CN117892884A

CN117892884A - Comprehensive energy system optimization design method, device, equipment and medium

Info

Publication number: CN117892884A
Application number: CN202410263361.9A
Authority: CN
Inventors: 杨佳奇; 高飒; 单彬彬; 贾颉辉; 王绍琨; 宁爱华; 周振玲; 刘丰艺; 刘志坚; 吴迪
Original assignee: State Grid Corp of China SGCC; State Grid Beijing Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; State Grid Beijing Electric Power Co Ltd
Priority date: 2024-03-08
Filing date: 2024-03-08
Publication date: 2024-04-16

Abstract

The invention belongs to the technical field of comprehensive energy system optimization, and particularly relates to a comprehensive energy system optimization design method, a comprehensive energy system optimization design device, comprehensive energy system optimization design equipment and comprehensive energy system optimization design media. The scheme comprises power type energy storage equipment, energy type energy storage equipment and a compressed air energy storage system, and the advantage complementation of various energy storage devices is realized by combining various energy storage equipment. On a time scale in the day, the super capacitor is used as a power type energy storage device to bear high-frequency fluctuation components, the lithium battery is used as an energy type energy storage device to bear low-frequency fluctuation, the power fluctuation is decomposed through variation modal decomposition, the power respectively born by the power type energy storage device and the energy type energy storage device is determined, the lowest annual operation cost is used as an optimization target, and then the rated power and rated capacity of the super capacitor and the lithium battery are determined. The comprehensive energy system obtained by the method can effectively stabilize the fluctuation of renewable energy and the electric load of a user in actual operation, and ensures the stability, flexibility and economy of the system.

Description

Comprehensive energy system optimization design method, device, equipment and medium

Technical Field

The invention belongs to the technical field of comprehensive energy system optimization, and particularly relates to a comprehensive energy system optimization design method, a comprehensive energy system optimization design device, comprehensive energy system optimization design equipment and comprehensive energy system optimization design media.

Background

Along with the gradual aggravation of world fossil energy shortage and ecological environment deterioration, the acceleration of the transformation of energy structures mainly comprising fossil energy into renewable energy is the key to solve the current energy environment problem. The comprehensive energy system (INTEGRATED ENERGY SYSTEM, IES) can realize multi-energy complementation and energy cascade utilization, and is an energy system with multiple structures, flexible operation and strong controllability. Renewable energy sources are added into the comprehensive energy system, so that the use of natural gas and pollution emission can be reduced, and the energy conservation, emission reduction and consumption reduction are further promoted.

But the characteristics of intermittence, randomness and fluctuation of renewable energy sources, uncertainty of user load and the like bring challenges to the safe and stable operation of the comprehensive energy system. The energy storage device is used as a core device component of the comprehensive energy system and can play an important role in the aspects of renewable energy source absorption, peak clipping, valley filling and the like. However, a single energy storage technology cannot meet the stability and flexibility of the integrated energy system at the same time.

Disclosure of Invention

The invention aims to provide an optimal design method, device, equipment and medium for a comprehensive energy system, which are used for solving the problem that a single energy storage technology in the prior art cannot simultaneously meet the stability and flexibility of the comprehensive energy system.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

the invention provides an optimization design method of a comprehensive energy system, which comprises the following steps:

Acquiring a pre-constructed mathematical model of the comprehensive energy system; the comprehensive energy system comprises new energy power generation equipment, energy supply equipment, power type energy storage equipment, energy type energy storage equipment and a compressed air energy storage system;

Acquiring a pre-established daily operation strategy of the comprehensive energy system, and typical daily time-by-time load data and typical daily wind-light output data;

Based on a mathematical model of the comprehensive energy system, a day-ahead running strategy of the comprehensive energy system, the typical day-by-day load data and the typical day-to-wind power output data, constructing a day-ahead optimization model with the aim of economic optimization, and solving the day-ahead optimization model to obtain an output plan of energy supply equipment and a compressed air energy storage system;

Determining wind-solar output data and an electric load of a user in the day, and determining energy deviation between power supply of a comprehensive energy system and the electric load of the user in the day based on the output plans of the energy supply equipment and the compressed air energy storage system, the wind-solar output data and the electric load of the user in the day; taking the energy deviation as a power instruction, and carrying out variation modal decomposition on the power instruction to obtain K eigen-modal functions;

Dividing K eigen mode functions according to a preset frequency standard to obtain a high-frequency component and a low-frequency component; reconstructing the high-frequency component and the low-frequency component respectively to obtain high-frequency power and low-frequency power; wherein high frequency power is carried by the power type energy storage device and low frequency power is carried by the energy type energy storage device;

Based on the high-frequency power and the low-frequency power, an energy storage optimization model is built by taking the total economical efficiency of the power type energy storage equipment and the energy type energy storage equipment as a target; and solving the energy storage optimization model to obtain rated power and rated capacity of the power type energy storage equipment and rated power and rated capacity of the energy type energy storage equipment respectively.

Further, the new energy power generation equipment comprises wind power generation equipment and photovoltaic power generation equipment; the energy supply equipment comprises an internal combustion engine, an organic Rankine cycle unit, a trough type solar heat collector, a heat storage system, a cylinder liner water heat exchanger, a tail heat exchanger, an absorption heat pump, an electric refrigerator and a gas boiler; the power type energy storage device is a super capacitor; the energy-type energy storage device is a lithium battery.

