A carbon emission planning method for integrated energy systems in a park considering generalized energy storage

Abstract

The present invention discloses a carbon emission planning method for a park comprehensive energy system taking into account generalized energy storage, including: collecting historical photovoltaic output data of the park comprehensive energy system and unit investment costs mainly based on photovoltaic and battery equipment; constructing scenarios of long-term uncertainty of multi-energy load growth and short-term uncertainty of photovoltaic output; establishing an energy hub model with the park hub as the core and equipment of the park's generalized energy storage resources as the main body; establishing a generalized energy storage model; calculating the 365-day operating cost and expansion cost of the park planning year, and outputting the optimal planning and operation strategy. The present invention fully considers the long-term uncertainty of load growth and the short-term uncertainty of photovoltaic output, uses the time transfer characteristics to achieve efficient utilization of multiple energy sources across time periods, taps the generalized energy storage resources in the park with data centers and buildings as the main body, effectively promotes the consumption of new energy, and ensures that the planning and scheduling scheme of the comprehensive energy system takes into account both economy and environmental protection.

Description

Technical Field

[0001] The present invention relates to a carbon emission planning method for a park integrated energy system taking into account generalized energy storage, and belongs to the field of integrated energy system planning and expansion. Background Art

[0002] Reasonable planning of the park's comprehensive energy system plays an important supporting role in improving the park's energy structure and promoting the low-carbon transformation of the industry. At present, there are many deficiencies in the planning of the park's comprehensive energy system. For example, the research at the planning level focuses on the medium- and long-term energy balance, ignores the flexibility balance of short-term operation, and does not fully tap the flexible scheduling potential of various energy resources in the park. Generalized energy storage resources can make full use of the time transfer characteristics of the energy of each energy-consuming entity in the park, store heat during flat and valley periods, release energy during peak periods, and realize the cross-time and high-value utilization of multiple energies. Summary of the invention

[0003] The present invention proposes a method for planning a comprehensive energy system for a park by utilizing the generalized energy storage effect.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A carbon emission planning method for a comprehensive energy system of a park taking into account generalized energy storage, comprising:

[0006] Step 1: Collect historical photovoltaic output data of the park's integrated energy system and unit investment costs mainly based on photovoltaic and battery equipment;

[0007] Step 2: Based on the known park parameters, taking the sum of the expansion cost and operating cost of the park's integrated energy system as the objective function, construct scenarios for long-term uncertainty in multi-energy load growth and short-term uncertainty in photovoltaic output; the known park parameters refer to the operating parameters of each device in the park, such as the efficiency of CCHP, CHP and other units.

[0008] Step 3: Establish an energy hub model with the park hub as the core and the equipment of the park's generalized energy storage resources as the main body;

[0009] Step 4: Establish a generalized energy storage model based on battery energy storage, data center thermal characteristics, and building thermal characteristics;

[0010] Step 5: Calculate the park’s planned 365-day operating costs and expansion costs, and output the optimal planning and operating strategy.

[0011] Specifically, the long-term uncertainty scenarios of multi-energy load growth in step 2 specifically include: generating three types of long-term uncertainty scenarios of upper multi-energy load according to seasonal differences, namely transition season, summer and winter;

[0012] The short-term uncertainty scenarios of photovoltaic output specifically include: based on the historical photovoltaic output data, the k-means clustering method is used to generate four categories of short-term uncertainty scenarios of lower-level photovoltaic output.

