Cogen photovoltaic / photothermal / aa-caes capacity configuration method

Abstract

The application discloses a capacity configuration method of a combined cooling, heating and power system of photovoltaic / photothermal / AA-CAES, which firstly establishes a model of a combined cooling, heating and power system of a micro integrated energy system containing AA-CAES, and then solves the model by adopting a double-layer planning method. In the double-layer planning method, the upper layer plans the configuration of capacity with the minimum sum of capacity configuration cost and scheduling cost of the lower layer as the target, the lower layer uses the parameters of the upper layer to schedule the lower layer model according to seasons, with the minimum sum of energy supply cost and carbon dioxide treatment cost as the target, and returns the scheduling result to the upper layer to assist the decision of the upper layer capacity. The method considers the capacity of the compressed air energy storage system of the combined cooling, heating and power system, and establishes a model suitable for capacity planning and scheduling. Simulation results prove that the double-layer planning method can obtain appropriate capacity configuration and energy scheduling.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a photovoltaic / solar thermal / AA-CAES capacity configuration method for combined cooling, heating and power (CCHP). Background Technology

[0002] With the continuous improvement of global economic production and consumption levels, the shortage of non-renewable energy and environmental pollution have become increasingly severe, making the development and utilization of renewable resources crucial for solving these problems. Combined cooling, heating, and power (CCHP) is considered an effective way to address environmental and energy shortage issues and is also an important topic for realizing the energy internet. Adding energy storage devices to micro-integrated energy systems can improve the system's renewable energy absorption capacity and operational flexibility. [1] Therefore, the proper allocation of energy storage system capacity becomes crucial.

[0003] Among numerous energy storage technologies, adiabatic compressed air energy storage (AA-CAES) stands out due to its advantages such as low capacity cost, long operating life, and large capacity. Furthermore, AA-CAES possesses combined cooling, heating, and power (CCHP) capabilities, effectively matching the multi-energy coupling characteristics of micro-integrated energy systems and achieving an overall energy utilization rate of over 70%. [2] In recent years, my country has established several small-scale compressed air energy storage demonstration platforms, promoting the application of compressed air energy storage technology. [3] .

[0004] Currently, scholars have conducted relevant research on AA-CAES. Reference [3] constructed a combined generation model of AA-CAES and wind power, and evaluated the generation cost and power supply reliability. Reference [4] added AA-CAES to the microgrid to construct the model, in order to The scheduling strategy of the system was studied with the goal of minimizing the load. However, the capacity configuration of AA-CAES was not considered. Reference [5] established a CAES power station based on a small island with the goal of absorbing wind power and proposed a capacity optimization method. Reference [6] considered adding AA-CAES to the integrated energy system to realize the combined heat and power load, and thus planned the capacity. Although the above references proposed the capacity configuration of CAES, none of them considered the scenario of combined cooling, heating and power supply, and did not consider the impact of the initial ratio of thermal storage and gas storage chamber on the system.

[0005] [1] Jiang Haiyang, Du Ershun, Zhu Guiping, et al. Review and prospect of seasonal energy storage for high-proportion renewable energy power systems [J]. Automation of Electric Power Systems, 2020, 44(19):194-207.

[0006] [2] Mei Shengwei, Li Rui, Chen Laijun, et al. Research progress and prospects of advanced adiabatic compressed air energy storage technology [J]. Proceedings of the CSEE, 2018, 38(10):2893-2907.

[0007] [3] Wu Chenxi, Chen Zehao, Zhang Jie, et al. Cost / Power Supply Reliability Assessment of Wind Power Generation System Considering Advanced Adiabatic Compressed Air Energy Storage [J]. Electric Power Automation Equipment, 2020, 40(2):62-71.

[0008] [4] Wu Chenxi, He Zhanglu, Ye Jianxiong, Hong Hanxiao. Based on Evaluation of multi-energy flow energy-saving dispatch. Electric Power Science and Engineering. 2021, 37(8):41-50.

[0009] [5]ZAFIRAKIS D, KALDELLIS J K. Autonomous Dual-mode CAES Systems for Maximum Wind Energy Contribution in Remote Island Networks[J]. Energy Conversion and Management, 2010, 51(11): 2150-2161.

[0010] [6] Ning Guangtao, Li Linwei, He Lipeng, Chen Mingfan, Zheng Zhu. Energy storage system capacity planning method for micro integrated energy systems on green islands [J]. Electric Power Automation Equipment, 2021, 41(02):8-15. Summary of the Invention

[0011] To address the shortcomings of existing technologies, this invention proposes a photovoltaic / solar thermal / AA-CAES capacity configuration method for combined cooling, heating and power (CCHP). This method employs a two-layer optimization approach to plan the capacity of relevant components of the AA-CAES energy storage system.

