A method and device for low-carbon transformation treatment of a comprehensive park energy system

Abstract

The present invention discloses a method and device for low-carbon transformation of a comprehensive energy system in a park. Through a hydrogen energy equipment model and a stepped carbon trading mechanism, the method realizes the coupled utilization of fossil energy, renewable energy, and hydrogen energy. At the same time, considering the time-series characteristics and uncertainties of the source and load, an ordered clustering and scenario generation method based on Fisher optimal segmentation is adopted to obtain typical scenarios. On this basis, a system evaluation model is constructed to identify the weak links in the energy supply and consumption of the existing PIES, and a stochastic optimization model for low-carbon transformation is constructed to obtain a low-carbon transformation strategy including five operations: new addition, expansion, replacement, decommissioning, and hydrogen blending transformation. After the transformation, the PIES has a lower comprehensive annualized cost because of lower operation and carbon trading costs. After the transformation, the PIES no longer uses CB with high carbon emissions, and the adequacy of heat energy supply is improved through the charging and discharging of HS. The difference in photovoltaic output among different seasons is compensated, and the utilization of hydrogen energy is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system integration, and particularly relates to a method and device for low-carbon transformation of a park integrated energy system. Background Art

[0002] At present, the traditional energy industry urgently needs to carry out low-carbon transformation. PIES (Park Integrated Energy System) has the potential to integrate and utilize various clean energies and is an effective way to achieve energy conservation and emission reduction on the user side.

[0003] In the prior art, the research on low-carbon transformation in the energy production and utilization links mainly focuses on the equipment and operation mechanism levels. At the equipment level, hydrogen blending transformation is carried out on gas turbines (GT), gas boilers (GB), etc.; new energy and electrolytic hydrogen production equipment are adopted to increase the penetration rate of renewable energy. At the operation mechanism level, a carbon trading mechanism is introduced. However, the current methods do not consider the replacement and retirement of old equipment, the carbon trading mechanism, hydrogen energy utilization, renewable energy power generation and other methods and their synergistic effects on low-carbon transformation at the same time. Therefore, the transformation measures and mechanisms are relatively single, and the potential of transformation to reduce the carbon emissions of existing PIES is not fully explored. Summary of the Invention

[0004] Therefore, the present invention provides a method and device for low-carbon transformation of a park integrated energy system to solve the problem that the traditional technology transformation measures and mechanisms are relatively single and the potential of transformation to reduce the carbon emissions of existing PIES is not fully explored.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for low-carbon transformation of a park integrated energy system, comprising:

[0006] Constructing a hydrogen energy equipment model and a stepped carbon trading model, coupling the utilization of fossil energy and renewable energy through the hydrogen energy equipment model and the stepped carbon trading model, and guiding the park integrated energy system to reduce carbon emissions, so as to realize the low-carbon transformation of the park integrated energy system; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-blended combustion equipment sub-model and a hydrogen fuel cell sub-model; the parameters of the stepped carbon trading model include PIES carbon emission quotas, actual carbon emissions and carbon emissions participating in carbon trading;

[0007] The historical data of photovoltaic power output, electrical and thermal loads are segmented respectively by the ordered clustering method based on Fisher optimal segmentation. Then, according to the corresponding relationship of the segmentation points of each group of data in time, the long-time series samples are segmented again. Each segmented section corresponds to a typical day, and the numerical values of photovoltaic power output, electrical and thermal loads in the typical day are obtained by clustering the historical data corresponding to the segmented section. Uncertainty scenarios corresponding to each typical day are generated by Latin hypercube sampling according to the obtained segmentation results; and according to the determined time series, the uncertainty scenarios corresponding to each typical day are arranged to obtain the annual time series scenario.

[0008] Build a system evaluation model and a low-carbon transformation model. The system evaluation model evaluates the PIES using the indicators of annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate, and comprehensive annualized cost to obtain the PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include the upper limit constraint of equipment capacity, the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, the SOC constraint, the balance constraints of electric power, thermal power, and hydrogen energy. Through the low-carbon transformation model, according to the hydrogen energy equipment model, the stepped carbon trading model, and the annual time series scenario, a low-carbon transformation strategy including new construction, expansion, replacement, retirement, and hydrogen blending transformation operations is obtained.

[0009] As an optimal solution for the low-carbon transformation processing method of the park integrated energy system, the formula of the electrolyzer sub-model is:

[0010]

[0011] In the formula, P EZ is the input electric power of the electrolyzer; is the calorific value of hydrogen; Q P is the conversion value between the two units of kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the electrolytic hydrogen production efficiency of the electrolyzer;

[0012] The formula of the seasonal hydrogen storage sub-model is:

[0013]

[0014] In the formula, is the seasonal hydrogen storage energy storage state at hour h on day d, and Δh and Δd are the scheduling periods in hours and days respectively; is the seasonal hydrogen storage energy storage state in the next hour; is the seasonal hydrogen storage energy storage state at hour l on day d; is the seasonal hydrogen storage energy storage state in the next day; is the seasonal hydrogen storage energy storage state at the first moment of the whole year; The seasonal hydrogen storage and energy storage state at the last moment of the whole year; The hydrogen charging and discharging efficiencies respectively; The hydrogen charging and discharging masses respectively; Q SHS The capacity of seasonal hydrogen storage; D is the number of days in a year;

[0015] The formula of the hydrogen-doped combustion equipment sub-model is:

[0016]

[0017] In the formula, X ∈ {HGT, HGB}, The electric power and thermal power output by the X-type hydrogen-doped equipment respectively, HGT is the hydrogen-doped gas turbine, and HGB is the hydrogen-doped gas boiler; η X,P 、η X,H The electric energy and thermal energy conversion efficiencies respectively; The total energy of the mixed gas; The energies of natural gas and hydrogen respectively; The calorific value of natural gas; The masses of natural gas and hydrogen consumed respectively;

[0018] The formula of the hydrogen fuel cell sub-model is:

[0019]

[0020] In the formula, P HFC 、H HFC The electric power and thermal power output by the hydrogen fuel cell respectively, The electric and thermal conversion efficiencies of the hydrogen fuel cell respectively; The mass of hydrogen consumed.

[0021] As the optimal solution of the low-carbon transformation treatment method for the park integrated energy system, the formula of the ladder carbon trading model is:

[0022] E quo =E quo,grid +E quo,GT +E quo,GB +E quo,HGT +E quo,HGB +E quo,CB

[0023] E act =E act,grid +E act,gas +E act,coal

[0024] E tra =E act -E quo

[0025] In the formula, Equo PIES carbon emission quota; E act is the actual carbon emissions; E tra E is the carbon emissions involved in carbon trading; quo,grid 、E quo,GT 、E quo,GB 、E quo,HGT 、E quo,HGB 、E quo,CB They are the carbon emission quotas for electricity purchased from the external network, gas turbine equipment, gas boiler equipment, hydrogen-blended gas turbine equipment, hydrogen-blended gas boiler equipment, and coal-fired boiler equipment; E act,grid 、E act,gas 、E act,coal They are the carbon emissions generated by purchasing electricity, gas and coal from the external grid.

[0026] As the optimal solution for the low-carbon transformation of the park's integrated energy system, the ordered clustering method based on Fisher's optimal segmentation is used to segment the photovoltaic output and the historical data of electricity and heat loads. The process includes:

[0027] 1) Divide the historical data into 24-hour intervals to obtain a long time series sample containing 364 points to be divided;

[0028] 2) Split the long time series samples into n+1 short time series x. There are a total of , let the short time series set Φ obtained by the mth segmentation method be m for:

[0029]

[0030] In the formula, Represents a short time series set Φ m The i-th group of short time series in ; Represents a short time series set Φ m The jth data sample in, i∈[1,n+1],j∈[1,j i ];

[0031] 3) Calculate the total sum of squares of all segmentation methods B(m):

[0032]

[0033] 4) Determine the segmentation method with the minimum total sum of squared deviations:

[0034]

[0035] In the formula, represents the mean of the i-th group of short time series;

[0036] 5) Use the silhouette coefficient index S n Evaluate the segmentation effect:

[0037]

[0038] where a w is the average Euclidean distance from the w-th data to other data in its group; b w is the minimum of the average Euclidean distances from the w-th data to all data in the adjacent group of its group in the time period;

[0039] 6) Let n = n + 1. If n ≠ N, repeat steps (2)-(4) to obtain segmentation schemes with different numbers of segmentation points, and select the scheme with the largest silhouette coefficient as the segmentation scheme for the long time series sample.