[0013] Specifically, the objective function in step 2 is:

[0014]

[0015] In formula (A-1), the subscript t represents the scheduling time; To expand the capacity of photovoltaic units, To expand the capacity of the battery unit, To expand the capacity of ground source heat pump units, To expand the capacity of the trigeneration unit; is the unit expansion cost of the photovoltaic unit, is the unit expansion cost of the battery unit, is the unit expansion cost of the ground source heat pump unit, is the unit expansion cost of the CCHP unit; Ω g is a typical day set, ω g is the weight of each typical day; Ω PV is the photovoltaic output scene set, ω PV is the photovoltaic output scenario weight; P t E is the power purchased by the upper power grid during period t, is the gas volume purchased by the upper-level gas network during period t; is the electricity price in period t, is the gas price in period t; I E is the carbon emission cost per unit of electricity purchased, I G is the carbon emission cost per unit of gas purchased;

[0016] In formula (A-1), the first four items are the annualized expansion costs of each expansion unit, namely photovoltaic units, battery units, ground source heat pump units and trigeneration units. The fifth item is the annual operating cost, which takes into account the carbon emission costs of purchasing electricity and gas;

[0017] The constraints of each planning variable are:

[0018]

[0019] In formula (A-2), The capacity limit for photovoltaic units expansion is To expand the capacity limit of the battery unit, The capacity limit for the ground source heat pump unit expansion is To expand the capacity limit of the trigeneration unit, is the original photovoltaic installed capacity, is the original battery capacity, is the original CCHP unit capacity, is the original ground source heat pump unit capacity; the first four items are the upper limit constraints of the investment in the expanded unit, and the last four items are the upper limit constraints of the operation of the expanded unit.

[0020] Specifically, the Step 3 Energy Hub Model is:

[0021] The energy flow balance constraint of the park is:

[0022]

[0023] Where P t LC is the cooling load demand during period t, is the industrial cooling capacity of the GSHP unit during period t, P is the cooling capacity of CCHP unit in period t; t LH is the heat load demand during period t, The heat supply for CCHP unit during period t; The heat supply for the GSHP unit during period t; is the heat supply of the gas boiler during period t, P is the heat supplied by the electric boiler during period t; t PV is the photovoltaic output during period t, P t dis is the battery charge during period t, P t LE is the electric load demand during period t, P t GSHP is the power consumption of industrial GSHP units during period t, P t EB is the power consumption of the electric boiler during period t, P t ch is the discharge amount of the battery unit during period t, P t BD is the power consumption of the building GSHP unit during period t, P t DC is the power consumption of the data center during period t; P is the power provided by the CCHP unit during period t; t CCHP is the gas consumption of CCHP unit during period t, P t GB is the gas consumption of the gas boiler in period t. Constraint (A-2) is the balance equation of the cold bus, heat bus, electricity bus and gas bus from top to bottom;

[0024] The operating constraints of conventional multi-energy conversion equipment in the park are:

[0025]

[0026] In the formula, is the cold conversion efficiency of CCHP unit, P t CCHP，C The amount of natural gas input to the CCHP unit for refrigeration; is the heat conversion efficiency of CCHP unit, P t CCHP，H is the amount of natural gas input to the CCHP unit for heat conversion; is the electricity conversion efficiency of the CCHP unit, P t CCHP,E is the amount of natural gas input to the CCHP unit for power generation; P t TF is the power input to the transformer, η TF is the electrical conversion efficiency of the transformer; η EB is the heat conversion efficiency of the electric boiler, η GB is the heat conversion efficiency of the gas boiler; the first three items are the CCHP unit operation equations, and the last three items are the transformer, electric boiler and gas boiler operation equations respectively;

[0027] The equipment operation constraints involving the park's generalized energy storage resources include batteries, ground source heat pumps, and data centers, among which:

[0028] The operating constraints of the battery unit are:

[0029]

[0030] Where P t ch is the energy storage charging power during period t, P t dis is the energy storage discharge power during period t; is the energy storage charging state variable during period t, E is the energy storage discharge state variable during period t; t is the energy storage capacity during period t; E is the energy storage self-discharge coefficient during period t, η ch is the energy storage charging efficiency during period t, η dis E is the energy storage discharge efficiency during period t; min is the lower limit of energy storage capacity, E max The upper limit of energy storage capacity;

[0031] The operating constraints of the ground source heat pump unit are:

[0032]

