Zero-carbon building integrated energy system planning method considering energy supply reliability

Abstract

The invention discloses a zero-carbon building comprehensive energy system planning method considering energy supply reliability. The method comprises the following steps: firstly, establishing a comprehensive energy system model oriented to urban buildings; secondly, solving the integrated energy system model by adopting an upper and lower layer planning method, establishing an objective function on the upper layer, and obtaining the optimal photovoltaic panel area and wind power plant capacity in fixed cost according to the objective function; and 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 a function with the minimum sum of the energy supply cost and the carbon emission cost as an objective for solving, and optimal scheduling is obtained. According to the method, economy and reliability of the power system are considered, energy supply reliability evaluation is introduced, after capacity configuration planning, safety evaluation of the load of the power system is achieved, and safety and reliability of the comprehensive energy system are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of power system operation and is a zero-carbon building integrated energy system planning method taking energy supply reliability into consideration. Background Art

[0002] Scientific and technological progress and social development have brought burdens to the global environment, making energy conservation and emission reduction and vigorously developing new energy an inevitable trend in the development of the power industry. As an important form of multi-energy coupling, the integrated energy system can guide energy-related industries to increase the proportion of renewable energy and reduce carbon emissions. Vigorously developing integrated energy systems is an important way to achieve energy conservation and emission reduction in buildings and optimize the energy structure, and it also meets the requirements of green and low-carbon development. Due to the continuous development of renewable energy and distributed energy, better combining the two and making rational use of them has become a new direction for the power system. Therefore, establishing a regional optimized intelligent integrated energy system for buildings, considering energy supply reliability and achieving energy conservation and emission reduction while ensuring economy, is an important means to solve energy and environmental problems.

[0003] In order to adapt to social development and meet people's needs, and at the same time improve energy utilization, integrated energy systems came into being. Among them, the integrated energy system with combined cooling, heating and power as the core has a high energy utilization rate and a flexible supply mode, and has been widely used in recent years. Another widely used energy system is the wind, solar and storage multi-energy complementary energy system, which has multiple energy sources and can meet the energy needs of users, so the configuration optimization of the system has always received attention. One of the goals of system capacity configuration planning is to maximize the benefits of the system.

[0004] In addition, with the development of multi-energy complementary energy systems, optimization objectives have also diversified, and multi-objective optimization has gradually become a hot topic of research. Many studies mostly start from a single perspective, and the optimization design of energy systems fails to comprehensively consider the total cost and carbon emissions of the system, or does not take into account the impact of different installed capacities on the comprehensive operating costs of the system, or adopts traditional capacity planning configuration methods. Evaluating the energy supply reliability of the system is also an important part of the construction. In the evaluation process, the Monte Carlo method is used to evaluate the reliability of the system while taking into account the economy of the system, and the Pareto optimal solution set is obtained. Summary of the invention

[0005] The problem to be solved by the present invention is to propose a modeling method that considers introducing energy supply reliability for evaluation after the capacity configuration planning obtained by relying on data, based on the integrated energy system for zero-carbon buildings. It explores that introducing energy supply reliability to evaluate the integrated energy system of zero-carbon buildings on the basis of planned capacity configuration is an effective measure to ensure the long-term stable operation of the power system. Therefore, while ensuring economy, the present invention also takes reliability into account. Based on the planning of capacity configuration, energy supply reliability is introduced to ensure the operation of the system and improve energy utilization. With the goal of minimizing the system operating cost, the modeling of the integrated energy system planning for zero-carbon buildings is completed.

[0006] A comprehensive energy system planning method for zero-carbon buildings considering energy supply reliability is proposed.

[0007] The integrated energy system uses multiple energy sources for joint energy supply. When configuring capacity, it is a problem of combining the installed capacity of multiple energy sources. When operating, it is a problem of economic scheduling. Because the time scales of the two are different, an optimization model for the upper and lower capacity configuration of the integrated energy system is established. The upper-level capacity planning configuration takes the minimum capacity configuration cost and operating cost as the objective function, and the lower-level optimization model takes the minimum system scheduling cost as the objective function. The output result of the upper layer is used as the basis for the capacity of the lower-level operating equipment. The lower layer uses the equipment capacity configured by the upper layer as the installed capacity, and uses the load data of a typical day to solve the equipment optimization operation strategy, and then obtains the scheduling cost, and passes the scheduling cost to the upper-level capacity planning configuration model, and then realizes sequential iteration. The constraints of the upper-level model are to meet the load demand constraints and equipment installation condition constraints; the constraints of the lower-level model are to meet the operating equipment constraints, system balance constraints, and reliability assessment constraints.