Further, a pre-established daily operation strategy of the comprehensive energy system is obtained, wherein the daily operation strategy of the comprehensive energy system is specifically as follows:

the electric power generated by the new energy power generation equipment is preferentially used to meet the electric load of a user, and if the electric quantity remains, the redundant electric energy generation capacity is stored by the compressed air energy storage system; if the electric quantity gap occurs, determining the partial load rate of the internal combustion engine at the moment; when the partial load rate of the internal combustion engine is larger than or equal to a preset value, starting the internal combustion engine and the organic Rankine cycle unit to generate power, if the internal combustion engine and the organic Rankine cycle unit cannot meet the electric quantity gap, generating power by the compressed air energy storage system, and if the compressed air energy storage system cannot meet the electric quantity gap, purchasing power from a power grid to meet the electric quantity gap; when the partial load rate of the internal combustion engine is smaller than a preset value, directly enabling the compressed air energy storage system to generate power, and if the compressed air energy storage system cannot generate power yet meet the electric quantity gap, purchasing power from a power grid to meet the electric quantity gap; the absorption heat pump and the electric refrigerator cooperate to meet the cold load of a user, and if a cold gap occurs, the heat provided by the gas-fired boiler enters the absorption heat pump to complement the cold gap; the groove type solar heat collector and the tail heat exchanger jointly meet the heat load of a user, and if heat remains, the heat storage system stores redundant heat; if the heat gap occurs, the heat storage system provides heat, and if the heat storage system still cannot meet the heat gap, the gas-fired boiler is started to complement the heat gap.

Further, a day-ahead optimization model is built by taking economical optimization as a target based on the mathematical model of the comprehensive energy system, the day-ahead operation strategy of the comprehensive energy system, the typical day-by-day load data and the typical day wind-light output data; the day-ahead optimization model is as follows:

constructing an objective function by taking economical optimization as a target:

Wherein: OMC represents the economic goal of the comprehensive energy system and operates the maintenance cost; c _ope、C_grid、C_gas is the operation maintenance cost, the power grid electricity purchasing cost and the natural gas purchasing cost respectively; c _ope,i represents the cost per unit capacity of the device i; e _i,t is the output of the equipment i at the time t; m _i is the number of devices i; c _gas is the price of natural gas; n _gas,t is the total amount of natural gas consumed by the system at the moment t, and the total amount of natural gas consumed by the system at the moment t comprises the gas consumption N _ICE,t of the internal combustion engine at the moment t and the gas consumption N _GB,t;c_grid,t of the gas boiler at the moment t; e _grid,t is the electricity purchasing quantity at the moment t;

constraint conditions of the day-ahead optimization model are determined as follows:

Wherein: e _ICE,t is the power generation amount of the internal combustion engine at the moment t; e _ORC,t is the generated energy of the organic Rankine cycle unit at the moment t; e _PV,t is the generated energy of the photovoltaic power generation equipment at the moment t; e _WT,t is the generated energy of the wind power generation equipment at the moment t; e _grid,t is the electricity purchasing quantity of the power grid at the moment t; e _EC,t is the power consumption of the electric refrigerator at the moment t; h _GB,t is the heat provided by the gas boiler at the moment t; h _tank,t is the heat stored in the heat storage water tank at the moment t; h _AHP,t is the heat consumed by the absorption heat pump at the moment t; e _CAE,out,t、E_{CAE,in ,t} is the discharge capacity and the storage capacity of the compressed air energy storage system at the moment t respectively; h _ex,t is the heat generation amount of the heat exchanger at the moment t; e _load,t、H_load,t and C _load,t are the electrical, thermal and cold loads of the user at time t, respectively; c _AHP,t is the refrigerating capacity of the absorption heat pump at the moment t; c _EC,t is the refrigerating capacity of the electric refrigerator at the moment t; c _waste,t is the cold rejection at time t.

Further, determining an energy deviation of the integrated energy system power supply and the daily user electrical load based on the output plan of the energy supply device and the compressed air energy storage system, the daily wind-light output data and the daily user electrical load, includes:

Wherein: subscript t2 is the time interval within a day; subscripts rq, rn represent before and during the day, respectively; p _HESS is a power instruction to be born by the energy storage device; p _ICE is the engine power; p _ORC is the power generated by the organic rankine cycle unit; p _WT is the power generated by the fan; p _PV is photovoltaic power; p _grid is the power purchased by the power grid; p _load is the user electrical load power; p _EC is the user cooling power; p _CAE is the compressed air energy storage system power; p _SC is the power of the super capacitor; p _BAT is the power of the lithium battery.

Further, reconstructing the high frequency component and the low frequency component to obtain a high frequency power and a low frequency power, respectively, including:

Wherein t represents the moment; p _H、P_L is high frequency power and low frequency power, respectively; Is an intrinsic mode function; the number of low frequency components is/> ; k represents the number of eigenmode functions.

Further, an energy storage optimization model is built by taking the total economical efficiency of the power type energy storage equipment and the energy type energy storage equipment as a target; wherein, the energy storage optimization model is as follows:

wherein: c _all is the annual total cost sum of the lithium battery and the super capacitor; c _BAT、C_SC is the investment operation cost of the lithium battery and the super capacitor respectively; lambda is the rate of occurrence; y _BAT、Y_SC is the service life of the lithium battery and the super capacitor respectively; beta _BAT,1、β_BAT,2、β_BAT,3 is the unit power cost, the unit capacity cost and the operation maintenance cost of the lithium battery respectively; beta _SC,1、β_SC,2、β_SC,3 is the unit power cost, the unit capacity cost and the operation and maintenance cost of the super capacitor respectively; p _SC,N is the rated power of the super capacitor; e _SC,N is the rated capacity of the super capacitor; p _BAT,N is the rated power of the lithium battery; e _BAT,N is the rated capacity of the lithium battery.