[0012] The method for configuring photovoltaic / solar thermal / AA-CAES capacity in combined cooling, heating and power (CCHP) systems includes the following steps:

[0013] Step 1: Establish a micro-integrated energy system model incorporating AA-CAES (Anaerobic-Anaerobic-Cooling-Heating-Power), including renewable energy inputs such as wind power, photovoltaic power, and solar thermal power; a cogeneration unit consisting of a gas generator and a waste heat recovery boiler; refrigeration equipment including absorption refrigeration and electric refrigeration; and an AA-CAES energy storage device. Assume that in this system model, air is an ideal gas, satisfying the ideal gas law; the temperature of the gas storage chamber is approximately equal to the ambient temperature; the temperature of the heat storage chamber is approximately equal to the rated temperature; and the heat exchange medium is water.

[0014] Step 2: To maximize the net benefit brought by AA-CAES, establish the upper-level objective function max B. bf :

[0015] max B bf =C noCAES -C CAES -C TCC -C O&M (1)

[0016] Among them, C noCAES This is the energy cost without AA-CAES, C CAES This is the energy cost after configuring AA-CAES, which comes from the lower-level scheduling, C TCC It is the annualized investment cost, C O&M This is the system's annualized operating and maintenance cost.

[0017] The upper-level decision variable x includes:

[0018] x={A PV A SF ,P CAESc ,P CAESt V v V h ,ω v ,ω h} (2)

[0019] Among them, A PV It is the number of photovoltaic panels, A SF P represents the area of ​​the mirror field. CAESc P is the rated power of the compressor. CAESt V is the expansion power of the expander. v V is the volume of the gas storage chamber. h ω is the volume of the heat storage chamber; v The initial proportion of gas in the storage chamber; ω h This represents the initial percentage of hot water in the thermal storage chamber.

[0020] The upper constraint is the land area required for photovoltaic and solar thermal collection:

[0021] A PV S PV +A SF ≤S MAX (3)

[0022] Among them, S PV S is the land area of ​​a single photovoltaic panel. MAX It is the maximum total land area.

[0023] Step 3: Establish the lower-level objective function with the goal of minimizing both the energy supply cost and carbon dioxide treatment cost after configuring AA-CAES:

[0024]

[0025] Among them, D spa D su D w These represent the number of days in the transitional seasons, summer, and winter of the year, respectively; P spab P sub P wb Electricity purchases on typical days during the transition season, summer, and winter, respectively; C e This refers to electricity prices; this article uses time-of-use pricing. spaGT P suGT P wGT These represent the gas turbine output on typical days during the transition season, summer, and winter, respectively; τ is the correlation coefficient between gas turbine output and natural gas; C gas The purchase cost per unit of natural gas; ψ e ψ is the conversion factor for CO2 per unit of electricity generated from the power grid. e C is the conversion factor for CO2 produced per unit of natural gas combustion. co2 The cost of treating a unit of CO2.

[0026] The lower-level decision variable x includes:

[0027] x={P CAESc,t ,P CAESg,t ,P GT,t ,P b,t ,P cold,t ,P rb,t M tesc,t M tesx,t M tescold,t} (5)

[0028] Among them, P CAESc,t P is the output of the compressor at time t; CAESg,t P is the output of the expander at time t; GT,t P is the output of the gas turbine at time t; b,t P is the amount of electricity purchased from the grid at time t; rb,t P is the amount of electricity consumed by the heat pump at time t; cold,t M is the amount of electricity used for cooling at time t; tesc,t M is the mass of hot water stored in the heat storage chamber at time t; tesx,t M is the mass of hot water supplied at time t; tescold,t It is the mass of hot water used for absorption refrigeration at time t.

[0029] The lower-level constraints include:

[0030] (1) Power balance constraint

[0031] P b,t +P GT,t +P WT,t +P PV,t +P t,t =P L,t +P c,t +P bc,t (6)

[0032] In the formula, P WT,t For the output of wind power at that moment; P bc,t P represents the amount of electricity used for electric refrigeration. L,t This refers to the electrical load's electrical charge.

[0033] (2) Thermal power balance constraint

[0034] H gl,t +M tesg,t σ h +P SF,t +M c,2 σ h +P rb σ er =H L,t +M tesc,t σ h +M g,2 σ h +M tescold,t σ h (7)

[0035] In the formula, H L,t M represents the heat load at time t; c,2 and M g,2 These represent the mass of water compressed / expanded, generating heat / consuming water, respectively; σ h This is the conversion coefficient between hot water mass and heat.

