CN110197312A - A kind of user class integrated energy system Optimization Scheduling based on Multiple Time Scales - Google Patents

Abstract

The patent of the present invention discloses a user-level integrated energy system optimization scheduling method based on multiple time scales. The controllable unit start-stop plan and output; in the intraday stage, layered control is realized according to the dynamic characteristics and time constant differences of cold and heat energy and electric energy. For short electric power fluctuations, the unit output can be corrected through the unit regulation penalty cost guidance and regulation range constraints. The forecast deviation of renewable energy ensures the safe, stable, efficient and economical operation of the integrated energy system.

Description

An optimal scheduling method for user-level integrated energy systems based on multiple time scales

Field of study

The invention belongs to the field of integrated energy systems, in particular to a user-level integrated energy system optimization scheduling method based on multiple time scales.

Background technique

Energy is the foundation of human survival and development. The traditional energy system construction tends to plan, design and operate independently of each subsystem. There are differences and barriers in the development of different energy supply systems, and the degree of coupling of electricity, heat, gas and other multi-energy systems is not The lack of multi-energy complementation and coordinated control mechanism may lead to problems such as low energy utilization efficiency, insufficient system operation security, and lack of system self-healing ability in case of failure.

In recent years, energy technologies such as distributed generation, renewable energy, combined cooling, heating and power, microgrids, and information and communication technologies have been rapidly updated, promoting the transformation of traditional discretely operating energy systems into integrated energy systems with multi-energy coupling and coordinated control. The support of energy policy has further accelerated the theoretical promotion and practical implementation of the integrated energy system. Building a comprehensive energy system platform with the power system as the core, and promoting the rapid development of multi-energy coupling, complementary interconnection, is of great significance for realizing the effective consumption of renewable energy, reducing system operating costs, improving energy production efficiency, and providing grid auxiliary services. It has a profound impact on the realization of cross-field, multi-dimensional and multi-level energy integration and energy technology innovation, and is an important measure for national energy conservation and emission reduction and international energy reform.

At present, scholars at home and abroad have done a lot of research on the modeling, simulation and optimal control of integrated energy systems, mainly including the modeling of dynamic characteristics of energy devices and the optimal operation of multi-energy complementary synergies. In terms of modeling the dynamic characteristics of energy devices, many scholars have constructed linear or nonlinear models of integrated energy systems from the perspectives of different time scales, space ranges, and energy links, such as the short-time-scale dynamic characteristics simulation model of the core energy coupling equipment gas turbine. Regional or cross-regional steady-state long-term power grid power flow model, gas network segmented linear transmission model, heat network energy flow transmission model, etc. The index system and evaluation method, multi-objective collaborative optimization method, optimal operation and safety control are studied. However, it needs to be further concerned that the response characteristics of electricity, heat and gas are different on the time scale, so the optimal scheduling strategy of the integrated energy system with the coupling of electricity, heat and gas is often not considered or cannot be integrated and Overcoming the difference in the time scale of the corresponding characteristics of electric and heating gas makes the integrated energy system scheduling unable to play its role to the greatest extent, with large errors and low work efficiency, thus overcoming and solving the integrated energy system with electricity-heat-gas multi-energy coupling The difference in time scale of different response characteristics becomes very important.

SUMMARY OF THE INVENTION

The present invention is precisely aimed at the problems in the prior art, and provides a user-level integrated energy system optimization scheduling method based on multiple time scales, which analyzes the difference between the scheduling objectives of the integrated energy system at different stages in the day-to-day period and the adjustment time scale of different energy forms. It can not only reduce the operating cost of the system as much as possible in the day-ahead scheduling stage, but also adjust the time difference between cold and heat energy and electric energy in the intra-day scheduling stage. With the shrinking of the time scale, the prediction deviations of the cooling and heating loads, as well as the electric load and the power generation of renewable energy are compensated in turn, so as to ensure the safe, stable, efficient and economical operation of the integrated energy system.

