CAPACITY CONFIGURATION METHOD FOR PHOTOVOLTAIC/PHOTOTHERMAL/AA-CAES OF COMBINED COOLING, HEATING AND POWER

Abstract

A capacity configuration method for photovoltaic/photothermal/Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) of combined cooling, heating and power (CCHP). The method includes: establishing a CCHP micro integrated energy system model containing AA-CAES, and then solving the model by using a dual-level planning approach. The upper level plans capacity configuration is planned with an objective of minimizing a sum of capacity configuration costs and lower-level scheduling costs, while the lower level employs parameters obtained from the upper level to implement season-based scheduling, with an objective of minimizing a sum of energy supply costs and carbon mitigation costs, and returns a scheduling result to the upper level to assist in upper-level capacity decisions. This method takes a CCHP capability of a compressed air energy storage system into consideration, and establishes a model suitable for capacity planning and scheduling.

Claims

1. A capacity configuration method for photovoltaic/photothermal/Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) of combined cooling, heating and power (CCHP), comprising the following steps: step 1: establishing a CCHP micro integrated energy system model containing AA-CAES, which comprises inputs from renewable energy sources of wind power, photovoltaic power, and solar thermal collection, a combined heat and power (CHP) unit with a gas generator and a waste heat recovery boiler, refrigeration equipment for absorption cooling and electric cooling, and an energy storage device of AA-CAES, wherein it is assumed that in the system model, air behaves as an ideal gas, following an ideal gas state equation, an air storage chamber has a temperature approximately equal to ambient temperature, a thermal storage chamber has a temperature approximately equal to a rated temperature, and water serves as a heat transfer medium; step 2: establishing an upper-level objective function max B.sub.bf, with an objective of maximizing a net benefit brought by AA-CAES:
max B.sub.bf=C.sub.noCAES?C.sub.CAES?C.sub.TCC?C.sub.O&M wherein C.sub.noCAES is energy costs without AA-CAES configuration; C.sub.CAES is energy costs with AA-CAES configuration, derived from lower-level scheduling; C.sub.TCC is annualized investment costs; and C.sub.O&M is annualized operation and maintenance costs of a system; upper-level decision variables x comprise:
x={A.sub.PV,A.sub.SF,P.sub.CAESc,P.sub.CAESt,V.sub.v,V.sub.h,?.sub.v,?.sub.h} wherein A.sub.PV is the number of photovoltaic panels; A.sub.SF is a ground area of a mirror field; P.sub.CAESc is a rated power of a compressor; P.sub.CAESt is an expansion power of an expander; V.sub.v is a volume of the air storage chamber; V.sub.h is a volume of the thermal storage chamber; ?.sub.v is an initial gas proportion in the air storage chamber; and ?.sub.h is an initial hot water proportion in the thermal storage chamber; upper-level constraints are ground areas for photovoltaic panels and solar thermal collection:
A.sub.PVS.sub.PV+A.sub.SF?S.sub.MAX wherein S.sub.PV is a ground area for a single photovoltaic panel; and S.sub.MAX is a maximum total ground area; step 3: establishing a lower-level objective function, with a scheduling objective of minimizing energy supply costs and carbon mitigation costs after configuring AA-CAES: $C_{\mathrm{CAES}}=D_{\mathrm{spa}}\left(P_{\mathrm{spa}}{C}_e+P_{\mathrm{spaGT}}?C_{\mathrm{gas}}+P_{\mathrm{spab}}{?}_e{C}_{{\mathrm{co}}_2}+P_{\mathrm{spaGT}}?{?}_g{C}_{{\mathrm{co}}_2}\right)+D_{\mathrm{su}}\left(P_{\mathrm{sub}}{C}_e+P_{\mathrm{suGT}}?C_{\mathrm{gas}}+P_{\mathrm{sub}}{?}_e{C}_{{\mathrm{co}}_2}+P_{\mathrm{suGT}}?{?}_g{C}_{{\mathrm{co}}_2}\right)+D_w\left(P_{\mathrm{wb}}{C}_e+P_{\mathrm{wGT}}?C_{\mathrm{gas}}+P_{\mathrm{wb}}{?}_e{C}_{{\mathrm{co}}_2}+P_{\mathrm{wGT}}?{?}_g{C}_{{\mathrm{co}}_2}\right)$ wherein D.sub.spa, D.sub.su, D.sub.w represent the number of days in transitional seasons, summer, and winter, respectively, during one year; P.sub.spab, P.sub.sub, P.sub.wb represent amounts of purchased electricity on typical days in transitional seasons, summer, and winter, respectively; C.sub.e is an electricity price, using time-of-use pricing; P.sub.spaGT, P.sub.suGT, P.sub.wGT represent outputs of a gas turbine on typical days in transitional seasons, summer, and winter, respectively; ? is a correlation coefficient between the output of the gas turbine and natural gas; C.sub.gas is purchase costs of natural gas per unit; ?.sub.e is a conversion coefficient of CO.sub.2 per unit of grid electricity; ?.sub.e is a conversion coefficient of CO.sub.2 per unit of natural gas combustion; and C.sub.co.sub.2 is mitigation costs per unit of CO.sub.2; lower-level decision variables x comprise:
x={P.sub.CAESc,t,P.sub.CAESg,t,P.sub.GT,t,P.sub.b,t,P.sub.cold,t,P.sub.rb,t,M.sub.tesc,t,M.sub.tesx,t,M.sub.tescold,t} wherein P.sub.CAESc,t is an output of the compressor at time t; P.sub.CAESg,t is an output of the expander at time t; P.sub.GT,t is an output of the gas turbine at time t; P.sub.b,t is an amount of electricity purchased from a power grid at time t; P.sub.rb,t is an amount of electricity consumed by a heat pump at time t; P.sub.cold,t is an amount of electricity used for cooling at time t; M.sub.tesc,t is a mass of hot water stored into the thermal storage chamber at time t; M.sub.tesx,t is a mass of hot water supplied from the thermal storage chamber at time t; and M.sub.tescold,t is a mass of hot water used for absorption cooling at time t; lower-level constraints comprise an electric power balance constraint, a thermal power balance constraint, a cold power balance constraint, AA-CAES module constraints, CHP constraints, thermal storage chamber constraints, heat pump and electric cooling constraints, and electricity purchase constraints; and step 4: performing crossover and mutation on decision variables by using a genetic algorithm (NSGA-II) at the upper level, with an objective of minimizing total costs, selecting new parent capacity values based on the total costs by applying an elite retention strategy, and passing down results to the lower level, wherein at the lower level, a Gurobi solver is employed to schedule a lower-level model after the results from the upper level are received, and scheduling results are then returned to the upper level to assist in capacity decisions, achieving dual-level planning.

