A zero-carbon building integrated energy system planning method considering energy supply reliability

Abstract

The application discloses a zero-carbon building comprehensive energy system planning method considering energy supply reliability, which firstly establishes a comprehensive energy system model for urban buildings. Secondly, the method of upper and lower layer planning is adopted to solve the comprehensive energy system model, the upper layer establishes an objective function, and the optimal photovoltaic panel area and wind power plant capacity in the fixed cost are obtained according to the objective function. Finally, the lower layer uses the optimal photovoltaic panel area and wind power plant capacity in the fixed cost of the upper layer to establish an objective function with the lowest sum of energy supply cost and carbon emission cost for solving, and the optimal scheduling is obtained. The application considers the economy and reliability of the power system, introduces the energy supply reliability evaluation, realizes the safety evaluation of the power system load after the capacity configuration planning, and guarantees the safety and reliability of the comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and is a planning method for a zero-carbon building integrated energy system that takes into account the reliability of energy supply. Background Technology

[0002] Technological advancements and social development have placed a burden on the global environment, making energy conservation, emission reduction, and the vigorous development of new energy sources an inevitable trend in the power industry. Integrated energy systems, as an important form of multi-energy coupling, can guide energy-related industries to increase the proportion of renewable energy and reduce carbon emissions. Vigorously developing integrated energy systems is a crucial pathway to building energy conservation, emission reduction, and energy structure optimization, while also meeting the requirements of green and low-carbon development. Due to the continuous development of renewable and distributed energy sources, better combining and rationally utilizing both has become a new direction for power systems. Therefore, establishing regionally optimized intelligent integrated energy systems for buildings, ensuring economic efficiency while considering power supply reliability and achieving energy conservation and emission reduction, is an important means of solving energy and environmental problems.

[0003] To adapt to social development and meet people's needs while improving energy efficiency, integrated energy systems have emerged. Among them, integrated energy systems with combined cooling, heating, and power (CCHP) at their core have high energy efficiency and flexible supply models, and have been widely used in recent years. Another widely used system is the wind-solar-storage multi-energy complementary energy system, which has multiple energy sources and can meet user energy demands; therefore, system configuration optimization has always been a focus. One of the goals of system capacity configuration planning is to maximize system benefits.

[0004] Furthermore, with the development of multi-energy complementary energy systems, optimization objectives have become increasingly diversified, and multi-objective optimization has gradually become a research hotspot. Many studies, however, take a single perspective, failing to comprehensively consider the total system cost and carbon emissions, or neglecting the impact of different installed capacities on the overall system operating cost, or employing traditional capacity planning and configuration methods. Assessing the system's power supply reliability is also a crucial aspect of construction. The Monte Carlo method is used in the assessment process to evaluate system reliability while simultaneously considering the system's economics, yielding a Pareto optimal solution set. Summary of the Invention

[0005] The problem this invention aims to solve is to propose a modeling method for evaluating the integrated energy system of zero-carbon buildings. This method incorporates power supply reliability assessment after data-driven capacity configuration planning. It explores how incorporating power supply reliability into the evaluation of the integrated energy system of zero-carbon buildings, based on planned capacity configuration, is an effective measure to ensure the long-term stable operation of the power system. Therefore, this invention balances economic efficiency with reliability, introducing power supply reliability into capacity configuration planning to ensure system operation and improve energy utilization. The goal is to minimize system operating costs, thereby completing the modeling of the integrated energy system planning for zero-carbon buildings.

[0006] A planning method for a zero-carbon building integrated energy system that takes into account the reliability of energy supply is proposed.