In order to achieve the above object, the technical solution adopted in the present invention is: a multi-time-scale-based user-level integrated energy system optimization scheduling method, comprising the following steps:

S1, determine and obtain the basic operating parameters of the integrated energy system;

S2, establish an optimal dispatch model in the day-ahead economic dispatch stage of the integrated energy system, and the objective function of the optimal dispatch model is:

Among them, C _fuel (t) is the fuel cost; C _grid (t) is the grid interaction cost; C _device (t) is the equipment maintenance cost; C _on-off (t) is the start and stop cost of the unit; C _heat-cold (t) ) is the cost of selling heat and cold;

Constraints in the optimal dispatch model include electric power balance constraints of the integrated energy system, thermal power balance constraints, cold power balance constraints, upper and lower limits of controllable units, ramp constraints of controllable units, battery-related constraints, and heat storage tank-related constraints. , Relevant constraints of cold storage tanks and interactive power constraints of tie lines;

S3, using a mixed linear integer programming solver to solve the optimal scheduling model of the economic scheduling stage established in step S2, and the obtained day-ahead optimization result is used as a benchmark operating point and an optimal scheduling basis for intra-day scheduling;

S4, establish a two-layer optimal scheduling model in the intraday rolling scheduling phase of the integrated energy system. The goal of the upper-layer scheduling model in the intraday rolling two-layer scheduling optimization model is: by adjusting the cooling and heating energy supply units, to stabilize the cooling and heating with a long scheduling time. Energy power fluctuation; the goal of the lower-level scheduling model in the intraday rolling double-layer scheduling optimization model is: by adjusting the electric energy scheduling units, according to the output changes of wind power, photovoltaic, electric load and the upper-level micro-gas turbine, the electric energy dispatching plan for the day is revised;

S5, use a quadratic programming solver to solve the double-layer optimal scheduling model model built in step S4 in the rolling scheduling stage, and obtain the real-time scheduling results of cooling and heating energy at the hour level and electric energy at the minute level.

As an improvement of the present invention, the objective function of the upper-layer scheduling model in the intraday rolling double-layer scheduling optimization model in the step S4 is:

in, is the unit price of natural gas; η _MT (t) is the power generation efficiency of the micro-combustion turbine; L is the low calorific value of natural gas, taking 9.7kW.h/m3; ΔP _MT (t), ΔP _EB (t), ΔP _EC (t), ΔQ _AC (t) is the adjusted power of the micro-gas turbine, electric boiler, electric refrigerator and absorption chiller, respectively; ω _MT , ω _EB , ω _EC , and ω _AC are the micro-gas turbine, electric boiler, electric refrigerator and absorption chiller unit penalty cost of the chiller;

The objective function of the lower-level scheduling model in the intraday rolling double-level scheduling optimization model is:

_Among them, _{η FC} ( _t ) is the power generation efficiency of the fuel cell _; Discharge adjustment power; ω _FC , ω _ex , ω _ESch , and ω _ESdis are the unit penalty costs of fuel cell, grid interaction power, battery charging, and battery discharging, respectively.

As a kind of improvement of the present invention, in described step S2, fuel cost C _fuel (t) is:

C _fuel =C _MT +C _FC

Among them, C _MT is the fuel cost of the micro-combustion engine; C _FC is the fuel cell cost;

The grid interaction cost C _grid (t) is:

Among them, SD(t) and GD(t) are the electricity sales and purchase prices of the upper-level distribution network to the user-level integrated energy system at time t, respectively; P _ex (t) is the grid interaction power at time t;

The equipment maintenance cost C _device (t) is:

Wherein, λ _device,i is the unit operation and maintenance cost of the ith equipment unit in the system; P _i (t) is the output of the ith equipment unit in the time period t; n is the total number of equipment units;

The start-stop cost C _on-off (t) of the unit is:

Among them, λ _on-off,j is the one-time start-stop cost of the controllable unit j (micro-gas turbine, fuel cell, electric boiler); S _j (t) is the start-stop state of the controllable unit j in the time period t; the Controllable unit j includes micro-combustion engine, fuel cell, electric boiler; ng is the total number of controllable units;

The cost of selling heat and cooling, C _heat (t) is:

C _heat-cold (t)＝λ _heat .Q _heat (t)+λ _cold .Q _cold (t)

Among them, λ _heat is the unit heating cost; Q _heat (t) is the total heating capacity of the microgrid in time period t; λ _cold is the unit cooling cost; Q _cold (t) is the total cooling capacity of the microgrid in time period t.