[0033] Where P t BD，Cis the electric energy used for heat transfer by the building GSHP unit during period t, P t BD，H is the electric energy used for cooling by the building GSHP unit during period t; and are the heat and cold energy delivered to the building by GSHP during period t, respectively; and are heating and cooling operation state variables respectively;

[0034] The data center operation constraints are;

[0035]

[0036] Where P t IT is the power consumption of IT equipment in the data center during period t, is the power of each k-type server in period t; is the fixed power consumption of type k server in period t, is the CPU power consumption during period t; C1 is the power consumption coefficient of the server, d' k，t is the data load that the k-type server in the data center needs to process during period t; a k，s，t f is the CPU s-speed operating frequency flag of type k server in data center during period t; k,s It is the S-speed operating frequency of the CPU of the K-type server in the data center.

[0037] Based on battery energy storage, data center thermal characteristics, and building thermal characteristics, a generalized energy storage model is established, including:

[0038] The generalized energy storage of the battery during period t is E t ;

[0039] The thermal characteristics of the data center and the generalized energy storage constraints are:

[0040]

[0041] In the formula, is the generalized energy storage of the data center during period t, H DC is the data center specific heat capacity, T t dc，in is the indoor temperature of the data center during period t, T t dc,in,max is the upper limit of indoor temperature during period t; η c is the efficiency of data center cooling equipment, Q t The cooling capacity T produced by the cooling equipment in the data center during period t t dc,out is the ambient temperature during period t, U C is the external heat transfer coefficient of the data center; is the lower limit of the temperature change rate of the data center, The upper limit of the temperature change rate of the data center; is the lower limit of the indoor temperature of the data center, The upper limit of the indoor temperature of the data center;

[0042] The thermal characteristics of the campus buildings and the generalized energy storage constraints are:

[0043]

[0044] In the formula, is the generalized energy storage of the data center during period t, H BD is the data center specific heat capacity, T t BD is the indoor temperature of the data center during period t, is the upper limit of indoor temperature during period t, is the lower limit of indoor temperature during period t; is the solar radiation through the building glaze, φ t is the solar radiation heat in period t, σ is the solar radiation loss rate; is the heat radiated outward from the building, T t E is the wall temperature of the park building during period t; T t U is the indoor temperature of the park during period t; A is the direct heat transfer coefficient of the building facade; U E is the heat transfer coefficient from the room to the wall; T t a is the ambient temperature during period t. t E is the building exterior wall temperature during period t, is the building exterior wall temperature during period t-1, H PE is the specific heat capacity of the building wall, T t a is the ambient temperature during period t.

[0045] The total amount of generalized energy storage in the park is:

[0046]

[0047] Calculate the park's planned 365-day operating costs and expansion costs, and output the optimal planning and operation strategy, including:

[0048] In the actual solution process, the objective function is the sum of the annualized investment cost and the planned annual operating cost, where the annualized investment cost is the total expansion cost divided by the planned period, and when calculating the planned annual operating cost, the operating constraints of each device must meet all loads and photovoltaic output conditions during the planning period.

[0049] The beneficial effects of the present invention are:

[0050] The present invention fully considers the long-term uncertainty of load growth and the short-term uncertainty of photovoltaic output, utilizes the time transfer characteristics to achieve efficient utilization of multiple energy sources across time periods, taps into the broad energy storage resources in the park with data centers and buildings as the main body, effectively promotes the consumption of new energy, and ensures that the planning and scheduling scheme of the comprehensive energy system takes into account both economy and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the method of the present invention.

[0052] Figure 2 This is the structural diagram of the campus energy hub.

[0053] Figure 3 This is a diagram of typical daily energy load conditions in the park.

[0054] Figure 4 This is a generalized energy storage situation map for the summer of the park planning year taking generalized energy storage into account.