2. The capacity configuration method for photovoltaic/photothermal/AA-CAES of CCHP according to claim 1, wherein the lower-level constraints specifically comprise: (1) electrical power balance constraint:
P.sub.b,t+P.sub.GT,t+P.sub.WT,t+P.sub.PV,t+P.sub.t,t=P.sub.L,t+P.sub.c,t+P.sub.bc,t wherein P.sub.WT,t is a wind power output at time t; P.sub.bc,t is an amount of electricity used for electric cooling; and P.sub.L,t is an amount of electricity for electric load; (2) thermal power balance constraint:
H.sub.gl,t+M.sub.tesg,t?.sub.h+P.sub.SF,t+M.sub.c,2?.sub.h+P.sub.rb?.sub.er=H.sub.L,t+M.sub.tesc,t?.sub.h+M.sub.g,2?.sub.h+M.sub.tescold,t?.sub.h wherein H.sub.L,t is thermal load at time t; M.sub.c,2 and M.sub.g,2 represent a mass of water for compression/expansion and a mass of water for heat generation, respectively; and ?.sub.h is a conversion coefficient between a hot water mass and heat; (3) cold power balance constraint:
P.sub.bc,t?.sub.ec+M.sub.tescold,t?.sub.h?.sub.hc+P.sub.t,cold,t=Cold.sub.L,t wherein ?.sub.ec is electric cooling efficiency; ?.sub.hc is absorption cooling efficiency; P.sub.t,cold,t is a cooling capacity of the expander at time t; and Cold.sub.L,t is cooling load at time t; (4) AA-CAES module constraints: $\{\begin{array}{c}0?u_{c,t}+u_{t,t}?1\\ 0?P_{c,t}?u_{c,t}{P}_{\mathrm{CAESc}}\\ 0?P_{t,t}?u_{t,t}{P}_{\mathrm{CAESt}}\\ V_v{?}_{\min }?M_{a,c}{u}_{c,t}-M_{a,t}{u}_{t,t}+M_{\mathrm{air},t}?V_v{?}_{\max }\end{array}$ wherein the first equation represents condition constraints for a compression turbine, and u.sub.c,t and u.sub.g,t represent conditions of the compression turbine at time t, which are binary variables; the second equation represents constraints on a compression power; the third equation represents constraints on an expansion power; and the fourth equation represents constraints on the air storage chamber, wherein M.sub.a,c and M.sub.a,t represent air masses during compression/expansion, and ?.sub.min/?.sub.max represent an air density corresponding to a minimum/maximum pressure set for the air storage chamber; (5) CHP constraints: $\{\begin{array}{c}{?}_{\mathrm{GT},t}{P}_{\mathrm{GT},\min }?P_{\mathrm{GT},t}?{?}_{\mathrm{GT},t}{P}_{\mathrm{GT},\min }\\ 0?H_{\mathrm{gl},t}?H_{\mathrm{gl},\max }\end{array}$ wherein the first equation represents an output constraint on the gas turbine, ?.sub.GT,t is a start-stop coefficient of the gas turbine and is a binary variable, and P.sub.GT,max and P.sub.GT,min represent a maximum output value and a minimum output value of the gas turbine; and the second equation represents an output constraint on the waste heat recovery boiler, wherein H.sub.gl,max is a maximum output value of the waste heat recovery boiler; (6) thermal storage chamber constraints: $\{\begin{array}{c}0?{?}_{\mathrm{hc},t}+{?}_{\mathrm{hx},t}?0\\ V_{\min }{?}_w?M_{\mathrm{tesc},t}{?}_{\mathrm{hc},t}-M_{\mathrm{tesx},t}{?}_{\mathrm{hx},t}+M_{\mathrm{tes},t}?V_h{?}_w\end{array}$ wherein in the first equation, ?.sub.hc,t and ?.sub.hx,t represent condition constraints for thermal storage/supply of the thermal storage chamber; and in the second equation, V.sub.min is a minimum thermal storage value set for the thermal storage chamber, ?.sub.w is a density at a set temperature of the thermal storage chamber, and M.sub.tes,t is a mass of stored hot water in the thermal storage chamber at time t; (7) heat pump and electric cooling constraints: $\{\begin{array}{c}0?P_{\mathrm{rb}}*{?}_{\mathrm{er}}?Q_{\mathrm{rb}}\\ 0?P_{\mathrm{bc}}*{?}_{\mathrm{ec}}?{\mathrm{Cold}}_{\mathrm{bc}}\end{array}$ wherein Q.sub.rb is a maximum output thermal power of the heat pump, and Cold.sub.bc is a maximum output power for electric cooling; and (8) electricity purchase constraints:
?P.sub.s,max?P.sub.b,t?P.sub.b,max wherein P.sub.b,max represents a maximum amount of purchased electricity, and P.sub.s,max represents a maximum amount of sold electricity.