As another improvement of the present invention, in the two-layer optimal scheduling model of the intraday rolling scheduling stage in the step S4, the constraints of the upper-layer scheduling model include the thermal power balance constraint, the cold power balance constraint and the comprehensive energy system after the comprehensive energy system changes. The system micro-combustion turbine adjustment amount constraint; the constraints of the lower-level dispatch model include the electric power balance constraint after the integrated energy system changes, the integrated energy system fuel cell adjustment constraint, the battery adjustment constraint, and the grid interactive power adjustment constraint.

Compared with the prior art, the patent of the present invention proposes a multi-time-scale-based integrated energy system optimization scheduling method, which can not only reduce the operating cost of the system as much as possible in the day-ahead scheduling stage, but also can reduce the operating cost of the system in the intra-day scheduling stage according to the cold and heat energy. The adjustment time difference between the power and the electric energy can be compensated for the cooling and heating load and the predicted deviation of the electric load and the power generation of renewable energy in turn with the shrinking of the time scale, so as to ensure the safe, stable, efficient and economical operation of the integrated energy system.

Description of drawings

1 is a system architecture diagram of a user-level integrated energy system structure of the present invention;

FIG. 2 is a graph of the day-ahead forecast data and the intra-day rolling correction data in Embodiment 2 of the present invention, wherein

Fig. 2a is a graph of daily data and intraday data of electric load;

Figure 2b is a graph of daily data and intraday data of photovoltaics;

Figure 2c is a graph of daily data and intraday data of wind power;

Figure 2d is a graph of the day-to-day data and intra-day data of the heat load;

Figure 2e is a graph of day-ahead data and intra-day data of cooling load;

Fig. 3 is the electricity purchase and sale price diagram of the power grid in Embodiment 2 of the present invention;

Fig. 4 is the electric heating and cooling power balance diagram dispatched before the day in Embodiment 2 of the present invention, wherein

Figure 4a is an electric power balance diagram;

Figure 4b is a thermal power balance diagram;

Figure 4c is a cold power balance diagram;

FIG. 5 is a power change diagram of each unit in the upper-layer cold and heat energy dispatching in the intraday rolling dispatching according to Embodiment 2 of the present invention, wherein

Fig. 5a is the power variation diagram of the micro-combustion engine;

Figure 5b is a power change diagram of an electric boiler;

Figure 5c is a graph of the power change of absorption refrigerant;

Fig. 5d is a power change diagram of an electric refrigerator;

FIG. 6 is a power change diagram of each unit in the lower-level electric energy dispatching in the intraday rolling dispatching according to Embodiment 2 of the present invention, wherein

Fig. 6a is a power change diagram of the fuel cell;

Fig. 6b is a change diagram of grid interaction power;

Figure 6c is a power change diagram of battery charging;

Figure 6d is a power change diagram of battery discharge.

Detailed ways

The present invention will be described in more detail below with reference to the accompanying drawings and embodiments.

Example 1

This embodiment is applied to an integrated energy system, and the structure of the integrated energy system is shown in FIG. 1 .

An optimal scheduling method for a user-level integrated energy system based on multiple time scales, comprising the following steps:

Step S1: Obtain basic operating parameters of the integrated energy system. The basic parameters in this embodiment include renewable energy sources such as photovoltaics and wind power, as well as daily forecasted output of cooling, heating and power loads, micro-combustion turbines in the system, electric boilers, fuel cells, and absorption. Capacity and ramp constraints of equipment such as refrigerators, electric refrigerators, batteries, heat storage tanks, and cold storage tanks, the interactive power constraints of the upper-level power grid tie line, and the electricity purchase and sale price of the power grid.

Step S2, establishing an optimal dispatch model for the day-ahead economic dispatch stage of the integrated energy system, including objective functions and constraints;

The objective function of the established integrated energy system day-ahead economic dispatch optimization model is:

Among them, C _fuel (t) is the fuel cost; C _grid (t) is the grid interaction cost; C _device (t) is the equipment maintenance cost; C _on-off (t) is the start and stop cost of the unit; C _heat-cold (t) ) is the cost of selling heat and cooling.

The formula for calculating the fuel cost C _fuel (t) is:

C _fuel =C _MT +C _FC

Among them, C _MT is the fuel cost of the micro-combustion engine; C _FC is the fuel cell cost.

The calculation formula of grid interaction cost C _grid (t) is:

Among them, SD(t) and GD(t) are the electricity sales and purchase prices of the upper-level distribution network to the user-level integrated energy system at time t, respectively; P _ex (t) is the grid interactive power at time t.

