An optimal scheduling method of an integrated energy system and a computer device

Abstract

The application belongs to the technical field of energy conversion and storage, and particularly relates to a comprehensive energy system optimization scheduling method and computer equipment. The method comprises the following steps: S1, constructing an optimization scheduling model of the comprehensive energy system; a target function of the optimization scheduling model is constructed with the lowest total cost as the target; the total cost comprises a carbon trading cost; the carbon trading cost is obtained according to actual carbon emissions and free carbon emission quotas of the comprehensive energy system; the actual carbon emissions comprise carbon emissions in manufacturing processes, carbon emissions in operation processes and carbon emissions in depreciation and recycling processes of each device of the comprehensive energy system; S2, solving the optimization scheduling model to obtain optimal operation parameters of decision variables in a scheduling period; and S3, controlling the comprehensive energy system to execute the optimal operation parameters obtained in S2 in the scheduling period. The application solves the technical problem that the optimization result is unreasonable due to inaccurate calculation of the carbon trading cost in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy conversion and storage, and particularly relates to an optimal scheduling method of an integrated energy system and a computer device. BACKGROUND

[0002] With the acceleration of energy low-carbon transformation, the installed capacity and power generation proportion of distributed energy are continuously increasing. The low installed capacity of renewable energy, the dispersion of distribution, and the large fluctuation of output restrict the consumption of renewable energy. The integrated energy system (IES) provides an effective way to improve overall energy efficiency, increase resource flexibility, and promote the integration of renewable energy, which has significant low-carbon benefits and is conducive to low-carbon development.

[0003] Through the coupling and complementation of multiple energies, combined with coordinated operation of loads, the new energy consumption capacity can be improved. The coordination of gas boilers (GB) and combined cold heat and power (CCHP) on the power supply side can improve the wind power and photovoltaic grid connection space. The power to gas (P2G) technology and the collaborative optimization model can efficiently convert renewable energy into natural gas for storage, thereby saving energy storage costs.

[0004] Currently, some studies have begun to focus on the low-carbon operation of IES, and the CO2 emission cost is considered in the economic cost analysis, which helps to more comprehensively evaluate the low-carbon operation of the integrated energy system, and balances the relationship between economy and carbon reduction.

[0005] The IES optimization model considering the carbon trading cost realizes the economic and carbon reduction targets. In addition, in order to limit carbon emissions, a stepped carbon trading mechanism is proposed, which constrains the carbon emissions by setting different carbon emission steps, and encourages enterprises to take more emission reduction measures to meet the carbon emission requirements set by different steps. For example, the Chinese invention patent application publication with the application publication number CN115809787A and the application publication date of March 17, 2023 discloses an integrated energy system low-carbon optimization scheduling method, which introduces carbon trading costs in the optimization target and constructs a stepped carbon trading mechanism to strengthen the constraints on carbon emissions in the optimization process, which is conducive to energy saving and emission reduction. However, the actual carbon emissions in the above technical solution are only based on the carbon emissions during the system operation, so the calculation of the carbon trading cost is not accurate, resulting in unreasonable optimization results. SUMMARY

[0006] The application aims to provide an optimal scheduling method of an integrated energy system and a computer device to solve the technical problem of unreasonable optimization results caused by inaccurate carbon trading cost calculation in the prior art.

[0007] To solve the above technical problem, the application provides an optimal scheduling method of an integrated energy system, which comprises the following steps:

[0008] S1, constructing an optimal scheduling model of the integrated energy system;

[0009] The decision variables of the optimal scheduling model are the operation parameters of each energy subsystem and each energy storage device of the integrated energy system;

[0010] The objective function of the optimal scheduling model is constructed with the lowest total cost as the target; the total cost comprises a carbon trading cost; the carbon trading cost is obtained according to the actual carbon emission of the integrated energy system and the free carbon emission quota; the actual carbon emission comprises the carbon emission in the manufacturing process of each device of the integrated energy system, the carbon emission in the operation process and the carbon emission in the depreciation and recycling process;

[0011] S2, solving the optimal scheduling model to obtain the optimal operation parameters of the decision variables in the scheduling period;

[0012] S3, controlling the integrated energy system to execute the optimal operation parameters obtained in S2 in the scheduling period.

[0013] The above technical solution has the beneficial effect that the optimal scheduling method of the integrated energy system of the application belongs to an improved invention. When the carbon trading cost is considered, the actual carbon emission not only considers the carbon emission in the operation process of the system, but also considers the carbon emission in the manufacturing process of each device of the system and the carbon emission in the depreciation and recycling process, so that the carbon trading cost is more accurate, and the final optimization result is more reasonable. The application solves the technical problem of unreasonable optimization results caused by inaccurate carbon trading cost calculation in the prior art.

[0014] Further, the carbon emission in the manufacturing process of the device comprises direct carbon emission in the manufacturing process of the device and indirect carbon emission in the manufacturing process of the device caused by purchasing electricity from the outside.

[0015] Further, the direct carbon emission is calculated according to the following formula:

[0016]

[0017] In the formula, CE PVde represents the direct carbon emission in the manufacturing process of the device in the scheduling period; P PVrepresents the planned installed capacity of the photovoltaic power station; α PVde represents the direct carbon emission factor, Z CRF represents the conversion coefficient of the total carbon emission amount allocated to each dispatching period.

[0018] Further, the indirect carbon emission amount is calculated according to the following formula:

[0019]

[0020] In the formula, CE PVinde represents the indirect carbon emission amount in the equipment manufacturing process in the dispatching period; E Pvinde represents the electric energy consumed in the equipment manufacturing process, α inde represents the indirect carbon emission factor, Z CRF represents the conversion coefficient of the total carbon emission amount allocated to each dispatching period.