3. The capacity configuration method for photovoltaic/photothermal/AA-CAES of CCHP according to claim 1, wherein an AA-CAES model comprises a compression phase, a compression heat transfer phase, a compression heat storage phase, an expansion heat transfer phase, an expansion heat release phase, and an expansion phase; and an output power of an i-th stage expander in the expansion phase is:
P.sub.t,i(t)=?.sub.tm.sub.ac.sub.pT.sub.i[1??.sub.t.sup.?(??1)/(?N)] wherein ?.sub.t is expansion efficiency, ?.sub.t is an expansion ratio of the expander, and a corresponding air mass is derived from the expansion power; an air outlet temperature of the i-th stage expander in the expansion phase is:
T.sub.t,i,out=T.sub.i{1??.sub.t[1??.sub.t.sup.?(??1)/(?N)]} a cooling capacity output of the expansion phase is:
P.sub.t,cold=m.sub.ac.sub.p(T.sub.0?T.sub.t,N,out) wherein T.sub.0 is the ambient temperature, and T.sub.t,N,out is an outlet temperature of a last-stage expander; a photovoltaic output power model is:
P.sub.pv=P.sub.STCI[1+k(T.sub.pv?T.sub.r)]/I.sub.STCA.sub.PV wherein P.sub.STC is a rated power of the photovoltaic panel under standard test conditions; I is light intensity; k is a power temperature coefficient; A.sub.PV is the number of photovoltaic panels; and T.sub.pv is a temperature of a photovoltaic power generation component:
T.sub.pv=T.sub.0+0.03I a solar thermal collection model is:
P.sub.SF=IA.sub.SFI.sub.LI.sub.T?.sub.OPT,R?.sub.END?.sub.CIN wherein A.sub.SF is a ground area of a mirror field; I.sub.L and I.sub.T are longitudinal and transverse components of an incidence angle adjustment rate; ?.sub.OPT,R is reference optical efficiency; ?.sub.END is terminal loss optical efficiency; ?.sub.CIN is a cleanliness coefficient of a mirror and a glass tube; and is a heat transfer coefficient of a solar cooling heat exchanger; and CHP generation comprises the gas turbine unit and the waste heat recovery boiler, and a relationship between the output of the gas turbine and recovered heat is as follows:
P.sub.CHP.sup.e(t)=?.sub.CHP.sup.eG.sub.CHP(t)LHV.sub.gas/3.6
P.sub.CHP.sup.h(t)=?.sub.CHP.sup.hG.sub.CHP(t)LHV.sub.gas/3.6 wherein ?.sub.CHP.sup.e and ?.sub.CHP.sup.e are electricity generation efficiency and heat generation efficiency of the unit; P.sub.CHP.sup.e and P.sub.CHP.sup.h are electric and thermal outputs of the unit, in kW; G.sub.CHP is gas consumption at time t of the CHP unit, in kg/h; LHV.sub.gas is a lower heating value of natural gas.

4. The capacity configuration method for photovoltaic/photothermal/AA-CAES of CCHP according to claim 1, wherein the annualized investment costs C.sub.TCC of the system comprise costs for an energy production module and an energy storage module: $C_{\mathrm{TCC}}=\left(P_{\mathrm{CAESc}}{c}_{\mathrm{psc}}+P_{\mathrm{CAESg}}{c}_{\mathrm{psg}}+V_v{c}_{\mathrm{ESa}}+V_h{c}_{\mathrm{ESw}}+A_{\mathrm{PV}}{c}_{\mathrm{pv}}+A_{\mathrm{SF}}{c}_{\mathrm{SF}}\right)\frac{{i\left(1+i\right)}^T}{{\left(1+i\right)}^T-1}$ wherein C.sub.psc and C.sub.psg represent investment costs per unit of rated compression power and rated expansion power, respectively; C.sub.ESa is investment costs per unit of the air storage chamber; C.sub.SF is investment costs per unit of the thermal storage chamber; C.sub.PV is investment costs of each photovoltaic panel; C.sub.SF is investment costs per unit of the mirror field; i is a discount rate; and T is a service life of system modules; the annualized operation and maintenance costs C.sub.O&M of the system are as follows:
C.sub.O&M=C.sub.O&ME(P.sub.CAESc+P.sub.CAESg)+C.sub.O&MpvA.sub.PVC.sub.O&MSFA.sub.SF wherein C.sub.O&ME is operation and maintenance costs per unit of compression turbine power; C.sub.O&MPV is operation and maintenance costs per unit of the photovoltaic panels; and C.sub.O&MSF is operation and maintenance costs per unit area of the mirror field.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0044] FIG. 1 is an energy flow diagram of a CCHP micro integrated energy system;