The formula for calculating equipment maintenance cost C _device (t) is:

Among them, λ _device,i is the unit operation and maintenance cost of the ith equipment unit in the system; P _i (t) is the output of the ith equipment unit in the period t; n is the total number of equipment units.

The formula for calculating unit start-stop cost C _on-off (t) is:

Among them, λ _on-off,j is the one-time start-stop cost of the controllable unit j (micro-gas turbine, fuel cell, electric boiler); S _j (t) is the start-stop state of the controllable unit j in the time period t; ng is Total number of controllable units.

The formula for calculating the cost of selling heat and cooling C _heat (t) is:

C _heat-cold (t)＝λ _heat .Q _heat (t)+λ _cold .Q _cold (t)

Among them, λ _heat is the unit heating cost; Q _heat (t) is the total heating capacity of the microgrid in time period t; λ _cold is the unit cooling cost; Q _cold (t) is the total cooling capacity of the microgrid in time period t.

Constraints in the optimal dispatch model include electric power balance constraints of the integrated energy system, thermal power balance constraints, cold power balance constraints, upper and lower limits of controllable units, ramp constraints of controllable units, battery-related constraints, and heat storage tank-related constraints. , Relevant constraints of cold storage tanks and interactive power constraints of tie lines, including:

Step S21, establishing the electric power balance constraint condition of the integrated energy system is:

P _MT (t)+P _FC (t)+P _WT (t)+P _PV (t)-P _ESch (t)+P _ESdis (t)+P _ex (t)=Pl(t)+P _EB ( t)+ _PEC (t) where P _WT (t) is the wind power in period t; P _PV (t) is the photovoltaic power in period t; P _ESch (t) is the battery charging power in period t; P _ESdis (t) ) is the battery discharge power in period t; P _MT (t) is the electric power of the micro-combustion gas in period t; P _FC (t) is the electric power of fuel cell in period t; _{PE B} (t) is the electric power of electric boiler in period t; P _EC ( t) The electromechanical power of the electric refrigerator in the period t; P _ex (t) the grid interaction power in the period t; Pl(t) the electric load power in the period t.

Step S22, establishing the thermal power balance constraint condition of the integrated energy system is:

Q _MTh (t)+Q _EB (t)-Q _HSch (t)+Q _HSdis (t)=Ql(t)+Q _AC (t)

Among them, Q _MTh (t) is the heating value provided by the waste heat of the flue gas in the period t; Q _EB (t) is the heating amount of the electric boiler in the period t; Q _AC (t) is the thermal power consumed by the absorption chiller in the period t ; Q _HSch (t) is the heat absorption power of the heat storage tank in the period t; Q _HSdis (t) is the heat release power of the heat storage tank in the period t; Ql(t) is the heat load power in the period t.

Step S23, establishing the cold power balance constraint condition of the integrated energy system is:

L _MTh (t)+L _AC (t)+L _ECa (t)+L _ECd (t)=Ll(t)

L _ECa (t) + L _ECc (t) = L _EC (t)

Among them, L _MTh (t) is the cooling power of the micro-combustion turbine in the period t; L _AC (t) is the cooling power of the absorption refrigerator in the period t; L _EC (t) is the output cooling power of the electric refrigerator in the period t ; L _ECa (t) is the cooling power of the electric refrigerator in the period t; L _ECc (t) is the ice storage power of the electric refrigerator in the period t; L _ECd (t) is the ice melting power of the electric refrigerator in the period t ; Ll(t) is the cooling load power of period t.

In step S24, the upper and lower limit constraints of the controllable units of the integrated energy system are established as follows:

Among them, S _i ^CG (t) is the operating state of the i-th controllable unit in time period t; P _i ^CG (t) is the output of the i-th controllable unit in time period t; is the lower limit of the capacity of the i-th controllable unit; is the upper limit of the capacity of the i-th controllable unit.

In step S25, the constraint conditions for building the controllable unit of the integrated energy system for the ramp up are:

in, are the up and down ramp rates of the controllable unit, respectively.

In step S26, the relevant constraint conditions for establishing an integrated energy system battery are:

Among them, E _ES (t) is the electric energy storage capacity in period t; ζ is the self-discharge rate of electric energy storage; P _ESch (t) and P _ESdis (t) are the charging and discharging power in period t, respectively; η _ESch and η _ESdis are respectively is the charge and discharge efficiency of the battery; γ _ESch and γ _ESdis are the maximum charge and discharge rates of the battery, respectively; C _apES is the total capacity of the battery; are the maximum and minimum states of charge of the battery, respectively.