[0007] Integrated energy systems utilize multiple energy sources for combined power supply. Capacity configuration involves combining the installed capacity of various energy sources, while operation becomes a matter of economic dispatch. Because these two aspects have different time scales, upper and lower-level capacity configuration optimization models are established for integrated energy systems. The upper-level capacity planning and configuration aims to minimize both capacity configuration and operating costs, while the lower-level optimization model aims to minimize system dispatch costs. The output of the upper-level model serves as the basis for the capacity of the lower-level operating equipment. The lower-level model uses the configured equipment capacity as its installed capacity and solves for optimal equipment operation strategies using typical daily load data to obtain the dispatch cost. This dispatch cost is then passed back to the upper-level capacity planning and configuration model for iterative processing. The upper-level model is constrained by load demand and equipment installation conditions; the lower-level model is constrained by operating equipment constraints, system balance constraints, and reliability assessment constraints.

[0008] The steps include the following:

[0009] Step 1: Establish a comprehensive energy system model for urban buildings, with a combined cooling, heating and power (CCHP) system as the core and distributed energy sources as a supplement to supply energy to the comprehensive energy system. Determine the model constraints based on equipment output and energy storage.

[0010] Step 2: Solve the integrated energy system model established in Step 1 using a top-bottom planning method. Establish the objective function at the top level, and obtain the optimal photovoltaic panel area and wind farm capacity in terms of fixed cost based on the objective function.

[0011] The upper layer establishes a capacity configuration plan with the objective of minimizing the sum of capacity planning configuration costs and lower layer scheduling costs. The objective function is:

[0012]

[0013] In equation (1), B bf C represents the net benefit brought by batteries at the upper level of the integrated energy system.noELE For the energy cost of not having a battery, C ELE The energy cost after configuring batteries, based on the scheduling results from the lower level, C TCC It is the annualized fixed cost, C O&M It is the annualized operation and maintenance cost of the integrated energy system. To account for the operation and maintenance costs of each piece of equipment in the integrated energy system, C rel The cost of supplying energy to the integrated energy system;

[0014]

[0015] In equation (2), C ESa C represents the investment cost per unit of storage battery. ESb The investment cost per unit of thermal storage device; C pv This is the investment cost of each photovoltaic panel; C wind The investment cost per unit of wind power; i is the discount rate; Z is the lifespan of the integrated energy system; V e and V h A represents the capacity of the battery and the thermal storage device, respectively. PV Let A be the area of ​​the photovoltaic panel. wind For the capacity of the wind farm;

[0016] C O&M =C O&Mpv A PV +C O&Mwind A wind (3)

[0017] In equation (3), C O&Mpv This refers to the unit operation and maintenance cost of a photovoltaic panel; C O&Mwind It is the operation and maintenance cost per unit wind area.

[0018] Based on the objective function, the optimal area of ​​photovoltaic panels and the capacity of wind farm are obtained.

[0019] Step 3: Using the optimal photovoltaic panel area and wind farm capacity from the upper fixed cost, the lower layer establishes a solution with the objective function of minimizing the sum of energy supply cost and carbon emission cost to obtain the optimal scheduling. The wind power generation and photovoltaic output are obtained by combining the optimal wind turbine capacity and photovoltaic panel area obtained from the model in Step 1 with the solution in Step 2.

[0020]

[0021] Equation (4) represents the operation and maintenance cost of each device in the system. in P represents the unit power operation and maintenance cost of gas turbine, waste heat boiler, thermal storage device, absorption chiller, storage battery, electric chiller, electric boiler, electric heating, and gas boiler, respectively. GT P HB P EH P AC P t ch P t dis P EC P EB P P2H P GF P represents the operating power of the gas turbine, waste heat boiler, thermal storage device, absorption chiller, battery charging, battery discharging, electric chiller, electric boiler, electric heating, and gas boiler, respectively. wind P pv Let P represent the power curtailed from wind and solar power, respectively, and P is calculated from the area of ​​the photovoltaic panels and the capacity of the wind farm. w P p These are the actual wind power and photovoltaic power used, respectively. These represent the penalty costs for wind power and solar power, respectively.

[0022]

[0023] In equation (6), D spa D su D w These represent the number of days in the transitional seasons, summer, and winter of the year, respectively.

[0024] P spab P sub P wb Electricity purchases for typical days during the transition season, summer, and winter, respectively; C e It refers to electricity pricing, specifically time-of-use pricing; P spaGT P suGT P wGT τ represents 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 This refers to the purchase cost per unit of natural gas.