[0045] FIGS. 2A-B are structural diagrams of AA-CAES;

[0046] FIG. 3 is CCHP load in winter according to an embodiment;

[0047] FIG. 4 is CCHP load in transitional seasons according to an embodiment;

[0048] FIG. 5 is CCHP load in summer according to an embodiment;

[0049] FIG. 6 is a sunlight intensity curve according to an embodiment;

[0050] FIG. 7 shows wind power output of each season according to an embodiment;

[0051] FIG. 8 is a flowchart of a dual-level planning algorithm;

[0052] FIG. 9 shows a result of electricity scheduling in winter according to an embodiment;

[0053] FIG. 10 shows a result of electricity scheduling in summer according to an embodiment;

[0054] FIG. 11 shows a result of electricity scheduling in transitional seasons according to an embodiment;

[0055] FIG. 12 shows a result of heat scheduling in winter according to an embodiment;

[0056] FIG. 13 shows a result of heat scheduling in summer according to an embodiment;

[0057] FIG. 14 shows a result of heat scheduling in transitional seasons according to an embodiment;

[0058] FIG. 15 shows a result of cooling scheduling in winter according to an embodiment;

[0059] FIG. 16 shows a result of cooling scheduling in summer according to an embodiment;

[0060] FIG. 17 shows a result of cooling scheduling in transitional seasons according to an embodiment;

[0061] FIG. 18 shows a thermal water storage status in a thermal storage chamber according to an embodiment; and

[0062] FIG. 19 shows an air storage status in an air storage chamber according to an embodiment.

DETAILED DESCRIPTION

[0063] The present disclosure will be further illustrated below with reference to the accompanying drawings.

[0064] The capacity configuration method for photovoltaic/photothermal/AA-CAES of CCHP specifically includes the following steps:

[0065] Step 1: Assuming that air behaves as an ideal gas, following an ideal gas state equation, an air storage chamber has a temperature approximately equal to ambient temperature, and a thermal storage chamber has a temperature approximately equal to a rated temperature, establish a CCHP micro integrated energy system model containing AA-CAES, with water as a heat transfer medium, where a system structure and energy flow chart are as shown in FIG. 1, including a compressor, an expander, a motor, a generator, a thermal storage chamber, an air storage chamber, and a heat exchanger. The AA-CAES model includes a compression phase, a compression heat transfer phase, a compression heat storage phase, an expansion heat transfer phase, an expansion heat release phase, and an expansion phase, with a typical 2-stage compression and 2-stage expansion structure as shown in FIGS. 2A-B. In this embodiment, 4-stage compression and 4-stage expansion are selected, and system parameters are shown in Table 1:

TABLE-US-00001 TABLE 1 Parameter Value Ambient temperature T/K 293 Ambient pressure P0/(pa) 100000 Compression ratio per stage ?.sub.c 3.16 Expansion ratio per stage ?.sub.t 3.16 Heat exchanger efficiency ? 0.8 Isentropic efficiency of the compressor ?.sub.c 0.75 Isentropic efficiency of the expander ?.sub.t 0.8 Specific heat capacity of gas c.sub.p (J/(kg .Math. K)) 1 Specific heat capacity of water c.sub.w (J/(kg .Math. K)) 4200 Mass of heat transfer medium water m.sub.w (kg) 10 Specific heat capacity of air ? 1.4 Upper pressure limit of air storage chamber/bar 100 Lower pressure limit of air storage chamber/bar 60

[0066] An output power of the i-th stage expander in the expansion phase is:

P.sub.t,i(t)=?.sub.tm.sub.ac.sub.pT.sub.i[1??.sub.t.sup.?(??1)/(?N)](14)

[0067] where ?.sub.t is expansion efficiency, and ?.sub.t is an expansion ratio of the expander. A corresponding air mass can be derived from the expansion power.

[0068] An air outlet temperature of the i-th stage expander in the expansion phase is:

T.sub.t,i,out=T.sub.i{1??.sub.t[1??.sub.t.sup.?(??1)/(?N)]}(15)

[0069] A cooling capacity output of the expansion phase is:

P.sub.t,cold=m.sub.ac.sub.p(T.sub.0?T.sub.t,N,out) (16)

[0070] where T.sub.0 is the ambient temperature, and T.sub.t,N,out is an outlet temperature of the last-stage expander.