In step S27, the constraints related to the establishment of the integrated energy system heat storage tank are:

Among them, H _HS (t) is the thermal energy storage capacity in the period t; ψ is the heat dissipation loss rate of the thermal energy storage; Q _HSch (t) and Q _HSdis (t) are the endothermic and exothermic powers in the period t, respectively; η _HSch and η _HSdis are the heat absorption and release efficiencies of the heat storage tank, respectively; γ _HSch and γ _HSdis are the maximum charge and discharge rates of the heat storage tank, respectively; C _apHS is the total capacity of the heat storage tank; are the maximum and minimum thermal energy states of the heat storage tank, respectively.

In step S28, the relevant constraint conditions for establishing the integrated energy system cold storage tank are:

Among them, H _HS (t) is the capacity of the cold storage tank in the period t; σ is the self-loss coefficient of the cold storage tank; L _ECc (t) and L _ECd (t) are the ice storage and melting power of the cold storage tank in the period t, respectively; η _lch and η _ldis are the ice storage and ice melting coefficients of the cold storage tank, respectively; _SECa (t), _SECc (t), and _SECd (t) are the cooling, ice storage and ice melting states of the electric refrigerator, respectively; The minimum and maximum cooling capacity, maximum ice storage capacity, and maximum ice melting capacity of the electric refrigerator, respectively.

In step S29, the interactive power constraint condition of the tie line of the integrated energy system is established as follows:

P _ex,min ≤P _ex (t)≤P _ex,max

Among them, P _ex,min and P _ex,max are the upper and lower limits of the power grid interactive power output, respectively.

In step S3, the mixed linear integer programming solver is used to solve the model built in S2, and the optimization result before the day is used as the benchmark operating point of the intraday scheduling and the optimization scheduling basis;

Step S4, establishing a two-layer optimal scheduling model in the day-to-day rolling scheduling stage of the integrated energy system, including objective functions and constraints;

The upper-level scheduling objective of the established intraday rolling double-layer scheduling optimization model of the integrated energy system is to adjust the cooling and heating energy supply related units (micro-gas turbines, electric boilers, absorption chillers, electric chillers, and cold storage tanks) to stabilize the scheduling time. The power fluctuation of cold and heat energy, the scheduling time window is 2h, the control time domain is 1h, and the objective function is:

in, is the unit price of natural gas; η _MT (t) is the power generation efficiency of the micro-combustion turbine; L is the low calorific value of natural gas, taking 9.7kW.h/m ³ ; ΔP _MT (t), ΔP _EB (t), ΔP _EC (t), ΔQ _AC (t) is the adjusted power of the micro-gas turbine, electric boiler, electric refrigerator and absorption refrigerator, respectively; ω _MT , ω _EB , ω _EC , and ω _AC are the micro-gas turbine, electric boiler, electric refrigerator and Unit penalty cost of absorption chillers.

The constraints of the upper model of the established integrated energy system intraday rolling dispatch optimization model are as follows:

In step S41, the thermal power balance constraint condition after the change of the integrated energy system is established is:

in, The heating value provided for the residual heat of the micro-combustion machine flue gas after the adjustment of the time period t; is the heating capacity of the electric boiler after adjustment for time period t; Input thermal power for the absorption chiller adjusted for time period t.

In step S42, the cold power balance constraint condition after the change of the integrated energy system is established is:

in, is the cooling capacity of the micro-combustion engine adjusted for the period t; is the refrigerating power of the absorption chiller adjusted for the period t; is the cooling power of the electric refrigerator adjusted for the period t.

In step S43, the constraints on the adjustment amount of the micro-gas turbines in the integrated energy system are established as follows:

-0.05×P _MT,max ≤ΔP _MT (t)≤0.05×P _MT,max

Among them, ΔP _MT (t) is the power adjustment amount of the micro-gas turbine.