[0036] (3) Balance of cooling power

[0037] P bc,t σ ec +M tescold,t σ h σ hc +P t,cold,t =Cold L,t (8)

[0038] In the formula, σ ec For electric cooling efficiency; σ hc For absorption refrigeration efficiency; P t,cold,t Cold represents the cooling capacity of the expander at time t. L,t Let t be the amount of cold load at time t.

[0039] (4) Constraints of the AA-CAES module

[0040]

[0041] In the formula, the first equation represents the operating condition constraint of the compression turbine, u c,t and u g,t The first equation represents the operating condition of the compression turbine at time t, and the second equation represents the constraint on the compression power. The third equation represents the constraint on the expansion power. The fourth equation represents the constraint on the gas storage chamber, where M... a,c and M a,t These represent the air mass during compression / expansion, ρ min / ρ max It is the air density corresponding to the lowest / highest pressure set in the gas storage chamber.

[0042] (5) Constraints on Cogeneration

[0043]

[0044] The first equation represents the output constraint of the gas turbine, μ. GT,t This is the start-stop coefficient of the gas turbine, a binary variable; P GT,max ,P GT,min These are the maximum and minimum output values ​​of the gas turbine, respectively. The second formula represents the output constraint of the waste heat recovery boiler, where H... gl,max It is the maximum output of the waste heat recovery boiler.

[0045] (6) Thermal storage chamber constraint

[0046]

[0047] In the first formula, μ hc,t and μ hx,t These are the operating constraints for heat storage / heat supply in the heat storage chamber; in the second formula, V min It is the minimum heat storage value set for the heat storage chamber; ρ w It is the density at the set temperature of the heat storage chamber; M tes,t It is the mass of the stored hot water in the thermal storage chamber at time t.

[0048] (7) Heat pump and electric refrigeration constraints

[0049]

[0050] In the formula, Q rb This is the maximum output heat power of the heat pump; Cold bc This is the maximum output power value of the electric cooling system.

[0051] (8) Electricity purchase constraints

[0052] -P s,max ≤P b,t ≤P b,max(13)

[0053] In the formula, P b,max Maximum purchase volume; P s,max This is the maximum amount of electricity sold.

[0054] Step 4: At the upper level, a genetic algorithm (NSGA-II) is used to minimize the total cost. By performing crossover and mutation on the decision variables, new parent capacity values ​​are selected based on the total cost and an elite retention strategy, and the results are passed to the lower level. The lower level uses the Gurobi solver. After receiving the results from the upper level, the lower level model is scheduled, and the scheduling results are returned to the upper level to assist in decision capacity, thus achieving bi-level programming.

[0055] The present invention has the following beneficial effects:

[0056] This method considers the combined cooling, heating and power (CCHP) capability of compressed air energy storage systems and establishes a model applicable to capacity planning and scheduling. The lower-level scheduling considers the typical daily operating characteristics of different seasons, and the optimization target takes into account the cost of carbon dioxide emission control. The upper-level planning considers the selection of the initial capacity values ​​of the gas storage chamber and the heat storage chamber, and comprehensively optimizes the compression / expansion power and the gas / heat storage capacity. Attached Figure Description

[0057] Figure 1 This is an energy flow diagram of a combined cooling, heating and power micro-integrated energy system;

[0058] Figure 2 This is a diagram of the AA-CAES structure;

[0059] Figure 3 This is the winter heating, cooling, and electrical load in the embodiment;

[0060] Figure 4 This refers to the transitional season's cooling, heating, and electrical loads in the embodiments;

[0061] Figure 5 This refers to the summer heating, cooling, and electrical loads in the embodiment;

[0062] Figure 6 This is the light intensity curve in the embodiment;

[0063] Figure 7 This refers to the wind power output in each season in the embodiments;

[0064] Figure 8 This is a flowchart of the bilevel programming algorithm;

[0065] Figure 9 This is the result of winter power dispatch in the embodiment;

[0066] Figure 10 This is the result of summer power dispatch in the embodiment;

[0067] Figure 11 This is the result of power dispatching during the transition season in the embodiment;

[0068] Figure 12 This is the result of winter thermal energy scheduling in the embodiment;

[0069] Figure 13 This is the result of summer thermal energy scheduling in the embodiment;

[0070] Figure 14 This is the result of thermal energy scheduling during the transition season in the embodiments;

[0071] Figure 15 This is the result of winter cold energy scheduling in the embodiment;

[0072] Figure 16 This is the result of summer cooling energy scheduling in the example;

[0073] Figure 17 This is the result of the transitional season cooling energy scheduling in the embodiment;

[0074] Figure 18 This describes the hot water storage state in the heat storage chamber of the embodiment.