In a second aspect of the present invention, there is provided an integrated energy system optimization design device, comprising:

The first data acquisition module is used for acquiring a mathematical model of the pre-constructed comprehensive energy system; the comprehensive energy system comprises new energy power generation equipment, energy supply equipment, power type energy storage equipment, energy type energy storage equipment and a compressed air energy storage system;

The second data acquisition module is used for acquiring a pre-established daily operation strategy of the comprehensive energy system, typical daily time-by-time load data and typical daily wind-light output data;

the day-ahead optimization module is used for constructing a day-ahead optimization model based on a mathematical model of the comprehensive energy system, a day-ahead operation strategy of the comprehensive energy system, the typical day-by-day load data and the typical day-to-wind-light output data, and solving the day-ahead optimization model by taking economical optimization as a target to obtain an output plan of energy supply equipment and a compressed air energy storage system;

The deviation calculation module is used for determining wind-solar power output data in the day and the electric load of the user in the day, and determining the energy deviation between the power supply of the comprehensive energy system and the electric load of the user in the day based on the output plans of the energy supply equipment and the compressed air energy storage system, the wind-solar power output data in the day and the electric load of the user in the day; taking the energy deviation as a power instruction, and carrying out variation modal decomposition on the power instruction to obtain K eigen-modal functions;

The power decomposition module is used for dividing K eigen mode functions according to a preset frequency standard to obtain a high-frequency component and a low-frequency component; reconstructing the high-frequency component and the low-frequency component respectively to obtain high-frequency power and low-frequency power; wherein high frequency power is carried by the power type energy storage device and low frequency power is carried by the energy type energy storage device;

The energy storage optimization module is used for constructing an energy storage optimization model based on the high-frequency power and the low-frequency power and taking the total economical efficiency of the power type energy storage equipment and the energy type energy storage equipment as the target; and solving the energy storage optimization model to obtain rated power and rated capacity of the power type energy storage equipment and rated power and rated capacity of the energy type energy storage equipment respectively.

In a third aspect of the present invention, an electronic device is provided, including a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the integrated energy system optimization design method as described above.

In a fourth aspect of the present invention, a computer readable storage medium is provided, where at least one instruction is stored, where the at least one instruction, when executed by a processor, implements an integrated energy system optimization design method as described above.

Compared with the prior art, the invention has the following beneficial effects:

According to the comprehensive energy system optimization design method provided by the invention, the designed comprehensive energy system comprises the power type energy storage equipment, the energy type energy storage equipment and the compressed air energy storage system, and the advantages of the various energy storage devices are complemented by combining the various energy storage equipment, so that the comprehensive energy system has the advantages in the aspects of stabilizing renewable energy fluctuation, improving the system economy and the like.

According to the comprehensive energy system optimization design method provided by the invention, the compressed air energy storage system is selected as energy storage equipment on a long-time scale before the day to play a role in peak clipping and valley filling. The mathematical model of each device in the comprehensive energy system is established, the operation strategy of energy utilization is determined, the economy is further used as an optimization target, key parameters of device operation are used as optimization variables, the daily optimal operation strategy of the comprehensive energy system under the economic target is obtained through optimization, and the economy of the system is effectively improved. On the time scale in the day, the renewable energy volatility and the user electrical load instability in the comprehensive energy system are considered, the super capacitor is selected as the power type energy storage device to bear the high-frequency fluctuation component, the lithium battery is selected as the energy type energy storage device to bear the low-frequency fluctuation, and the hybrid energy storage system is constructed together with the compressed air energy storage system. Further, power fluctuation is decomposed through variation modal decomposition, power born by the power type energy storage equipment and the power born by the energy type energy storage equipment are determined, annual operation cost is the lowest as an optimization target, and rated power and rated capacity of the super capacitor and the lithium battery are further determined. The comprehensive energy system in the scheme can effectively stabilize the fluctuation of renewable energy and the electric load of the user, and ensures the stability, flexibility and economy of the system.

Drawings

FIG. 1 is a schematic flow chart of an optimization design method of a comprehensive energy system according to an embodiment of the invention;

FIG. 2 is a block diagram of a comprehensive energy system in an embodiment of the invention;

FIG. 3 is a block diagram of an apparatus for optimizing design of an integrated energy system according to an embodiment of the present invention;

Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The application will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.

Example 1

As shown in fig. 1, the method for optimizing the design of the integrated energy system comprises the following steps:

s10, acquiring a mathematical model of the pre-built comprehensive energy system.