[0040] As the optimal scheme for the low-carbon transformation treatment method of the park integrated energy system, the objective function of the low-carbon transformation model is:

[0041]

[0042]

[0043]

[0044] where N s is the number of scenarios, p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod are the annualized investment costs generated by new construction, expansion, replacement, retirement, and hydrogen blending transformation respectively; are the unit investment costs of the corresponding operations; N CM is the number of equipment types; respectively represent the changes in equipment capacity brought about by the five transformation operations of new construction, expansion, replacement, retirement, and hydrogen blending transformation; r is the discount rate; Y k is the life cycle of the k-th type of equipment; R k is the salvage value of the k-th type of equipment; is the capacity of the k-th type of equipment before transformation; δ k is the net salvage value rate of the k-th type of equipment; L k is the number of operating years of the k-th type of equipment from the start of configuration to transformation.

[0045] As the optimal scheme for the low-carbon transformation treatment method of the park integrated energy system, the equipment capacity upper limit constraint formula is:

[0046]

[0047] Wherein, represents the upper limit of the capacity of the k-th type of equipment;

[0048] The operation power constraint of the energy supply equipment after transformation, the operation power constraint of the energy storage equipment, and the SOC constraint formula are:

[0049]

[0050] Wherein, are the maximum charge and discharge energy coefficients of the energy storage equipment respectively; is the capacity of the k-th type of equipment after transformation; are the minimum and maximum state coefficients of the SOC of the energy storage equipment respectively;

[0051] The electric power, heat power, and hydrogen energy balance constraint formula are:

[0052]

[0053]

[0054]

[0055] Wherein, is the externally purchased grid electric power at the h-th moment of the d-th typical day in scenario s; is the power generation power of the gas turbine; is the power generation power of the hydrogen-blended gas turbine; is the photovoltaic power generation power; is the discharge power of the electric energy storage; is the electric power of the hydrogen fuel cell; is the electric load power; is the charging power of the electric energy storage; is the electric power of the electrolyzer; is the heat power of the gas turbine; is the heat power of the gas boiler; is the heat power of the hydrogen-blended gas turbine; is the heat power of the hydrogen-blended gas boiler; is the heat release power of the thermal energy storage; is the heat power of the coal-fired boiler; is the heat power of the hydrogen fuel cell; is the heat load power; is the heat storage power of the thermal energy storage; is the hydrogen production mass of the electrolyzer; is the hydrogen release mass of the seasonal hydrogen storage; is the hydrogen consumption mass of the hydrogen-blended gas turbine; is the hydrogen consumption mass of the hydrogen-blended gas boiler; For seasonal hydrogen storage and hydrogen retention quality.

[0056] The present invention also provides a low-carbon transformation treatment device for a park integrated energy system, including:

[0057] An energy coupling analysis module, configured to construct a hydrogen energy equipment model and a stepped carbon trading model, perform coupled utilization of fossil energy and renewable energy through the hydrogen energy equipment model and the stepped carbon trading model, and guide the park integrated energy system to reduce carbon emissions, so as to achieve the low-carbon transformation of the park integrated energy system; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-doped combustion equipment sub-model, and a hydrogen fuel cell sub-model; the parameters of the stepped carbon trading model include PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading;

[0058] An annual time series scenario analysis module, configured to respectively segment the historical data of photovoltaic power output and electric and thermal loads based on the ordered clustering method of Fisher optimal segmentation, and then re-segment the long-time series samples according to the corresponding relationship in time of the segmentation points of each group of data. Each segmented segment corresponds to a typical day, and the photovoltaic power output and electric and thermal load values in the typical day are obtained by clustering the historical data corresponding to the segmented segment; generate uncertainty scenarios corresponding to each typical day by using Latin hypercube sampling according to the obtained segmentation results; and arrange the uncertainty scenarios corresponding to each typical day according to the determined time series to obtain the annual time series scenario;

[0059] An energy system transformation evaluation analysis module, configured to construct a system evaluation model and a low-carbon transformation model. The system evaluation model evaluates the PIES by using indicators such as annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate, and comprehensive annualized cost to obtain a PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include equipment capacity upper limit constraint, operating power constraint of the energy supply equipment after transformation, operating power constraint of the energy storage equipment, SOC constraint, electric power, thermal power, and hydrogen energy balance constraint. The low-carbon transformation model obtains a low-carbon transformation strategy including new construction, expansion, replacement, retirement, and hydrogen-doped transformation operations according to the hydrogen energy equipment model, the stepped carbon trading model, and the annual time series scenario.

[0060] As an optimal solution for the low-carbon transformation treatment device of the park integrated energy system, in the energy coupling analysis module, the formula of the electrolyzer sub-model is:

[0061]

[0062] In the formula, P EZ is the input electric power of the electrolyzer; is the calorific value of hydrogen; Q Pis the conversion value between the two units of kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the electrolytic hydrogen production efficiency of the electrolyzer;

[0063] In the energy coupling analysis module, the formula of the seasonal hydrogen storage sub-model is:

[0064]

[0065] In the formula, is the seasonal hydrogen storage energy storage state at the hth hour of the dth day, and Δh and Δd are the scheduling periods in hours and days respectively; is the seasonal hydrogen storage energy storage state at the next hour; is the seasonal hydrogen storage energy storage state at the lth hour of the dth day; is the seasonal hydrogen storage energy storage state at the next day; is the seasonal hydrogen storage energy storage state at the first moment of the whole year; is the seasonal hydrogen storage energy storage state at the last moment of the whole year; are the hydrogen charging and discharging efficiencies respectively; are the hydrogen charging and discharging masses respectively; Q SHS is the capacity of seasonal hydrogen storage; D is the number of days in a year;

[0066] In the energy coupling analysis module, the formula of the hydrogen-doped combustion equipment sub-model in the energy coupling analysis module is:

[0067]

[0068] In the formula, X ∈ {HGT, HGB}, are the electric power and thermal power output by the X-type hydrogen-doped equipment respectively, HGT is the hydrogen-doped gas turbine, and HGB is the hydrogen-doped gas boiler; η X,P 、η X,H are the electric energy and thermal energy conversion efficiencies respectively; is the total energy of the mixed gas; are the energies of natural gas and hydrogen respectively; is the calorific value of natural gas; are the masses of natural gas and hydrogen consumed respectively;

[0069] In the energy coupling analysis module, the formula of the hydrogen fuel cell sub-model is:

[0070]

[0071] In the formula, P HFC 、H HFC are the electric power and thermal power output by the hydrogen fuel cell respectively, The electrical and thermal conversion efficiencies of the hydrogen fuel cell, respectively; is the mass of hydrogen consumed;

[0072] In the energy coupling analysis module, the formula of the ladder carbon trading model is:

[0073] E quo = E quo,grid + E quo,GT + E quo,GB + E quo,HGT + E quo,HGB + E quo,CB

[0074] E act = E act,grid + E act,gas + E act,coal

[0075] E tra = E act - E quo

[0076] In the formula, E quo is the PIES carbon emission quota; E act is the actual carbon emission; E tra is the carbon emission participating in the carbon trading; E quo,grid , E quo,GT , E quo,GB , E quo,HGT , E quo,HGB , E quo,CB are the carbon emission quotas of external power purchase, gas turbine equipment, gas boiler equipment, hydrogen-doped gas turbine equipment, hydrogen-doped gas boiler equipment, and coal-fired boiler equipment, respectively; E act,grid , E act,gas , E act,coal are the carbon emissions generated by external power purchase, gas, and coal, respectively.

[0077] As an optimal solution for the low-carbon transformation treatment device of the park integrated energy system, the annual time series scenario analysis module includes:

[0078] The long-time series sample division sub-module is used to divide the historical data at an interval of 24 hours to obtain a long-time series sample containing 364 points to be segmented;

[0079] The long-time series sample segmentation sub-module is used to segment the long-time series sample into n + 1 short-time series x, and there are a total of ways of segmentation. Denote the set of short-time series Φ m obtained by the m-th segmentation method as:

[0080]

[0081] In the formula, represents the i-th group of short time series in the short time series set Φ m ; represents the j-th data sample in the short time series set Φ m , i ∈ [1, n + 1], j ∈ [1, j i ;

[0082] The total deviation sum of squares calculation sub-module is used to calculate the total deviation sum of squares B(m) of all segmentation methods:

[0083]

[0084] The minimum total deviation sum of squares determination sub-module is used to determine the segmentation method with the minimum total deviation sum of squares:

[0085]

[0086] In the formula, represents the mean value of the i-th group of short time series;

[0087] The segmentation effect evaluation sub-module is used to evaluate the segmentation effect using the silhouette coefficient index S n :

[0088]

[0089] In the formula, a w is the average Euclidean distance from the w-th data to other data in its belonging group; b w is the minimum value of the average Euclidean distance from the w-th data to all data in the group adjacent to its belonging group in the time period;

[0090] The segmentation scheme generation sub-module is used to obtain segmentation schemes with different numbers of segmentation points, and select the scheme with the largest silhouette coefficient as the segmentation scheme of the long time series sample.