[0008] The steps include:

[0009] Step 1: Establish an integrated energy system model for urban buildings, with the trigeneration system as the core and distributed energy as the auxiliary to supply energy to the integrated energy system, and determine the model constraints based on equipment output and energy storage.

[0010] Step 2: Use the upper and lower level planning method to solve the integrated energy system model established in step 1. The upper level establishes the objective function and obtains the optimal photovoltaic panel area and wind farm capacity in the fixed cost according to the objective function.

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

[0012]

[0013] In formula (1), B bf It represents the net benefit of the battery in the upper layer of the integrated energy system, CnoELE is the energy cost without battery, C ELE is the energy cost after configuring the battery, which comes from the scheduling result of the lower layer, C TCC is the annualized fixed cost, C O&M is the annual operation and maintenance cost of the integrated energy system, is the operation and maintenance cost of each device in the integrated energy system, C rel Penalize costs for supplying energy to integrated energy systems;

[0014]

[0015] In formula (2), C ESa is the investment cost of a unit battery, C ESb is the investment cost of the unit heat storage device; C pv is the investment cost of each photovoltaic panel; C wind is the investment cost per unit of wind power; i is the discount rate; Z is the service life of the integrated energy system, V e and V h are the capacities of the battery and heat storage device, A PV is the area of ​​the photovoltaic panel, A wind is the capacity of the wind farm;

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

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

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

[0019] Step 3: The lower layer uses the optimal photovoltaic panel area and wind farm capacity in the upper layer fixed cost to establish an objective function with the minimum sum of energy supply cost and carbon emission cost to solve and obtain the optimal scheduling, where wind power generation and photovoltaic output are obtained by combining the model in step one with the optimal wind turbine capacity and photovoltaic panel area solved in step two.

[0020]

[0021] Formula (4) represents the operation and maintenance cost of each device in the system in represents the unit power operation and maintenance cost of gas turbine, waste heat boiler, heat storage device, absorption refrigerator, battery, electric refrigerator, 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 Respectively represent the operating power of gas turbine, waste heat boiler, heat storage device, absorption refrigerator, battery charging, battery discharging, electric refrigerator, electric boiler, electric heating, and gas boiler, P wind , P pv They represent the power of wind power abandonment and solar power abandonment, and P is obtained from the area of ​​PV panels and the capacity of wind farms. w , P p are the wind power and photovoltaic power actually used, represent the penalty costs for wind and photovoltaic respectively.

[0022]

[0023] In formula (6), D spa , D su , D w are the number of days in the transition season, summer, and winter in a year,

[0024] P spab , P sub , P wb are the electricity purchases on typical days in transition season, summer and winter respectively; C e is the electricity price, which adopts the time-of-use electricity price; P spaGT , P suGT , P wGT are the gas turbine outputs on typical days in transition season, summer and winter respectively; τ is the correlation coefficient between gas turbine output and natural gas; C gas The purchase cost of natural gas per unit.

[0025]

[0026] Formula (7) represents the energy supply reliability penalty cost, s represents the season type index, S represents the total number of seasons, T represents the time period, and D s Indicates the number of days of the sth season, Q Loss,k,l represents the total energy shortage of energy k at load rate l, σ k is the penalty unit price for lack of k types of energy.

[0027] The upper layer adopts a multi-objective genetic algorithm with the goal of maximizing net benefits. It combines the elite retention strategy to screen out new parent capacity values ​​and pass them to the lower layer. The lower layer builds an optimal scheduling model for the power system on the Yalmip platform. Combined with the results of the upper-level planning, the Cplex solver is used to optimize the scheduling of the integrated energy system model to obtain the optimal output results of each device and the optimal configuration plan.

[0028] The patent of this invention is different from the existing research work in the following characteristics.

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

[0030] (2) The optimal capacity configuration is obtained based on data calculations, and capacity planning is performed and applied to actual capacity configuration collection.