The lower-level scheduling objective of the established intraday rolling double-layer scheduling optimization model of the integrated energy system is to adjust the electric energy dispatch related units (fuel cell, grid interactive power, electric energy storage), according to the change of wind power, photovoltaic, electric load and the output of the upper micro-gas turbine. , to amend the energy dispatching plan before the day, the dispatching time window is 1h, the control time domain is 5min, and its objective function is:

_Among them, _{η FC} ( _t ) is the power generation efficiency of the fuel cell _; Discharge adjustment power; ω _FC , ω _ex , ω _ESch , and ω _ESdis are the unit penalty costs of fuel cell, grid interaction power, battery charging, and battery discharging, respectively.

The constraints of the lower model of the established integrated energy system intraday rolling dispatch optimization model are as follows:

In step S44, the electric power balance constraint condition after the change of the comprehensive energy system is established is:

in, is the daily corrected power of wind power in period t; is the daily corrected power of photovoltaics for the period t; are the adjusted period t fuel cell, grid interaction power, battery charging, and battery discharging power, respectively.

Step S45, establishing the fuel cell adjustment constraints of the integrated energy system as follows:

S _FC (t)×P _FC,min ≤P _FC (t)+ΔP _FC (t)≤S _FC (t)×P _FC,max

Among them, S _FC (t) is the state of the fuel cell in time period t; P _FC,min and P _FC,max are the upper and lower output limits of the fuel cell respectively; ΔP _FC (t) is the fuel cell power adjustment amount in time period t.

Step S46, establish the battery adjustment constraints of the integrated energy system as follows:

-0.05×C _apES ＜=η _ESch ×ΔP _{ESch -ΔP ESdis} /η _ESdis ＜=0.05*C _apES

0<=P _ESch +ΔP _ESch <=y _ES,C *C _apES

0 <= P _ESdis +ΔP _ESdis <= 2*y _ES,C *C _apES

Among them, ΔP _ESch and ΔP _ESdis are the charging and discharging capacity of the battery, respectively.

In step S47, the constraint condition for establishing the power grid interactive power adjustment of the integrated energy system is:

-0.05×P _ex,max ≤ΔP _ex (t)≤0.05×P _ex,max

Among them, ΔP _ex (t) is the change of grid interactive power.

In step S5, the quadratic programming solver is used to solve the model built in S4, and the real-time scheduling results of cooling and heating energy at the hour level and electric energy at the minute level are obtained, so as to ensure the safe, stable, efficient and economical operation of the integrated energy system.

Example 2

The user-level integrated energy system structure diagram of this embodiment is shown in Figure 1. The equipment components in the system include: photovoltaic, wind power, fuel cell, micro-gas turbine, electric boiler, absorption refrigerator, electric refrigerator, electric energy storage, thermal Energy storage, ice storage tanks, etc., and power interaction with the upper-level distribution network. The basic data of photovoltaic, wind power and load include daily forecast data and intraday rolling correction data, as shown in Figure 2, Figure 2a is the daily data and intraday data of electric load, Figure 2b is the daily data and intraday data of photovoltaic, Figure 2c is wind power Figure 2d shows the day-to-day data and intra-day data of heat load, Figure 2e shows the day-to-day data and intra-day data of cooling load; the electricity purchase and sale price of the grid is shown in Figure 3; The related parameters such as slope constraint are shown in Table 1.

Table 1 Relevant operating parameters of controllable units

After the steps according to the present invention are run, the electric, heating and cooling power balances scheduled before the day are shown in Figure 4, including the electric power balance diagram in Figure 4a, the thermal power balance diagram in Figure 4b, and the cold power balance diagram in Figure 4c; The power change of each unit of thermal energy dispatch is shown in Figure 5, including the power change diagram of the micro-combustion turbine--Fig. 5a, the power change diagram of the electric boiler--Fig. 5b, and the absorption refrigerant power change diagram--Fig. 5c and The power change diagram of the electric refrigerator--Fig. 5d; the power change of the lower-level electric energy dispatching units in the daily rolling dispatch is shown in Fig. 6, including the power change diagram of the fuel cell--Fig. 6a, and the change diagram of the grid interactive power--Fig. 6b , the power change diagram of battery charging--Fig. 6c and the power change diagram of battery discharge--Fig. 6d; it can be seen that on the basis of consuming renewable energy to the greatest extent, the start-stop and output of each controllable unit are coordinated and controlled. , which can not only jointly meet the energy demand of various energy forms including cold, heat and electricity in the user-level integrated energy system, but also ensure the economical operation of the system under the guidance of electricity prices and operating costs of various equipment. At the same time, using the time-shifting characteristics of energy storage systems such as batteries, heat storage tanks, and cold storage tanks can improve the flexibility of system operation, and achieve multi-time-scale optimal scheduling of the system through two-stage scheduling within the day before and double-layer scheduling of cold, thermal and electrical energy. Gradually and accurately adjust the output of controllable equipment to effectively compensate for the power prediction error of load and renewable energy.