[0091] As the preferred scheme for the low-carbon transformation treatment device of the park integrated energy system, in the energy system transformation evaluation and analysis module, the objective function of the low-carbon transformation model is:

[0092]

[0093]

[0094]

[0095] In the formula, N s is the number of scenarios, p sis the probability of the occurrence of scenario s; C add , C exp , C rep , C dem , C mod are the annualized investment costs generated by new construction, expansion, replacement, retirement, and hydrogen blending retrofit respectively; are the unit investment costs for the corresponding operations; N CM is the number of equipment types; represent the changes in equipment capacity brought about by the five retrofit operations of new construction, expansion, replacement, retirement, and hydrogen blending retrofit respectively; r is the discount rate; Y k is the life cycle of the k-th type of equipment; R k is the salvage value of the k-th type of equipment; is the capacity of the k-th type of equipment before retrofit; δ k is the net salvage value rate of the k-th type of equipment; L k is the number of operating years of the k-th type of equipment from the start of configuration to retrofit;

[0096] In the energy system retrofit evaluation and analysis module, the equipment capacity upper limit constraint formula is:

[0097]

[0098] In the formula, represents the upper limit of the capacity of the k-th type of equipment;

[0099] In the energy system retrofit evaluation and analysis module, the operating power constraint of the energy supply equipment after retrofit, the operating power constraint of the energy storage equipment, and the SOC constraint formula are:

[0100]

[0101] In the formula, are the maximum charge and discharge energy coefficients of the energy storage equipment respectively; is the capacity of the k-th type of equipment after retrofit; are the minimum and maximum state coefficients of the SOC of the energy storage equipment respectively;

[0102] In the energy system retrofit evaluation and analysis module, the electric power, thermal power, and hydrogen energy balance constraint formula is:

[0103]

[0104]

[0105]

[0106] In the formula, is the externally purchased electric power at the h-th hour of the d-th typical day under scenario s; is the power generation of the gas turbine; is the power generation of the hydrogen-blended gas turbine; is the power generation of the photovoltaic power generation; is the discharging power of the electrical energy storage; is the electrical power of the hydrogen fuel cell; is the power of the electrical load; is the charging power of the electrical energy storage; is the electrical power of the electrolyzer; is the thermal power of the gas turbine; is the thermal power of the gas boiler; is the thermal power of the hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; is the heat release power of the thermal energy storage; is the thermal power of the coal-fired boiler; is the thermal power of the hydrogen fuel cell; is the power of the thermal load; is the heat storage power of the thermal energy storage; is the hydrogen production mass of the electrolyzer; is the hydrogen release mass of the seasonal hydrogen storage; is the hydrogen consumption mass of the hydrogen-blended gas turbine; is the hydrogen consumption mass of the hydrogen-blended gas boiler; is the hydrogen storage mass of the seasonal hydrogen storage.

[0107] The present invention has the following advantages: Through the hydrogen energy equipment model and the stepped carbon trading mechanism, the coupled utilization of fossil energy, renewable energy and hydrogen energy is realized; at the same time, considering the time series characteristics and uncertainties of the source and load, the ordered clustering and scenario generation method based on Fisher optimal segmentation is adopted to obtain typical scenarios; on this basis, a system evaluation model is constructed to identify the weak links in the energy supply and use of the existing PIES, and a stochastic optimization model for low-carbon transformation is constructed to obtain a low-carbon transformation strategy including five operations: new construction, expansion, replacement, retirement and hydrogen blending transformation. After the transformation, the PIES has a lower comprehensive annualized cost due to lower operation and carbon trading costs; after the transformation, the PIES no longer uses the CB with high carbon emissions, and the adequacy of the heat energy supply is improved through the charging and discharging of the HS; the stored hydrogen energy is released in winter and autumn with insufficient sunlight and converted into electrical energy and heat energy for utilization, thus making up for the difference in photovoltaic output between different seasons and promoting the utilization of hydrogen energy. Description of the Drawings

[0108] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only exemplary. For those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained based on the provided drawings.

[0109] Figure 1 Schematic diagram of the low-carbon transformation processing method for the park integrated energy system provided in the embodiment of the present invention;

[0110] Figure 2 Schematic diagram of the Fisher optimal segmentation result provided in the embodiment of the present invention;

[0111] Figure 3 Uncertainty scenarios and PV output, electricity, and heat load curves in a typical day provided in the embodiment of the present invention;

[0112] Figure 4 Hydrogen energy balance in a typical scenario of the PIES after low-carbon transformation provided in the embodiment of the present invention;

[0113] Figure 5 Thermal power balance of the existing PIES before and after transformation provided in the embodiment of the present invention;

[0114] Figure 6 Hydrogen energy balance in a typical scenario of the PIES after low-carbon transformation provided in the embodiment of the present invention;

[0115] Figure 7 Annual hydrogen energy balance of the existing PIES after transformation provided in the embodiment of the present invention;

[0116] Figure 8 Architecture diagram of the device for low-carbon transformation processing of the park integrated energy system provided in the embodiment of the present invention. Specific embodiments

[0117] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0118] Embodiment 1

[0119] See Figure 1 , the embodiment of the present invention provides a method for low-carbon transformation processing of a park integrated energy system, including the following steps:

[0120] S1. Build a hydrogen energy equipment model and a stepped carbon trading model, and through the hydrogen energy equipment model and the stepped carbon trading model, couple the utilization of fossil energy and renewable energy to guide the integrated energy system of the park to reduce carbon emissions, so as to achieve the low-carbon transformation of the integrated energy system of the park; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-doped combustion equipment sub-model, and a hydrogen fuel cell sub-model; the parameters of the stepped carbon trading model include PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading;

[0121] S2. Use the ordered clustering method based on Fisher optimal segmentation to segment the historical data of photovoltaic power output and electrical and thermal loads respectively, and then re-segment the long-time series samples according to the corresponding relationship in time of the segmentation points of each group of data. Each segmented segment corresponds to a typical day, and the numerical values of photovoltaic power output and electrical and thermal loads in the typical day are obtained by clustering the historical data corresponding to the segmented segment; generate the uncertainty scenarios corresponding to each typical day by using Latin hypercube sampling according to the obtained segmentation results; and arrange the uncertainty scenarios corresponding to each typical day according to the determined time series to obtain the annual time series scenario;

[0122] S3. Build a system evaluation model and a low-carbon transformation model. The system evaluation model uses indicators such as annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate, and comprehensive annualized cost to evaluate PIES to obtain a PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include the upper limit constraint of equipment capacity, the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, the SOC constraint, the balance constraint of electric power, thermal power, and hydrogen energy. Through the low-carbon transformation model, according to the hydrogen energy equipment model, the stepped carbon trading model, and the annual time series scenario, obtain a low-carbon transformation strategy including new construction, expansion, replacement, retirement, and hydrogen-doped transformation operations.

[0123] In this embodiment, in step S1, in the electrolyzer sub-model, the electrolyzer (EZ) converts electrical energy into hydrogen by electrolyzing water, and the formula of the electrolyzer sub-model is:

[0124]

[0125] In the formula, P EZ is the input electrical power of the electrolyzer; is the calorific value of hydrogen; Q P is the conversion value between the two units of kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the electrolytic hydrogen production efficiency of the electrolyzer;

[0126] In step S1, in the seasonal hydrogen storage sub-model, seasonal hydrogen storage (SHS) realizes long-term cross-seasonal hydrogen storage through a low-pressure gas storage tank. The formula of the seasonal hydrogen storage sub-model is:

[0127]

[0128] In the formula, is the seasonal hydrogen storage energy storage state at hour h on day d. Δh and Δd are the scheduling periods in hours and days respectively; is the seasonal hydrogen storage energy storage state for the next hour; is the seasonal hydrogen storage energy storage state at hour l on day d; is the seasonal hydrogen storage energy storage state for the next day; is the seasonal hydrogen storage energy storage state at the first moment of the whole year; is the seasonal hydrogen storage energy storage state at the last moment of the whole year; are the hydrogen charging and discharging efficiencies respectively; are the hydrogen charging and discharging masses respectively; Q SHS is the capacity of seasonal hydrogen storage; D is the number of days in a year;

[0129] In step S1, in the hydrogen-blended combustion equipment sub-model, through hydrogen-blended transformation, ordinary gas turbines (GT) and gas boilers (GB) can be transformed into HGT and HGB, and burn with a mixed gas of natural gas and hydrogen as fuel: The formula of the hydrogen-blended combustion equipment sub-model is:

[0130]

[0131] In the formula, X ∈ {HGT, HGB}, are the electric power and thermal power output by the X-type hydrogen-blended equipment respectively. HGT is the hydrogen-blended gas turbine and HGB is the hydrogen-blended gas boiler; η X,P 、η X,H are the electric energy and thermal energy conversion efficiencies respectively; is the total energy of the mixed gas; are the energies of natural gas and hydrogen respectively; is the calorific value of natural gas; are the masses of natural gas and hydrogen consumed respectively;

[0132] In step S1, in the hydrogen fuel cell sub-model, the hydrogen fuel cell (HFC) generates electric energy and thermal energy by allowing hydrogen to undergo an oxidation reaction on the proton exchange membrane. The formula of the hydrogen fuel cell sub-model is:

[0133]

[0134] In the formula, P HFC 、H HFCare the electric power and thermal power output by the hydrogen fuel cell respectively, are the electrical and thermal conversion efficiencies of the hydrogen fuel cell respectively; is the mass of hydrogen consumed.