As shown in fig. 2, the comprehensive energy system in the scheme comprises new energy power generation equipment, energy supply equipment, power type energy storage equipment, energy type energy storage equipment and a compressed air energy storage system; the power type energy storage device, the energy type energy storage device and the compressed air energy storage system form a hybrid energy storage system. Specific: the new energy power generation equipment comprises wind power generation equipment, photovoltaic power generation equipment and the like; the wind power generation equipment mainly generates power through a fan, and the photovoltaic power generation equipment mainly generates power through a photovoltaic panel; the energy supply equipment comprises an internal combustion engine, a cylinder liner water heat exchanger, an Organic Rankine cycle unit (ORC unit) RANKINE CYCLE, a heat storage system, a trough type solar heat collector, a tail heat exchanger, an absorption heat pump, an electric refrigerator, a gas boiler and the like; the power type energy storage device is a super capacitor; the energy type energy storage device is a lithium battery; the compressed air energy storage system comprises a first heat exchanger, a second heat exchanger, a third heat exchanger, a fourth heat exchanger, a first compressor, a second compressor, an air storage chamber, a first expander, a second expander, a motor and a generator; the user backwater is heated by the waste heat of the internal combustion engine in the cylinder sleeve water heat exchanger and then becomes low-temperature water, the low-temperature water sequentially enters the first heat exchanger and the second heat exchanger to be heated again, so that medium-temperature water is obtained, and the medium-temperature water enters the tail heat exchanger, so that high-temperature water is obtained; the motor drives the first compressor to compress air, the air after the first compression is cooled in the first heat exchanger, then enters the second compressor to compress again, the air after the second compression is cooled in the second heat exchanger, and then the air is stored in the air storage chamber; when the compressed air energy storage system generates power, compressed air in the air storage chamber enters the first expander to apply work after being heated for the first time in the third heat exchanger to drive the generator to generate power, and then enters the fourth heat exchanger to enter the second expander to apply work after being heated again to drive the generator to generate power; or compressed air in the air storage chamber directly and sequentially enters the third heat exchanger and the fourth heat exchanger to heat up for two times, and then enters the second expander to apply work to drive the generator to generate electricity.

Specifically, the mathematical model of the integrated energy system includes the following:

1) Internal combustion engine

The internal combustion engine is used as main equipment of the comprehensive energy system, and the partial load rate PLR _ICE has the following calculation formula:

（1）

Wherein: w _ICE and G _ICE are the actual power generation and rated capacity of the internal combustion engine, kW, respectively.

2) Organic Rankine cycle unit

R113 is used as an organic working medium of the organic Rankine cycle unit, and the calculation formula of the power generation of the organic Rankine cycle unit is as follows:

（2）

Wherein: The power generation amount of the organic Rankine cycle unit; q _m,R113 is the mass flow of the organic working medium, kg/s; h ₁ and h ₂ are the specific enthalpies of the organic working medium R113 flowing into and out of the turbine, respectively.

3) Compressed air energy storage system

In this scheme, compressed air energy storage system includes first heat exchanger, second heat exchanger, third heat exchanger, fourth heat exchanger, first compressor, second compressor, air receiver, first expander, second expander, motor and generator. It should be noted that, in the present solution, when the compressed air energy storage system is running, air is regarded as ideal gas, and the specific heat capacity is unchanged; the heat capacity of air is equal to that of the heat storage medium; irrespective of the momentum change and gravitational potential energy change of the fluid in the process; the compressor is thermally insulated from the operation of the expander.

(1) Energy storage stage

When storing energy, the compressed air process is regarded as an irreversible adiabatic variable process; the compressor outlet air temperature T _co,out is calculated as follows:

（3）

Wherein: t _co,in is the inlet temperature of the compressor, K; kappa is the adiabatic polytropic index; beta _c is the pressure ratio of the compressor; η _c is the isentropic efficiency of the compressor.

The compression work (W _co, J) for compressing the mkg air is obtained by:

（4）

Wherein: The constant pressure specific heat capacity of air, J/(kg.K).

In addition, the efficiency of the heat exchanger in the compressed air energy storage system is shown in formula (5):

（5）

Wherein: c is the specific heat capacity of the corresponding fluid at constant pressure, J/(kg.K); q is the mass flow of the fluid in the heat exchanger, kg/s; t _in、T_out respectively represents the inlet and outlet fluid temperature of the heat exchanger, K; subscripts c and h represent cold and hot side fluids, respectively; (cq) _min represents: and taking the minimum value of the constant-pressure specific heat capacity in the fluid at the cold side and the hot side of the heat exchanger and the minimum value of the mass flow in the fluid at the cold side and the hot side of the heat exchanger, and multiplying the two minimum values.

The pressure loss coefficient of the heat exchanger is shown as (6):

（6）

The heat exchanger outlet fluid temperature is calculated as follows:

（7）

Wherein: t _1,in and T _2,in are the temperatures of the different fluids on both sides of the heat exchanger, respectively.

(2) Energy release stage

The expander outlet air temperature is calculated as follows:

（8）

Wherein: t _e,in is the temperature of the inlet air of the expander, K; beta _e is the expansion ratio of the expander; Is the isentropic efficiency of the expander.

The work performed by the mkg air in the expander is calculated by formula (9):

（9）

(3) Air storage chamber

① Energy storage process

In the energy storage process, the air storage chamber and the outside have no exchange of substances and energy, and the change conditions of the pressure ratio beta and the temperature in the air storage chamber along with time are as follows:

（10）

（11）

Wherein: t _in is the inlet air temperature of the air storage chamber, K; h _c is the convection heat transfer coefficient of the gas and the wall surface of the gas storage chamber, W/(m ²·K);A_c) is the inner surface area of the gas storage chamber, m ²;T_w is the inner wall temperature of the gas storage chamber, K, T is the inner air temperature of the gas storage chamber, K, T is time, s, q _m,b is the air mass flow rate of the inlet of the gas storage chamber, kg/s, p ₀ is the atmospheric pressure, pa, c _v is the specific heat capacity of the air, J/(kg.K), R _g is the gas constant, J/(kg.K), and V is the volume of the gas storage chamber, m ³.