[0071] A photovoltaic output power model is:

P.sub.pv=P.sub.STCI[1+k(T.sub.pv?T.sub.r)]/I.sub.STCA.sub.PV (17)

[0072] where P.sub.STC is a rated power of the photovoltaic panel under standard test conditions (I.sub.STC is 1000 w/m.sup.2, and T.sub.r is 25? C.); I is light intensity; k is a power temperature coefficient; A.sub.PV is the number of photovoltaic panels; and T.sub.pv is a temperature of a photovoltaic power generation component:

T.sub.pv=T.sub.0+0.03I (18)

[0073] A linear Fresnel concentrating heat collection module is selected as a solar concentrating heat collection subsystem in the system, with a model expression as follows:

P.sub.SF=IA.sub.SFI.sub.LI.sub.T?.sub.OPT,R?.sub.END?.sub.CIN(19)

[0074] where A.sub.SF is a ground area of a mirror field; I.sub.L and I.sub.T are longitudinal and transverse components of an incidence angle adjustment rate; ?.sub.OPT,R is reference optical efficiency; ?.sub.END is terminal loss optical efficiency; ?.sub.CIN is a cleanliness coefficient of a mirror and a glass tube; and is a heat transfer coefficient of a solar cooling heat exchanger. The chosen numerical values for these parameters in this embodiment are shown in Table 2:

TABLE-US-00002 TABLE 2 Parameter Value Reference optical efficiency 0.67 Cleanliness coefficient of mirror and glass tube 0.98 Heat transfer coefficient of solar cooling heat exchanger 0.7 Length of individual concentrating heat collection reflector/m 180 Focal length of individual concentrating heat collection 16.56 reflector/m

[0075] CHP generation includes the gas turbine unit and the waste heat recovery boiler. A relationship between the output of the gas turbine and the recovered heat is as follows:

P.sub.CHP.sup.e(t)=?.sub.CHP.sup.eG.sub.CHP(t)LHV.sub.gas/3.6 (20)

P.sub.CHP.sup.h(t)=?.sub.CHP.sup.hG.sub.CHP(t)LHV.sub.gas/3.6 (21)

[0076] where ?.sub.CHP.sup.e and ?.sub.CHP.sup.h are electricity generation efficiency and heat generation efficiency of the unit; P.sub.CHP.sup.e and P.sub.CHP.sup.h are electric and thermal outputs of the unit, in kW; G.sub.CHP is gas consumption at time t of the CHP unit, in kg/h; LHV.sub.gas is a lower heating value of natural gas. The chosen numerical values for these parameters in this embodiment are shown in Table 3:

TABLE-US-00003 TABLE 3 Equipment Parameter Value Gas turbine Output upper limit/(kW) 430 Output lower limit/(kW) 100 LHV.sub.gas (MJ/kg) 50 Heat pump Heating efficiency 0.8 Heating upper limit (kW) 800 Electric refrigerator Cooling efficiency 0.8 Cooling upper limit (kW) 800

[0077] Unit construction costs for each piece of equipment are shown in Table 4:

TABLE-US-00004 TABLE 4 Parameter Value Investment costs per unit of compression power/(CNY .Math. kW.sup.?1) 2340 Investment costs per unit of electric power/(CNY .Math. kW.sup.?1) 1950 Investment costs per unit volume of air storage chamber/ 180 (CNY .Math. m.sup.?3) Investment costs per unit volume of hot water tank/(CNY .Math. m.sup.?3) 800 Operation and maintenance costs per unit power/(CNY .Math. kW.sup.?1) 66 Investment costs per unit area of mirror field/($ .Math. m.sup.?2) 130

[0078] The load, wind power output, and solar radiation intensity curves for each season are shown in FIG. 3 to FIG. 7.

[0079] Step 2: Solve the model by using a dual-level planning approach, where the upper level plans capacity configuration with an objective of minimizing a sum of capacity configuration costs and lower-level scheduling costs, while the lower level is responsible for season-based categorized scheduling due to significant differences in cooling, heating, and power load across seasons. The lower level employs the capacity parameters obtained from the upper level for scheduling, with an objective of minimizing a sum of energy supply costs and carbon mitigation costs, and returns a scheduling result to the upper level to assist in capacity decisions. In this embodiment, a typical day consists of 24 hours, with a scheduling step of 1 hour. It is assumed that the operational lifespan of the system is 20 years, and the discount rate of the system is 8%. Peak-valley electricity prices are set, with peak hours (10:00-12:00 and 16:00-22:00) having a buying price of 1.35 CNY per kWh and a selling price of 1.03 CNY per kWh, off-peak hours (07:00-10:00, 12:00-16:00, and 22:00-23:00) having a buying price of 0.90 CNY per kWh and a selling price of 0.67 CNY per kWh, and valley hours (23:00 to 07:00 of the next day) having a buying price of 0.40 CNY per kWh and a selling price of 0.27 CNY per kWh. The gas purchase price is fixed at 2.01 CNY per m.sup.3. The CO.sub.2 emissions and mitigation costs for the gas turbine and the power grid are as shown in Table 5:

TABLE-US-00005 TABLE 5 Equipment Equipment parameter Value Gas turbine CO.sub.2 emissions 0.16 kg/(kWh) Grid CO.sub.2 emissions 0.616 kg/(kWh) CO.sub.2 mitigation costs 0.11CNY/(kWh)

[0080] Upper-level decision variables x include:

x={A.sub.PV,A.sub.SF,P.sub.CAESc,P.sub.CAESt,V.sub.v,V.sub.h,?.sub.v,?.sub.h}(22)

[0081] where P.sub.CAESc is a rated power of a compressor; P.sub.CAESt is an expansion power of an expander; V.sub.v is a volume of the air storage chamber; V.sub.h is a volume of the thermal storage chamber; ?.sub.v is an initial gas proportion in the air storage chamber; and ?.sub.h is an initial hot water proportion in the thermal storage chamber.