[0021] Further, the carbon emission amount in the depreciation and recycling process is calculated according to the following formula:

[0022]

[0023] In the formula, CE Pvre represents the carbon emission amount in the depreciation and recycling process in the dispatching period; P PV represents the planned installed capacity of the photovoltaic power station; α PVde represents the depreciation carbon emission factor, t represents the time of a dispatching period, Z CRF represents the conversion coefficient of the total carbon emission amount allocated to each dispatching period.

[0024] Further, the carbon emission amount in the system operation process includes the carbon emission amount generated by each device in the system operation process and the carbon emission amount generated from the purchased electricity and natural gas from the outside.

[0025] Further, the carbon trading cost is obtained according to a reward and punishment step carbon trading model: the carbon emission trading amount includes the carbon emission buying amount and the carbon emission selling amount, the carbon emission trading amount is the difference between the actual carbon emission amount and the free carbon emission quota; the carbon trading unit price increases with the increase of the end point of the interval in which the carbon emission trading amount is located, and the carbon trading cost is obtained by integrating the carbon trading unit price with the carbon emission trading amount.

[0026] Further, the integrated energy system includes a combined cooling, heating and power system composed of a gas turbine and a lithium bromide chiller heat pump, and the constraint condition for the combined cooling, heating and power system in the optimal dispatching model is:

[0027]

[0028] In the formula, P CCHP (t) represents the electric power of the combined cooling, heating and power system; PCCHPmin and P CCHPmax respectively represent the minimum / maximum electric power of the CCHP system; V CCHP (t) represents the methane gas flow rate input into the CCHP system per unit time; V CCHPmin and V CCHPmax respectively represent the minimum / maximum methane gas flow rate input into the CCHP system per unit time; H CCHP (t) represents the heat power of the CCHP system; H CCHP (t) represents the refrigeration power of the CCHP system; η p,CCHP , η H,CCHP , and η U,CCHP respectively represent the electric, heat, and cold conversion rates; U heat and U cold respectively represent binary variables of the working mode of the CCHP system, when U heat is 1, the CCHP system works in the cogeneration mode, and when U cold is 1, the CCHP system works in the cooling power generation mode. represents the heat value of methane; K represents the conversion coefficient between electric energy and heat energy.

[0029] Further, the constraint conditions of the comprehensive energy system include at least one of the balance constraints of the electric energy system, the hydrogen energy system, the heat energy system, the cold energy system, and the methane system; the balance constraint is that the output of a kind of energy of the comprehensive energy system minus the consumption of the kind of energy in a unit time period is equal to the energy storage change of the energy storage device of the kind of energy.

[0030] Further, the balance equation corresponding to the balance constraint of the electric energy system is:

[0031]

[0032] In the formula, is the power generation amount of the photovoltaic power station in a unit time period t; is the electric energy generated by the CCHP system in a unit time period t; is the electric energy output by the fuel cell in a unit time period t; is the electric energy absorbed by the IES from the power grid in a unit time period t; is the electric energy consumed by the electrolytic cell in a unit time period t; is the electricity load in a unit time period t; is the energy storage amount of the electric energy storage device at the end of a unit time period t; is the energy storage amount of the electric energy storage device at the beginning of a unit time period t; is the electric energy consumed by the air conditioner in a unit time period t;

[0033] The balance equation of the hydrogen energy system is:

[0034]

[0035] wherein, is the hydrogen energy consumed by the fuel cell in the unit time period t; is the hydrogen energy stored in the hydrogen storage tank at the end of the unit time period t; is the hydrogen energy stored in the hydrogen storage tank at the end of the unit time period t; is the hydrogen energy generated by the electrolyzer in the unit time period t; is the hydrogen energy sold by the IES in the unit time period t;

[0036] The balance equation of the thermal energy system is:

[0037]

[0038] wherein, is the thermal energy generated by the fuel cell in the unit time period t; is the thermal energy generated by the CCHP system in the unit time period t, is the thermal energy generated by the steam turbine in the unit time period t; is the energy stored in the thermal storage device at the beginning of the unit time period t; is the energy stored in the thermal storage device at the end of the unit time period t; is the thermal load in the unit time period t;

[0039] The balance equation of the cold energy system is:

[0040]

[0041] wherein, is the cold energy output by the air conditioner in the unit time period t; is the cold energy generated by the CCHP system in the unit time period t; is the energy stored in the thermal storage device at the beginning of the unit time period t; is the energy stored in the thermal storage device at the end of the unit time period t; is the cold load in the unit time period t;

[0042] The balance equation of the methane system is:

[0043]

[0044] wherein, is the methane consumed by the CCHP system in the unit time period t; is the methane consumed by the steam turbine in the unit time period t; is the methane absorbed by the IES from the gas network in the time period t; is the methane storage of the methane storage device at the beginning of the time period t; is the methane storage of the methane storage device at the end of the time period t.

[0045] The application also provides a technical solution of a computer device: comprising a memory, a processor, and a computer program stored in the memory, the processor being configured to execute the computer program to implement the steps of the optimization scheduling method of the integrated energy system. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is the capacity factor curve of the photovoltaic power station in the method embodiment of the application;

[0047] Figure 2 is the typical daily curve of the electricity-gas-heat load in the method embodiment of the application;

[0048] Figure 3 is the curve of the change of the electricity price with time in a day in the method embodiment of the application;

[0049] Figure 4a is the grid balance curve of the IES under scenario 1 in the method embodiment of the application;

[0050] Figure 4b is the grid balance curve of the IES under scenario 7 in the method embodiment of the application;

[0051] Figure 5a is the heat grid balance curve of the IES under scenario 1 in the method embodiment of the application;

[0052] Figure 5b is the heat grid balance curve of the IES under scenario 7 in the method embodiment of the application;

[0053] Figure 6a is the cold grid balance curve of the IES under scenario 1 in the method embodiment of the application;

[0054] Figure 6b is the cold grid balance curve of the IES under scenario 7 in the method embodiment of the application;

