a waste heat recovery device, waste heat is recovered through the waste heat recovery device, the cascade utilization of energy is realized, and the model expression is as follows:

wherein the content of the first and second substances,

the electric power output by the cogeneration unit at the moment t; eta_GTThe generating efficiency of the cogeneration unit;

when energy memory simulation model was established, energy memory carried out the energy storage when the energy was surplus or the price is low, released the energy when demand peak moment or price are high, and heat-retaining process is similar with the accumulate process, adopts the energy storage model of generalized to express as:

wherein x is an energy type, x is e representing electricity, and x is h representing heat; e_x,t+1The energy is the energy after charging or discharging; e_x,tEnergy before charging or discharging; delta_xThe energy loss rate of the energy storage system; p_x,c,t、P_x,d,tRespectively charge and discharge energy power; mu.s_xIs a variable of 0,1, mu_x0 represents energy release, mu_x1 represents charging; e_x,min、E_x,maxMinimum and maximum stored energy, respectively; and the delta t is 1h, and the scheduling period is 24 h.

In this embodiment, the method for setting the operation strategy of the hybrid energy storage device in the cogeneration system comprises the following steps: in the wind abandoning period, the electricity storage device is started to store electric energy at low price, and when the electricity storage device reaches the maximum power and still cannot absorb all the abandoned wind, the electric boiler is started to increase the electric load valley value, reduce the lower limit of the electric power of the thermoelectric generator set and increase the wind power grid space; if the electric boiler still cannot absorb all the abandoned wind, starting the heat storage device again to improve the wind power grid space; in the peak period of the electric load and the low-power period of the wind power, the electric energy is discharged by the electric storage device, the peak value of the electric load is cut, and the income is increased by utilizing the price difference between the peak value and the valley value; meanwhile, the electric boiler stops heating, and the heat storage device releases heat for heat supply load.

It should be noted that the operation strategy of the hybrid energy storage device can be set according to various scenes, the hybrid energy storage device is not limited to the heat storage device and the electricity storage device, and can also be other energy storage devices (electricity-to-gas storage devices), and the priority operation strategies of different energy storage devices are set, so that the absorption and the wind curtailment of the system and the low-carbon operation are realized; and when the operation strategy of the hybrid energy storage device in the combined heat and power system is set, researching an electric/thermal hybrid energy storage coordination operation strategy, and preferentially starting the energy storage equipment with good economic performance. The electricity storage efficiency is higher, and the electric boiler needs to convert high-quality electric energy into low-quality heat energy, so that the efficiency is low and uneconomical, and the priority for starting the electric energy storage device to absorb the abandoned wind is higher than that of the electric boiler; the electric boiler can be used for consuming abandoned wind through two ways, on one hand, the electric load valley can be filled, and on the other hand, the thermal output of the thermal power unit can be reduced, so that the electrical output of the thermal power unit is reduced, and the wind power internet space is increased. The way that the heat storage device absorbs the abandoned wind is only equivalent to the first way that the electric boiler absorbs the abandoned wind, so that the equivalent abandoned wind power is absorbed, the required capacity of the electric boiler is smaller, the efficiency is higher, and the priority of starting the electric boiler to absorb the abandoned wind is higher than that of the heat storage device.

In this embodiment, a scene analysis method is used to describe wind power generation uncertainty in the cogeneration system, a wind power output scene and corresponding scene probabilities corresponding to a plurality of wind power simulation errors are generated, a scene reduction technology is used to process the wind power output scene to generate a wind power output random scene, and the wind power output random scene participates in optimal scheduling and solving of the cogeneration system, and specifically includes:

taking wind speed as a random variable, generating uniformly distributed quasi-random sequences in a [0,1] interval according to the distribution characteristics of the wind speed, converting the uniformly distributed quasi-random sequences into normally distributed random numbers by adopting inverse transformation, simulating probability distribution in an actual wind power output sample, establishing a probability density model, and obtaining the probability of a corresponding scene; the quasi-random sequence comprises a halton sequence and a sobol sequence; the inverse transformation of the normal distribution comprises a Box-Muller algorithm and a Moro algorithm;

And determining the rest scene sets as:

In this embodiment, an upper-layer capacity optimization model is established: based on different hybrid energy storage device capacity combinations, establishing a target function with the minimum input cost and the minimum system operation cost of the hybrid energy storage devices in all random scenes, setting related constraint conditions, and constructing an energy storage device capacity configuration optimization model, wherein the method comprises the following steps:

wherein L is_εNumber of random scenes, n_εIs the probability of occurrence of scene epsilon; combined heat and power system operating cost C_rdlh,εObtaining the data from a lower scheduling optimization model;

investment cost: c_cn,tz,ε＝C_glP_gl+C_tesS_tes+C_dlP_dl+C_swS_sw；C_glCost per unit electric boiler capacity; p_glThe construction capacity of the electric boiler; c_tesIs the price per unit capacity of the heat storage device; s_tesFor construction of heat-storage devicesSetting the capacity; c_dlIs the unit electricity storage power cost; p_dlThe construction capacity for the stored power; c_swA price per capacity that is the capacity of the electricity storage device; s_swIs the construction capacity of the electricity storage device;

operating and maintaining cost: c_cn,yx,ε＝C_batP_bat,ε+C_tsP_ts,ε；C_batConverting the cost coefficient for depreciation of the power storage device; p_bat,εPower for charging or discharging; c_tsA depreciation conversion cost coefficient of the heat storage device; p_ts,εThe thermal power of the heat storage device under the scene epsilon;

setting a constraint condition:

remaining power constraint conditions of the power storage device:

SOC_min≤SOC_i≤SOC_max；

P_bat,ε≤P_bat,ε.max；

therein, SOC_iFor the remaining capacity of the electricity storage means at any point in time, SOC_i-1The residual capacity at the previous time point; eta_bc,ε、η_bd,εThe charging and discharging efficiencies of the electric storage device under the scene epsilon are respectively; SOC_min、SOC_maxRespectively the minimum and maximum residual electric quantity of the electric storage device under the scene epsilon; p is_bat.ε，maxThe rated power of the power storage device under the scene epsilon;

Q_i,ε≤Q_ε,max；

P_ts,ε≤P_ts,ε.max；

wherein Q_i,εThe residual heat of the heat storage device at any time point under the scene epsilon; q_i-1,εThe residual heat of the previous time point under the scene epsilon; eta_tc,ε、η_td,εThe heat storage efficiency and the heat release efficiency of the heat storage device under the scene epsilon are respectively; q_ε,maxThe maximum residual heat of the heat storage device under the scene epsilon; p_ts,ε.maxThe rated power of the heat storage device under the scene epsilon;

Electric power and electric quantity balance constraint: p_t,w,ε＝P_t,gl,ε+P_ess,t,ε；P_t,w,εPlanned wind power output at the moment t under the scene epsilon; p_ess,tAnd (4) the abandoned wind power is consumed by the power storage device at the moment t under the scene epsilon.

In this embodiment, a lower layer scheduling optimization model is established: based on the operation income and the cost of the combined heat and power system, the method establishes a target function which maximizes the system income under all random scenes, sets related constraint conditions, and constructs a low-carbon economic operation model of the combined heat and power system for wind absorption and curtailment, and comprises the following steps:

wherein L is_εFor random number of scenes, pi_εIs the probability of occurrence of scene ε;

cogeneration power revenue:

cogeneration heat recovery:

the thermal output Q generated by the nth cogeneration unit at the moment t under the scene epsilon_t,in,εIs the heat storage power, Q, of the heat storage device under the scene epsilon_t,out,εThe heat release power of the heat storage device under the scene epsilon;

electric power assisted peak shaving revenue:

heat storage and income: i is_tes,ε＝c_peakβQ_t,tes,ε；

Wherein Q_t,tes,εThe heat storage device finally stores heat every day under the scene epsilon; beta is the thermoelectric ratio of the cogeneration unit;

and (3) electricity selling income: i is_dl，ε＝c_dlW_sz,ε；c_dlFor selling electricity from the system to the grid；W_sz,εAbandoning the total electric quantity of wind for consumption;

the running cost of the cogeneration unit is as follows:

wherein the content of the first and second substances,

the wind power operation and maintenance cost is as follows:

t is the tth period; k is a radical of_wThe cost coefficient is the wind power operation maintenance cost coefficient; p is_t,w,εPlanned wind power output at the moment t under the scene epsilon;

the system carbon emission trading cost is as follows:

the actual carbon emission of the unit;

allocating amount for unit carbon emission right;