[0075] Figure 19 This refers to the gas storage state of the gas storage chamber in the embodiment. Detailed Implementation

[0076] The present invention will be further explained below with reference to the accompanying drawings;

[0077] The method for configuring photovoltaic / solar thermal / AA-CAES capacity in combined cooling, heating and power (CCHP) systems includes the following steps:

[0078] Step 1: Assumptions: Air is an ideal gas, satisfying the ideal gas law; the temperature of the gas storage chamber is approximately equal to the ambient temperature, and the temperature of the heat storage chamber is approximately equal to the rated temperature; the heat exchange medium is water. Establish a micro-integrated energy system model containing AA-CAES (Advanced Combined Cooling, Heating, and Power). The system structure and energy flow diagram are as follows: Figure 1 As shown, the system includes a compressor, expander, motor, generator, heat storage chamber, gas storage chamber, and heat exchanger equipment. The AA-CAES model includes a compression stage, a compression heat transfer stage, a compression heat storage stage, an expansion heat transfer stage, an expansion heat release stage, and an expansion stage. Its typical two-stage compression and two-stage expansion structure is shown below. Figure 2 As shown in Table 1, this embodiment selects a compression and expansion stage of 4 stages, and the system parameters are as follows:

[0079]

[0080] Table 1

[0081] The output power of the i-th stage expander in the expansion phase is:

[0082]

[0083] In the formula, η t It is the expansion efficiency; β t This is the expansion ratio of the expander. An air mass can be obtained from the expansion power.

[0084] The outlet temperature of the i-th stage expander during the expansion phase is:

[0085]

[0086] The output cooling capacity during the expansion phase is:

[0087] P t,cold =m a c p (T0-T t,N,out (16)

[0088] In the formula, T0 is the ambient temperature, T t,N,out It is the output temperature of the last stage expander.

[0089] The output power model of photovoltaics is:

[0090] P pv =P STC I[1+k(T pv -T r )] / I STC A PV (17)

[0091] In the formula, P STC Under standard test conditions (I) STC 1000w / m 2 T r The rated power of the photovoltaic panel at 25℃; I is the light intensity; k is the power temperature coefficient; A PV It is the number of photovoltaic panels, T pv The temperature of the photovoltaic power generation module:

[0092] T pv =T0+0.03I (18)

[0093] A linear Fresnel concentrating solar collector module is selected as the solar concentrating solar collector subsystem in the system. The model expression is as follows:

[0094]

[0095] In the formula, A SF For the area of ​​the mirror field, I L and I T These are the longitudinal and lateral components of the incident angle adjustment rate, ηOPT,R It is the reference optical efficiency, η END It is the terminal loss optical efficiency, η CIN It is the cleanliness coefficient of the reflective mirror surface, the glass tube surface; The heat transfer coefficient of the solar cooling heat exchanger is shown in Table 2 in this embodiment.

[0096]

[0097] Table 2

[0098] Cogeneration includes gas turbine units and waste heat recovery boilers. The relationship between gas turbine output and recovered heat is as follows:

[0099]

[0100]

[0101] In the formula: and These are the unit's power generation efficiency and heat production efficiency, respectively. and These represent the unit's electrical output and thermal output, respectively, in kW; G CHP LHV is the gas consumption of the CHP unit at time t, in kg / h. gas The lower heating value of natural gas is shown in Table 3 in this embodiment:

[0102]

[0103] Table 3

[0104] The unit construction cost of each piece of equipment is shown in Table 4:

[0105]

[0106] Table 4

[0107] The curves of load, wind power output, and solar radiation intensity for each season are shown below. Figures 3-7 As shown.

[0108] Step 2: This method employs a two-level programming approach to solve the model. The upper level plans capacity allocation with the objective of minimizing the sum of capacity configuration cost and scheduling cost in the lower level. Due to significant seasonal differences in heating, cooling, and electrical loads, the lower level performs seasonal scheduling. The lower level uses the capacity parameters obtained from the upper level for scheduling, aiming to minimize the sum of energy supply cost and carbon dioxide control cost, and returns the scheduling results to the upper level to assist in capacity decision-making. In this embodiment, a typical day is 24 hours long, the scheduling step size is 1 hour, the system lifespan is assumed to be 20 years, and the system discount rate is 8%. Peak-valley electricity pricing is implemented. During peak hours (10:00-12:00 and 16:00-22:00), the purchase price of electricity is 1.35 yuan / kWh, and the selling price is 1.03 yuan / kWh. During normal hours (07:00-10:00, 12:00-16:00, and 22:00-23:00), the purchase price is 0.90 yuan / kWh, and the selling price is 0.67 yuan / kWh. During off-peak hours (23:00 to 07:00 the next day), the purchase price is 0.40 yuan / kWh, and the selling price is 0.27 yuan / kWh. The gas purchase price is uniformly 2.01 yuan / m³. 3 Table 5 shows the CO2 emissions and treatment costs from gas turbines and the power grid:

[0109]

[0110] Table 5

[0111] The upper-level decision variable x includes:

[0112] x={A PV A SF ,P CAESc ,P CAESt V v V h ,ω v ,ω h} (twenty two)

[0113] Among them, P CAESc P is the rated power of the compressor. CAESt V is the expansion power of the expander. v V is the volume of the gas storage chamber. h ω is the volume of the heat storage chamber; v The initial proportion of gas in the storage chamber; ω h This represents the initial percentage of hot water in the thermal storage chamber.