[0031] (3) Based on the integrated energy system, the present invention introduces energy supply reliability assessment, and realizes safety assessment of the power system load after capacity configuration planning, thereby ensuring the safety and reliability of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a diagram of the integrated energy system structure for zero-carbon buildings;

[0033] Figure 2 Implementation flowchart for the system;

[0034] Figure 3 This is a schematic diagram comparing the output of equipment electrical load and cooling load before and after the power storage device is installed in summer;

[0035] Figure 4 This is a schematic diagram comparing the electrical load and thermal load output of equipment before and after the power storage device is installed and not installed in winter;

[0036] Figure 5 This is a schematic diagram comparing the equipment electrical load and cooling load output before and after the power storage device is installed during the transition season. DETAILED DESCRIPTION

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

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

[0039] The entire integrated energy system Figure 1As shown, it includes an electricity-gas-cold-heat coupling architecture, which mainly includes the production, conversion, storage and consumption of energy. The energy production link first connects to electricity, natural gas and renewable energy. After entering the system, it passes through a variety of energy conversion equipment, and different energy sources are deeply coupled and hierarchically utilized. Energy that cannot be consumed in a short time can be introduced into energy storage devices for storage, and finally provided to the user side electric load, heat load and cold load through line pipelines. Because the various energy sources within the system are closely connected, when a single energy supply is insufficient, it can be quickly converted by the conversion equipment; at the same time, the energy storage device can give full play to the flexible adjustment of energy supply through storage to achieve multi-energy complementarity.

[0040] Step 1: Establish a comprehensive energy system model for urban buildings, such as Figure 1 As shown in the figure, the integrated energy system is mainly based on the trigeneration system of cooling, heating and electricity, and the distributed energy is used to supply energy to the system. Through photovoltaic power generation, wind power and power purchase from the distribution network, the electric energy in the system mainly flows to batteries, electric refrigerators, electric heating and electric boilers. Considering the output of equipment 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] Formula (1) represents the photovoltaic equipment output constraint: P pv is the output power of the photovoltaic cell; P STC is the rated power of the photovoltaic panel under standard test conditions; k is the power temperature coefficient; A PV is the area of ​​the photovoltaic panel; I is the solar radiation intensity; I STC 1000w / m 3 , T r is a fixed constant of 25°C; T pv is the operating temperature of the photovoltaic panel surface, which can be estimated by formula (2):

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

[0044] Where T0 is the air temperature.

[0045]

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

[0047]

[0048] Formula (4) represents the energy storage device constraint, where P t dis,k is the energy release power of the kth energy storage device in time period t; P t ch,k is the charging power of the kth energy storage device at time t; P t k is the energy state of the k-th energy storage device at time t; Δt is the time step value of 30min, W k is the rated capacity of the energy storage device; η ch is the energy storage device charging efficiency; η dis is the energy storage device discharge efficiency, P t ch ,k,max , P t dis,k,max are the maximum charging and discharging power of the kth energy storage device in time period t; P t k,min , P t k,max They are respectively the minimum and maximum energy storage value of the kth energy storage device at time t.

[0049] For example, the power storage device mainly stores electrical energy. When there is excess electrical energy, it will be charged and released when the system needs electricity. The heat storage device will also store excess heat in the system and release the heat energy when the system needs heat energy.

[0050]

[0051] Formula (5) represents the wind power output, v t represents the predicted wind speed in period t, v in 、v r 、v out Respectively represent the cut-in wind speed, rated wind speed, and cut-out wind speed; A wind Indicates the rated capacity of the wind farm;

[0052]

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

[0054]

[0055] Formula (7) represents the gas turbine output constraint, where P GT is the power generated by the gas turbine during period t, in kW; η GT is the gas turbine power generation efficiency, is the natural gas consumption of the gas turbine during period t, in m 3 ; L gas It is the low calorific value of natural gas;

[0056]

[0057] Part of the energy of the gas turbine is released in the form of flue gas waste heat, which can be fully utilized by collecting it. Formula (8) represents the waste heat of flue gas discharged by the gas turbine, W GT To discharge the residual heat in the flue gas; η loss is the gas turbine heat loss coefficient.

[0058]

[0059] Formula (9) represents the upper limit constraint of gas turbine power generation. is the maximum output of the gas turbine, kW.