[0135] In this embodiment, in step S1, the formula of the stepped carbon trading model is:

[0136] E quo = E quo,grid + E quo,GT + E quo,GB + E quo,HGT + E quo,HGB + E quo,CB

[0137] E act = E act,grid + E act,gas + E act,coal

[0138] E tra = E act - E quo

[0139] In the formula, E quo is the PIES carbon emission quota; E act is the actual carbon emission; E tra is the carbon emission participating in carbon trading; E quo,grid , E quo,GT , E quo,GB , E quo,HGT , E quo,HGB , E quo,CB are the carbon emission quotas of external power grid power purchase, gas turbine equipment, gas boiler equipment, hydrogen-blended gas turbine equipment, hydrogen-blended gas boiler equipment, and coal-fired boiler equipment respectively; E act,grid , E act,gas , E act,coal are the carbon emissions generated by external power grid power purchase, gas and coal respectively.

[0140] Among them, the stepped carbon trading cost is as follows:

[0141]

[0142] In the formula, b is the carbon trading price base value; l is the carbon emission interval length; λ is the growth coefficient of the carbon trading price.

[0143] In this embodiment, in step S2, the process of respectively segmenting the historical data of photovoltaic output and electrical and thermal loads based on the ordered clustering method of Fisher optimal segmentation includes:

[0144] 1) Divide the historical data at intervals of 24 hours to obtain a long - time series sample with 364 points to be segmented; set the maximum number of segmentation points of the Fisher optimal segmentation method to 364, and let the initial number of segmentation points;

[0145] 2) Divide the long - time series sample into n + 1 short - time series x. There are a total of ways of division. Denote the set of short - time series obtained by the m - th division method as Φ m as:

[0146]

[0147] where, represents the i - th group of short - time series in the set of short - time series Φ m ; represents the j - th data sample in the set of short - time series Φ m . i ∈ [1, n + 1], j ∈ [1, j i ;

[0148] 3) Calculate the total sum of squared deviations B(m) for all division methods:

[0149]

[0150] 4) Determine the division method with the minimum total sum of squared deviations:

[0151]

[0152] where, represents the mean value of the i - th group of short - time series; the smaller this value, the smaller the difference within the group;

[0153] 5) Use the silhouette coefficient index S n to evaluate the division effect:

[0154]

[0155] where, a w is the average Euclidean distance from the w - th data to other data in its group; b w is the minimum of the average Euclidean distances from the w - th data to all data in the adjacent group in the time period of its group; the silhouette coefficient index S n ranges from [- 1, 1], and the larger the value, the better the division effect;

[0156] 6) Let n = n + 1. If n ≠ N, repeat steps (2) - (4) to obtain division schemes with different numbers of segmentation points, and select the scheme with the largest silhouette coefficient as the division scheme of the long - time series sample.

[0157] Among them, the historical data of photovoltaic (PV) output and electrical and thermal loads are segmented respectively, and then the long-time series samples are segmented again according to the corresponding relationship of the segmentation points of each group of data in time. Each segmented section corresponds to a typical day, and the specific PV output and electrical and thermal load values in the typical day are obtained by clustering the historical data corresponding to the segmented section. The segmentation results are as Figure 2 shown, and the specific segmentation is shown in Table 1.

[0158] Table 1 Results of time period division

[0159]

[0160] Among them, based on the obtained segmentation results, assuming that the PV output and electrical and thermal loads respectively follow the Beta and normal distributions, 2000 uncertainty scenarios corresponding to each typical day are generated by Latin hypercube sampling. Further, according to the determined time series, the scenarios of each typical day are arranged to obtain the annual time series scenarios. To improve the calculation efficiency, K-means clustering is used for scenario reduction, and finally 5 annual time series scenarios are obtained. The results are as Figure 3 shown.

[0161] In this embodiment, in step S3, four indicators, namely annual carbon emissions, primary energy utilization rate, PV penetration rate, and comprehensive annualized cost, are used to evaluate the PIES. To comprehensively analyze the situation before the transformation of the existing PIES and obtain the weak links restricting the existing PIES in aspects such as energy conservation and emission reduction and operation efficiency improvement. First, the initial planning data and the actual operation data of the existing PIES are obtained, and the original data is processed. Secondly, qualitative and quantitative analyses are carried out on equipment configuration, system operation conditions, and system carbon emissions, and the indicators in Table 2 are calculated. Identify the problem links that cause high carbon emissions, low energy utilization efficiency, and insufficient energy supply in aspects such as energy supply, conversion, and storage. The results of the evaluation strategy can help formulate targeted transformation plans.

[0162] Table 2 Definitions and descriptions of evaluation indicators

[0163]

[0164] Among them, "+" is a positive indicator, and the higher the value, the better; "-" is a negative indicator, and the lower the value, the better.

[0165] In this embodiment, in step S3, the low-carbon transformation of the existing PIES includes not only the expansion, replacement, retirement, and hydrogen blending transformation of the original equipment, but also the investment in new equipment. Therefore, the objective function of the low-carbon transformation model is:

[0166]

[0167]

[0168]

[0169] In the formula, N s is the number of scenarios, and p s is the probability of the occurrence of scenario s; C add , C exp , C rep , C dem , C mod are the annualized investment costs generated by new construction, expansion, replacement, retirement, and hydrogen blending retrofit respectively; are the unit investment costs for the corresponding operations; N CM is the number of equipment types; represent the changes in equipment capacity brought about by the five retrofit operations of new construction, expansion, replacement, retirement, and hydrogen blending retrofit respectively; r is the discount rate; Y k is the life cycle of the k-th type of equipment; R k is the salvage value of the k-th type of equipment; is the capacity of the k-th type of equipment before retrofit; δ k is the net salvage value rate of the k-th type of equipment; L k is the number of operating years of the k-th type of equipment from the start of configuration to retrofit.

[0170] Among them, the operation and maintenance cost is specifically:

[0171]

[0172] In the formula, is the number of typical days represented by the s-th type of scenario; p s,d is the probability of the d-th typical day of scenario s occurring; and are the natural gas and coal consumption of GB, GT, and CB at the h-th hour on the d-th typical day under scenario s; c grid ,h is the electricity price at the h-th hour; is the externally purchased power.

[0173] In this embodiment, in step S3, the equipment capacity upper limit constraint formula is:

[0174]

[0175] In the formula, represents the upper limit of the capacity of the k-th type of equipment;

[0176] In step S3, the operation power constraint of the reconstructed energy supply equipment, the operation power constraint of the energy storage equipment, and the SOC constraint formula are:

[0177]

[0178] In the formula, are the maximum charge and discharge energy coefficients of the energy storage equipment respectively; is the capacity of the k-type equipment after reconstruction; are the minimum and maximum state coefficients of the SOC of the energy storage equipment respectively;

[0179] In step S3, the electric power, thermal power, and hydrogen energy balance constraint formula are:

[0180]

[0181]

[0182]

[0183]

[0184] In the formula, is the externally purchased grid electric power at the h-th moment of the d-th typical day in scenario s; is the power generation power of the gas turbine; is the power generation power of the hydrogen-blended gas turbine; is the photovoltaic power generation power; is the discharge power of the electric energy storage; is the electric power of the hydrogen fuel cell; is the electric load power; is the charging power of the electric energy storage; is the electric power of the electrolyzer; is the thermal power of the gas turbine; is the thermal power of the gas boiler; is the thermal power of the hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; is the heat release power of the thermal energy storage; is the thermal power of the coal-fired boiler; is the thermal power of the hydrogen fuel cell; is the thermal load power; is the heat storage power of the thermal energy storage; is the hydrogen production mass of the electrolyzer; is the hydrogen release mass of the seasonal hydrogen storage; is the hydrogen consumption mass of the hydrogen-blended gas turbine; is the hydrogen consumption mass of the hydrogen-blended gas boiler; is the hydrogen storage mass of the seasonal hydrogen storage.