[0055] Figure 7a is the natural gas grid balance curve of the IES under scenario 1 in the method embodiment of the application;

[0056] Figure 7b is the natural gas grid balance curve of the IES under scenario 7 in the method embodiment of the application;

[0057] Figure 8a is the hydrogen grid balance curve of the IES under scenario 1 in the method embodiment of the application;

[0058] Figure 8b Hydrogen grid balance curve for IES under scenario 7 in the embodiment of the method of the application;

[0059] Figure 9a Influence of carbon price growth rate on system carbon emission net value and system total cost in the embodiment of the method of the application;

[0060] Figure 9b Benchmark carbon price λ in the embodiment of the method of the application c Influence on system carbon emission net value and system total cost;

[0061] Figure 9c Influence of carbon price interval length l on system carbon emission net value and system total cost in the embodiment of the method of the application;

[0062] Figure 9d Benchmark carbon price λ in the embodiment of the method of the application c Change process of system to each device planned capacity;

[0063] Figure 10 Flow chart of the optimization scheduling method in the embodiment of the method of the application. DETAILED DESCRIPTION

[0064] The application considers not only the carbon emissions in the system operation process, but also the carbon emissions in the manufacturing process of each device of the system and the carbon emissions in the depreciation and recovery process when considering the carbon trading cost, so that the carbon trading cost is more accurate, and the final optimization result is more reasonable. The application solves the technical problem of inaccurate calculation of rolling carbon trading cost in the prior art, which leads to unreasonable optimization results.

[0065] Embodiment of the optimization scheduling method of the integrated energy system:

[0066] An optimization scheduling method of an integrated energy system, as shown in Figure 10 The method comprises the following steps:

[0067] First, an optimization scheduling model of the integrated energy system is constructed; the decision variables of the optimization scheduling model are the operation parameters of each energy subsystem and each energy storage device of the integrated energy system; the objective function of the optimization scheduling model is constructed with the lowest total cost as the target; the total cost includes a carbon trading cost; the carbon trading cost is obtained according to the actual carbon emissions and the free carbon emissions quota of the integrated energy system; the actual carbon emissions include the carbon emissions in the manufacturing process of each device of the integrated energy system, the carbon emissions in the operation process and the carbon emissions in the depreciation and recovery process.

[0068] Secondly, the optimal operation parameters of each energy subsystem and each energy storage device in the comprehensive energy system in the scheduling period are obtained by solving the optimal scheduling model.

[0069] Finally, the optimal operation parameters obtained above are executed by the comprehensive energy system in the scheduling period.

[0070] Specifically, in the embodiment, one scheduling period is one day.

[0071] The comprehensive energy system of the embodiment comprises an electric energy system, an energy storage system and a CCHP system, wherein the energy storage system comprises five energy storage means of electricity, heat, cold, hydrogen and methane; the electric energy system comprises a new energy power station (a photovoltaic power station), a hydrogen fuel cell and an electrolytic cell for electrolyzing water to produce hydrogen energy storage, and further comprises a methanation device for converting hydrogen into methane energy storage; the CCHP system comprises a gas turbine and a lithium bromide cold and heat pump, wherein the lithium bromide cold and heat pump can work in condensing and heating modes to realize heat-electricity and cold-electricity cogeneration; in addition, a steam turbine and an air conditioner are arranged, and when the waste heat supply of the CCHP is insufficient or the cold load cannot be met, the steam turbine produces heat / air conditioner refrigeration to make up for the insufficient heat / cold energy supply.

[0072] The above content will be further described in combination with embodiments. Figure 10

[0073] S1, an optimal scheduling model of the comprehensive energy system is constructed; the decision variables of the optimal scheduling model are the operation parameters of each energy subsystem and each energy storage device of the comprehensive energy system.

[0074] The optimal scheduling method of the comprehensive energy system of the embodiment is an optimal scheduling method considering carbon trading, and the optimal scheduling model of the embodiment takes the lowest total system cost as the objective function, wherein the total system cost comprises the power purchase cost from the power grid, the one-time cost of construction of each device and the operation cost of each module, in addition to the carbon trading cost, and specifically comprises:

[0075]

[0076] In the formula, C is the total system cost, is the carbon trading cost of the system and the outside carbon market, C e is the power purchase cost of the system from the power grid, C CAPEX is the one-time cost of construction of each device of the system, C OPEX is the operation cost of each module, and the annual usage is calculated in hours.

[0077] ​The actual carbon emissions used in the calculation of the carbon trading cost in this embodiment are obtained based on the full life cycle of the system equipment. A full life cycle carbon dioxide emission model is established, and the carbon emissions of each device in the system can be calculated from three aspects: carbon emissions in the manufacturing process, carbon emissions in the operation process, and carbon emissions in the depreciation and recycling process.

[0078] The carbon emissions in the construction process of the system equipment can be divided into direct carbon emissions and indirect carbon emissions. Direct carbon emissions refer to the amount of carbon dioxide directly emitted during the construction process. In a photovoltaic power station, the amount of carbon emitted during the manufacturing process of photovoltaic solar panels is mainly considered, and the direct carbon emission coefficient in the production process is 18.4 kg / kW. Indirect carbon emissions in the production process of the equipment represent the amount of carbon dioxide generated by using other forms of energy during the production process. The carbon emission coefficient of purchasing electricity from the power grid is multiplied by the electricity consumption in the manufacturing process.

[0079]

[0080] In the formula, represents the indirect carbon emissions in the construction process, represents the energy consumed in the construction process, is the indirect carbon emission factor, which is 0.997 kg / kWh.