[0082] Establish an upper-level objective function max B.sub.bf, with an objective of maximizing a net benefit brought by AA-CAES:

[00006]$\begin{array}{cc}\max B_{\mathrm{bf}}=C_{\mathrm{noCAES}}-C_{\mathrm{CAES}}-C_{\mathrm{TCC}}-C_{O\&M}& \left(23\right)\end{array}$

[0083] where C.sub.noCAES is energy costs without AA-CAES configuration; C.sub.CAES is energy costs with AA-CAES configuration, derived from lower-level scheduling; C.sub.TCC is annualized investment costs, including costs for an energy production module and an energy storage module:

[00007]$\begin{array}{cc}{C}_{\mathrm{TCC}}=\left(P_{\mathrm{CAESc}}{c}_{\mathrm{psc}}+P_{\mathrm{CAESg}}{c}_{\mathrm{psg}}+V_v{c}_{\mathrm{ESa}}+V_h{c}_{\mathrm{ESw}}+A_{\mathrm{PV}}{c}_{\mathrm{pv}}+A_{\mathrm{SF}}{c}_{\mathrm{SF}}\right)\frac{{i\left(1+i\right)}^T}{{\left(1+i\right)}^T-1}& \left(24\right)\end{array}$

[0084] where C.sub.psc and C.sub.psg represent investment costs per unit of rated compression power and rated expansion power, respectively; C.sub.ESa is investment costs per unit of the air storage chamber; C.sub.SF is investment costs per unit of the thermal storage chamber; C.sub.PV is investment costs of each photovoltaic panel; C.sub.SF is investment costs per unit of the mirror field; i is a discount rate; and T is a service life of system modules.

[0085] C.sub.O&M is annualized operation and maintenance costs of the system:

[00008]$\begin{array}{cc}{C}_{O\&M}=C_{O\&\mathrm{ME}}\left(P_{\mathrm{CAESc}}+P_{\mathrm{CAESg}}\right)+C_{O\&\mathrm{Mpv}}{A}_{\mathrm{PV}}{C}_{O\&\mathrm{MSF}}{A}_{\mathrm{SF}}& \left(25\right)\end{array}$

[0086] where C.sub.O&ME is operation and maintenance costs per unit of compression turbine power; C.sub.O&MPV is operation and maintenance costs per unit of the photovoltaic panels; and C.sub.O&MSF is operation and maintenance costs per unit area of the mirror field.

[0087] Upper-level constraints are ground areas for photovoltaic panels and solar thermal collection:

[00009]$\begin{array}{cc}{A}_{\mathrm{PV}}{S}_{\mathrm{PV}}+A_{\mathrm{SF}}?S_{\mathrm{MAX}}& \left(26\right)\end{array}$

[0088] where S.sub.PV is a ground area for a single photovoltaic panel; and S.sub.MAX is a maximum total ground area.

[0089] Lower-level decision variables x include:

x={P.sub.CAESc,t,P.sub.CAESg,t,P.sub.GT,t,P.sub.b,t,P.sub.cold,t,P.sub.rb,t,M.sub.tesc,t,M.sub.tesx,t,M.sub.tescold,t}(27)

[0090] where P.sub.CAESc,t is an output of the compressor at time t; P.sub.CAESg,t is an output of the expander at time t; P.sub.GT,t is an output of the gas turbine at time t; P.sub.b,t is an amount of electricity purchased from a power grid at time t; P.sub.rb,t is an amount of electricity consumed by a heat pump at time t; P.sub.cold,t is an amount of electricity used for cooling at time t; M.sub.tesc,t is a mass of hot water stored into the thermal storage chamber at time t; M.sub.tesx,t is a mass of hot water supplied from the thermal storage chamber at time t; and M.sub.tescold,t is a mass of hot water used for absorption cooling at time t.

[0091] Step 3: Establish a lower-level objective function, with a scheduling objective of minimize energy supply costs and carbon mitigation costs after configuring AA-CAES:

[00010]$\begin{array}{cc}{C}_{\mathrm{CAES}}=D_{\mathrm{spa}}\left(P_{\mathrm{spa}}{C}_e+P_{\mathrm{spaGT}}?C_{\mathrm{gas}}+P_{\mathrm{spab}}{?}_e{C}_{{\mathrm{co}}_2}+P_{\mathrm{spaGT}}?{?}_g{C}_{{\mathrm{co}}_2}\right)+D_{\mathrm{su}}\left(P_{\mathrm{sub}}{C}_e+P_{\mathrm{suGT}}?C_{\mathrm{gas}}+P_{\mathrm{sub}}{?}_e{C}_{{\mathrm{co}}_2}+P_{\mathrm{suGT}}?{?}_g{C}_{{\mathrm{co}}_2}\right)+D_w\left(P_{\mathrm{wb}}{C}_e+P_{\mathrm{wGT}}?C_{\mathrm{gas}}+P_{\mathrm{wb}}{?}_e{C}_{{\mathrm{co}}_2}+P_{\mathrm{wGT}}?{?}_g{C}_{{\mathrm{co}}_2}\right)& \left(28\right)\end{array}$