[0060] W HB =W GT η HB (10)

[0061] Formula (10) represents the heat generation condition constraint of the waste heat boiler, W HB is the output heat of the waste heat boiler during period t; η HB is the waste heat boiler efficiency.

[0062]

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

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

[0065] Formula (12) shows the flow direction of heat generated by the waste heat boiler, W EH is the heat input to the heat storage device, kW; W AC is the heat input into the absorption chiller.

[0066]

[0067] Formula (13) represents the maximum power constraint of the heat storage device, is the maximum power of the heat storage device.

[0068] P GF =η GF P gas，GF (14)

[0069] Formula (14) represents the constraints of the gas boiler, η GF is the gas heat conversion efficiency of the gas boiler, P gas，GF , P GFThey are the natural gas power entering the gas boiler and the heat production power respectively.

[0070]

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

[0072]

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

[0074] C AC =η AC W AC (17)

[0075] The absorption refrigerator achieves cooling by absorbing heat, which reduces heat waste and makes full use of resources. Equation (17) represents the output constraint of the absorption refrigerator, C AC is the cooling capacity of the absorption chiller;

[0076] η AC is the coefficient of performance of the absorption chiller.

[0077] C EC =η EC P EC (18)

[0078] Formula (18) represents the cooling constraint of the electric refrigerator, C EC represents the cooling capacity of the electric refrigerator in time period t; P EC represents the cooling power consumption of the electric refrigerator in time period t; η EC Indicates the refrigeration performance coefficient of the electric refrigerator.

[0079] P EB,h (t) = η EB P EB (t) (19)

[0080] Formula (19) represents the constraints of the electric boiler, P EB,h (t), P EB (t) are the heating power and power consumption of the electric boiler respectively; η EB is the conversion efficiency of the electric boiler.

[0081]

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

[0083]

[0084] Formula (21) represents the constraints of electric heating, is the electric power consumed by the electric heating equipment, β P2H is the conversion efficiency of the electric heating equipment, P P2H The power to generate 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] Formula (23) represents the balance constraint of cold energy, C AC and C EC Indicates the cooling power of absorption refrigerator and electric refrigerator, C L,t Represents the cooling energy load power in time period t.

[0089]

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

[0091]

[0092] Formula (25) represents the balance constraint of electric energy, P eload represents the electric energy load power in time period t, is the battery discharge power, is the battery charging power, P wind , P pv , P GT , P P2H They represent wind power generation, photovoltaic power generation, gas turbine power generation, and electric heating power consumption respectively.

[0093] -P s,max ≤P b,t ≤P b,max (26) In formula (26), P b,max is the maximum amount of electricity purchased; P s,max The maximum amount of electricity sold.

[0094]

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

[0096]

[0097] In formulas (28) and (29), r t,k,l Indicates the count, if the energy supply is sufficient, it is 1, otherwise it is 0; R k,l is the energy supply guarantee degree, that is, the ratio of the number of hours of insufficient energy supply of k types of energy to the total time under the load rate l in the simulation period; C t,k,l Indicates the total output of the system at time t, kW; Load t,k,l represents the total load of the user at time t, kW; N represents the total operating hours of the simulation.

[0098]

[0099] In the formula, x SAIDI,k,l Indicates the count. If the energy supply is insufficient, the number is recorded as 1, otherwise it is recorded as 0; X SAIDI,k,l Indicates the duration of insufficient energy supply, T k Represents the number of days of energy supply period of k types of energy.

[0100]

[0101] In formulas (32) and (33), Q t,k,l Indicates the total amount of energy shortage from t1 to t2, kW; Q LOSS,k,l It represents the total amount of insufficient energy supply of k types of energy due to some reasons in the integrated energy system under load l during the annual energy supply period.