[0055] Figure 5 It is a scenario of short-term uncertainty in photovoltaic output. DETAILED DESCRIPTION

[0056] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention to further illustrate the present invention. Obviously, the described embodiments are only partial embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work belong to the protection scope of the present invention.

[0057] like Figure 1 As shown, the present invention proposes a carbon emission planning method for a park integrated energy system taking into account generalized energy storage. The park integrated energy system planning method in an embodiment of the present invention includes the following steps:

[0058] Step 1: Collect historical photovoltaic output data of the park's integrated energy system and other park operating parameters and unit investment costs of photovoltaic, battery and other equipment.

[0059] Step 2: Generate scenarios for long-term uncertainty in multi-energy load growth and short-term uncertainty in PV output, taking the sum of the system’s planned annual expansion cost and operating cost as the objective function.

[0060] Generate scenarios for long-term uncertainty in multi-energy load growth and short-term uncertainty in PV output:

[0061] There are three types of long-term uncertainty scenarios for upper multi-energy loads generated according to seasonal differences, namely transition season, summer and winter. Figure 3Based on the historical photovoltaic output data, the k-means clustering method is used to generate four types of short-term uncertainty scenarios for the lower photovoltaic output, such as Figure 5 .

[0062] Based on the uncertainty scenario, the objective function is constructed with the sum of expansion cost and operation cost as:

[0063]

[0064] In the formula, the subscript t represents the scheduling time; To expand the capacity of photovoltaic units, To expand the capacity of the battery unit, To expand the capacity of ground source heat pump units, To expand the capacity of the trigeneration unit; is the unit expansion cost of the photovoltaic unit, is the unit expansion cost of the battery unit, is the unit expansion cost of the ground source heat pump unit, is the unit expansion cost of the CCHP unit; Ω g is a typical day set, ω g is the weight of each typical day; Ω PV is the photovoltaic output scene set, ω PV is the photovoltaic output scenario weight; P t E is the power purchased by the upper power grid during period t, is the gas volume purchased by the upper-level gas network during period t; is the electricity price in period t, is the gas price in period t; I E is the carbon emission cost per unit of electricity purchased, I G is the carbon emission cost per unit of gas purchased.

[0065] In formula (A-1), the first four items are the annual expansion costs of each expansion unit, including photovoltaic units, battery units, ground source heat pump units and trigeneration units. The fifth item is the annual operating cost, which takes into account the carbon emission costs of purchasing electricity and gas.

[0066] The constraints of each planning variable are:

[0067]

[0068] In formula (A-2), The capacity limit for photovoltaic units expansion is To expand the capacity limit of the battery unit, Expand the capacity cap for GSHP units, Expand the capacity ceiling for CCHP units. is the original photovoltaic installed capacity, is the original battery capacity, is the original CCHP unit capacity, is the original GSHP unit capacity. The first four items are the upper limit constraints of the expansion unit investment, and the last four items are the upper limit constraints of the expansion unit operation.

[0069] Step 3: Establish an energy hub model with the park hub as the core:

[0070] The energy flow balance constraint of the park is:

[0071]

[0072] Where P t LC is the cooling load demand during period t, is the industrial cooling capacity of the GSHP unit in period t, where the GSHP unit is the ground source heat pump unit. P is the cooling capacity of CCHP unit in period t, CCHP unit is a trigeneration unit; t LH is the heat load demand during period t, The heat supply for CCHP unit during period t; The heat supply for the GSHP unit during period t; is the heat supply of the gas boiler during period t, P is the heat supplied by the electric boiler during period t; t PV is the photovoltaic output during period t, P t dis is the battery charge during period t, P t LE is the electric load demand during period t, P t GSHP is the power consumption of industrial GSHP units during period t, P t EB is the power consumption of the electric boiler during period t, P t ch is the discharge amount of the battery unit during period t, P t BD is the power consumption of the building GSHP unit during period t, P t DC is the power consumption of the data center during period t; P is the power provided by the CCHP unit during period t; t CCHP is the gas consumption of CCHP unit during period t, P t GB is the gas consumption of the gas boiler in period t. Constraints (A-2) are the balance equations of the cold bus, heat bus, electricity bus and gas bus from top to bottom.