[0025]

[0026] Equation (7) represents the energy supply reliability penalty cost, s represents the seasonal type index, S represents the total number of seasons, T represents the time period, and D represents the total number of seasons. s Q represents the number of days in the s-th type of season. Loss,k,l σ represents the total energy deficit of energy k at load factor l. k The penalty unit price for the lack of k types of energy.

[0027] The upper layer employs a multi-objective genetic algorithm with the goal of maximizing net benefit. It combines an elite retention strategy to select new parent capacity values ​​and pass them to the lower layer. The lower layer uses a power system optimization scheduling model built on the Yalmip platform. Combined with the planning results from the upper layer, the Cplex solver is used to optimize the scheduling of the integrated energy system model, obtaining the optimal output results of each device and the optimal configuration scheme.

[0028] This invention patent differs from existing research in the following ways.

[0029] (1) Unlike traditional power system economic planning and dispatching, this invention takes into account both the economy and reliability of the power system.

[0030] (2) The optimal capacity configuration is calculated based on the data, thereby carrying out capacity planning and applying it to the actual capacity configuration collection.

[0031] (3) Based on the integrated energy system, this invention introduces energy supply reliability assessment, and after capacity configuration planning, realizes the safety assessment of the power system load, thus ensuring the safety and reliability of the integrated energy system. Attached Figure Description

[0032] Figure 1 A structural diagram of an integrated energy system for zero-carbon buildings;

[0033] Figure 2 A flowchart illustrating the specific implementation of the system;

[0034] Figure 3 A schematic diagram comparing the electrical load and cooling load output of equipment before and after the presence or absence of energy storage devices in summer;

[0035] Figure 4 A schematic diagram comparing the electrical and thermal load output of equipment before and after the presence or absence of energy storage devices in winter;

[0036] Figure 5 This is a schematic diagram comparing the electrical and cooling load output of equipment before and after the transition season, with and without energy storage devices. Detailed Implementation

[0037] The method of the present invention will be described in detail below with reference to the accompanying drawings:

[0038] A planning method for a zero-carbon building integrated energy system that considers energy supply reliability includes the following steps:

[0039] The entire integrated energy system, such as Figure 1As shown, the system comprises an electricity-gas-cooling-heating coupled architecture, mainly including energy production, conversion, storage, and consumption stages. The energy production stage first connects to electricity, natural gas, and renewable energy sources. After entering the system, these sources undergo various energy conversion devices, achieving deep coupling and tiered utilization between different energy sources. Energy that cannot be consumed in a short time can be stored in energy storage devices. Finally, the energy is supplied to the user-side electrical, heating, and cooling loads through pipelines. Due to the close connection between the various energy sources within the system, when the supply of a single energy source is insufficient, the conversion devices can quickly switch to other sources. Simultaneously, the energy storage devices, through storage, fully utilize flexible energy supply adjustment to achieve multi-energy complementarity.

[0040] Step 1: Establish a comprehensive energy system model for urban buildings, such as... Figure 1 As shown, the integrated energy system is mainly based on a combined cooling, heating, and power (CCHP) system, supplemented by distributed energy resources. Electricity is supplied through photovoltaic power generation, wind power generation, and power grid purchases. Within the system, the main flow of electricity is to batteries, electric chillers, electric heating systems, and electric boilers. Considering equipment output and energy storage, the model conditions are determined as follows:

[0041] P pv =P STC I[1+k(T pv -T r )] / I STC A PV (1)

[0042] Equation (1) represents the output constraint of the photovoltaic equipment: P pv P represents the output power of the photovoltaic cell. STC The rated power of the photovoltaic panel under standard test conditions; k is the power temperature coefficient; A PV I represents the area of ​​the photovoltaic panel; I represents the solar irradiance; I STC 1000w / m 3 T r The constant is 25℃; T pv The operating temperature of the photovoltaic panel surface can be estimated using equation (2):

[0043] T pv =T0+0.03I (2)

[0044] Where T0 is the air temperature.