[0114] With the goal of maximizing the net benefit brought by AA-CAES, a higher-level objective function maxB is established. bf :

[0115] maxB bf =CnoCAES -C CAES -C TCC -C O&M (twenty three)

[0116] Among them, C noCAES This is the energy cost without AA-CAES, C CAES This is the energy cost after configuring AA-CAES, which comes from the lower-level scheduling, C TCC This is the annualized investment cost, including the capacity module and the energy storage module:

[0117]

[0118] In the formula C psc C psg These are the investment costs per unit of rated compression power and rated expansion power, respectively; C ESa C represents the investment cost per unit of gas storage room; SF The investment cost per unit of thermal storage chamber; C PV This is the investment cost of each photovoltaic panel; C SF is the investment cost per unit mirror field; i is the discount rate; T is the lifespan of the system module.

[0119] C O&M This refers to the system's annualized operating and maintenance costs:

[0120] C O&M =C O&ME (P CAESc +P CAESg )+C O&Mpv A PV C O&MSF A SF (25)

[0121] In the formula, C O&ME It is the maintenance cost per unit of compressed turbine power; C O&MPV This refers to the operation and maintenance cost per unit of photovoltaic panel; C O&MSF It is the operation and maintenance cost per unit mirror field area.

[0122] The upper constraint is the land area required for photovoltaic and solar thermal collection:

[0123] A PV S PV +A SF ≤S MAX (26)

[0124] Among them, S PV S is the land area of ​​a single photovoltaic panel. MAX It is the maximum total land area.

[0125] Step 3: The lower-level decision variable x includes:

[0126] x={P CAESc,t ,P CAESg,t ,P GT,t ,P b,t ,P cold,t ,P rb,t M tesc,t M tesx,t M tescold,t} (27)

[0127] Among them, P CAESc,t P is the output of the compressor at time t; CAESg,t P is the output of the expander at time t; GT,t P is the output of the gas turbine at time t; b,t P is the amount of electricity purchased from the grid at time t; rb,t P is the amount of electricity consumed by the heat pump at time t; cold,t M is the amount of electricity used for cooling at time t; tesc,t M is the mass of hot water stored in the heat storage chamber at time t; tesx,t M is the mass of hot water supplied at time t; tescold,t It is the mass of hot water used for absorption refrigeration at time t.

[0128] With the goal of minimizing both energy supply cost and carbon dioxide treatment cost after configuring AA-CAES, a lower-level objective function is established:

[0129]

[0130] In the formula, D spa D su D w These represent the number of days in the transitional seasons, summer, and winter of the year, respectively; P spab P sub P wb Electricity purchases on typical days during the transition season, summer, and winter, respectively; C e This refers to electricity prices; this article uses time-of-use pricing. spaGT P suGT P wGT These represent the gas turbine output on typical days during the transition season, summer, and winter, respectively; τ is the correlation coefficient between gas turbine output and natural gas; C gas The purchase cost per unit of natural gas; ψ e ψ is the conversion factor for CO2 per unit of electricity generated from the power grid. e The conversion factor for CO2 produced per unit of natural gas combustion; The cost of treating a unit of CO2.

[0131] The lower-level constraints include:

[0132] (1) Power balance constraint

[0133] P b,t +P GT,t +P WT,t +P PV,t +P t,t =P L,t +P c,t +P bc,t (29)

[0134] In the formula, P WT,t For the output of wind power at that moment; P bc,t P represents the amount of electricity used for electric refrigeration. L,t This refers to the electrical load's electrical charge.

[0135] (2) Thermal power balance constraint

[0136] H gl,t +M tesg,t σ h +P SF,t +M c,2 σ h +P rb σ er =H L,t +M tesc,t σ h +M g,2 σ h +M tescold,t σ h (30)

[0137] In the formula, H L,t M represents the heat load at time t; c,2 and M g,2 These represent the mass of water compressed / expanded, generating heat / consuming water, respectively; σ h This is the conversion coefficient between hot water mass and heat.

[0138] (3) Balance of cooling power

[0139] P bc,t σ ec +M tescold,t σ h σ hc +P t,cold,t =Cold L,t (31)

[0140] In the formula, σ ec For electric cooling efficiency; σ hc For absorption refrigeration efficiency; P t,cold,t Cold represents the cooling capacity of the expander at time t. L,t Let t be the amount of cold load at time t.