[0081]

[0082] The carbon emissions in the depreciation and recycling process of each device in the system refer to the recycling or disposal of renewable energy power generation equipment after retirement. According to the national power industry standard “Photovoltaic Power Generation Project Full Life Cycle Carbon Emission Quantification Method and Evaluation Standard”, the carbon emission intensity in the recycling and disposal stage is 0.52 g CO2e / (kWh), as shown in the following formula:

[0083]

[0084] The carbon emissions in the full life cycle process of the system are considered, but the optimization range is only one day, so the total cost needs to be depreciated. Considering the service life of the one-time cost, the depreciation of the one-time cost allocated to each year is defined as follows:

[0085]

[0086] In the formula, i is the depreciation rate, which is set to 3%, and n is the equipment operation life, which is set to 25 years.

[0087] Therefore, the total carbon emissions of photovoltaic power generation in the construction, depreciation, and recycling processes are as follows:

[0088]

[0089] The other carbon emissions during system operation refer to the carbon emissions generated in the process of consuming electricity and natural gas purchased from outside and fuel cell consumption of the equipment already installed in the system. The annual carbon emissions of the equipment already installed in the IES are shown in the following formula:

[0090]

[0091] In the formula, is the carbon emissions during system operation, is the carbon emissions of 1 m 3 of natural gas combustion, which is set to 2.165 kg / m 3 .

[0092] The essence of establishing a carbon trading model is to establish a reasonable carbon emissions allocation and trading system through establishing a carbon market, so as to control the carbon emissions of enterprises and users. First, the market will allocate free carbon quotas to all parties. Within a statistical cycle, if the actual carbon emissions of an enterprise are less than its free emissions quota, the remaining carbon emissions quota can be sold for profit, and otherwise it needs to meet the demand by purchasing the quota.

[0093] The allocation of carbon quotas for each device within the system is mainly in the form of free emissions quota in China. The baseline method is conducive to encouraging enterprises to actively reduce emissions. The free carbon emissions quota of the electricity purchaser is calculated according to the following formula:

[0094]

[0095] In the formula, represents the initial allocation of free carbon quota for each device, , represent the free carbon quota allocation factors of coal-fired equipment and electricity-consuming equipment, which are 0.798 kg / kWh and 0.385 kg / kWh, respectively.

[0096] The actual carbon trading cost paid by the electricity purchaser will not be calculated repeatedly. The electricity purchased from the public power grid is included in the carbon trading accounting. This embodiment considers that the electricity purchased from the public power grid comes from coal-fired generating units.

[0097] The quota trading mechanism between the system and the outside carbon market can adopt a step pricing mechanism to further limit carbon emissions. Compared with the traditional carbon trading pricing mechanism, the step pricing mechanism divides multiple purchase intervals, and the more carbon emissions quotas are purchased, the higher the price of the purchase interval. The step carbon trading cost is shown in the following formula:

[0098]

[0099] where λ c represents the benchmark price of the carbon trading market; LCEex θ represents the carbon trading volume; θ represents the carbon price growth rate; l represents the range length of carbon emissions. This indicates the cost of carbon trading.

[0100] The constraints of the optimized scheduling model in this embodiment are based on the settings of each subsystem of the IES. The following is a detailed description of the establishment of the model of each subsystem of the IES and the setting of its constraints.

[0101] The model for a hydrogen fuel cell is as follows:

[0102]

[0103] In the formula, It is the heat generated by the operation of the fuel cell. It is the hydrogen energy consumed by the fuel cell to operate. This is the calorific value of hydrogen, taken as 33 kWh / kg; It refers to the efficiency of gas-to-electricity conversion in fuel cells; It refers to the efficiency of releasing heat during the fuel cell reaction process; The energy generated by the operation of a fuel cell.

[0104] The model of the electrolytic cell is as follows:

[0105]

[0106] In the formula, It is the calorific value of hydrogen; It is the efficiency of heat consumption during the electrolytic cell reaction process; It is the efficiency of converting electrical energy into gaseous chemical energy during the electrolytic cell reaction process; It is the energy required for the electrolytic cell to operate; It is the heat absorbed by the electrolytic cell during operation.

[0107] For photovoltaic power plants, the energy-power formula can be derived from the photovoltaic power generation curve:

[0108]

[0109] In the formula, This represents the actual photovoltaic power generation capacity factor. Plan the capacity for photovoltaic power station systems.

[0110] The energy storage system model is as follows: five energy storage methods are established: electricity, heat, cooling, hydrogen, and methane, to reduce the impact of the uncertainty of renewable energy on the system. The working principle of the energy storage system is similar. Taking hydrogen energy storage as an example, the model is as follows:

[0111]

[0112] In the formula, the planned volume of the hydrogen storage tank, the maximum ramping power input / output of the hydrogen storage tank.

[0113] In order to control the energy loss in the storage tank in each operation cycle, it is necessary to ensure that the gas volume in the storage tank is the same at the beginning and end of the scheduling cycle:

[0114]

[0115] The model of the methanation device is as follows: the methane production can be calculated by considering the energy loss of hydrogen as follows:

[0116]

[0117] In the formula, is the efficiency of the methanation process, K is the conversion coefficient between electrical energy and thermal energy, which is 3.6 MJ / kWh, corresponds to the heat value of methane, which is 35.6 MJ / m 3 .

[0118] Model of the thermal energy system: In the microgrid, when the waste heat supply of combined heat and power is insufficient or the cold load cannot be met, the heat production of the steam turbine / air conditioner can make up for the insufficient part of the heat / cold energy supply. The mathematical model of the steam turbine / air conditioner is established based on energy conservation as follows:

[0119]

[0120] In the formula, , are the heating / cooling efficiencies of the steam turbine / air conditioner, respectively, which are 80% and 380%.