[0073] The operating constraints of conventional multi-energy conversion equipment in the park are:

[0074]

[0075] In the formula, is the cold conversion efficiency of CCHP unit, P t CCHP，C The amount of natural gas input to the CCHP unit for refrigeration; is the heat conversion efficiency of CCHP unit, P t CCHP，H is the amount of natural gas input to the CCHP unit for heat conversion; is the electricity conversion efficiency of the CCHP unit, P t CCHP，E is the amount of natural gas input to the CCHP unit for power generation; P t TF is the amount of electricity input to the transformer, η TF is the electrical conversion efficiency of the transformer; η EB is the heat conversion efficiency of the electric boiler, η GB is the heat conversion efficiency of the gas boiler. The first three items are the CCHP unit operation equations, and the last three items are the transformer, electric boiler and gas boiler operation equations respectively.

[0076] The equipment operation constraints involving the park's generalized energy storage resources include batteries, ground source heat pumps, and data centers, among which:

[0077] The operating constraints of the battery unit are:

[0078]

[0079] Where P t ch is the energy storage charging power during period t, P t dis is the energy storage discharge power during period t; is the energy storage charging state variable during period t, E is the energy storage discharge state variable during period t; t is the energy storage capacity during period t; E is the energy storage self-discharge coefficient during period t, η ch is the energy storage charging efficiency during period t, η dis E is the energy storage discharge efficiency during period t; min is the lower limit of energy storage capacity, E max The upper limit of energy storage capacity.

[0080] The operating constraints of the ground source heat pump unit are:

[0081]

[0082] Where Pt BD，C is the electric energy used for heat transfer by the building GSHP unit during period t, P t BD，H is the electric energy used for cooling by the building GSHP unit during period t; and are the heat and cold energy delivered to the building by GSHP during period t, respectively; and are the heating and cooling operation state variables respectively. The operation constraints of industrial GSHP units are the same as those of industrial GSHP units, so they will not be described in detail.

[0083] The data center operation constraints are;

[0084]

[0085] Where P t IT is the power consumption of IT equipment in the data center during period t, is the power of each k-type server in period t; is the fixed power consumption of type k server in period t, is the CPU power consumption during period t; C1 is the power consumption coefficient of the server, d' k，t is the data load that the k-type server in the data center needs to process during period t; a k，s，t f is the CPU s-speed operating frequency flag of type k server in data center during period t; k，s It is the S-speed operating frequency of the CPU of the K-type server in the data center.

[0086] Step 4: Establish a generalized energy storage model based on battery energy storage, data center thermal characteristics, and campus building thermal characteristics.

[0087] The generalized energy storage of the battery during period t is E t .

[0088] The thermal characteristics of the data center and the generalized energy storage constraints are:

[0089]

[0090] In the formula, is the generalized energy storage of the data center during period t, H DC is the data center specific heat capacity, T t dc，in is the indoor temperature of the data center during period t, T t dc，in，max is the upper limit of indoor temperature during period t; η c is the efficiency of data center cooling equipment, Q t The cooling capacity T produced by the cooling equipment in the data center during period t t dc,outis the ambient temperature during period t, U C is the external heat transfer coefficient of the data center; is the lower limit of the temperature change rate of the data center, The upper limit of the temperature change rate of the data center; is the lower limit of the indoor temperature of the data center, It is the upper limit of the indoor temperature of the data center.