② Energy release process

The relation between the pressure ratio in the gas storage chamber and the temperature along with the time change is as follows:

（12）

（13）

Wherein: q _m,e is the air mass flow at the outlet of the air storage chamber, kg/s.

③ Energy storage and release interval process

In the interval process of energy storage and energy release, the relation between the internal pressure ratio of the gas storage and the temperature along with the time is as follows:

（14）

（15）

(4) Throttle valve

（16）

Wherein: h _val,in and h _val,out respectively represent specific enthalpy of gas at the inlet and outlet of the throttle valve, J/kg.

4) Trough type solar heat collector

The heat collection amount (H _PTC, kW) and the effective solar radiation intensity (R _eff,kW/m²) of the trough type solar heat collector are shown as formulas (17) and (18):

（17）

（18）

（19）

Wherein: a _PTC is the heat collection area of the trough solar collector, m ²;R_b,n is the direct solar radiation intensity, kW/m ²; is the incident angle, °; the/> is declination angle, °; and/> is the solar hour angle, °.

The declination angle and the solar hour angle/> are calculated as:

（20）

（21）

（22）

（23）

（24）

Wherein: n is 1 month 1 day per year to the number of days calculated; when AST is the sun, min; LST is min when the local standard; SL is the longitude of the place where the local standard is located, beijing time longitude is selected, and east longitude is 120 degrees; LL is local longitude, °; ET is the time difference, min, caused by the change of motion and rotation speed when the earth revolves around the sun.

The effective heat collection amount H _PTC,eff of the groove type solar heat collector is shown as the formula:

（25）

wherein: c _p,oil is the constant pressure specific heat capacity of the PTC heat conduction oil, kJ/(kg.K); m _oil is the heat conduction oil mass flow, kg/s; t _oil,out and T _oil,in are respectively the inlet and outlet temperatures K of the heat conduction oil in the PTC; η _PTC is the PTC heat collection efficiency and its formula H _PTC,eff is as follows:

（26）

Wherein: a is the intercept efficiency of the trough solar collector, and is selected to be 0.762; b and c are heat loss coefficients, 0.2125 and 0.001672 are selected respectively; Is ambient temperature.

5) Heat storage system

In this scheme, heat accumulation system is the heat accumulation water tank, and the operation model is as follows:

（27）

（28）

（29）

wherein: h _tank (t) is the heat stored in the heat storage water tank at the moment t, and kW; phi _in、φ_out is the heat accumulation and release power coefficient respectively; h _tank,in、H_tank,out is the stored heat and the output heat of the heat storage water tank, kW; h _in,max、H_out,max is the maximum heat release power and the minimum heat release power of the heat storage water tank respectively, and kW.

6) Cylinder sleeve water heat exchanger

The cylinder sleeve water heat exchanger provides a heat source required by user backwater, and the calculation formula is as follows:

（30）

wherein: h _j,out,H_j,in is the heat quantity coming in and going out of the cylinder sleeve water heat exchanger, and kW; η _j is the heat exchange efficiency of the cylinder liner water heat exchanger, and 0.8.

7) Wind power generation equipment

The wind power generation device mainly comprises a wind power generator, and the relation between the output power (P _WT, kW) and the wind speed (v, m/s) of the wind power generator is as follows:

（31）

Wherein v _in、v_N、v_out is the cut-in wind speed, rated wind speed and cut-out wind speed of the fan, and m/s respectively; p _WT ^N is the rated power of the fan, kWh.

8) Photovoltaic power generation equipment

The photovoltaic power generation equipment mainly generates power through a photovoltaic panel, and the power generated by the photovoltaic panel (P _PV (t) and kW) is influenced by working current (I (t), A), working voltage (V (t), V) and actual working temperature (t _pv,real and DEG C). The actual power generation power is determined by the formula (32), the actual operating current is determined by the formula (34), the actual operating voltage is determined by the formula (35), and the actual operating temperature is determined by the formula (39).

（32）

（33）

（34）

（35）

（36）

（37）

Wherein: p ₀ is the theoretical generated power (kW) of the photovoltaic panel; f _real is the temperature correction coefficient of the photovoltaic panel; f _s is a dust accumulation factor, and 0.98 is taken; r _eff is the solar irradiation intensity, wherein the unit is W/m ²;R_eff,0 is the solar irradiation amount of the PV panel under the standard condition, and W/m ²;I_sc,0 is the short-circuit current of the PV panel with the area of Sm ²; i _pm0 is the peak current of the PV panel with area Sm ²; v _pm0 is the peak voltage of the PV panel with area Sm ². Is the actual operating temperature of the photovoltaic panel,/> is the ambient temperature.

9) Tail heat exchanger

The tail heat exchanger provides heat (H _ex,out, kW) as follows:

（38）

Wherein: h _ex,in is waste heat released to the tail heat exchanger by flue gas, and kW; η _ex is the heat exchange efficiency of the tail heat exchanger, 0.8.

10 Absorption heat pump)

The refrigerating capacity (C _AHP, kW) of the absorption heat pump is calculated as follows:

（39）

Wherein: h _AHP is the heat consumed by the absorption heat pump, kW; COP _AHP is the coefficient of performance of the absorption heat pump and takes a value of 0.7.