[0185] The effects of the embodiments of the present invention are verified through the following numerical examples:

[0186] The equipment configuration of the existing PIES before transformation is shown in Table 3. It is configured with a certain capacity of PV and ES. Since it has been in operation for 5 years, the capacity of ES has decayed, and its state of health (SOH) is only 81.79%, which restricts the ability of the system to suppress the fluctuations of the source and load. At the same time, the system is configured with 1 GT, 2 GBs, and 1 CB for energy supply, but their capacities are insufficient and it is difficult to match the growing heat load demand.

[0187] Table 3 Equipment configuration results before transformation and integration scheme

[0188]

[0189] The power balance of the existing PIES before transformation is as Figure 4 shown. In terms of power supply, due to the degradation of the ES capacity, the energy stored and released is reduced, which limits the ability to suppress the fluctuations of PV output and load. In terms of heat supply, the capacities of GT and GB cannot meet the growing load demand and are often in a full-load state, which restricts the heat supply adequacy of the system.

[0190] Among them, the evaluation indexes of the existing PIES are shown in Table 4. The PV capacity in the existing PIES is small, resulting in a low PV penetration rate in the system. Secondly, the carbon emissions of the existing PIES are relatively high. On the one hand, the use of CB and the purchase of electricity from the grid are the main sources of carbon emissions, and their low energy conversion efficiency leads to a low primary energy utilization rate of the existing PIES; on the other hand, the system uses less renewable energy. GT and GB also generate relatively high carbon emissions. The high carbon emissions lead to a significant increase in carbon trading costs, and the excessive purchase of electricity from the grid also increases the electricity purchase cost. These two factors jointly restrict the operation economy of the system.

[0191] Table 4 Evaluation indexes before transformation and integration scheme

[0192]

[0193] Therefore, in the low-carbon transformation of PIES, it is considered to expand PV to improve the PV penetration rate, and replace and expand the degraded ES to suppress the fluctuations of PV output; in addition, it is considered to increase high-efficiency power supply and heat supply equipment and carry out hydrogen blending transformation on existing gas equipment to improve the environmental protection and economy of energy supply; finally, it is considered to retire CB in advance to further reduce the system carbon emissions and recover costs.

[0194] The existing PIES is transformed using different schemes, and the equipment configurations of each device are shown in Table 3. Compared with the existing PIES, the transformed PIES expands PV and GT to improve the system's self-sufficiency level in electric energy and reduce the dependence on external power grid purchases. At the same time, ES is replaced and expanded to suppress the output fluctuation of PV; CB with relatively high carbon emissions is decommissioned, and HS is newly added. Hydrogen blending transformation is carried out on 1 GB and 2 GTs, increasing the system's hydrogen demand to produce more hydrogen and promoting the utilization of renewable energy.

[0195] The various evaluation indicators before and after the transformation of the existing PIES are shown in Table 4. In terms of the improvement of PV penetration rate, the transformed PIES has diversified hydrogen energy utilization links, which promotes the consumption of PV. In terms of annual carbon emissions, due to the configuration of more PVs, the application of more hydrogen energy equipment and the realization of hydrogen blending combustion of gas equipment in the transformed PIES, the emission reduction effect is significantly improved compared with other schemes. In terms of the comprehensive annualized cost, the transformed PIES has a lower comprehensive annualized cost due to lower operation and carbon trading costs.

[0196] Select the typical daily scenarios corresponding to time periods 1, 5, 8, and 14, which represent winter, spring, summer, and autumn respectively, and then analyze the actual operation of each transformation scheme. The electric power balance of the existing PIES before and after the transformation is as Figure 5 shown. The transformed PIES improves the PV consumption and reduces the external power grid purchase volume. Among them, the transformed PIES uses EZ to produce hydrogen during periods with sufficient sunlight and uses HFC to supply power during periods with insufficient sunlight. Through the conversion and storage between solar energy, electric energy, and hydrogen energy, the PV consumption level and the energy cross-season transfer ability are improved. After the hydrogen blending transformation, the HGT has more output than the GT of the existing PIES, reducing the system's carbon emissions and carbon trading costs, and at the same time reducing the gas purchase cost. The thermal power balance of the existing PIES before and after the transformation is as Figure 5 shown. The transformed PIES no longer uses CB with relatively high carbon emissions, and improves the adequacy of thermal energy supply through the charging and discharging of HS.

[0197] The hydrogen energy balance in the typical scenario of the PIES after the low-carbon transformation is as Figure 6 shown. After the transformation of the existing PIES, EZ can be used to produce hydrogen, which is consumed by devices such as HFC, and the excess hydrogen energy is stored in SHS. Through the transfer of hydrogen energy at different times, the flexibility of the PIES' energy supply within the day and between days is enhanced, and its dependence on fossil fuels is reduced, significantly reducing the system's carbon emissions. Figure 7 Shows the annual hydrogen energy balance of the existing PIES after the transformation. The PIES uses PV to produce and store hydrogen in spring and summer with abundant sunlight, and releases the stored hydrogen energy in winter and autumn with insufficient sunlight, which is converted into electric energy and thermal energy for utilization, thus making up for the difference in PV output between different seasons and promoting the utilization of hydrogen energy.

[0198] In summary, the present invention constructs a hydrogen energy equipment model and a stepped carbon trading model, and through the hydrogen energy equipment model and the stepped carbon trading model, the coupled utilization of fossil energy and renewable energy is carried out to guide the integrated energy system of the park to reduce carbon emissions, so as to realize the low-carbon transformation of the integrated energy system of the park; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-blended combustion equipment sub-model and a hydrogen fuel cell sub-model; the parameters of the stepped carbon trading model include the carbon emission quota of the PIES, the actual carbon emissions and the carbon emissions participating in carbon trading; the historical data of photovoltaic power output and electric and heat loads are respectively segmented by the ordered clustering method based on Fisher optimal segmentation, and then according to the corresponding relationship in time of the segmentation points of each group of data, the long-time series samples are re-segmented. Each segmented segment corresponds to a typical day, and the numerical values of photovoltaic power output and electric and heat loads in the typical day are obtained by clustering the historical data corresponding to the segmented segment; according to the obtained segmentation results, Latin hypercube sampling is used to generate the uncertainty scenarios corresponding to each typical day; and according to the determined time series, the uncertainty scenarios corresponding to each typical day are arranged to obtain the annual time series scenarios; a system evaluation model and a low-carbon transformation model are constructed. The system evaluation model uses indicators such as annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost to evaluate the PIES, and obtains the PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include the upper limit constraint of equipment capacity, the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, the SOC constraint, the balance constraint of electric power, heat power and hydrogen energy. Through the low-carbon transformation model, according to the hydrogen energy equipment model, the stepped carbon trading model and the annual time series scenarios, a low-carbon transformation strategy including new construction, expansion, replacement, retirement and hydrogen-blended transformation operations is obtained. The present invention realizes the coupled utilization of fossil energy, renewable energy and hydrogen energy through the hydrogen energy equipment model and the stepped carbon trading mechanism; at the same time, considering the time series characteristics and uncertainties of the source and load, the ordered clustering and scenario generation method based on Fisher optimal segmentation is adopted to obtain typical scenarios; on this basis, a system evaluation model is constructed to identify the weak links in the energy supply and use of the existing PIES, and a stochastic optimization model for low-carbon transformation is constructed to obtain a low-carbon transformation strategy including five operations of new construction, expansion, replacement, retirement and hydrogen-blended transformation. After the transformation, the PIES has a lower comprehensive annualized cost because of lower operation and carbon trading costs; after the transformation, the PIES no longer uses the CB with higher carbon emissions, and the adequacy of heat energy supply is improved through the charging and discharging of HS; the stored hydrogen energy is released in winter and autumn with insufficient light and converted into electric energy and heat energy for utilization, thus making up for the difference in photovoltaic power output between different seasons and promoting the utilization of hydrogen energy.