[0091] The thermal characteristics of the campus buildings and the generalized energy storage constraints are:

[0092]

[0093] In the formula, is the generalized energy storage of the data center during period t, H BD is the data center specific heat capacity, T t BD is the indoor temperature of the data center during period t, is the upper limit of indoor temperature during period t, is the lower limit of indoor temperature during period t; is the solar radiation through the building glaze, φ t is the solar radiation heat in period t, σ is the solar radiation loss rate; is the heat radiated outward from the building, T t E is the wall temperature of the park building during period t; T t U is the indoor temperature of the park during period t; A is the direct heat transfer coefficient of the building facade; U E is the heat transfer coefficient from the room to the wall; T t a is the ambient temperature during period t. t E is the building exterior wall temperature during period t, is the building exterior wall temperature during period t-1, H PE is the specific heat capacity of the building wall, T t a is the ambient temperature during period t.

[0094] The total amount of generalized energy storage in the park is:

[0095]

[0096] Step 5: Calculate the park’s planned 365-day operating costs and expansion costs, and output the optimal planning and operating strategy.

[0097] In the actual solution process, the objective function is the sum of the annualized investment cost and the planned annual operating cost, where the annualized investment cost is the total expansion cost divided by the planned period, and when calculating the planned annual operating cost, the operating constraints of each device must meet all loads and photovoltaic output conditions during the planning period.

[0098] Example Analysis

[0099] The typical daily energy load of an industrial park in Jiangsu Province was selected as the test load, and the photovoltaic input data was derived from the Netherlands Solar Forecast and Smart Grid Project Database. The typical day selection is shown in Table 1, and the time-of-use energy price is shown in Table 2.

[0100] Table 1 Typical day selection

[0101]

[0102] Table 2 Time-of-use electricity and gas purchase prices [￥ / kWh]

[0103]

[0104] In this embodiment, we compare the method proposed in the present invention with the park planning method without considering generalized energy storage in terms of investment cost, total cost and carbon emissions. In the method without considering generalized energy storage, the temperature of the building and the data center are controlled at a constant value, and there is no peak shaving and valley filling capability, as shown in Table 3. The investment cost of the park comprehensive energy system planning method considering the flexible operation mode of generalized energy storage resources in the present invention is slightly higher than that of the traditional method, but the annual planning total cost is reduced by 1.707 million yuan, and the annual carbon emissions are reduced by 0.15 kilotons. The temperature of the building and the data center can change within a specific comfort range to realize energy transfer across time periods. Therefore, the planning method considering the flexible operation of the park's generalized energy storage body improves the economic and environmental protection of the park's comprehensive energy system.

[0105] Table 3 Comparison of investment cost and carbon emissions with and without considering the park's generalized energy storage resources

[0106]

[0107] In this example, the generalized energy storage in the summer of the park planning year taking into account the generalized energy storage is as follows: Figure 4 As shown. Figure 4It can be seen that on a typical summer day, the electricity price is lower during the 2-7 period and the 15-18 period, and the generalized energy storage stores energy first, that is, the energy storage bar graph shows a "double peak" characteristic, in which the battery purchases electricity during the flat and valley period, and releases electricity during the 12-14 period and the 19-22 period to meet the peak load demand. Data centers and buildings store thermal energy during the peak period of new energy output and the flat and valley period of electricity prices, and release part of the stored energy during the 18-23 period. From the above results, it can be seen that the generalized energy storage uses its flexible charging / discharging characteristics to achieve peak shaving and valley filling of the diversified energy load in the park.