[0092] where D.sub.spa, D.sub.su, D.sub.w represent the number of days in transitional seasons, summer, and winter, respectively, during one year; P.sub.spab, P.sub.sub, P.sub.wb represent amounts of purchased electricity on typical days in transitional seasons, summer, and winter, respectively; C.sub.e is an electricity price, using time-of-use pricing; P.sub.spaGT, P.sub.suGT, P.sub.wGT represent outputs of a gas turbine on typical days in transitional seasons, summer, and winter, respectively; ? is a correlation coefficient between the output of the gas turbine and natural gas; C.sub.gas is purchase costs of natural gas per unit; ?.sub.e is a conversion coefficient of CO.sub.2 per unit of grid electricity; ?.sub.e is a conversion coefficient of CO.sub.2 per unit of natural gas combustion; and C.sub.co.sub.2 is mitigation costs per unit of CO.sub.2.

[0093] Lower-level constraints include:

(1) Electrical Power Balance Constraint:

[0094]
P.sub.b,t+P.sub.GT,t+P.sub.WT,t+P.sub.PV,t+P.sub.t,t=P.sub.L,t+P.sub.c,t+P.sub.bc,t (29)

[0095] where P.sub.WT,t is a wind power output at time t; P.sub.bc,t is an amount of electricity used for electric cooling; and P.sub.L,t is an amount of electricity for electric load.

(2) Thermal Power Balance Constraint:

[0096]
H.sub.gl,t+M.sub.tesg,t?.sub.h+P.sub.SF,t+M.sub.c,2?.sub.h+P.sub.rb?.sub.er=H.sub.L,t+M.sub.tesc,t?.sub.h+M.sub.g,2?.sub.h+M.sub.tescold,t?.sub.h (30)

[0097] where H.sub.L,t is thermal load at time t; M.sub.c,2 and M.sub.g,2 represent a mass of water for compression/expansion and a mass of water for heat generation, respectively; and ?.sub.h is a conversion coefficient between a hot water mass and heat.

(3) Cold Power Balance Constraint:

[0098]
P.sub.bc,t?.sub.ec+M.sub.tescold,t?.sub.h?.sub.hc+P.sub.t,cold,t=Cold.sub.L,t (31)

[0099] where ?.sub.ec is electrical cooling efficiency; ?.sub.hc is absorption cooling efficiency; P.sub.t,cold,t is a cooling capacity of the expander at time t; and Cold.sub.L,t is cooling load at time t.

(4) AA-CAES Module Constraints:

[0100] [00011]$\begin{array}{cc}\{\begin{array}{c}0?u_{c,t}+u_{t,t}?1\\ 0?P_{c,t}?u_{c,t}{P}_{\mathrm{CAESc}}\\ 0?P_{t,t}?u_{t,t}{P}_{\mathrm{CAESt}}\\ V_v{?}_{\min }?M_{a,c}{u}_{c,t}-M_{a,t}{u}_{t,t}+M_{\mathrm{air},t}?V_v{?}_{\max }\end{array}& \left(32\right)\end{array}$

[0101] where the first equation represents condition constraints for a compression turbine, and u.sub.c,t and u.sub.g,t represent conditions of the compression turbine at time t, which are binary variables; the second equation represents constraints on a compression power; the third equation represents constraints on an expansion power; and the fourth equation represents constraints on the air storage chamber, where M.sub.a,c and M.sub.a,t represent air masses during compression/expansion, and ?.sub.min/?.sub.max represent an air density corresponding to a minimum/maximum pressure set for the air storage chamber.

(5) CHP Constraints:

[0102] [00012]$\begin{array}{cc}\{\begin{array}{c}{?}_{\mathrm{GT},t}{P}_{\mathrm{GT},\min }?P_{\mathrm{GT},t}?{?}_{\mathrm{GT},t}{P}_{\mathrm{GT},\max }\\ 0?H_{\mathrm{gl},t}?H_{\mathrm{gl},\max }\end{array}& \left(33\right)\end{array}$

[0103] where the first equation represents an output constraint on the gas turbine, ?.sub.GT,t is a start-stop coefficient of the gas turbine and is a binary variable, and P.sub.GT,max and P.sub.GT,min represent a maximum output value and a minimum output value of the gas turbine; and the second equation represents an output constraint on the waste heat recovery boiler, where H.sub.gl,max is a maximum output value of the waste heat recovery boiler.

(6) Thermal Storage Chamber Constraints:

[0104] [00013]$\begin{array}{cc}\{\begin{array}{c}0?{?}_{\mathrm{hc},t}+{?}_{\mathrm{hx},t}?0\\ V_{\min }{?}_w?M_{\mathrm{tesc},t}{?}_{\mathrm{hc},t}-M_{\mathrm{tesx},t}{?}_{\mathrm{hx},t}+M_{\mathrm{tes},t}?V_h{?}_w\end{array}& \left(34\right)\end{array}$

[0105] where in the first equation, ?.sub.hc,t and ?.sub.hx,t represent condition constraints for thermal storage/supply of the thermal storage chamber; and in the second equation, V.sub.min is a minimum thermal storage value set for the thermal storage chamber, ?.sub.w is a density at a set temperature of the thermal storage chamber, and M.sub.tes,t is a mass of stored hot water in the thermal storage chamber at time t.