[0141] (4) Constraints of the AA-CAES module

[0142]

[0143] In the formula, the first equation represents the operating condition constraint of the compression turbine, u c,t and u g,t The first equation represents the operating condition of the compression turbine at time t, and the second equation represents the constraint on compression power. The third equation represents the constraint on expansion power. The fourth equation represents the constraint on the gas storage chamber, where M... a,c and M a,t These represent the air mass during compression / expansion, ρ min / ρ max It is the air density corresponding to the lowest / highest pressure set in the gas storage chamber.

[0144] (5) Constraints on Cogeneration

[0145]

[0146] The first equation represents the output constraint of the gas turbine, μ. GT,t This is the start-stop coefficient of the gas turbine, a binary variable; P GT,max ,P GT,min These are the maximum and minimum output values ​​of the gas turbine, respectively. The second formula represents the output constraint of the waste heat recovery boiler, where H... gl,max It is the maximum output of the waste heat recovery boiler.

[0147] (6) Thermal storage chamber constraint

[0148]

[0149] In the first formula, μ hc,t and μ hx,t These are the operating constraints for heat storage / heat supply in the heat storage chamber; in the second formula, V min It is the minimum heat storage value set for the heat storage chamber; ρ w It is the density at the set temperature of the heat storage chamber; M tes,t It is the mass of the stored hot water in the thermal storage chamber at time t.

[0150] (7) Heat pump and electric refrigeration constraints

[0151]

[0152] In the formula, Q rb This is the maximum output heat power of the heat pump; Cold bc This is the maximum output power value of the electric cooling system.

[0153] (8) Electricity purchase constraints

[0154] -P s,max ≤P b,t ≤P b,max (36)

[0155] In the formula, P b,max Maximum purchase volume; P s,max This is the maximum amount of electricity sold.

[0156] Step 4: At the upper level, a genetic algorithm (NSGA-II) is used to minimize the total cost. This is achieved by performing crossover and mutation on the decision variables, selecting new parent generation capacity values ​​based on the total cost and an elite retention strategy, and then passing the results to the lower level. The lower level uses the Gurobi solver. After receiving the results from the upper level, the lower-level model is scheduled, and the scheduling results are returned to the upper level to assist in decision-making, thus implementing a two-level programming approach. The algorithm flowchart is shown below. Figure 8 As shown in Table 6, simulations were performed in MATLAB software to obtain the investment cost, scheduling cost, and scheduling cost without the AA-CAES system.

[0157]

[0158] Table 6

[0159] Simulation results show that configuring AA-CAES reduces system scheduling costs. Although the initial investment cost of AA-CAES is high, its long lifespan allows for cost recovery in approximately three years. The scheduling of various output modules in different seasons after configuring AA-CAES is shown below. Figures 9-17 As shown, where Figure 9 , Figure 10 , Figure 11 The figures show the power dispatch situation in winter, summer, and transitional seasons. As can be seen from the figures, gas turbines, as high-quality power output devices, generally operate at a relatively high load. The compression period of AA-CAES energy storage devices generally occurs during off-peak hours, which is also the period when large amounts of electricity are purchased from the grid, indicating the good valley-filling effect of AA-CAES. Its expansion period occurs during peak hours, and the time when electricity is sold to the grid generally also occurs at this time, indicating the good peak-shaving effect of AA-CAES.

[0160] Figure 12 , Figure 13 , Figure 14 The figures show the thermal energy dispatch situation in winter, summer, and transitional seasons. As can be seen from the figures, due to the high load operation of the gas turbine, the heat output of the waste heat boiler is also relatively high. The heat output of electric pumps and compressors both occur during off-peak electricity periods. Although the heat demand is relatively low during these times, the excess heat can be stored in the heat storage chamber and released during peak heat demand. This avoids purchasing high-priced electricity for heating, which would increase dispatch costs.

[0161] Figure 15 , Figure 16 , Figure 17 The graph shows the cooling energy dispatching for winter, summer, and transitional seasons. As can be seen, in winter, due to higher heat demand, the demand is mainly met by electric cooling and expansion cooling. In the transitional season, due to higher electricity demand, the demand is mainly met by absorption cooling and electric cooling. In summer, expansion mainly occurs during peak electricity periods, combining with absorption cooling and electric cooling for cooling. The graph demonstrates that AA-CAES can stably provide cooling through expansion in every season. The inherent combined cooling, heating, and power (CCHP) capability of AA-CAES is well-matched with the CCHP micro-integrated energy system, increasing the energy flexibility of the CCHP microgrid.