σ_jthe carbon emission intensity of the wind turbine generator is shown;

the electric output of a jth wind turbine generator set under a scene epsilon; sigma_nThe electric power carbon emission intensity of the cogeneration unit is obtained; s is the total number of the wind turbine generators;

q is the electric power carbon emission quota;

the wind abandonment penalty cost is as follows:

setting the constraint conditions of the combined heat and power system:

electric power balance constraint:

P_t,gl,εthe power of the electric boiler at the moment t under the scene epsilon; p_t,load,εThe electric load demand at the moment t under the scene epsilon;

and thermal power balance constraint:

η_chthe efficiency of the electric boiler; p is_t,load,ε' is the total heat load demand under the scene epsilon;

The calculation of the carbon emission is closely related to the electric output of the unit, because the output of the thermoelectric unit comprises two forms of electric energy and heat energy, the heating load needs to be converted into equivalent electric quantity when the carbon emission and the carbon emission right quota amount of the thermoelectric unit are calculated, the carbon emission intensity is introduced to measure the capacity of the unit for carbon emission, the higher the carbon emission intensity is, the more carbon emission is released by the unit in the same environment, the carbon emission right quota amount mainly depends on the carbon emission level allowed by the area, and different quota amounts are distributed according to the electric output of the unit; in the establishment of the upper-layer optimization model, the cogeneration income, the peak regulation income, the heat accumulation income, the electricity selling income, the cogeneration operation cost and the wind power operation maintenance cost are considered, the system carbon emission transaction cost and the wind abandonment punishment cost are also considered, the combined heat and power system wind abandonment optimization operation model under the low-carbon environment is established, the thermal output and the wind abandonment rate are favorably reduced, the thermal output of a thermoelectric unit with higher carbon emission intensity can be preferentially reduced under the action of a carbon emission transaction mechanism, the total coal consumption is reduced, the wind power consumption level is improved, and the low-carbon requirement can be met.

In this embodiment, the upper and lower layer models are solved interactively: performing bidirectional interactive solution on the energy storage device capacity configuration optimization model and the combined heat and power system wind-curtailment low-carbon economic operation model to obtain an optimal operation scheme of the combined heat and power system and an optimal configuration capacity of the energy storage device, wherein the method comprises the following steps:

taking the capacity configuration information of the corresponding energy storage device as a decision variable of a lower-layer scheduling optimization model, solving a wind-absorption and wind-abandoning low-carbon economic operation model of the thermoelectric combined system to obtain an optimal operation scheme, and returning to an upper-layer capacity optimization model;

and the upper-layer capacity optimization model recalculates the system operation cost in the objective function according to the fed-back optimal operation scheme, updates the fitness function, performs iterative optimization again, and obtains the optimal configuration capacity of the energy storage device according to the optimal operation scheme and the energy storage device capacity configuration information.

In this embodiment, the algorithm adopted by the solution of the upper-layer capacity optimization model and the lower-layer scheduling optimization model includes: particle swarm optimization algorithm, whale algorithm, genetic algorithm and mixed integer programming method.

In practical applications, the solving of the energy storage device capacity configuration optimization model to obtain energy storage device capacity configuration information includes:

the whale algorithm is improved, and the method comprises the following steps: embedding a secondary interpolation method in a spiral updating position mechanism; replacing a contraction surrounding mechanism with a random Levy flight strategy with alternate long and short search step lengths;

randomly generating an initial whale population according to a constraint condition, taking N whales generated randomly as the initial whale population, and taking the spatial position of the whale individual as a decision variable of capacity configuration;

calculating the fitness value of each individual in the whale colony by using an objective function in the capacity configuration optimization model, and finding and storing the optimal whale individual position in the current colony;