Therefore, the method in this paper can not only reduce the operating cost of the system as much as possible in the day-ahead scheduling stage, but also make up for the cooling and heating load and the electric load and the available energy in the intraday scheduling stage according to the adjustment time difference between the cooling and heating energy and the electric energy with the reduction of the time scale. Renewable energy generation power prediction deviation, so as to ensure the safe, stable, efficient and economical operation of the integrated energy system.

The foregoing has shown and described the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above examples, the above examples and descriptions only illustrate the principles of the present invention, and the present invention will have various changes without departing from the spirit and scope of the present invention. and improvements, which fall within the scope of the claimed invention. The claimed scope of the present invention is defined by the appended claims and their equivalents.

Claims (7)

1. a user-level integrated energy system optimization scheduling method based on multiple time scales, is characterized in that, comprises the following steps: S1, determine and obtain the basic operating parameters of the integrated energy system; S2, establish an optimal dispatch model in the day-ahead economic dispatch stage of the integrated energy system, and the objective function of the optimal dispatch model is: Among them, C _fuel (t) is the fuel cost; C _grid (t) is the grid interaction cost; C _device (t) is the equipment maintenance cost; C _on-off (t) is the start and stop cost of the unit; C _heat-cold (t) ) is the cost of selling heat and cold; Constraints in the optimal dispatch model include electric power balance constraints of the integrated energy system, thermal power balance constraints, cold power balance constraints, upper and lower limits of controllable units, ramp constraints of controllable units, battery-related constraints, and heat storage tank-related constraints. , Relevant constraints of cold storage tanks and interactive power constraints of tie lines; S3, using a mixed linear integer programming solver to solve the optimal scheduling model of the economic scheduling stage established in step S2, and the obtained day-ahead optimization result is used as a benchmark operating point and an optimal scheduling basis for intra-day scheduling; S4, establish a two-layer optimal scheduling model in the intraday rolling scheduling phase of the integrated energy system. The goal of the upper-layer scheduling model in the intraday rolling two-layer scheduling optimization model is: by adjusting the cooling and heating energy supply units, to stabilize the cooling and heating with a long scheduling time. Energy power fluctuation; the goal of the lower-level scheduling model in the intraday rolling double-layer scheduling optimization model is: by adjusting the electric energy scheduling units, according to the output changes of wind power, photovoltaic, electric load and the upper-level micro-gas turbine, the electric energy dispatching plan for the day is revised; S5, use a quadratic programming solver to solve the double-layer optimal scheduling model model built in step S4 in the rolling scheduling stage, and obtain the real-time scheduling results of cooling and heating energy at the hour level and electric energy at the minute level. 2. a kind of user-level integrated energy system optimization scheduling method based on multiple time scales as claimed in claim 1, it is characterized in that: in described step S4, the objective function of the upper-level scheduling model in the intraday rolling double-layer scheduling optimization model is: in, is the unit price of natural gas; η _MT (t) is the power generation efficiency of the micro-combustion turbine; L is the low calorific value of natural gas, taking 9.7kW.h/m3; ΔP _MT (t), ΔP _EB (t), ΔP _EC (t), ΔQ _AC (t) is the adjusted power of the micro-gas turbine, electric boiler, electric refrigerator and absorption chiller, respectively; ω _MT , ω _EB , ω _EC , and ω _AC are the micro-gas turbine, electric boiler, electric refrigerator and absorption chiller unit penalty cost of the chiller; The objective function of the lower-level scheduling model in the intraday rolling double-level scheduling optimization model is: _Among them, _{η FC} ( _t ) is the power generation efficiency of the fuel cell _; Discharge adjustment power; ω _FC , ω _ex , ω _ESch , and ω _ESdis are the unit penalty costs of fuel cell, grid interaction power, battery charging, and battery discharging, respectively. 