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

[0103] After completing the comprehensive energy system modeling in step 1, the objective function of the system is established. The present invention aims to maximize the net benefit brought by the battery:

[0104]

[0105] In formula (34), B bf It represents the net benefit of the battery in the upper layer of the integrated energy system, C noELE is the energy cost without battery, C ELE is the energy cost after configuring the battery, which comes from the scheduling result of the lower layer, C TCC is the annualized fixed cost, C O&M is the annual operation and maintenance cost of the system, is the operation and maintenance cost of each device in the integrated energy system, C rel Penalize costs for supplying energy to integrated energy systems;

[0106]

[0107] In formula (35), C ESa is the investment cost of a unit battery, C ESb is the investment cost of the unit heat storage device; C pv is the investment cost of each photovoltaic panel; C wind is the investment cost per unit of wind power; i is the discount rate; Z is the service life of the integrated energy system, V e and V h are the capacities of the battery and heat storage device, A PV is the area of ​​the photovoltaic panel, A wind is the capacity of the wind farm;

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

[0109] In formula (36), C O&Mpv is the operation and maintenance cost of a unit 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 transmitted by the upper layer to schedule the lower layer model, establishes an objective function with the minimum sum of energy supply cost and carbon emission cost, and solves it to obtain the optimal schedule.

[0111]

[0112] Formula (37) represents the operation and maintenance cost of each device in the system in represents the unit power operation and maintenance cost of gas turbine, waste heat boiler, heat storage device, absorption refrigerator, battery, electric refrigerator, 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 They represent the operating power of the gas turbine, waste heat boiler, heat storage device, absorption refrigerator, battery charging, battery discharging, electric refrigerator, electric boiler, electric heating, and gas boiler respectively. Represent the power of wind power abandonment and solar power abandonment, P w , P p are the wind power and photovoltaic power actually used, represent the penalty costs for wind and photovoltaic respectively.

[0113]

[0114] In formula (39), D spa , D su , D w The numbers of days in the transition season, summer, and winter in a year respectively;

[0115] P spab , P sub , P wb are the electricity purchases on typical days in transition season, summer and winter respectively; C e is the electricity price, which adopts the time-of-use electricity price; P spaGT , P suGT , P wGT are the gas turbine outputs in the transition season, summer, and typical days in winter; τ is the correlation coefficient between gas turbine output and natural gas; C gas is the purchase cost of natural gas per unit;

[0116]

[0117] Formula (40) represents the energy supply reliability penalty cost, s represents the season type index, S represents the total number of seasons, T represents the time period, and D s Indicates the number of days of the sth season, Q Loss,k,l represents the total energy shortage of energy k at load rate l, σ k is the penalty unit price for lack of k types of energy.

[0118] The upper layer adopts a multi-objective genetic algorithm with the goal of maximizing net benefits, combined with an elite retention strategy to screen out new parent capacity values ​​and pass them to the lower layer.

[0119] Step 4: On the Yalmip platform, the lower layer uses the Cplex solver to solve the running model in combination with the results of the upper layer planning, and returns the solved cost to the upper layer. The upper layer algorithm then calculates the net benefit objective function based on the returned value, iterates repeatedly, and finally obtains the optimal configuration solution. 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 selects and crosses the target variables, combines the paternal population with the daughter population, and selects and stores new parent capacity values ​​according to the elite retention strategy of the algorithm based on the minimum total target cost, and iterates back and forth to find the optimal solution.

[0120] The experimental results are as follows Figure 3 , Figure 4 , Figure 5 As shown, Figure 3 From left to right, they are the comparison of electric load and cooling load output with and without power storage devices in summer. Figure 4 From left to right, the comparison of electrical load and thermal load output with and without power storage devices in winter is shown below. Figure 5 From left to right, the comparison of electric load and cooling load output with and without power storage devices in the transition season is shown. The figure shows that when there is no power storage device, the system's energy supply flexibility is insufficient and it does not have the ability to shave peaks and fill valleys. The power storage device improves the absorption rate of renewable energy and reduces the phenomenon of wind and solar power abandonment. Table 1 is a cost comparison with and without power storage devices. It can be seen from the table that when the system includes power storage devices, it has a greater impact on the economy of the entire integrated energy system and can save operating costs. Combined with Table 2, considering the reliability of energy supply, it also ensures the safety of the integrated energy system.

[0121] Table 1

[0122]

[0123] Table 2

[0124]

Claims

1. A zero-carbon building integrated energy system planning method considering energy supply reliability, characterized in that: The steps include: Step 1: Establish a comprehensive energy system model for urban buildings; Step 2: Use the upper and lower level planning method to solve the integrated energy system model. The upper level establishes the objective function and obtains the optimal photovoltaic panel area and wind farm capacity in the fixed cost according to the objective function. Step 3: The lower layer uses the optimal photovoltaic panel area and wind farm capacity in the upper layer’s fixed costs to establish an objective function with the minimum sum of energy supply cost and carbon emission cost to solve and obtain the optimal dispatch.