updating the positions of whale groups according to different values of the parameters, and updating the parameters of each whale group when the number of iterations is less than the iteration number: a, A, B, l, p₁,p₂(ii) a Checking whether any whale individual updated position exceeds the search space, and correcting the whale position exceeding the search space; the updating of the whale flock position includes: when the parameter p is random₁If the absolute value of A is less than 1, updating the position of the whale individual by adopting a surrounding predation mechanism; when p is₁When the absolute value of A is more than or equal to 1 and less than 0.5, updating the current position of the whale individual by adopting an improved shrink wrapping mechanism; when the parameter p is random₁≥0.5，p₂When the value is more than or equal to 0.6, a secondary interpolation method updating mechanism is adopted; when the parameter p is random₁≥0.5，p₂When the position is less than 0.6, a spiral position updating mechanism is adopted;

calculating the updated fitness value of each whale colony individual through the target function, finding and storing the optimal whale individual in the colony, judging whether the optimal solution of the target function is met, and if so, outputting the spatial position of the optimal whale individual and the corresponding fitness value, namely the optimal solution of capacity configuration optimization; otherwise, the next iteration is performed again. In the process of solving the upper-layer optimization model and the lower-layer optimization model, an improved genetic algorithm and an improved whale algorithm are respectively designed, and compared with the traditional genetic algorithm and the traditional whale algorithm, the method has the advantages that the convergence of an objective function can be obviously accelerated, the global optimization capability is strong, and the calculation accuracy is improved.

It should be noted that the embedded quadratic interpolation method can improve the development capability of the algorithm and perform a fine search on a potential space. In addition, the characteristic of random search of Levy flight with self-adaptive step length is combined, so that the algorithm can jump out of local optimum for global search, and the problems of premature convergence and low accuracy of an optimization result caused by the fact that the original whale optimization algorithm is easily trapped into the local optimum are solved. A certain interactive relation exists between the upper-layer optimization model and the lower-layer optimization model, the optimal solution of the capacity allocation obtained in the lower-layer optimization model is input into the upper-layer optimization model, the solution of the wind-abandoning low-carbon economic operation model of the thermoelectric combined system is carried out according to the capacity allocation, the operation state and the operation parameters of the equipment under the low-carbon operation of the system are obtained, the two layers of optimization models are mutually influenced, and the optimal operation parameters of the system are obtained through interactive solution.

In practical application, the method for solving the wind-curtailed low-carbon economic operation model of the combined heat and power system to obtain the optimal operation scheme comprises the following steps:

the genetic algorithm is improved, and comprises the following steps: and (3) improving an operation operator: calculating individual fitness value, combining roulette selection method with optimal storage strategy, and reserving excellent individuals; and (3) crossing and variation improvement: replacing the traditional fixed cross probability with a dynamically changing cross probability strategy, and then performing cross recombination between individuals in the population; carrying out mutation operation on individuals in the population by adopting a self-adaptive mutation probability strategy;

setting relevant parameters of the combined heat and power system, wherein the relevant parameters at least comprise parameters of a wind turbine generator, a cogeneration generator, an energy storage device, electricity price and load information;

setting a population scale and the maximum iteration times, obtaining individuals by adopting an improved operator selection method, judging whether the fitness value of the individual meets an iteration termination condition, and if the fitness value of the individual meets the maximum iteration times, decoding to obtain the optimal individual of the fitness value; otherwise, continuing iteration;

calculating the cross probability and the mutation probability by adopting an improved cross and mutation strategy;

performing selection, crossing and variation operations to generate a new generation of population, and repeating iteration;

and when the optimal individual of the fitness value is obtained after iteration and the maximum iteration number is reached, outputting the minimum value of the objective function to obtain the optimal control strategy of the wind curtailment of the combined heat and power system.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims

1. A thermoelectric combined system optimal scheduling method based on scene analysis and hybrid energy storage is characterized by comprising the following steps:

establishing a hybrid energy storage-based digital twin model of the combined heat and power system;

2. The optimal scheduling method of a cogeneration system of claim 1,

the method for establishing the digital twin model of the combined heat and power system based on the hybrid energy storage comprises the following steps:

carrying out virtual-real fusion on the physical model, the logic model and the simulation model, and constructing a system-level digital twin model of a physical entity of the hybrid energy storage-based thermoelectric combined system in a virtual space;

and accessing multi-working-condition real-time operation data of the hybrid energy storage-based thermoelectric combined system into the system-level digital twin model, and performing self-adaptive identification correction on the simulation result of the system-level digital twin model by adopting a reverse identification method to obtain the identified and corrected digital twin model of the hybrid energy storage-based thermoelectric combined system.