[0162] Figure 18 The hot water storage status of the thermal storage chamber is shown. The optimal initial thermal storage ratio is 0.3 in winter, summer and transition seasons, and the thermal storage chamber can return to the initial stored hot water volume after one day. Figure 19 The gas storage status of the gas storage chamber is shown, with an optimal initial gas ratio of 0.7. Due to low electricity prices, more electricity is purchased from the grid, resulting in a significant amount of electricity being stored in the AA-CAES, leading to a rapid increase in gas storage volume during this period. After one day, the gas storage volume returns to its initial value. The system optimized the initial ratio of gas to thermal storage in the AA-CAES, demonstrating that this initial ratio significantly impacts system capacity planning.

Claims

1. A method for configuring photovoltaic / solar thermal / AA-CAES capacity in combined cooling, heating and power (CCHP) systems, characterized in that: The method specifically includes the following steps: Step 1: Establish a micro-integrated energy system model incorporating AA-CAES (Anaerobic-Anaerobic-Cooling-Heating-Power), including renewable energy inputs such as wind power, photovoltaic power, and solar thermal collection; cogeneration units consisting of gas generators and waste heat recovery boilers; refrigeration equipment including absorption refrigeration and electric refrigeration; and energy storage devices using AA-CAES. It is assumed that in this system model, air is an ideal gas satisfying the ideal gas law; the temperature of the gas storage chamber is equal to the ambient temperature; the temperature of the heat storage chamber is equal to the rated temperature; and the heat exchange medium is water. Step 2: Establish the upper-level objective function with the goal of maximizing the net benefit brought by AA-CAES. : (1) Among them, C noCAES This is the energy cost without AA-CAES, C CAES This refers to the energy cost after configuring AA-CAES, which comes from the underlying scheduling layer, C. TCC It is the annualized investment cost, C O&M This is the system's annualized operating and maintenance cost; Upper-level decision variables include: (2) Among them, A PV It is the number of photovoltaic panels, A SF P represents the area of ​​the mirror field. CAESc P is the rated power of the compressor. CAESt V is the expansion power of the expander. v V is the volume of the gas storage chamber. h ω is the volume of the heat storage chamber; v The initial proportion of gas in the storage chamber; ω h This represents the initial proportion of hot water in the thermal storage chamber. The upper constraint is the land area required for photovoltaic and solar thermal collection: (3) in, This refers to the land area occupied by a single photovoltaic panel. It is the maximum total land area; Step 3: Establish the lower-level objective function with the goal of minimizing both the energy supply cost and carbon dioxide treatment cost after configuring AA-CAES: (4) Among them, D spa D su D w These represent the number of days in the transitional seasons, summer, and winter of the year, respectively; P spab P sub P wb Electricity purchases on typical days during the transition season, summer, and winter, respectively; C e This refers to electricity prices; this article uses time-of-use pricing. spaGT P suGT P wGT These represent the gas turbine output on typical days during the transition season, summer, and winter, respectively; τ is the correlation coefficient between gas turbine output and natural gas; C gas The purchase cost per unit of natural gas; ψ e ψ is the conversion factor for CO2 per unit of electricity generated from the power grid. e The conversion factor for CO2 produced per unit of natural gas combustion; The cost of treating one unit of CO2; Lower-level decision variables include: (5) Among them, P CAESc,t P is the output of the compressor at time t; CAESg,t P is the output of the expander at time t; GT,t P is the output of the gas turbine at time t; b,t P is the amount of electricity purchased from the grid at time t; rb,t P is the amount of electricity consumed by the heat pump at time t; cold,t M is the amount of electricity used for cooling at time t; tesc,t M is the mass of hot water stored in the heat storage chamber at time t; tesx,t M is the mass of hot water supplied at time t; tescold,t It is the mass of hot water used for absorption refrigeration at time t; The lower-level constraints include electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, AA-CAES module constraints, cogeneration constraints, thermal storage chamber constraints, heat pump and electric cooling constraints, and electricity purchase constraints. Step 4: Use a genetic algorithm at the upper level to minimize the total cost. By performing crossover and mutation on the decision variables, select new parent capacity values ​​based on the total cost and the elite retention strategy, and pass the results to the lower level. The lower level uses the Gurobi solver. After receiving the results from the upper level, it schedules the lower level model and returns the scheduling results to the upper level to assist in decision capacity, thus realizing a two-level programming.