3. a kind of user-level integrated energy system optimization scheduling method based on multiple time scales as claimed in claim 1 or 2, is characterized in that: in described step S2, fuel cost C _fuel (t) is: C _fuel =C _MT +C _FC Among them, C _MT is the fuel cost of the micro-combustion engine; C _FC is the fuel cell cost; The grid interaction cost C _grid (t) is: Among them, SD(t) and GD(t) are the electricity sales and purchase prices of the upper-level distribution network to the user-level integrated energy system at time t, respectively; P _ex (t) is the grid interaction power at time t; The equipment maintenance cost C _device (t) is: Wherein, λ _device,i is the unit operation and maintenance cost of equipment unit i in the system; P _i (t) is the output of equipment unit i in time period t; n is the total number of equipment units; The start-stop cost C _on-off (t) of the unit is: Among them, λ _on-off,j is the one-time start-stop cost of the controllable unit j (micro-gas turbine, fuel cell, electric boiler); S _j (t) is the start-stop state of the controllable unit j in the time period t; the Controllable unit j includes micro-combustion engine, fuel cell, electric boiler; ng is the total number of controllable units; The cost of selling heat and cooling, C _heat (t) is: C _heat-cold (t)＝λ _heat .Q _heat (t)+λ _cold .Q _cold (t) Among them, λ _heat is the unit heating cost; Q _heat (t) is the total heating capacity of the microgrid in time period t; λ _cold is the unit cooling cost; Q _cold (t) is the total cooling capacity of the microgrid in time period t. 4. A kind of user-level integrated energy system optimization scheduling method based on multiple time scales as claimed in claim 3, it is characterized in that: in described step S2, controllable unit upper and lower limit constraints are: in, is the operating state of the i-th controllable unit in time period t; P _i ^CG (t) is the output of the i-th controllable unit in time period t; is the lower limit of the capacity of the i-th controllable unit; is the upper limit of the capacity of the i-th controllable unit; The relevant constraints of the heat storage tank are: Among them, H _HS (t) is the thermal energy storage capacity in the period t; ψ is the heat dissipation loss rate of the thermal energy storage; Q _HSch (t) and Q _HSdis (t) are the endothermic and exothermic powers in the period t, respectively; η _HSch and η _HSdis are the heat absorption and release efficiencies of the heat storage tank, respectively; γ _HSch and γ _HSdis are the maximum charge and discharge rates of the heat storage tank, respectively; C _apHS is the total capacity of the heat storage tank; are the maximum and minimum thermal energy states of the heat storage tank, respectively; The relevant constraints of the cold storage tank are: Among them, H _HS (t) is the capacity of the cold storage tank in the period t; σ is the self-loss coefficient of the cold storage tank; L _ECc (t) and L _ECd (t) are the ice storage and melting power of the cold storage tank in the period t, respectively; η _lch and η _ldis are the ice storage and ice melting coefficients of the cold storage tank, respectively; _SECa (t), _SECc (t), and _SECd (t) are the cooling, ice storage and ice melting states of the electric refrigerator, respectively; The minimum and maximum cooling capacity, maximum ice storage capacity, and maximum ice melting capacity of the electric refrigerator, respectively. 5. A user-level integrated energy system optimization scheduling method based on multiple time scales as claimed in claim 3, characterized in that: in said step S4, in the two-layer optimal scheduling model of the intraday rolling scheduling stage, the constraints of the upper-level scheduling model The conditions include thermal power balance constraints, cooling power balance constraints, and adjustment amount constraints of the integrated energy system after the integrated energy system changes; the constraints of the lower-level dispatch model include the electric power balance constraints after the integrated energy system changes, the integrated energy system constraints Fuel cell regulation constraints, battery regulation constraints and grid interaction power regulation constraints. 6. A kind of user-level integrated energy system optimization scheduling method based on multiple time scales as claimed in claim 5, it is characterized in that: the micro-gas turbine adjustment quantity constraint condition in the upper-layer scheduling model of described step S4 is: -0.05×P _MT,max ≤ΔP _MT (t)≤0.05×P _MT,max Among them, ΔP _MT (t) is the power adjustment amount of the micro-gas turbine. 7. A kind of user-level integrated energy system optimization scheduling method based on multiple time scales as claimed in claim 5, it is characterized in that: the grid interactive power adjustment constraint condition in described step S4 lower layer scheduling model is: -0.05×P _ex,max ≤ΔP _ex (t)≤0.05×P _ex,max Among them, ΔP _ex (t) is the change of grid interactive power.