[0121] The CCHP system is composed of a gas turbine and a lithium bromide cold heat pump device, wherein the lithium bromide cold heat pump can work in condensing and heating modes, thereby realizing heat-electricity and cold-electricity combined production. The model of the CCHP system is as follows:

[0122]

[0123] In the formula, P CCHP (t) represents the electric power of the combined cooling heating and power system; P CCHPmin and P CCHPmax represent the minimum / maximum electric power of the combined cooling heating and power system; V CCHP (t) represents the methane gas flow rate input per unit time of the combined cooling heating and power system; V CCHPmin and V CCHPmax represent the minimum / maximum methane gas flow rate input per unit time of the combined cooling heating and power system; H CCHP (t) represents the heat production power of the combined cooling heating and power system; HCCHP (t) represents the cooling power of the combined cooling, heating and power system; η p,CCHP , η H,CCHP and η U,CCHP represent the power conversion rates of electricity, heat and cooling, respectively; U heat and U cold are binary variables representing the working mode of the combined cooling, heating and power system, when U heat is 1, the combined cooling, heating and power system works in the combined heat and power mode, and when U cold is 1, the combined cooling, heating and power system works in the combined cooling and power mode.

[0124] According to the fuel cell model, the electrolytic cell model, the electric energy system model, the thermal energy model and the energy storage system model, the electric-hydrogen-thermal-cooling-methane network balance equation is constructed.

[0125] The electric energy system balance equation is:

[0126]

[0127] In the formula, is the power generation of the solar power station in the corresponding unit period t; is the power generation of the combined cooling, heating and power device; is the power generation of the fuel cell; is the energy absorbed by the system from the power grid per hour; is the energy consumption of the electrolytic cell; is the electric load; is the energy storage of the energy storage device at time t, is the energy consumption of the air conditioner.

[0128] The thermal energy system balance equation is:

[0129]

[0130] In the formula, is the heat generated by the fuel cell; is the heat generation of the combined cooling, heating and power device, is the heat generation of the steam turbine; is the energy storage of the thermal energy storage device at time t; is the thermal load of the system.

[0131]

[0132] In the formula, is the cooling generated by the air conditioner; is the cooling power of the combined cooling, heating and power device; is the energy storage of the thermal energy storage device at time t; is the cooling load of the system.

[0133] The hydrogen energy system balance equation is:

[0134]

[0135] In the formula, is the hydrogen energy consumed by the fuel cell operation; is the hydrogen energy stored in the hydrogen storage tank at time t; is the hydrogen energy stored in the hydrogen storage tank at the last time; is the hydrogen energy generated by the electrolyzer operation; is the hydrogen energy sold by the system.

[0136] The methane balance equation is:

[0137] In the formula, is the methane consumption of the CCHP; is the methane consumption of the turbine operation; is the amount of methane absorbed by the system from the gas network per hour; is the gas storage amount of the energy storage device at time t.

[0138] S2, solve the optimal scheduling model established in step S1 to obtain the optimal operation parameters of each energy subsystem and each energy storage device of the integrated energy system in the scheduling day.

[0139] S3, control the integrated energy system to execute the optimal operation parameters obtained above in the scheduling day.

[0140] In order to reduce the effectiveness of the system carbon emissions and economic cost of the present application, eight scenes are set for comparative analysis in this paper: the specific scene setting is shown in Table 1:

[0141] Table 1 Different equipment and transaction mechanism scenes

[0142]

[0143] In the table, indicates the carbon trading cost; HS indicates the heat energy storage planning capacity; ES indicates the electric energy storage planning capacity; GS indicates the methane energy storage planning capacity; H2S indicates the hydrogen energy storage planning capacity; CS indicates the cold energy storage planning capacity; PV indicates the photovoltaic power station generation capacity; C indicates the total cost of the system; LCEex indicates the carbon trading amount.

[0144] Scenario 1: considering the step carbon trading mechanism, containing five kinds of energy storage devices of heat, electricity, hydrogen, cold and methane, considering the solar power station generation, wherein, in the initial state, the carbon price growth rate is 0.25, and the benchmark carbon price is 400 yuan / ton.

[0145] Scenarios 2-6: based on scenario 1, respectively exclude heat, electricity, hydrogen, cold and methane energy storage forms, containing only four kinds of energy storage devices;

[0146] Scenario 7: Based on Scenario 1, five types of energy storage devices are included, including thermoelectric, hydrogen, and cold methane, without considering solar power generation.

[0147] Scenario 8: Consider the impact of three coefficients in the tiered carbon trading mechanism on the system's net carbon emissions and planned equipment capacity, including five energy storage forms: thermoelectric, hydrogen, and cold methane, and consider solar power generation.

[0148] As shown in Table 1, Scenario 1 has the lowest total cost and carbon emissions, at 1.74 × 10⁻⁶ respectively. 6 Yuan and 0.987×10 6 The carbon emission reductions for various storage tanks were 109.8%, 40.3%, 68.5%, 87.37%, and 45.76%, respectively. Further comparing the impact of different storage tanks on the system, thermal energy storage, methane storage tanks, and hydrogen storage tanks showed the most significant carbon reductions. The absence of thermal energy storage significantly increased the planned capacity of the steam turbine and the amount of natural gas purchased from the gas grid. In scenario 5, the absence of hydrogen storage led to intermittent hydrogen production and consumption within the system. In both scenarios, the system's hydrogen production decreased, while the amount of natural gas purchased from the gas grid increased, thus increasing the system's total carbon emissions. The absence of methane storage tanks also reduced the system's peak shaving and valley filling capabilities, increasing the amount of natural gas purchased from the gas grid. The carbon reduction effect of cold energy storage was limited because the cooling load was relatively small compared to other loads, and the cooling equipment was singular (CCHP), with limited regulation capabilities. From the planned PV capacity in the table above, it can be seen that the larger the scale of the solar power plant, the less carbon trading volume the system requires, the lower the system's dependence on external electricity and natural gas grids, and the lower the carbon trading costs.