[0199] Example 2

[0200] See Figure 8, Embodiment 2 of the invention also provides a device for low-carbon transformation of a comprehensive park energy system, including:

[0201] An energy coupling analysis module 100, configured to construct a hydrogen energy equipment model and a stepped carbon trading model, and perform the coupled utilization of fossil energy and renewable energy through the hydrogen energy equipment model and the stepped carbon trading model, and guide the comprehensive park energy system to reduce carbon emissions, so as to realize the low-carbon transformation of the comprehensive park energy system; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-doped combustion equipment sub-model, and a hydrogen fuel cell sub-model; the parameters of the stepped carbon trading model include PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading;

[0202] An annual time series scenario analysis module 200, configured to respectively segment the historical data of photovoltaic power output and electrical and thermal loads based on the ordered clustering method of Fisher optimal segmentation, and then re-segment the long time series samples according to the corresponding relationship in time of the segmentation points of each group of data. Each segmented segment corresponds to a typical day, and the numerical values of photovoltaic power output and electrical and thermal loads in the typical day are obtained by clustering the historical data corresponding to the segmented segment; generate uncertainty scenarios corresponding to each typical day by using Latin hypercube sampling according to the obtained segmentation results; and arrange the uncertainty scenarios corresponding to each typical day according to the determined time series to obtain the annual time series scenario;

[0203] An energy system transformation evaluation and analysis module 300, configured to construct a system evaluation model and a low-carbon transformation model, the system evaluation model evaluates PIES by using indicators such as annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate, and comprehensive annualized cost to obtain a PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include equipment capacity upper limit constraint, operating power constraint of the energy supply equipment after transformation, operating power constraint of the energy storage equipment, SOC constraint, electrical power, thermal power, and hydrogen energy balance constraint. The low-carbon transformation strategy including new construction, expansion, replacement, retirement, and hydrogen-doped transformation operations is obtained through the low-carbon transformation model according to the hydrogen energy equipment model, the stepped carbon trading model, and the annual time series scenario.

[0204] In this embodiment, in the energy coupling analysis module 100, the formula of the electrolyzer sub-model is:

[0205]

[0206] In the formula, P EZ is the input electric power of the electrolyzer; is the calorific value of hydrogen; Q P is the conversion value between the two units of kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZis the electrolytic hydrogen production efficiency of the electrolyzer;

[0207] In the energy coupling analysis module 100, the formula of the seasonal hydrogen storage sub-model is:

[0208]

[0209] In the formula, is the seasonal hydrogen storage energy storage state in the next hour; is the seasonal hydrogen storage energy storage state at the l-th period on the d-th day; is the seasonal hydrogen storage energy storage state in the next day; is the seasonal hydrogen storage energy storage state at the first moment of the whole year; is the seasonal hydrogen storage energy storage state at the last moment of the whole year; are the hydrogen charging and discharging efficiencies respectively; are the hydrogen charging and discharging masses respectively; Q SHS is the capacity of seasonal hydrogen storage; D is the number of days in a year;

[0210] In the energy coupling analysis module, the formula of the hydrogen-doped combustion equipment sub-model in the energy coupling analysis module is:

[0211]

[0212] In the formula, X ∈ {HGT, HGB}, are the electric power and thermal power output by the X-type hydrogen-doped equipment respectively, HGT is the hydrogen-doped gas turbine, and HGB is the hydrogen-doped gas boiler; η X,P 、η X,H are the electric energy and thermal energy conversion efficiencies respectively; is the total energy of the mixed gas; are the energies of natural gas and hydrogen respectively; is the calorific value of natural gas; are the masses of natural gas and hydrogen consumed respectively;

[0213] In the energy coupling analysis module 100, the formula of the hydrogen fuel cell sub-model is:

[0214]

[0215] In the formula, P HFC 、H HFC are the electric power and thermal power output by the hydrogen fuel cell respectively, are the electric and thermal conversion efficiencies of the hydrogen fuel cell respectively; is the mass of hydrogen consumed;

[0216] In the energy coupling analysis module 100, the formula of the stepped carbon trading model is:

[0217] E quo = E quo,grid + E quo,GT + E quo,GB + E quo,HGT + E quo,HGB + E quo,CB

[0218] E act = E act,grid + E act,gas + E act,coal

[0219] E tra = E act - E quo

[0220] In the formula, E quo is the PIES carbon emission quota; E act is the actual carbon emission; E tra is the carbon emission involved in carbon trading; E quo,grid , E quo,GT , E quo,GB , E quo,HGT , E quo,HGB , E quo,CB are respectively the carbon emission quotas of externally purchased grid power, gas turbine equipment, gas boiler equipment, hydrogen-blended gas turbine equipment, hydrogen-blended gas boiler equipment, and coal-fired boiler equipment; E act,grid , E act,gas , E act,coal are respectively the carbon emissions generated by externally purchased grid power, gas, and coal.

[0221] In this embodiment, the annual time-series scenario analysis module 200 includes:

[0222] The long-time series sample division sub-module 201 is used to divide the historical data at intervals of 24 hours to obtain a long-time series sample containing 364 points to be segmented;

[0223] The long-time series sample segmentation sub-module 202 is used to segment the long-time series sample into n + 1 short-time series x, and there are a total of ways of segmentation. Denote the set of short-time series Φ m obtained by the m-th segmentation method as:

[0224]

[0225] In the formula, represents the i-th group of short-time series in the set of short-time series Φ m ; represents the set of short-time series Φm the j-th data sample in, i ∈ [1, n + 1], j ∈ [1, j i ;

[0226] The total deviation square calculation sub-module 203 is used to calculate the total deviation square sum B(m) of all segmentation methods:

[0227]

[0228] The minimum total deviation square sum determination sub-module 204 is used to determine the segmentation method with the minimum total deviation square sum:

[0229]

[0230] In the formula, represents the mean value of the i-th group of short time series;

[0231] The segmentation effect evaluation sub-module 205 is used to evaluate the segmentation effect using the silhouette coefficient index S n :

[0232]

[0233] In the formula, a w is the average Euclidean distance from the w-th data to other data in its affiliated group; b w is the minimum value of the average Euclidean distance from the w-th data to all data in the group adjacent in time period to its affiliated group;

[0234] The segmentation scheme generation sub-module 206 is used to obtain segmentation schemes with different numbers of segmentation points, and select the scheme with the largest silhouette coefficient as the segmentation scheme of the long time series sample.

[0235] In this embodiment, in the energy system transformation evaluation and analysis module 300, the objective function of the low-carbon transformation model is:

[0236]

[0237]

[0238] In the formula, N s is the number of scenarios, p s is the probability of the occurrence of scenario s; C add , C exp , C rep , C dem , C mod are the annualized investment costs generated by new construction, expansion, replacement, retirement, and hydrogen blending transformation respectively; are the unit investment costs of the corresponding operations; N CMis the number of equipment types; respectively represent the changes in equipment capacity brought about by five types of retrofit operations: new addition, expansion, replacement, retirement, and hydrogen blending retrofit; r is the discount rate; Y k is the life cycle of the k-th type of equipment; R k is the salvage value of the k-th type of equipment; is the capacity of the k-th type of equipment before retrofit; δ k is the net salvage rate of the k-th type of equipment; L k is the number of operating years of the k-th type of equipment from the start of configuration to retrofit;

[0239] In the energy system retrofit evaluation and analysis module 300, the equipment capacity upper limit constraint formula is:

[0240]

[0241] In the formula, represents the upper limit of the capacity of the k-th type of equipment;

[0242] In the energy system retrofit evaluation and analysis module 300, the operating power constraint of the post-retrofit energy supply equipment, the operating power constraint of the energy storage equipment, and the SOC constraint formula are:

[0243]

[0244] In the formula, are the maximum charge and discharge energy coefficients of the energy storage equipment respectively; is the capacity of the k-th type of equipment after retrofit; are the minimum and maximum state coefficients of the SOC of the energy storage equipment respectively;

[0245] In the energy system retrofit evaluation and analysis module, the electric power, thermal power, and hydrogen energy balance constraint formula are:

[0246]

[0247]

[0248]

[0249] In the formula, is the externally purchased electric power at the h-th moment of the d-th typical day in scenario s; is the power generation of the gas turbine; is the power generation of the hydrogen-blended gas turbine; is the power generation of the photovoltaic power generation; is the discharge power of the electric energy storage; is the electric power of the hydrogen fuel cell; is the electric load power; is the charging power of the electric energy storage; is the electric power of the electrolyzer; is the thermal power of the gas turbine; is the thermal power of the gas-fired boiler; is the thermal power of the hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas-fired boiler; is the heat release power of the thermal energy storage; is the thermal power of the coal-fired boiler; is the thermal power of the hydrogen fuel cell; is the heat load power; is the heat storage power of the thermal energy storage;

[0250] is the hydrogen production mass of the electrolyzer; is the hydrogen release mass of the seasonal hydrogen storage; is the hydrogen consumption mass of the hydrogen-blended gas turbine; is the hydrogen consumption mass of the hydrogen-blended gas-fired boiler; is the hydrogen storage mass of the seasonal hydrogen storage.