3. The optimal scheduling method of a cogeneration system of claim 2,

when the electric boiler simulation model is constructed, the electric boiler device converts electric energy into heat energy, the optimization of a heat load curve is realized under the guidance of time-of-use electricity price, and an output heat power model is expressed as follows:

Q_eb(t)＝η_ebP_eb(t)；

wherein Q is_eb(t) the output thermal power of the electric boiler at the moment t; eta_ebThe heating efficiency is improved; p_eb(t) isInputting electric power into the electric boiler;

the electric boiler output cost is expressed as:

C_eb(t)＝k_ebP_eb(t)；

wherein, C_eb(t) the output cost of the electric boiler at the moment t; k is a radical of formula_ebIs the unit output cost coefficient of the electric boiler;

wherein, P_w,tThe output is wind power; v is the actual wind speed; v. of_ci、v_coRespectively cut-in and cut-out wind speeds; p_weInstalling capacity for wind power; v. of_rRated wind speed;

when the simulation model of the cogeneration unit is constructed,

wherein, the first and the second end of the pipe are connected with each other,

when the simulation model of the energy storage device is built,

4. The optimal scheduling method of a cogeneration system of claim 3,

the method for setting the operation strategy of the hybrid energy storage device in the combined heat and power system comprises the following steps:

in the wind abandoning period, the electricity storage device is started to store electric energy at low price, and when the electricity storage device reaches the maximum power and still cannot absorb all the abandoned wind, the electric boiler is started to increase the electric load valley value, reduce the lower limit of the electric power of the thermoelectric generator set and increase the wind power grid space; if the electric boiler still cannot absorb all the abandoned wind, the heat storage device is started again, and the wind power online space is increased; in the peak period of the electric load and the low-power period of the wind power, the electric energy is discharged by the electric storage device, the peak value of the electric load is cut, and the income is increased by utilizing the price difference between the peak value and the valley value; meanwhile, the electric boiler stops heating, and the heat storage device releases heat for heat supply load.

5. The optimal scheduling method of a cogeneration system of claim 4,

the method comprises the steps of describing wind power generation uncertainty in the combined heat and power system by adopting a scene analysis method, generating wind power output scenes corresponding to a plurality of wind power simulation errors and corresponding scene probabilities, processing the wind power output scenes by adopting a scene reduction technology to generate wind power output random scenes, and participating in optimal scheduling solution of the combined heat and power system, and specifically comprises the following steps:

the method for generating the wind power output scene by adopting the quasi Monte Carlo method comprises the following steps:

And determining the rest scene sets as:

6. The optimal scheduling method of a cogeneration system of claim 5,

establishing an upper-layer capacity optimization model: based on different capacity combinations of the hybrid energy storage devices, establishing a target function which takes the minimum input cost and the minimum system operation cost of the hybrid energy storage devices under all random scenes as well as setting related constraint conditions and constructing an energy storage device capacity configuration optimization model, wherein the method comprises the following steps:

wherein L is_εFor random number of scenes, pi_εIs the probability of occurrence of scene ε; combined heat and power system operating cost C_rdlh,εObtaining the data from a lower scheduling optimization model;

investment cost: c_cn,tz,ε＝C_glP_gl+C_tesS_tes+C_dlP_dl+C_swS_sw；C_glCost per unit electric boiler capacity; p_glThe construction capacity of the electric boiler; c_tesIs the price per unit capacity of the heat storage device; s_tesThe construction capacity of the heat storage device; c_dlIs the unit electricity storage power cost; p_dlThe capacity is the construction capacity of the stored power; c_swA price per capacity that is the capacity of the electricity storage device; s_swIs the construction capacity of the electricity storage device;