[0045]

[0046] Equation (3) represents the upper limit constraint on the output of photovoltaic equipment, where It is the maximum output of photovoltaic power.

[0047]

[0048] Equation (4) represents the constraints of the energy storage device, where P t dis,k P represents the energy release power of the k-th type of energy storage device during time period t. t ch,k P represents the charging power of the k-th type of energy storage device at time t. t k The energy state of the k-th energy storage device at time t; Δt is the time step, which takes a value of 30 min, W k η is the rated capacity of the energy storage device. ch Energy storage device charging efficiency; η dis For the energy release efficiency of the energy storage device, P t ch ,k,max P t dis,k,max These represent the maximum charging and discharging power of the k-th type of energy storage device during time period t; P t k,min P t k,max These are the minimum and maximum values ​​of the energy stored by the k-th type of energy storage device at time t, respectively.

[0049] For example, energy storage devices mainly serve to store electrical energy. When there is excess electrical energy, they will be charged and released when the system needs electricity. Similarly, thermal storage devices will store excess heat in the system and release it when the system needs thermal energy.

[0050]

[0051] Equation (5) represents the wind power output, v t V represents the predicted wind speed during time period t. in v r v out These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; A wind Indicates the rated capacity of the wind farm;

[0052]

[0053] Equation (6) represents the upper and lower limits of wind power output.

[0054]

[0055] Equation (7) represents the gas turbine output constraint, where P GT η represents the power generation of the gas turbine during time period t, in kW; GT For gas turbine power generation efficiency, The gas turbine consumes natural gas in the time period t, in cubic meters. 3 L gas It has a low calorific value for natural gas;

[0056]

[0057] Part of the energy from a gas turbine is released as waste heat from the flue gas, and proper collection can ensure its full utilization. Equation (8) represents the waste heat from the flue gas discharged by the gas turbine, W. GT To remove residual heat from the flue gas; η loss This represents the heat loss coefficient of the gas turbine.

[0058]

[0059] Equation (9) represents the upper limit constraint on the power generation capacity of the gas turbine. This represents the maximum output of the gas turbine, in kW.

[0060] W HB =W GT η HB (10)

[0061] Equation (10) represents the heat production condition constraint of the waste heat boiler, W HB η is the output heat of the waste heat boiler during time period t; HB This refers to the efficiency of the waste heat boiler.

[0062]

[0063] Equation (11) represents the upper limit power constraint of the waste heat boiler. This is the maximum input power of the waste heat boiler.

[0064] W HB =W EH +W AC (12)

[0065] Equation (12) represents the direction of heat flow generated by the waste heat boiler, W EH The heat input to the thermal storage device is expressed in kW or W. AC The heat input to the absorption chiller.

[0066]

[0067] Equation (13) represents the maximum power constraint of the thermal storage device. This represents the maximum power of the thermal storage device.

[0068] P GF =η GF P gas，GF (14)

[0069] Equation (14) represents the constraint of the gas-fired boiler, η GF For the gas-heat conversion efficiency of a gas-fired boiler, P gas，GF P GFThese represent the power output of the natural gas entering the gas-fired boiler and the heat generation power, respectively.

[0070]

[0071] Equation (15) represents the maximum and minimum power constraints of the gas-fired boiler.

[0072]

[0073] Equation (16) represents the maximum power constraint of the absorption chiller. This is the maximum input power of the absorption chiller.

[0074] C AC =η AC W AC (17)

[0075] Absorption chillers achieve cooling by absorbing heat, reducing heat waste and making full use of resources. Equation (17) represents the output constraint of the absorption chiller, C AC This refers to the cooling capacity of the absorption chiller.

[0076] η AC The coefficient of performance (COP) of the absorption chiller.

[0077] C EC =η EC P EC (18)

[0078] Equation (18) represents the refrigeration constraint of the electric chiller, C EC P represents the cooling capacity of the electric chiller during time period t; EC η represents the power consumption of the electric chiller during time period t; EC This represents the coefficient of performance (COP) of an electric refrigeration unit.