(7) Heat Pump and Electric Cooling Constraints:

[0106] [00014]$\begin{array}{cc}\{\begin{array}{c}0?P_{\mathrm{rb}}*{?}_{\mathrm{er}}?Q_{\mathrm{rb}}\\ 0?P_{\mathrm{bc}}*{?}_{\mathrm{ec}}?{\mathrm{Cold}}_{\mathrm{bc}}\end{array}& \left(35\right)\end{array}$

[0107] where Q.sub.rb is a maximum output thermal power of the heat pump, and Cold.sub.bc is a maximum output power for electric cooling.

(8) Electricity Purchase Constraints:

[0108]
?P.sub.s,max?P.sub.b,t?P.sub.b,max (36)

[0109] where P.sub.b,max represents a maximum amount of purchased electricity, and P.sub.s,max represents a maximum amount of sold electricity.

[0110] Step 4: Perform crossover and mutation on decision variables by using a genetic algorithm (NSGA-II) at the upper level, with an objective of minimizing total costs, select new parent capacity values based on the total costs by applying an elite retention strategy, and pass down results to the lower level. At the lower level, a Gurobi solver is employed to schedule a lower-level model after the results from the upper level are received; and scheduling results are then returned to the upper level to assist in capacity decisions, achieving dual-level planning. The algorithm flowchart is shown in FIG. 8. Simulation was conducted in MATLAB software to obtain investment costs, scheduling costs, and scheduling costs without the AA-CAES system, as shown in Table 6:

TABLE-US-00006 Parameter Value Parameter Value Ground area of photovoltaic panels/m.sup.2 1100 Investment costs of energy storage system/ 2863890 CNY Ground area of mirror field/m.sup.2 896 Scheduling costs with energy storage/CNY 3348047 Rated compression power/kW 690 Scheduling costs without AA-CAES 4374189 configuration/CNY Rated expansion power/kW 321 Benefit brought by AA-CAES configuration/ 1026142 CNY Rated volume of air storage chamber/m.sup.3 427 Payback period/years 3 Initial volume ratio of air storage chamber 0.7 Rated volume of thermal storage chamber/m.sup.3 125 Initial volume ratio of thermal storage chamber 0.3

Table 6

[0111] From the simulation results, it can be observed that the scheduling costs of the system are reduced after the configuration of AA-CAES. Although the initial investment costs of AA-CAES are relatively high, AA-CAES has a long operational lifespan, and the investment costs can be recovered in approximately 3 years. After the system is configured with AA-CAES, the scheduling of various output modules in different seasons is illustrated in FIG. 9 to FIG. 17. FIG. 9, FIG. 10, and FIG. 11 depict the energy scheduling during winter, summer, and transitional seasons, respectively. It can be seen from the figures that the gas turbine, as high-quality electricity output equipment, generally operates at high load. The compression phase of the AA-CAES energy storage equipment typically occurs during valley electricity hours, which is also the period when a substantial amount of electricity is purchased from the power grid, indicating an effective valley-filling capability of AA-CAES. The expansion phase occurs during peak electricity hours, which usually is also the period when electricity is sold back to the power grid, indicating an effective peak-shaving capability of AA-CAES.

[0112] FIG. 12, FIG. 13, and FIG. 14 depict the thermal energy scheduling during winter, summer, and transitional seasons, respectively. From the figures, it can be observed that due to the high-load operation of the gas turbine, the heat generation from the waste heat boiler is also high. Heat generation from the electric pump and compression occurs during valley electricity hours. Although the heat demand during these hours is relatively low, excess heat can be stored in the thermal storage chamber and released during peak heat demand, thus avoiding the purchase of expensive electricity for heating, which would increase scheduling costs.

[0113] FIG. 15, FIG. 16, and FIG. 17 illustrate the cooling energy scheduling during winter, summer, and transitional seasons, respectively. It can be observed from the figures that in winter, due to higher heat demand, electric cooling and expansion cooling are mainly used. During the transitional seasons, with higher electricity demand, absorption cooling and electric cooling are the main sources of cooling. In summer, expansion cooling primarily occurs during peak electricity hours, while absorption cooling and electric cooling are used in combination to meet cooling demand. These figures show that AA-CAES can provide stable cooling through expansion throughout each season. The inherent ability of AA-CAES to provide combined cooling, heating, and power aligns well with the CCHP micro integrated energy system, enhancing the flexibility of energy supply within CCHP micro grids.

[0114] FIG. 18 shows the hot water storage status in the thermal storage chamber. The optimal initial thermal storage ratio for winter, summer, and transitional seasons is 0.3, and after one day, the thermal storage chamber can restore its initial hot water volume. FIG. 19 shows the gas storage status in the air storage chamber. The optimal initial gas ratio for the air storage chamber is 0.7. During periods of low electricity prices when a substantial amount of electricity is purchased from the power grid, a significant amount of electricity is stored in AA-CAES, leading to rapid growth in the air storage volume of the air storage chamber during this time. After one day, the air storage chamber can restore its initial gas volume. The system has optimized the initial ratios for gas storage and thermal storage in AA-CAES, demonstrating that the initial ratios are values that affect the capacity planning of the system.