the operation and maintenance cost is as follows: c_cn,yx,ε＝C_batP_bat,ε+C_tsP_ts,ε；C_batConverting the cost coefficient for depreciation of the power storage device; p_bat,εPower for charging or discharging; c_tsA depreciation conversion cost coefficient of the heat storage device; p_ts,εThe thermal power of the heat storage device under the scene epsilon;

transaction cost: c_cn,jy＝C_biW_bi-C_siW_si；C_bi、W_bi、C_si、W_siAre respectively one in a dayPurchase electricity price, purchase electricity quantity, sale electricity price and sale electricity quantity in a time period;

setting a constraint condition:

the remaining capacity constraint condition of the power storage device is as follows:

SOC_min≤SOC_i≤SOC_max；

P_bat,ε≤P_bat,ε.max；

Q_i,ε≤Q_ε,max；

P_ts,ε≤P_ts,ε.max；

thermal load demand constraints: p_t,load,ε′≤η_tdQ_t,out,ε+P_t,gl,ε；P_t,load,εIs a thermal load under scene epsilonA total demand; p_t,gl,εThe power of the electric boiler at the moment t under the scene epsilon; q_t,out,εFor the heat-releasing power of the heat-storage device under the scene epsilon

7. The optimal scheduling method of a cogeneration system of claim 6,

the establishment of a lower-layer scheduling optimization model: based on the operation income and the cost of the combined heat and power system, the method establishes a target function which maximizes the system income under all random scenes, sets related constraint conditions, and constructs a low-carbon economic operation model of the combined heat and power system for wind absorption and curtailment, and comprises the following steps:

cogeneration power revenue:

cogeneration heat recovery:

c_hsupplying heat unit price for the cogeneration unit; q_t,s,εFor combined heat and power machine under scene epsilonThe total heat output power of the bank and the heat storage,

the heat output Q generated by the nth cogeneration unit under the scene epsilon at the moment t_t,in,εIs the heat storage power, Q, of the heat storage device under the scene epsilon_t,out,εThe heat release power of the heat storage device under the scene epsilon;

electric power assisted peak shaving revenue:

wherein, c_peakPeak-shaving electricity price for the cogeneration unit to participate in wind abandoning and consumption; p_t,s,εThe method comprises the following steps that' planned power generation output of a cogeneration unit under a scene epsilon is not considered when abandoned wind peak shaving is not considered; p is_t,c,εAnd P_t,d,εRespectively charging power and discharging power of the power storage device under the scene epsilon;

heat storage and income: i is_tes,ε＝c_peakβQ_t,tes,ε；

the running cost of the cogeneration unit is as follows:

wherein the content of the first and second substances,

the wind power operation and maintenance cost is as follows:

the system carbon emission trading cost is as follows:

the actual carbon emission of the unit;

allocating amount for unit carbon emission right;

σ_jthe carbon emission intensity of the wind turbine generator is shown;

q is the electric power carbon emission quota;

the wind abandon penalty cost is:

setting the constraint conditions of the combined heat and power system:

electric power balance constraint:

P_t,gl,εthe power of the electric boiler at the moment t under the scene epsilon; p is_t,load,εThe electric load demand at the moment t under the scene epsilon;

and thermal power balance constraint:

η_chefficiency of the electric boiler; p_t,load,ε' is the total heat load demand under the scene epsilon;

cogeneration units related constraints: the method comprises the following steps of thermoelectric unit output restraint:

8. The optimal scheduling method of a cogeneration system of claim 7,

and the upper and lower layer models are solved interactively: the method comprises the following steps of performing bidirectional interactive solution on an energy storage device capacity configuration optimization model and a combined heat and power system wind-curtailment low-carbon economic operation model to obtain an optimal operation scheme of the combined heat and power system and an optimal configuration capacity of the energy storage device, wherein the bidirectional interactive solution comprises the following steps:

9. The optimal scheduling method of a cogeneration system of claim 8,

the algorithm adopted by the solution of the upper-layer capacity optimization model and the lower-layer scheduling optimization model comprises the following steps: particle swarm optimization algorithm, whale algorithm, genetic algorithm and mixed integer programming method.