2. According to claim 1, a zero-carbon building integrated energy system planning method considering energy supply reliability is characterized in that: The integrated energy system model takes the trigeneration system as the core and uses distributed energy as the auxiliary to supply energy to the integrated energy system. The model constraints are determined according to the equipment output and energy storage.

3. A zero-carbon building integrated energy system planning method considering energy supply reliability according to claim 2, characterized in that: The specific implementation process of step 2 is as follows: The upper layer establishes an objective function, with the minimum sum of capacity planning configuration cost and lower layer scheduling cost as the goal of capacity planning configuration. The objective function is: In the formula, B bf It represents the net benefit of the battery in the upper layer of the integrated energy system, C noELE is the energy cost without battery, C ELE is the energy cost after configuring the battery, which comes from the scheduling result of the lower layer, C TCC is the annual fixed cost, C O&M is the annual operation and maintenance cost of the integrated energy system, is the operation and maintenance cost of each device in the integrated energy system, C rel Penalize costs for supplying energy to integrated energy systems; In the formula, C ESa is the investment cost of a unit battery, C ESb is the investment cost of the unit heat storage device; C pv is the investment cost of each photovoltaic panel; C wind is the investment cost per unit of wind power; i is the discount rate; Z is the service life of the integrated energy system, V e and V h are the capacities of the battery and heat storage device, A PV is the area of ​​the photovoltaic panel, A wind is the capacity of the wind farm; C O&M =C O&Mpv A PV +C O&Mwind A wind In the formula, C O&Mpv is the operation and maintenance cost of a unit photovoltaic panel; C O&Mwind is the operation and maintenance cost per unit wind area; According to the objective function, the optimal area of ​​photovoltaic panels and capacity of wind farm are obtained.

4. A zero-carbon building integrated energy system planning method considering energy supply reliability according to claim 3, characterized in that: The specific implementation process of step three is as follows: The lower layer uses the optimal photovoltaic panel area and wind farm capacity in the upper layer’s fixed costs to establish an objective function to minimize the sum of energy supply cost and carbon emission cost, and obtain the optimal dispatch. In the formula, Indicates the operation and maintenance cost of each device in the system. represents the unit power operation and maintenance cost of gas turbine, waste heat boiler, heat storage device, absorption refrigerator, battery, electric refrigerator, 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 Respectively represent the operating power of gas turbine, waste heat boiler, heat storage device, absorption refrigerator, battery charging, battery discharging, electric refrigerator, electric boiler, electric heating, and gas boiler, P wind , P pv They represent the power of wind power abandonment and solar power abandonment, which are obtained from the area of ​​PV panels and the capacity of wind farms, respectively. w , P p are the wind power and photovoltaic power actually used, denote the penalty costs of wind and PV respectively; C ELE =D spa (P spa C e +P spaGT τC gas ) +D su (P sub C e +P suGT τC gas ) +D w (P wb C e +P wGT τC gas ) Where D spa , D su , D w are the number of days in the transition season, summer, and winter in a year; P spab , P sub , P wb are the electricity purchases on typical days in transition season, summer and winter respectively; C e is the electricity price, which adopts the time-of-use electricity price; P spaGT , P suGT , P wGT are the gas turbine outputs on typical days in transition season, summer and winter respectively; τ is the correlation coefficient between gas turbine output and natural gas; C gas is the purchase cost of natural gas per unit; In the formula, C rel represents the energy reliability penalty cost, s represents the season type index, S represents the total number of seasons, T represents the time period, and D s Indicates the number of days of the sth season, Q Loss,k,l represents the total energy shortage of energy k at load rate l, σ k is the penalty unit price for lack of k types of energy.

5. A zero-carbon building integrated energy system planning method considering energy supply reliability according to claim 4, characterized in that: The step three also includes that the upper layer adopts a multi-objective genetic algorithm with the goal of maximizing net benefits, and combines the elite retention strategy to screen out new parent capacity values ​​and pass them to the lower layer. The lower layer uses the power system optimization scheduling model built on the Yalmip platform, combines the results of the upper-level planning, and uses the Cplex solver to optimize the scheduling of the integrated energy system model, obtain the optimal output results of each device, and obtain the optimal configuration plan.

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