[0251] It should be noted that for the information interaction, execution process, etc. among the above device modules, since they are based on the same concept as the method embodiment in Embodiment 1 of this application, the technical effects brought by them are the same as those of the method embodiment of this application. For specific content, reference can be made to the description in the method embodiment shown above in this application, and details will not be elaborated here.

[0252] Embodiment 3

[0253] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which program codes for the low-carbon transformation processing method of the park integrated energy system are stored, and the program codes include instructions for executing the low-carbon transformation processing method of the park integrated energy system in Embodiment 1 or any possible implementation manner thereof.

[0254] The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).

[0255] Embodiment 4

[0256] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0257] The processor and the memory complete communication with each other through a bus; the memory stores program instructions executable by the processor, and the processor can execute the park integrated energy system low-carbon transformation processing method of Embodiment 1 or any possible implementation manner by invoking the program instructions.

[0258] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in the memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0259] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.).

[0260] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules respectively, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present invention is not limited to any specific combination of hardware and software.

[0261] Although the present invention has been described in detail above with general descriptions and specific embodiments, on the basis of the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. A method for low-carbon transformation treatment of an integrated park energy system, characterized in that, Including: Construct a hydrogen energy equipment model and a stepped carbon trading model, and through the hydrogen energy equipment model and the stepped carbon trading model, couple the utilization of fossil energy and renewable energy to guide the integrated energy system of the park to reduce carbon emissions, so as to realize the low-carbon transformation of the integrated energy system of the park; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-doped combustion equipment sub-model, and a hydrogen fuel cell sub-model; the parameters of the stepped carbon trading model include the carbon emission quota of the integrated energy system PIES of the park, the actual carbon emissions, and the carbon emissions participating in carbon trading. Based on the ordered clustering method of Fisher optimal segmentation, segment the historical data of photovoltaic output and electrical and thermal loads respectively, and then re-segment the long-time series samples according to the corresponding relationship in time of the segmentation points of each group of data. Each segment after segmentation corresponds to a typical day, and the numerical values of photovoltaic output and electrical and thermal loads in the typical day are obtained by clustering the historical data corresponding to the segment where it is located; generate the uncertainty scenarios corresponding to each typical day by using Latin hypercube sampling according to the obtained segmentation results; and arrange the uncertainty scenarios corresponding to each typical day according to the determined time series to obtain the annual time series scenarios. Construct a system evaluation model and a low-carbon transformation model. The system evaluation model uses indicators such as annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate, and comprehensive annualized cost to evaluate the PIES and obtain the PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include the upper limit constraint of equipment capacity, the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, the SOC constraint, the balance constraint of electric power, thermal power, and hydrogen energy. Through the low-carbon transformation model, according to the hydrogen energy equipment model, the stepped carbon trading model, and the annual time series scenarios, obtain a low-carbon transformation strategy including new construction, expansion, replacement, retirement, and hydrogen-doped transformation operations. The objective function of the low-carbon transformation model is: Where N s is the number of scenarios, and p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod are the annualized investment costs generated by new construction, expansion, replacement, retirement, and hydrogen blending transformation respectively; are the unit investment costs for the corresponding operations; N CM is the number of equipment types; represent the changes in equipment capacity brought about by the five transformation operations of new construction, expansion, replacement, retirement, and hydrogen blending transformation respectively; r is the discount rate; Y k is the life cycle of the k-th type of equipment; R k is the salvage value of the k-th type of equipment; is the capacity of the k-th type of equipment before transformation; δ k is the net salvage value rate of the k-th type of equipment; L k is the number of operating years of the k-th type of equipment from the start of configuration to transformation.

2. The low-carbon transformation processing method of a comprehensive energy system in a park according to claim 1, characterized in that The formula of the electrolyzer sub-model is: Where, P EZ is the input electric power of the electrolyzer; Q H2 is the calorific value of hydrogen; Q P is the conversion value between kilowatt-hour and joule; m EZ,H2 is the hydrogen production mass of the electrolyzer; η EZ is the electrolytic hydrogen production efficiency of the electrolyzer; The formula of the seasonal hydrogen storage sub-model is: In the formula, is the seasonal hydrogen storage and energy storage state at the hth period on the dth day, and Δh and Δd are the scheduling periods in hours and days respectively; is the seasonal hydrogen storage and energy storage state for the next hour; is the seasonal hydrogen storage and energy storage state at the lth period on the dth day; is the seasonal hydrogen storage and energy storage state for the next day; is the seasonal hydrogen storage and energy storage state at the first moment of the whole year; is the seasonal hydrogen storage and energy storage state at the last moment of the whole year; are the hydrogen charging and discharging efficiencies respectively; are the hydrogen charging and discharging masses respectively; Q SHS is the capacity of seasonal hydrogen storage; D is the number of days in a year; The formula of the hydrogen-doped combustion equipment sub-model is: Where X ∈ {HGT, HGB}, are the electric power and thermal power output by the X-type hydrogen-doped equipment respectively, HGT is a hydrogen-doped gas turbine, and HGB is a hydrogen-doped gas boiler; η X,P and η X,H are the conversion efficiencies of electrical energy and thermal energy respectively; is the total energy of the mixed gas; are the energies of natural gas and hydrogen respectively; Q CH4 is the calorific value of natural gas; are the masses consumed by natural gas and hydrogen respectively;​ The formula of the hydrogen fuel cell sub-model is: Wherein, P HFC and H HFC are respectively the electric power and the heat power output by the hydrogen fuel cell, are respectively the electric and heat conversion efficiencies of the hydrogen fuel cell; m HFC,H2 is the mass of hydrogen consumed.

3. A method for low-carbon transformation treatment of an integrated park energy system according to claim 2, characterized in that, The formula of the stepped carbon trading model is: E quo = E quo,grid + E quo,GT + E quo,GB + E quo,HGT + E quo,HGB + E quo,CB E act = E act,grid + E act,gas + E act,coal E tra = E act - E quo where, E quo is the PIES carbon emission quota; E act is the actual carbon emission; E tra is the carbon emission participating in carbon trading; E quo,grid , E quo,GT , E quo,GB , E quo,HGT , E quo,HGB , E quo,CB are the carbon emission quotas of externally purchased electricity, gas turbine equipment, gas boiler equipment, hydrogen-blended gas turbine equipment, hydrogen-blended gas boiler equipment, and coal-fired boiler equipment respectively; E act,grid , E act,gas , E act,coal are the carbon emissions generated by externally purchased electricity, gas, and coal respectively.

4. A method for low-carbon transformation treatment of a comprehensive energy system in a park according to claim 3, characterized in that, The process of respectively segmenting the historical data of photovoltaic output and electrical and thermal loads based on the ordered clustering method of Fisher optimal segmentation includes: 1) Divide the historical data at an interval of 24 hours to obtain a long-time series sample containing 364 points to be segmented. 2) Divide the long time series sample into n + 1 short time series x. There are a total of ways, and denote the set Φ m of short time series obtained by the m-th division method as: In the formula, represents the i-th group of short time series in the short time series set Φ m ; represents the j-th data sample in the short time series set Φ m , where i ∈ [1, n + 1] and j ∈ [1, j i ; 3) Calculate the total within-group sum of squares B(m) for all segmentation methods: 4) Determine the segmentation method with the minimum total within-group sum of squares: In the formula, represents the mean value of the i-th short time series; 5) Use the silhouette coefficient index S n to evaluate the segmentation effect: where a w is the average Euclidean distance from the w-th data to other data in its group; b w is the minimum value of the average Euclidean distance from the w-th data to all data in the group adjacent in time to its group. 6) Let n = n + 1. If n ≠ N, repeat steps (2)-(4) to obtain segmentation schemes with different numbers of segmentation points, and select the scheme with the largest silhouette coefficient as the segmentation scheme of the long-time series sample.