[0079] P EB,h (t)=η EB P EB (t) (19)

[0080] Equation (19) represents the constraint of the electric boiler, P EB,h (t), P EB (t) represents the heating power and power consumption of the electric boiler, respectively; η EB This refers to the conversion efficiency of the electric boiler.

[0081]

[0082] Equation (20) represents the maximum and minimum power constraints of the electric boiler.

[0083]

[0084] Equation (21) represents the constraint of electric heating. β represents the electrical power consumed by the electric heating equipment. P2H For the conversion efficiency of electric heating equipment, P P2H The power that generates heat energy for electric heating.

[0085]

[0086] Equation (22) represents the maximum and minimum output constraints of electric heating.

[0087] C AC +C EC =C L,t (twenty three)

[0088] Equation (23) represents the balance constraint of cold energy, C AC and C EC C represents the cooling capacity of absorption chillers and electric chillers. L,t This represents the cooling load power during time period t.

[0089]

[0090] Equation (24) represents the thermal energy balance constraint. P P2H P EB,h P GF P represents the output thermal power of the thermal storage device, electric heating device, electric boiler, and gas boiler, respectively. hload This represents the thermal load power during the time period t.

[0091]

[0092] Equation (25) represents the balance constraint of electrical energy, P eload This represents the electrical load power during time period t. This refers to the battery discharge power. For battery charging power, P wind P pv P GT P P2H These represent wind power generation, photovoltaic power generation, gas turbine power generation, and power consumption for electric heating, respectively.

[0093] -P s,max ≤P b,t ≤P b,max In equation (26), P b,max Maximum purchase volume; P s,max This is the maximum amount of electricity sold.

[0094]

[0095] Equation (27) represents the reliability constraints of the integrated energy system.

[0096]

[0097] In equations (28) and (29), r t,k,l This indicates a count; 1 represents a sufficient power supply, and 0 represents an unsufficient power supply. R k,l Energy supply security is the ratio of the number of hours when k energy sources are insufficient to supply energy at a load factor of l during the simulation period to the total time; C t,k,l The load represents the total output of the system at time t, in kW; t,k,l The total load of the user at time t is represented in kW; N represents the total number of simulation operating hours.

[0098]

[0099] In the formula, x SAIDI,k,l This represents a count; if the power supply is insufficient, the count is recorded as 1; otherwise, it is recorded as 0. X SAIDI,k,l T represents the duration of insufficient energy supply. k This represents the number of days for which k types of energy are available.

[0100]

[0101] In equations (32) and (33), Q t,k,l Q represents the total amount of insufficient energy supply from time t1 to t2, in kW; LOSS,k,l This represents the total amount of k types of energy supply shortage caused by certain reasons in the integrated energy system under load l within the annual energy supply period.

[0102] Step 2: The upper layer establishes a capacity configuration plan with the goal of minimizing the sum of capacity configuration cost and lower layer scheduling cost.

[0103] After completing the integrated energy system modeling in step one, we begin to establish the system's objective function. This invention aims to maximize the net benefits brought by the battery.

[0104]

[0105] In equation (34), B bf C represents the net benefit brought by batteries at the upper level of the integrated energy system. noELE For the energy cost of not having a battery, C ELE The energy cost after configuring batteries, based on the scheduling results from the lower level, C TCC It is the annualized fixed cost, C O&M This is the system's annualized operation and maintenance cost. To account for the operation and maintenance costs of each piece of equipment in the integrated energy system, C rel The cost of supplying energy to the integrated energy system;

[0106]

[0107] In equation (35), C ESa C represents the investment cost per unit of storage battery. ESb The investment cost per unit of thermal storage device; C pv This is the investment cost of each photovoltaic panel; C wind The investment cost per unit of wind power; i is the discount rate; Z is the lifespan of the integrated energy system; V e and V h A represents the capacity of the battery and the thermal storage device, respectively. PV Let A be the area of ​​the photovoltaic panel. wind For the capacity of the wind farm;

[0108] C O&M =C O&Mpv A PV +C O&Mwind A wind (36)

[0109] In equation (36), C O&Mpv This refers to the operation and maintenance cost per unit of photovoltaic panel; C O&Mwind It is the operation and maintenance cost per unit wind area.