11 Electric refrigerator

The refrigerating capacity (C _EC, kW) of the electric refrigerator is calculated as follows:

（40）

wherein: e _EC is the electricity consumption of the electric refrigerator, kW; COP _EC is the coefficient of performance of the electric refrigerator, and is a value of 4.

12 Gas boiler

The supplementary load required by the user is provided by the gas boiler, and the heat release amount H _GB is calculated as follows:

（41）

Wherein: n _GB is the natural gas consumption of the gas boiler and kW; η _GB is the heat exchange efficiency of the gas boiler.

The gas boiler provides heat for supplementing the supplemental heat load and supplemental cold load required by the user, so the following formula is also required:

（42）

wherein: h _GB,h、H_GB,c is the heat and cold quantity, kW that gas boiler provided for the user respectively.

13 Super capacitor)

The operation model of the super capacitor is as follows:

（43）

（44）

（45）/>

（46）

（47）

Wherein: e _SC is the capacity of the super capacitor, kWh; phi _sc is the charge-discharge coefficient of the super capacitor; p _SC,in、P_SC,out is the input power and the output power of the super capacitor, kW respectively; p _SC,N is the rated power of the super capacitor, kW; SOC _SC is the state of charge of the super capacitor; e _SC,N is the rated capacity of the super capacitor, kWh; t represents the time.

14 Lithium battery

（48）

（49）

（50）

（51）

（52）

Wherein: e _BAT is the capacity of the lithium battery, kWh; phi _BAT is the charge and discharge coefficient of the lithium battery; p _BAT,in、P_BAT,out is the input power and the output power of the lithium battery, kW respectively; p _BAT,N is the rated power of the lithium battery, kW; SOC _BAT is the state of charge of the lithium battery; e _BAT,N is the rated capacity of the lithium battery, kWh; t represents the time.

S20, acquiring a pre-established daily operation strategy of the comprehensive energy system, and typical daily time-by-time load data and typical daily wind-light output data.

Specifically, the daily operation strategy of the comprehensive energy system is specifically as follows:

S30, constructing a day-ahead optimization model based on the mathematical model of the comprehensive energy system, the day-ahead operation strategy of the comprehensive energy system, the typical day-by-day load data and the typical day wind-light output data, and solving the day-ahead optimization model by taking economical efficiency optimization as a target to obtain an output plan of energy supply equipment and a compressed air energy storage system.

Specifically, the day-ahead optimization model constructed in the scheme is as follows:

（53）

（54）

（55）

（56）

（57）

Wherein: OMC (Operation Maintenance Cost) represents the economic goal of the integrated energy system operation and maintenance costs; c _ope、C_grid、C_gas is the operation maintenance cost, the power grid electricity purchasing cost and the natural gas purchasing cost respectively; c _ope,i represents the cost per unit capacity of the device i; e _i,t is the output of the equipment i at the time t, and kWh; m _i is the number of devices i; c _gas is the price of natural gas, and the price of natural gas is 0.22 yuan/kWh; n _gas,t is the total amount of natural gas consumed by the system at the moment t, and the total amount of natural gas consumed by the system at the moment t comprises the gas consumption N _ICE,t of the internal combustion engine at the moment t and the gas consumption N _GB,t,kWh;c_grid,t of the gas boiler at the moment t; e _grid,t is the electricity purchasing quantity at the moment t;

determining constraint conditions of a day-ahead optimization model, including cold-hot electric power balance constraint, wherein the constraint conditions comprise the following specific steps:

（58）

（59）

（60）

Wherein: the subscript t represents the time; e _ICE,t is the power generation amount of the internal combustion engine at the moment t; e _ORC,t is the generated energy of the organic Rankine cycle unit at the moment t; e _PV,t is the generated energy of the photovoltaic power generation equipment at the moment t; e _WT,t is the generated energy of the wind power generation equipment at the moment t; e _grid,t is the electricity purchasing quantity of the power grid at the moment t; e _EC,t is the power consumption of the electric refrigerator at the moment t; h _GB,t is the heat provided by the gas boiler at the moment t; h _tank,t is the heat stored in the heat storage water tank at the moment t; h _AHP,t is the heat consumed by the absorption heat pump at the moment t; e _CAE,out,t、E_{CAE,in ,t} is the discharge capacity and the storage capacity of the compressed air energy storage system at the moment t respectively, and kWh is calculated; h _ex,t is the heat generation amount (a cylinder sleeve water heat exchanger, an energy storage side heat exchanger and a tail heat exchanger) of the heat exchanger at the time t, and kWh; e _load,t、H_load,t and C _load,t are respectively the electric, thermal and cold loads of the user at time t, kWh; c _AHP,t is the refrigerating capacity of the absorption heat pump at the moment t; c _EC,t is the refrigerating capacity of the electric refrigerator at the moment t; c _waste,t is the cold rejection at time t.

The daily optimization model constructed above selects the partial load rate PLR _ICE of the internal combustion engine, the smoke ratio r _ORC flowing into the organic Rankine cycle unit, the low-temperature smoke temperature T _s,L entering the tail heat exchanger and the electric refrigeration ratio r _EC as optimization variables. The change of the partial load rate PLR _ICE of the internal combustion engine can obviously influence the power generation efficiency of the internal combustion engine, thereby influencing the energy utilization rate of the system; the ratio r _ORC of the flue gas flowing into the organic Rankine cycle unit and the temperature T _s,L of the low-temperature flue gas entering the tail heat exchanger are adjusted according to the load demand, so that flexible output of cold, heat and electric energy is realized; meanwhile, the change of the electric refrigeration duty ratio r _EC can obviously influence the refrigeration capacity of the absorption heat pump and the electric refrigerator, thereby influencing the electric energy consumption and the energy efficiency of the system.