Claims

1. A carbon emission planning method for a park integrated energy system taking into account generalized energy storage, characterized in that: include: Step 1: Collect historical photovoltaic output data of the park's integrated energy system and unit investment costs mainly based on photovoltaic and battery equipment; Step 2: Based on the known park parameters, taking the sum of the expansion cost and the operating cost of the park's integrated energy system as the objective function, construct scenarios for the long-term uncertainty of multi-energy load growth and the short-term uncertainty of photovoltaic output; the objective function is: In formula (A-1), the subscript t represents the scheduling time; To expand the capacity of photovoltaic units, To expand the capacity of the battery unit, To expand the capacity of ground source heat pump units, To expand the capacity of the trigeneration unit; is the unit expansion cost of the photovoltaic unit, is the unit expansion cost of the battery unit, is the unit expansion cost of the ground source heat pump unit, is the unit expansion cost of the CCHP unit; Ω g is a typical day set, ω g is the weight of each typical day; Ω PV is the photovoltaic output scene set, ω PV is the photovoltaic output scenario weight; P t E is the power purchased by the upper power grid during period t, is the gas purchase volume of the upper-level gas network in period t; is the electricity price in period t, is the gas price in period t; I E is the carbon emission cost per unit of electricity purchased, I G is the carbon emission cost per unit of gas purchased; In formula (A-1), the first four items are the annualized expansion costs of each expansion unit, namely photovoltaic units, battery units, ground source heat pump units and trigeneration units. The fifth item is the annual operating cost, which takes into account the carbon emission costs of purchasing electricity and gas; The constraints of each planning variable are: In formula (A-2), E t is the energy storage capacity in period t; The capacity limit for photovoltaic units expansion is To expand the capacity limit of the battery unit, The capacity limit for ground source heat pump units is expanded. To expand the capacity limit of the trigeneration unit, is the original photovoltaic installed capacity, is the original battery capacity, is the original CCHP unit capacity, is the original ground source heat pump unit capacity; the first four items are the upper limit constraints of the expansion unit investment, and the last four items are the upper limit constraints of the expansion unit operation; Step 3: Establish an energy hub model with the park hub as the core and the equipment of the park's generalized energy storage resources as the main body; Step 4: Establish a generalized energy storage model based on battery energy storage, data center thermal characteristics, and building thermal characteristics; Step 5: Calculate the park’s planned 365-day operating costs and expansion costs, and output the optimal planning and operating strategy.

2. According to claim 1, a carbon emission planning method for a park integrated energy system taking into account generalized energy storage is characterized in that: The construction of long-term uncertainty scenarios of multi-energy load growth in step 2 specifically includes: generating three types of long-term uncertainty scenarios of upper multi-energy load according to seasonal differences, namely transition season, summer and winter; The short-term uncertainty scenarios of photovoltaic output specifically include: based on the historical photovoltaic output data, the k-means clustering method is used to generate four categories of short-term uncertainty scenarios of lower-level photovoltaic output.

3. The carbon emission planning method for a park integrated energy system taking into account generalized energy storage according to claim 1 is characterized in that: Step 3 specifically includes: The energy flow balance constraint of the park is: Where P t LC is the cooling load demand during period t, is the industrial cooling capacity of the GSHP unit during period t, P is the cooling capacity of CCHP unit in period t; t LH is the heat load demand during period t, The heat supply for CCHP unit during period t; The heat supply for the GSHP unit during period t; is the heat supply of the gas boiler during period t, P is the heat supplied by the electric boiler during period t; t PV is the photovoltaic output during period t, P t LE is the electric load demand during period t, P t GSHP is the power consumption of industrial GSHP units during period t, P t EB is the power consumption of the electric boiler during period t, P t ch is the discharge amount of the battery unit during period t, P t BD is the power consumption of the building GSHP unit during period t, P t DC is the power consumption of the data center during period t; P is the power provided by the CCHP unit during period t; t CCHP is the gas consumption of CCHP unit during period t, P t GB is the gas consumption of the gas boiler in period t; constraints (A-3) are the balance equations of the cold bus, heat bus, electricity bus and gas bus from top to bottom; The operating constraints of conventional multi-energy conversion equipment in the park are: In the formula, is the cold conversion efficiency of CCHP unit, P t CCHP,C The amount of natural gas input to the CCHP unit for refrigeration; is the heat conversion efficiency of CCHP unit, P t CCHP,H is the amount of natural gas input to the CCHP unit for heat conversion; is the electricity conversion efficiency of the CCHP unit, P t CCHP,E is the amount of natural gas input to the CCHP unit for power generation; P t TF is the amount of electricity input to the transformer, η TF is the electrical conversion efficiency of the transformer; η EB is the heat conversion efficiency of the electric boiler, η GB is the heat conversion efficiency of the gas boiler; the first three items are the CCHP unit operation equations, and the last three items are the transformer, electric boiler and gas boiler operation equations respectively; The equipment operation constraints involving the park's generalized energy storage resources include batteries, ground source heat pumps, and data centers, among which: The operating constraints of the battery unit are: Where P t ch is the energy storage charging power during period t, P t dis is the energy storage discharge power during period t; is the energy storage charging state variable during period t, is the energy storage discharge state variable during period t; E is the energy storage self-discharge coefficient during period t, η ch is the energy storage charging efficiency during period t, η dis E is the energy storage discharge efficiency during period t; min is the lower limit of energy storage capacity, E max The upper limit of energy storage capacity; The operating constraints of the ground source heat pump unit are: Where P t BD,C is the electric energy used for heat transfer by the building GSHP unit during period t, P t BD,H is the electric energy used for cooling by the building GSHP unit during period t; and are the heat and cold energy delivered to the building by GSHP during period t, respectively; and are heating and cooling operation state variables respectively; The data center operation constraints are; Where P t IT is the power consumption of IT equipment in the data center during period t, is the power of each k-type server in period t; is the fixed power consumption of type k server in period t, is the CPU power consumption during period t; C1 is the power consumption coefficient of the server, d' k,t is the data load that the k-type server in the data center needs to process during period t; a k,s,t f is the CPU s-speed operating frequency flag of type k server in data center during period t; k,s It is the S-speed operating frequency of the CPU of the K-type server in the data center.