2. The photovoltaic / solar thermal / AA-CAES capacity configuration method for combined cooling, heating and power as described in claim 1, characterized in that: The specific constraints at the lower level are as follows: (1) Electric power balance constraint (6) In the formula, P WT,t For the output of wind power at that moment; P bc,t P represents the amount of electricity used for electric refrigeration. L,t The electrical load's electrical charge; (2) Thermal power balance constraint (7) In the formula, H L,t M represents the heat load at time t; c,2 and M g,2 These represent the mass of water compressed / expanded, and the mass of water generated / consumed, respectively; σ h The conversion coefficient between hot water mass and heat. (3) Balance of cooling power (8) In the formula, σ ec For electric cooling efficiency; σ hc For absorption refrigeration efficiency; P t,cold,t Cold represents the cooling capacity of the expander at time t. L,t Let be the amount of cold load at time t; (4) Constraints of the AA-CAES module (9) In the formula, the first equation represents the operating condition constraint of the compression turbine, u c,t and u g,t The first equation represents the operating condition of the compression turbine at time t, and the second equation represents the constraint on the compression power. The third equation represents the constraint on the expansion power. The fourth equation represents the constraint on the gas storage chamber, where M... a,c and M a,t These represent the air mass during compression / expansion, ρ min / ρ max It is the air density corresponding to the lowest / highest pressure set in the gas storage chamber; (5) Constraints on Cogeneration (10) The first equation represents the output constraint of the gas turbine, μ. GT,t This is the start-stop coefficient of the gas turbine, a binary variable; P GT,max , P GT,min These are the maximum and minimum output values ​​of the gas turbine, respectively; the second formula represents the output constraint of the waste heat recovery boiler, where H... gl,max This is the maximum output of the waste heat recovery boiler; (6) Thermal storage chamber constraint (11) In the first formula, μ hc,t and μ hx,t These are the operating constraints for heat storage / heat supply in the heat storage chamber; in the second formula, V min It is the minimum heat storage value set for the heat storage chamber; ρ w It is the density at the set temperature of the heat storage chamber; M tes,t It is the mass of the stored hot water in the thermal storage chamber at time t; (7) Constraints of heat pumps and electric refrigeration (12) In the formula, Q rb This is the maximum output heat power of the heat pump; Cold bc This is the maximum output power value of the electric cooling system; (8) Electricity purchase constraints (13) In the formula, P b,max Maximum purchase volume; P s,max This is the maximum amount of electricity sold.

3. The photovoltaic / solar thermal / AA-CAES capacity configuration method for combined cooling, heating and power as described in claim 1, characterized in that: The AA-CAES model includes a compression stage, a compression heat transfer stage, a compression heat storage stage, an expansion heat transfer stage, an expansion heat release stage, and an expansion stage. The output power of the i-th stage expander in the expansion stage is: (14) In the formula, It is the expansion efficiency; It is the expansion ratio of the expander; an air mass can be obtained from the expansion power. The outlet temperature of the i-th stage expander during the expansion phase is: (15) The output cooling capacity during the expansion phase is: (16) In the formula, T0 is the ambient temperature, T t,N,out It is the output temperature of the last stage expander; The output power model of photovoltaics is: (17) In the formula, P STC I is the rated power of the photovoltaic panel under standard test conditions; I is the light intensity. k is the power temperature coefficient; A PV It is the number of photovoltaic panels, T pv The temperature of the photovoltaic power generation module: (18) The solar thermal collector model is as follows: (19) In the formula, A SF For the area of ​​the mirror field, I L and I T These are the longitudinal and lateral components of the incident angle adjustment rate. It refers to optical efficiency. It refers to the terminal's optical efficiency loss. ϛ is the cleanliness coefficient of the reflective mirror surface and the glass tube surface; ϛ is the heat transfer coefficient of the solar cooling heat exchanger. Cogeneration includes gas turbine units and waste heat recovery boilers. The relationship between gas turbine output and recovered heat is as follows: (20) (21) In the formula: and These are the unit's power generation efficiency and heat production efficiency, respectively. and These represent the unit's electrical output and thermal output, respectively, in kW; G CHP LHV is the gas consumption of the CHP unit at time t, in kg / h. gas It is the lower heating value of natural gas.

4. The photovoltaic / solar thermal / AA-CAES capacity configuration method for combined cooling, heating and power as described in claim 1, characterized in that: The system's annualized investment cost Includes production capacity modules and energy storage modules: (24) In the formula C psc C psg These are the investment costs per unit of rated compression power and rated expansion power, respectively; C ESa C represents the investment cost per unit of gas storage room; SF The investment cost per unit of thermal storage chamber; C PV This is the investment cost of each photovoltaic panel; C SF is the investment cost per unit mirror field; i is the discount rate; T is the lifespan of the system module; The annual maintenance cost of the system C O&M for: (25) In the formula, C O&ME It is the maintenance cost per unit of compressed turbine power; C O&MPV This refers to the unit operation and maintenance cost of a photovoltaic panel; C O&MSF It is the operation and maintenance cost per unit mirror field area.

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