In order to improve the calculation speed, the scheme adopts a parallel genetic algorithm to solve a day-ahead optimization model, and simultaneously, in order to avoid the premature phenomenon in the optimization process, a self-adaptive crossing and mutation process is introduced to avoid sinking into a local optimal solution.

S40, determining wind-solar output data and an electric load of a user in the day, and determining energy deviation between power supply of a comprehensive energy system and the electric load of the user in the day based on output plans of the energy supply equipment and the compressed air energy storage system, the wind-solar output data and the electric load of the user in the day; and taking the energy deviation as a power instruction, and carrying out variation modal decomposition on the power instruction to obtain K eigenmode functions.

Specifically, determining an energy deviation between a power supply of the integrated energy system and an electric load of the user in the day based on the output plan of the energy supply device and the compressed air energy storage system, the wind-solar output data in the day, and the electric load of the user in the day includes:

（61）

（62）

Wherein: subscript t2 is an intra-day time interval, which in this embodiment is 1 min; subscripts rq, rn represent before and during the day, respectively; p _HESS is a power instruction to be born by the energy storage device; p _ICE is the engine power; p _ORC is the power generated by the organic rankine cycle unit; p _WT is the power generated by the fan; p _PV is photovoltaic power; p _grid is the power purchased by the power grid; p _load is the user electrical load power; p _EC is the user cooling power; p _CAE is the compressed air energy storage system power; p _SC is the power of the super capacitor; p _BAT is the power of the lithium battery.

Specifically, decomposing a power instruction P _HESS needed to be born by the energy storage device through the VMD; wherein, adopting genetic algorithm to optimize the decomposition layer number K and penalty factor alpha; k eigenmode functions (INTRINSIC MODE FUNCTIONS, IMF) are obtained.

S50, dividing the K eigen mode functions according to a preset frequency standard to obtain a high-frequency component and a low-frequency component; reconstructing the high-frequency component and the low-frequency component respectively to obtain high-frequency power and low-frequency power; wherein the high frequency power is carried by the power type energy storage device and the low frequency power is carried by the energy type energy storage device.

Specifically, after K eigenmode functions (INTRINSIC MODE FUNCTIONS, IMF) are obtained, reconstruction is performed according to the response characteristics of different energy storage devices. Specifically, the energy storage device in the scheme comprises a power type energy storage device and an energy type energy storage device; in the scheme, 1 min is selected as a response demarcation point of the lithium battery and the super capacitor, namely an intrinsic mode function with the frequency larger than 1.67 multiplied by 10 ^-2 Hz is used as a high-frequency component, and an IMF with the frequency smaller than 1.67 multiplied by 10 ^-2 Hz is used as a low-frequency component.

Specifically, the high-frequency component and the low-frequency component are respectively reconstructed to obtain high-frequency power and low-frequency power, as follows:

（63）

wherein P _H、P_L is high-frequency power and low-frequency power respectively; Is an intrinsic mode function; and/() is the number of low frequency components.

S60, constructing an energy storage optimization model by taking the total economy optimization of the power type energy storage equipment and the energy type energy storage equipment as a target based on the high-frequency power and the low-frequency power; and solving the energy storage optimization model to obtain rated power and rated capacity of the power type energy storage equipment and rated power and rated capacity of the energy type energy storage equipment respectively.

It should be noted that the rated power of the energy storage device must not be lower than the power of the charge and discharge task that the energy storage device needs to complete at time t, and the rated powers of the super capacitor and the lithium battery are as follows:

（64）

（65）

（66）

（67）

In order to avoid the overcharge and overdischarge conditions of the energy storage devices in the operation process, the rated capacities of the energy storage devices are calculated according to the maximum value and the minimum value of the operation accumulated capacity, and the rated capacities of the lithium battery and the super capacitor are as follows:

（68）

（69）

Wherein: SOC _SC,max and SOC _SC,min are the upper and lower limits of the state of charge of the supercapacitor, respectively.

Specifically, the energy storage optimization model constructed in the scheme is as follows:

（70）

（71）

（72）

Wherein: c _all is the annual total cost sum of the lithium battery and the super capacitor; c _BAT、C_SC is the investment operation cost of the lithium battery and the super capacitor respectively; lambda is the rate of occurrence,%; y _BAT、Y_SC is the service life of the lithium battery and the super capacitor respectively; beta _BAT,1、β_BAT,2、β_BAT,3 is the unit power cost, the unit capacity cost and the operation maintenance cost of the lithium battery respectively; beta _SC,1、β_SC,2、β_SC,3 is the unit power cost, the unit capacity cost and the operation and maintenance cost of the super capacitor respectively; p _SC,N is the rated power of the super capacitor; e _SC,N is the rated capacity of the super capacitor; p _BAT,N is the rated power of the lithium battery; e _BAT,N is the rated capacity of the lithium battery.

It can be understood that the high-frequency power and the low-frequency power are obtained in the scheme, so that the annual comprehensive cost sum of the power type energy storage equipment and the energy type energy storage equipment can be calculated. And finally obtaining the corresponding rated power and rated capacity when the economy is optimal through continuous iterative optimization.