[0110] Step 3: The lower layer uses the capacity parameters passed from the upper layer to schedule the lower layer model, and establishes a solution with the objective function of minimizing the sum of energy supply cost and carbon emission cost to obtain the optimal scheduling.

[0111]

[0112] Equation (37) represents the operation and maintenance cost of each device in the system. in P represents the unit power operation and maintenance cost of gas turbine, waste heat boiler, thermal storage device, absorption chiller, storage battery, electric chiller, electric boiler, electric heating, and gas boiler, respectively. GT P HB P EH P AC P t ch P t dis P EC P EB P P2H P GF These represent the operating power of the gas turbine, waste heat boiler, thermal storage device, absorption chiller, battery charging, battery discharging, electric chiller, electric boiler, electric heating, and gas boiler, respectively. P represents the power of wind and solar power curtailment, respectively. w P p These are the actual wind power and photovoltaic power used, respectively. These represent the penalty costs for wind power and solar power, respectively.

[0113]

[0114] In equation (39), D spa D su D w These represent the number of days in the transitional seasons, summer, and winter of the year, respectively.

[0115] P spab P sub P wb Electricity purchases on typical days during the transition season, summer, and winter, respectively; C e It refers to electricity pricing, specifically time-of-use pricing; P 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;

[0116]

[0117] Equation (40) represents the energy supply reliability penalty cost, s represents the seasonal type index, S represents the total number of seasons, T represents the time period, and D represents the total number of seasons. s Q represents the number of days in the s-th type of season. Loss,k,l σ represents the total energy deficit of energy k at load factor l. k The penalty unit price for the lack of k types of energy.

[0118] The upper layer uses a multi-objective genetic algorithm to maximize net benefit, and combines an elite retention strategy to select new parent capacity values ​​and pass them to the lower layer.

[0119] Step 4: On the Yalmip platform, the lower layer combines the planning results from the upper layer and uses the Cplex solver to solve the running model. The cost obtained from the solution is returned to the upper layer, which then calculates the net benefit objective function based on the return value. This process is iterated repeatedly until the optimal configuration scheme is obtained. The specific implementation process is as follows: Figure 2 As shown, NSGA-II (Non-dominated sorting genetic algorithm) is used to solve the problem. NSGA-II combines the parent and offspring populations by selecting and crossover mutations on the target variable, and selects new parent capacity values ​​based on the lowest total cost combined with the elite retention strategy of the algorithm. The process is repeated to find the optimal solution.

[0120] Experimental results are as follows Figure 3 , Figure 4 , Figure 5 As shown, Figure 3 From left to right, the comparison shows the electrical and cooling load outputs in summer with and without energy storage devices. Figure 4 From left to right, the comparison shows the electrical and thermal load outputs in winter with and without energy storage devices. Figure 5 From left to right, the figures compare the electrical and cooling load outputs during the transition season with and without energy storage devices. The figures show that without energy storage devices, the system lacks sufficient energy supply flexibility and peak-shaving / valley-filling capabilities. Energy storage devices improve the absorption rate of renewable energy and reduce wind and solar curtailment. Table 1 compares the costs of having and not having energy storage devices. It can be seen from the table that including energy storage devices in the system has a significant impact on the overall economics of the integrated energy system, saving operating costs. Combined with Table 2 which considers energy supply reliability, this also ensures the safety of the integrated energy system.