[0149] Specifically, compared to scenario 1, scenario 7 shows a significant increase in total system cost and carbon emissions, with increases of 138.79% and 100.27%, respectively. Due to the high cost of electricity and low efficiency in converting it into other forms of energy, the planned capacity of each energy storage system is significantly reduced.

[0150] The invention will be further explained and illustrated below with a specific example:

[0151] The power curve for photovoltaic (PV) power generation comes from simulation data on the Renewable Ninja website, and the capacity factor curve for PV power generation is as follows: Figure 1 As shown.

[0152] Considering that the investment in a photovoltaic power station is divided into primary costs (construction costs) and secondary costs (operating costs), this case uses the following parameters: primary investment, with solar power generation calculated at 5719 yuan / kWh, and the annual operating cost of photovoltaic power is converted into 3% of the annual primary investment cost (CAPEX).

[0153] As shown in Table 2, the fuel cell, electrolytic cell, CCHP, methanation device efficiency and cost have been given, the one-time investment cost, device operation efficiency, the one-time investment cost of hydrogen and methane storage tank operated at standard atmospheric pressure is 12600 yuan / kg, the cost of electric storage tank is 1200 yuan / kWh, and the one-time cost of heat and cold storage tank is 420 yuan / kWh. The storage tank does not require operating cost. The CCHP has been built in advance and is not considered as cost, and the installed capacity is 2500 kW.

[0154] Table 2 Investment cost and operation efficiency of various devices

[0155]

[0156] The power load and heat load data curves are as shown in Figure 2 , and the cold load in this paper is 83.3% of the heat load. In order to verify the effectiveness of the model and the optimization scheme, an electricity price scenario is selected. The relevant electricity price parameters are as shown in Figure 3 . The price of natural gas is fixed at 2.93 yuan / m 3 .

[0157] Under this premise, the optimization results before and after the introduction of new energy power station (scene 1 and scene 7) are compared, and the network balance curve comparison is as shown in Figures 4a to 7b .

[0158] The electric energy optimization results are as shown in Figure 4a and 4b . After the introduction of new energy power station, solar power generation replaces CCHP and grid input to become the main power supply source, and the electric energy output and consumption in each time period are significantly improved. The electric energy is converted into other forms of energy, especially hydrogen energy, which is significantly improved. After the introduction of solar power station, the purchased power is reduced by 79.86%, which is mainly concentrated in the period with high solar output (4:00-16:00). The hydrogen production electricity consumption exceeds the electric load, reaching 51.88% of the total power consumption, leaving space for solar power generation, reducing the cost of system purchase of electric energy and natural gas, and improving the economic efficiency and environmental protection of the system.

[0159] Figures 5a to 7bThe heat-cold-natural gas network balance under scenarios 1 and 7 is shown. There are many ways to replace heat and natural gas. After adding solar power generation, the system heating sources include heat pump, CCHP, fuel cell heat and heat storage tank output. The solar power station system improves the total power generation of the system, thereby introducing fuel cells and electric heat pumps as electric heat conversion equipment. The heat production of CCHP is reduced by 13.95% compared with scenario 7. The cold production equipment in the cold network is relatively single. The cold energy storage planning capacity in scenario 1 is increased by 166.91% compared with scenario 7. The cold energy is stored during 4:00-12:00 when the sunlight is strong, and the energy peak load shifting is realized. The large-scale production of hydrogen greatly reduces the dependence of the system on the natural gas network. In scenario 7, the natural gas is completely dependent on the input of the natural gas network, and the gas purchase amount is 1.4395×10 4 m 3 In scenario 1, it is only 7.4357×10 3 m 3 , which is reduced by 48.35%.

[0160] Without the introduction of solar power stations, the cost of hydrogen production is high, so the system does not choose hydrogen as the energy carrier. As shown in Figure 8a and 8b , correspondingly, the planning capacity of electrolysis cell and fuel cell is 0. After the introduction of new energy, hydrogen becomes the preferred energy carrier of the system, and its energy curve is highly consistent with the power generation curve of the solar power station. Therefore, it can be known that hydrogen with fuel cell and electrolysis cell as energy converters can absorb new energy and effectively reduce cost.

[0161] Finally, by introducing the carbon trading mechanism, the influence of the ladder carbon price on the system carbon emission net value, the system total cost and the device capacity configuration is explored, so that the device output configuration of the carbon trading mechanism is more reasonable. Based on scenarios 1 and 8, the three coefficients of the benchmark carbon price, the interval length and the carbon price growth rate of the ladder carbon trading mechanism are discussed. Among them, Figure 9a shows the influence of the carbon price growth rate θ on the system carbon emission net value and the system total cost, Figure 9b shows the influence of the benchmark carbon price λ c on the system carbon emission net value and the system total cost, Figure 9c shows the influence of the carbon price interval length l on the system carbon emission net value and the system total cost, Figure 9d shows the change of the system to each device planning capacity in the change process of the benchmark carbon price λ c .

[0162] From Figures 9a to 9dIt can be seen that the increase in carbon price growth rate and benchmark carbon price increases the carbon emission cost of the system, resulting in a decrease in the carbon trading volume of the system. As both gradually increase, the equipment output of the integrated energy system tends to stabilize, and eventually the carbon emissions of the system tend to stabilize. The increase in the length of the carbon price range reduces the average carbon price of the system, thereby increasing the carbon emissions of the system.

[0163] according to Figure 9a , 9c It can be seen that the carbon price growth rate and carbon price range have a significant impact on the net carbon emissions of the system. A step increase in the capacity of photovoltaic power plants leads to an increase in carbon allowances, which in turn reduces the cost of carbon trading. Based on carbon emission calculations, it can be deduced that when the interval length is 2×10... 5 When the total carbon emissions are below kg, the system's total carbon emissions are in the fourth segment of the carbon price tier, while when the interval length is between 4 and 5.5 × 10⁻⁶ kg / kg... 5 When the system's total carbon emissions reach kg, it is in the third stage of the carbon price tier. At this point, the system's average carbon price will decrease significantly, and thus carbon emissions will experience a leap in increase.