In summary, the scheme provides a collaborative optimization method capable of adjusting the energy output of the system time by time on a long-time scale before the day, so that the economic efficiency is taken as an optimization target, key parameters of equipment operation are taken as optimization variables, and a parallel genetic algorithm is used for optimizing the system, so that a comprehensive energy system day optimal operation strategy under the economic target is obtained, and the economic efficiency of the system is effectively improved; on a time scale in the day, the renewable energy volatility and the user electrical load instability in the comprehensive energy system are considered, the super capacitor is used as a power type energy storage device, a high-frequency fluctuation component is born, the lithium battery is used as a short-time energy type energy storage device, low-frequency fluctuation is born, the power born by the hybrid energy storage system is decomposed through variation mode decomposition, the annual running cost is the lowest as an optimization target, and the rated power and rated capacity of the super capacitor and the lithium battery are further determined. The invention constructs the hybrid energy storage system by combining different types of energy storage equipment such as super capacitor, lithium battery and compressed air energy storage, and the like, thereby further improving the economy of the system while ensuring peak clipping and valley filling, and stabilizing renewable energy and user electric load fluctuation.

Example 2

As shown in fig. 3, the present invention also provides an integrated energy system optimization design device based on the same inventive concept as the above embodiment, including:

Example 3

As shown in fig. 4, the present invention also provides an electronic device 100 for implementing the integrated energy system optimization design method of embodiment 1; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 may be used to store a computer program 103, and the processor 102 implements a comprehensive energy system optimization design method step of embodiment 1 by running or executing the computer program stored in the memory 101 and invoking data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, memory 101 may include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. The at least one processor 102 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-programmable gate array (field-programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, the processor 102 being a control center of the electronic device 100, the various interfaces and lines being utilized to connect various portions of the overall electronic device 100. The memory 101 in the electronic device 100 stores a plurality of instructions to implement a comprehensive energy system optimization design method, and the processor 102 may execute the plurality of instructions to implement:

Example 4

The modules/units integrated with the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of each method embodiment described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, and a Read-Only Memory (ROM).

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Claims

1. The comprehensive energy system optimization design method is characterized by comprising the following steps of:

2. The integrated energy system optimization design method according to claim 1, wherein the new energy power generation equipment comprises wind power generation equipment and photovoltaic power generation equipment; the energy supply equipment comprises an internal combustion engine, an organic Rankine cycle unit, a trough type solar heat collector, a heat storage system, a cylinder liner water heat exchanger, a tail heat exchanger, an absorption heat pump, an electric refrigerator and a gas boiler; the power type energy storage device is a super capacitor; the energy-type energy storage device is a lithium battery.

3. The integrated energy system optimization design method according to claim 2, wherein a pre-established integrated energy system day-ahead operation strategy is obtained, wherein the integrated energy system day-ahead operation strategy is specifically as follows:

The electric power generated by the new energy power generation equipment is preferentially used to meet the electric load of a user, and if the electric quantity remains, the redundant electric energy generation capacity is stored by the compressed air energy storage system; if the electric quantity gap occurs, determining the partial load rate of the internal combustion engine at the moment; when the partial load rate of the internal combustion engine is larger than or equal to a preset value, starting the internal combustion engine and the organic Rankine cycle unit to generate power, if the internal combustion engine and the organic Rankine cycle unit cannot meet the electric quantity gap, generating power by the compressed air energy storage system, and if the compressed air energy storage system cannot meet the electric quantity gap, purchasing power from a power grid to meet the electric quantity gap; when the partial load rate of the internal combustion engine is smaller than a preset value, directly enabling the compressed air energy storage system to generate power, and if the compressed air energy storage system cannot generate power yet meet the electric quantity gap, purchasing power from a power grid to meet the electric quantity gap;

the absorption heat pump and the electric refrigerator cooperate to meet the cold load of a user, and if a cold gap occurs, the heat provided by the gas-fired boiler enters the absorption heat pump to complement the cold gap;

The groove type solar heat collector and the tail heat exchanger jointly meet the heat load of a user, and if heat remains, the heat storage system stores redundant heat; if the heat gap occurs, the heat storage system provides heat, and if the heat storage system still cannot meet the heat gap, the gas-fired boiler is started to complement the heat gap.

4. The integrated energy system optimization design method according to claim 3, wherein a day-ahead optimization model is built with the aim of economy optimization based on the mathematical model of the integrated energy system, the day-ahead operation strategy of the integrated energy system, the typical day-by-day load data and the typical day wind-light output data; the day-ahead optimization model is as follows:

5. The method of claim 4, wherein determining an energy deviation of the integrated energy system power supply from the daily user electrical load based on the output plan of the energy supply device and the compressed air energy storage system, the daily wind and light output data, and the daily user electrical load, comprises:

6. The method for optimizing design of a comprehensive energy system according to claim 5, wherein the reconstructing of the high frequency component and the low frequency component to obtain the high frequency power and the low frequency power comprises:

7. The method for optimizing design of a comprehensive energy system according to claim 6, wherein an energy storage optimization model is constructed with the aim of optimizing the total economy of the power type energy storage device and the energy type energy storage device; wherein, the energy storage optimization model is as follows:

8. An integrated energy system optimization design device, comprising:

9. An electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the integrated energy system optimization design method of any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the integrated energy system optimization design method of any one of claims 1-7.