[0121] Table 1

[0122]

[0123] Table 2

[0124]

Claims

1. A planning method for a zero-carbon building integrated energy system considering energy supply reliability, characterized in that, Includes the following steps: Step 1: Establish a comprehensive energy system model for urban buildings; Step 2: Solve the integrated energy system model using a layer-by-layer planning method. Establish an objective function at the upper layer, and obtain the optimal photovoltaic panel area and wind farm capacity within the fixed cost based on the objective function. The specific implementation process is as follows: The upper layer establishes an objective function, aiming to minimize the sum of capacity planning configuration costs and lower-level scheduling costs. The objective function is as follows: In the formula, This indicates the net benefits brought by batteries at the upper level of the integrated energy system. For the energy cost of not having a battery, The energy cost after configuring batteries is determined by the scheduling results from the lower level. It is the annualized fixed cost. It is the annualized operation and maintenance cost of the integrated energy system. To account for the operation and maintenance costs of each piece of equipment in the comprehensive energy system, The cost of supplying energy to the integrated energy system; In the formula, The investment cost per unit of storage battery. The investment cost per unit of thermal storage device; This is the investment cost of each photovoltaic panel; It is the investment cost per unit of wind power; The discount rate; For the lifespan of the integrated energy system, and These are the capacities of the storage battery and the thermal storage device, respectively. The area of ​​the photovoltaic panel. For the capacity of the wind farm; In the formula, This refers to the unit operation and maintenance cost of a photovoltaic panel; It is the operation and maintenance cost per unit wind area; Based on the objective function, the optimal area of ​​the photovoltaic panels and the capacity of the wind farm are obtained; Step 3: Using the optimal photovoltaic panel area and wind farm capacity from the upper-level fixed costs, the lower layer establishes a solution with the objective function of minimizing the sum of energy supply costs and carbon emission costs, to obtain the optimal scheduling. The specific implementation process is as follows: The lower layer uses the optimal photovoltaic panel area and wind farm capacity from the upper layer's fixed costs to establish an objective function that minimizes the sum of energy supply costs and carbon emission costs, and then solves for the optimal scheduling. In the formula, This represents the operation and maintenance costs of each device in the system. , , , , , , , , These represent the unit power operating and maintenance costs of gas turbines, waste heat boilers, thermal storage devices, absorption chillers, storage batteries, electric chillers, electric boilers, electric heating systems, and gas boilers, respectively. , , , , , , , , , These represent the operating power of the gas turbine, waste heat boiler, thermal storage device, absorption chiller, battery charging, battery discharging, electric chiller, electric boiler, electric heating, and gas boiler, respectively. , These represent the power curtailed from wind and solar power, respectively, calculated from the area of ​​the photovoltaic panels and the capacity of the wind farm. , These represent the penalty costs for wind power and solar power, respectively. In the formula, , , These represent the number of days in the transitional season, summer, and winter of the year, respectively. , , The electricity purchases are for typical days during the transition season, summer, and winter, respectively. It's an electricity price, and it uses time-of-use pricing. , , The gas turbine output for typical days during the transition season, summer, and winter, respectively; This is the correlation coefficient between gas turbine output and natural gas. The purchase cost per unit of natural gas; In the formula, This indicates the cost of penalties for unreliable power supply. Indicates a seasonal type index. Indicates the total number of seasons. Indicates time period, Indicates the first Number of days in a season Indicates in under load rate The total amount of energy shortage, The penalty unit price for the lack of k types of energy.

2. The method for planning a zero-carbon building integrated energy system considering energy supply reliability according to claim 1, characterized in that, The integrated energy system model takes a combined cooling, heating and power (CCHP) system as its core and distributed energy sources as a supplement to supply energy to the integrated energy system. The model constraints are determined based on the equipment output and energy storage.

3. The method for planning a zero-carbon building integrated energy system considering energy supply reliability according to claim 2, characterized in that, Step three also includes the following steps: the upper layer uses a multi-objective genetic algorithm with the goal of maximizing net benefit, and combines an elite retention strategy to select new parent capacity values ​​and pass them to the lower layer. The lower layer uses a power system optimization scheduling model built on the Yalmip platform. Combined with the planning results of the upper layer, the Cplex solver is used to optimize the scheduling of the integrated energy system model, obtain the optimal output results of each device, and obtain the optimal configuration scheme.

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