[0164] according to Figure 9b and 9d As shown, before the benchmark carbon price rises to 300 yuan / ton, the planned capacity of the solar power plants and electrolyzers in the system steadily increases, with installed capacities increasing by 639.79 kW and 1080.17 kW respectively compared to when carbon trading is not considered (c=0). When the carbon price is between 300-500 yuan / ton, the planned capacity of hydrogen storage tanks continuously decreases. At this point, due to the high carbon emissions from purchasing external natural gas, the demand for hydrogen to produce methane gradually increases. The cost savings from storing hydrogen as a backup energy source are lower than the cost of emission reduction. After the benchmark carbon price exceeds 500 yuan / ton, the output of the integrated energy system tends to stabilize, and ultimately the system's carbon emissions stabilize, with a total emission reduction of 1807.73 tons. Therefore, by reasonably setting a carbon trading benchmark price, the carbon emissions of IES can be effectively reduced, and the installed capacity of low-carbon equipment can be increased. The planned capacity of solar power plants also increases significantly. The tiered carbon trading mechanism has certain advantages in reducing the total carbon emissions of the system and increasing the absorption of new energy systems.

[0165] Computer equipment example:

[0166] A computer device includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the integrated energy system optimization scheduling method described above. The specific integrated energy system optimization scheduling method has been described in sufficient detail in the above-described integrated energy system optimization scheduling system embodiments and will not be repeated here.

[0167] This invention has the following characteristics:

[0168] The application takes the economy and the minimum environmental cost as the objective function to perform real-time dynamic energy management on the integrated energy system, effectively reduces the carbon emission of the integrated energy system, and improves the installed capacity of low-carbon equipment.

[0169] (1) By optimizing the configuration of various energy storage systems, the carbon emission reduction of each type of tank reaches 4.58%, 17.7%, 9.42%, 5.71%, and 0.882%, respectively. The carbon emission reduction of the electric energy storage system, the methane tank, and the hydrogen tank is the most obvious. The electric energy storage system affects the storage of electric energy during the solar energy peak period. The lack of hydrogen energy storage promotes the production and consumption of hydrogen energy in the system, thereby increasing the gas purchase amount from the natural gas network and the total carbon emission of the system. After the introduction of new energy, hydrogen energy becomes the preferred energy carrier in the system. Hydrogen energy with fuel cells and electrolytic cells as energy converters can absorb new energy and effectively reduce costs.

[0170] (2) The increase of the carbon price growth rate and the benchmark electricity price increases the carbon emission cost of the system, reduces the carbon trading amount of the system, and increases the average carbon price of the system. When the benchmark carbon price increases to 300 yuan / ton, the planning capacity of the solar power station and the electrolytic cell in the system steadily increases, which is 639.79 kW and 1080.17 kW higher than that without considering carbon trading, respectively. When the carbon price is between 300 yuan / ton and 500 yuan / ton, the planning capacity of the hydrogen tank continuously decreases. When the benchmark carbon price is higher than 500 yuan / ton, the carbon emission of the system tends to be stable, and the total carbon emission reduction reaches 1807.73 tons.

[0171] (3) By reasonably setting the benchmark price of carbon trading, the carbon emission of the integrated energy system is effectively reduced, and the installed capacity of low-carbon equipment is improved. The planning capacity of the solar power station also significantly increases. The step-by-step carbon trading mechanism has certain advantages in reducing the total carbon emission of the system and increasing the consumption of new energy systems.

[0172] Finally, it should be noted that the above-described preferred embodiments of the application are not intended to limit the application, although the application has been described in detail with reference to the foregoing embodiments. For those skilled in the art, the technical solutions described in the foregoing embodiments can be modified without creative labor, or some technical features can be replaced equivalently. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for optimal scheduling of an integrated energy system, characterized in that, The method comprises: S1, constructing an optimal scheduling model of the integrated energy system; The decision variables of the optimal scheduling model are the operation parameters of each energy subsystem and each energy storage device of the integrated energy system; The objective function of the optimization scheduling model is constructed with the lowest total cost as the target; the total cost includes the carbon trading cost obtained according to the reward and punishment step carbon trading model and the actual carbon emissions and free carbon emission quota of the system; the actual carbon emissions include the carbon emissions CE PVmade in the manufacturing process of each device of the integrated energy system, the carbon emissions CE ope in the running process and the carbon emissions CE PVre in the depreciation and recovery process; CE PVmade comprises direct carbon emissions CE emitted during the manufacturing of the device PVde and indirect carbon emissions CE caused by the purchase of electricity from the outside during the manufacturing of the device PVinde : ； CE PVre According to the following formula: ； P PV is the installed capacity of the photovoltaic power station; a PVde is the direct carbon emission factor; a CRF is the conversion factor of total carbon emissions allocated to each dispatching period; E PVinde is the electricity consumed in the equipment manufacturing process; a inde is the indirect carbon emission factor; a PVre is the depreciation carbon emission factor; t is the time of a dispatching period; CE ope includes carbon emissions generated by each device during operation and carbon emissions generated from purchased electricity and natural gas from outside S2, solving the optimal scheduling model to obtain the optimal operation parameters of the decision variables in the scheduling period; S3, controlling the integrated energy system to execute the optimal operation parameters obtained in S2 in the scheduling period.

2. The method of Claim 1, wherein, The carbon trading cost is obtained by integrating the carbon trading unit price and the carbon emission trading volume, the carbon emission trading volume includes carbon emission buying volume and carbon emission selling volume, the carbon emission trading volume is the difference between the actual carbon emission volume and the free carbon emission quota; the carbon trading unit price increases with the increase of the interval end point of the carbon emission trading volume.