4. The carbon emission planning method for a park integrated energy system taking into account generalized energy storage according to claim 1 is characterized in that: Based on battery energy storage, data center thermal characteristics, and building thermal characteristics, a generalized energy storage model is established, including: The thermal characteristics of the data center and the generalized energy storage constraints are: In the formula, is the generalized energy storage of the data center during period t, H DC is the data center specific heat capacity, T t dc,in is the indoor temperature of the data center during period t, T t dc,in,max is the upper limit of indoor temperature during period t; η c is the efficiency of data center cooling equipment, Q t The cooling capacity generated by the cooling equipment in the data center during period t, T t dc,out is the ambient temperature during period t, U C is the external heat transfer coefficient of the data center; is the lower limit of the temperature change rate of the data center, The upper limit of the temperature change rate of the data center; is the lower limit of the indoor temperature of the data center, The upper limit of the indoor temperature of the data center; The thermal characteristics of the campus buildings and the generalized energy storage constraints are: In the formula, is the generalized energy storage of the data center during period t, H BD is the data center specific heat capacity, T t BD is the indoor temperature of the data center during period t, is the upper limit of indoor temperature during period t, is the lower limit of indoor temperature during period t; is the solar radiation through the building glaze, φ t is the solar radiation heat in period t, σ is the solar radiation loss rate; is the heat radiated outward from the building, T t E is the wall temperature of the park building during period t; T t U is the indoor temperature of the park during period t; A is the direct heat transfer coefficient of the building facade; U E is the heat transfer coefficient from the room to the wall; where T t E is the building exterior wall temperature during period t, is the building exterior wall temperature during period t-1, H PE is the specific heat capacity of the building wall, T t a is the ambient temperature during period t; The total amount of generalized energy storage in the park is: 。 5. The carbon emission planning method for a park integrated energy system taking into account generalized energy storage according to claim 1 is characterized in that: Calculate the park's planned 365-day operating costs and expansion costs, and output the optimal planning and operation strategy, including: In the actual solution process, the objective function is the sum of the annualized investment cost and the planned annual operating cost, where the annualized investment cost is the total expansion cost divided by the planned period, and when calculating the planned annual operating cost, the operating constraints of each device must meet all loads and photovoltaic output conditions during the planning period.

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