3. The method of Claim 1, wherein, The integrated energy system includes a combined heat and power system composed of a gas turbine and a lithium bromide cold heat pump, and the constraint condition of the combined heat and power system in the optimal scheduling model is: ； where P CCHP (t) represents the electric power of the combined cooling heating and power system; P CCHPmin and P CCHPmax represent the minimum / maximum electric power of the combined cooling heating and power system; V CCHP (t) represents the flow rate of the methane gas input per unit time of the combined cooling heating and power system; V CCHPmin and V CCHPmax represent the minimum / maximum flow rate of the methane gas input per unit time of the combined cooling heating and power system; H CCHP (t) represents the heat production power of the combined cooling heating and power system; H CCHP (t) represents the refrigeration power of the combined cooling heating and power system; η p,CCHP η H,CCHP η U,CCHP represent the electricity, heat, and cold conversion rates, respectively; U heat and U cold are binary variables representing the working mode of the combined cooling, heating, and power system, where U heat = 1 indicates that the combined cooling, heating, and power system is working in the combined heat and power mode, and U cold = 1 indicates that the combined cooling, heating, and power system is working in the combined cooling and power mode; represents the heating value of methane; and K represents the conversion coefficient between electrical energy and thermal energy.

4. The method of Claim 3, wherein, The constraint condition of the integrated energy system includes at least one of the balance constraints of the electric energy system, the hydrogen energy system, the thermal energy system, the cold energy system and the methane system; the balance constraint is that in a unit time period, the output of a kind of energy of the integrated energy system minus the consumption of the kind of energy is equal to the energy storage change of the energy storage device of the kind of energy.

5. The method of claim 4, wherein, The balance equation corresponding to the balance constraint of the electric energy system is: ； In the formula, is the power generation of the photovoltaic power station in a unit time period t; is the power generated by the CCHP system in a unit time period t; is the power output by the fuel cell in a unit time period t; is the power absorbed by the IES from the power grid in a unit time period t; is the power consumed by the electrolytic cell in a unit time period t; is the power load in a unit time period t; is the energy storage of the electric energy storage device at the end of a unit time period t; is the energy storage of the electric energy storage device at the beginning of a unit time period t; is the power consumed by the air conditioner in a unit time period t; The balance equation corresponding to the balance constraint of the hydrogen energy system is: ； wherein is the hydrogen energy consumed by the fuel cell during the unit time period t; is the hydrogen energy stored in the hydrogen storage tank at the end of the unit time period t; is the hydrogen energy stored in the hydrogen storage tank at the beginning of the unit time period t; is the hydrogen energy produced by the electrolyzer during the unit time period t; is the hydrogen energy sold by the IES during the unit time period t; The balance equation of the thermal energy system is: ； wherein, is the heat generated by the fuel cell during the time period t; is the thermal energy generated by the CCHP system during the time period t, is the thermal energy generated by the steam turbine during the time period t; is the thermal energy stored in the thermal storage device at the end of the time period t; is the thermal energy stored in the thermal storage device at the beginning of the time period t; is the thermal load during the time period t; The balance equation of the cold energy system is: ； In the formula, is the cold energy output by the air conditioner in the unit time period t; is the cold energy generated by the CCHP system in the unit time period t; is the energy storage amount of the cold energy storage device at the end of the unit time period t; is the energy storage amount of the cold energy storage device at the beginning of the unit time period t; is the cold load in the unit time period t; The balance equation of the methane system is: ； wherein, is the methane consumed by the CCHP system in the time period t; is the methane consumed by the turbine in the time period t; is the methane absorbed by the IES from the gas grid in the time period t; is the methane storage of the methane storage device at the end of the time period t; is the methane storage of the methane storage device at the beginning of the time period t. 6.The method of Claim 1, wherein, Conversion factor Z CRF is obtained according to the formula: ； Wherein, i is the depreciation rate, n is the equipment operation life. 7.The method of claim 5, wherein, The integrated energy system includes a hydrogen fuel cell, and the model of the hydrogen fuel cell is: ； wherein, is the heat generated by the fuel cell operation, is the hydrogen energy consumed by the fuel cell operation, is the heat value of hydrogen gas; is the fuel cell gas-to-electricity efficiency; is the heat release efficiency during the fuel cell reaction process; is the energy generated by the fuel cell operation. 8.The method of Claim 5, wherein, The integrated energy system includes an electrolytic cell, and the model of the electrolytic cell is: ； wherein is the heat value of hydrogen gas; is the efficiency of heat consumption during the electrolysis cell reaction process; is the efficiency of the conversion of electrical energy into chemical energy of gas during the electrolysis cell reaction process; is the energy required for the electrolysis cell to work; is the heat absorbed by the electrolysis cell to work; is the hydrogen energy produced by the electrolysis cell to work. 9.The method of claim 5, wherein, The integrated energy system includes a hydrogen energy storage device, and the model of the hydrogen energy storage device is: ； where C VSS is the planned volume of the hydrogen tank, T in and T out are the maximum ramp-up power of the hydrogen tank input and output; is the hydrogen energy stored in the hydrogen tank at the end of the unit time period t; is the hydrogen energy stored in the hydrogen tank at the beginning of the unit time period t. 10.The method of Claim 5, wherein The integrated energy system includes a steam turbine and an air conditioner, and the model of the steam turbine and the air conditioner is: ； wherein η GB and η EC are the heating efficiency of the turbine and the refrigeration efficiency of the air conditioner, respectively; is the thermal energy produced by the turbine in a unit time period t; is the methane consumed by the turbine in a unit time period t; is the cold energy output by the air conditioner in a unit time period t; is the electrical energy consumed by the air conditioner in a unit time period t.

11. A computer device, comprising: The computer program is stored in the memory, and the processor is used to execute the computer program to realize the steps of the optimal scheduling method of the integrated energy system according to any one of claims 1-10.

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