5. A method for low-carbon transformation of an integrated energy system in a park according to claim 4, characterized in that, The formula for the upper limit constraint of equipment capacity is: In the formula, represents the upper limit of the capacity of the k-th type of device; The formulas for the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, and the SOC constraint are: In the formula, are the maximum charge and discharge energy coefficients of the energy storage device respectively; is the capacity of the k-th type of device after transformation; are the minimum and maximum state coefficients of the SOC of the energy storage device respectively; The formula for the balance constraint of electric power, thermal power, and hydrogen energy is: In the formula, is the external power purchase of electricity at the h-th moment of the d-th typical day under the scenario s; is the power generation of the gas turbine; is the power generation of the hydrogen-blended gas turbine; is the power generation of photovoltaic power generation; is the power discharge of the electrical energy storage; is the electrical power of the hydrogen fuel cell; is the electrical load power; is the power charge of the electrical energy storage; is the electrical power of the electrolyzer; is the thermal power of the gas turbine; is the thermal power of the gas boiler; is the thermal power of the hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; is the heat release power of the thermal energy storage; is the thermal power of the coal-fired boiler; is the thermal power of the hydrogen fuel cell; is the thermal load power; is the heat storage power of the thermal energy storage; is the hydrogen production mass of the electrolyzer; is the hydrogen release mass of the seasonal hydrogen storage; is the hydrogen consumption mass of the hydrogen-blended gas turbine; is the hydrogen consumption mass of the hydrogen-blended gas boiler; is the hydrogen storage mass of the seasonal hydrogen storage.

6. A low-carbon transformation treatment device for a comprehensive park energy system, characterized in that, Including: The energy coupling analysis module is used to construct a hydrogen energy equipment model and a ladder carbon trading model, and through the hydrogen energy equipment model and the ladder carbon trading model, carry out the coupled utilization of fossil energy and renewable energy and guide the park integrated energy system to reduce carbon emissions, so as to realize the low-carbon transformation of the park integrated energy system; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen-blended combustion equipment sub-model and a hydrogen fuel cell sub-model; the parameters of the ladder carbon trading model include the carbon emission quota of the park integrated energy system PIES, the actual carbon emission and the carbon emission participating in carbon trading; The annual time series scenario analysis module is used to segment the historical data of photovoltaic output and electric and thermal loads respectively based on the ordered clustering method of Fisher optimal segmentation, and then re-segment the long-time series samples according to the corresponding relationship of the segmentation points of each group of data in time. Each segmented segment corresponds to a typical day, and the photovoltaic output and electric and thermal load values in the typical day are obtained by clustering the historical data corresponding to the segmented segment; generate the uncertainty scenarios corresponding to each typical day by using Latin hypercube sampling according to the obtained segmentation results; and arrange the uncertainty scenarios corresponding to each typical day according to the determined time series to obtain the annual time series scenario; The energy system transformation evaluation and analysis module is used to construct a system evaluation model and a low-carbon transformation model. The system evaluation model evaluates the PIES by using the annual carbon emission, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to obtain the PIES evaluation strategy; the constraint conditions of the low-carbon transformation model include the upper limit constraint of equipment capacity, the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, the SOC constraint, the balance constraint of electric power, thermal power and hydrogen energy. Through the low-carbon transformation model, according to the hydrogen energy equipment model, the ladder carbon trading model and the annual time series scenario, a low-carbon transformation strategy including new construction, expansion, replacement, decommissioning and hydrogen-blended transformation operations is obtained; In the energy system transformation evaluation and analysis module, the objective function of the low-carbon transformation model is: where N s is the number of scenarios, and p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod are the annualized investment costs generated by new construction, expansion, replacement, retirement, and hydrogen blending retrofit, respectively; are the unit investment costs for the corresponding operations; N CM is the number of equipment types; represent the changes in equipment capacity brought about by the five retrofit operations of new construction, expansion, replacement, retirement, and hydrogen blending retrofit, respectively; r is the discount rate; Y k is the life cycle of the k-th type of equipment; R k is the salvage value of the k-th type of equipment; is the capacity of the k-th type of equipment before retrofit; δ k is the net salvage value rate of the k-th type of equipment; L k is the number of operating years of the k-th type of equipment from the start of configuration to retrofit.

7. A low-carbon transformation processing device for a comprehensive park energy system according to claim 6, characterized in that In the energy coupling analysis module, the formula of the electrolyzer sub-model is: Where, P EZ is the input electric power of the electrolyzer; is the calorific value of hydrogen; Q P is the conversion value between the two units of kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the electrolytic hydrogen production efficiency of the electrolytic cell; In the energy coupling analysis module, the formula of the seasonal hydrogen storage sub-model is: Wherein, is the seasonal hydrogen storage and energy storage state at the hth period on the dth day, and Δh and Δd are the scheduling periods in hours and days respectively; is the seasonal hydrogen storage and energy storage state for the next hour; is the seasonal hydrogen storage and energy storage state at the lth period on the dth day; is the seasonal hydrogen storage and energy storage state for the next day; is the seasonal hydrogen storage and energy storage state at the first moment of the whole year; is the seasonal hydrogen storage and energy storage state at the last moment of the whole year; are the hydrogen charging and discharging efficiencies respectively; are the hydrogen charging and discharging masses respectively; Q SHS is the capacity of seasonal hydrogen storage; D is the number of days in a year; In the energy coupling analysis module, the formula of the hydrogen-blended combustion equipment sub-model is: where X ∈ {HGT, HGB}, are respectively the electric power and the thermal power output by the X-type hydrogen-doped equipment, HGT is a hydrogen-doped gas turbine, and HGB is a hydrogen-doped gas boiler; η X,P 、η X,H are the conversion efficiencies of electrical energy and thermal energy respectively; is the total energy of the mixed gas; are the energies of natural gas and hydrogen respectively; Q CH4 is the calorific value of natural gas; are the masses consumed by natural gas and hydrogen respectively; In the energy coupling analysis module, the formula of the hydrogen fuel cell sub-model is: Wherein, P HFC and H HFC are respectively the electric power and the heat power output by the hydrogen fuel cell, are respectively the electric and heat conversion efficiencies of the hydrogen fuel cell; is the mass of hydrogen consumed; In the energy coupling analysis module, the formula of the ladder carbon trading model is: E quo = E quo,grid + E quo,GT + E quo,GB + E quo,HGT + E quo,HGB + E quo,CB E act = E act,grid + E act,gas + E act,coal E tra = E act - E quo Where E quo is the PIES carbon emission quota; E act is the actual carbon emission; E tra is the carbon emission participating in carbon trading; E quo,grid , E quo,GT , E quo,GB , E quo,HGT , E quo,HGB , E quo,CB are the carbon emission quotas of purchasing electricity from external grid, gas turbine equipment, gas boiler equipment, hydrogen-blended gas turbine equipment, hydrogen-blended gas boiler equipment, and coal-fired boiler equipment respectively; E act,grid , E act,gas , E act,coal are the carbon emissions generated from purchasing electricity from external grid, gas, and coal respectively.

8. The low-carbon transformation treatment device for a comprehensive park energy system according to claim 7, wherein, The annual time series scenario analysis module includes: The long-time series sample division sub-module is used to divide the historical data at an interval of 24 hours to obtain a long-time series sample containing 364 points to be segmented; The long-time series sample segmentation sub-module is used to segment the long-time series sample into n + 1 short-time series x. There are a total of ways, and the set Φ m of short-time series obtained by the m-th segmentation method is denoted as: In the formula, represents the i-th group of short time series in the short time series set Φ m ; represents the j-th data sample in the short time series set Φ m , where i ∈ [1, n + 1] and j ∈ [1, j i ; The total deviation square calculation sub-module is used to calculate the total deviation square sum B(m) of all segmentation methods: The minimum total deviation square sum determination sub-module is used to determine the segmentation method with the minimum total deviation square sum: In the formula, represents the mean value of the i-th group of short time series; The segmentation effect evaluation sub-module is used to evaluate the segmentation effect using the silhouette coefficient index S n : Where a w is the average Euclidean distance from the w-th data to other data in its group; b w is the minimum of the average Euclidean distances from the w-th data to all data in the group adjacent in time period to its group. The segmentation scheme generation sub-module is used to obtain the segmentation schemes with different numbers of segmentation points, and select the scheme with the largest silhouette coefficient as the segmentation scheme of the long-time series sample.

9. The low-carbon transformation treatment device for a comprehensive park energy system according to claim 8, wherein, In the energy system transformation evaluation and analysis module, the equipment capacity upper limit constraint formula is as follows: In the formula, represents the upper limit of the capacity of the k-th type of device; In the energy system transformation evaluation and analysis module, the operating power constraint of the energy supply equipment after transformation, the operating power constraint of the energy storage equipment, and the SOC constraint formula are as follows: In the formula, are the maximum charge and discharge energy coefficients of the energy storage device respectively; is the capacity of the k-th type of device after transformation; are the minimum and maximum state coefficients of the SOC of the energy storage device respectively; In the energy system transformation evaluation and analysis module, the electric power, heat power, and hydrogen energy balance constraint formula are as follows: Wherein, is the external online power purchase at the h-th moment of the d-th typical day under the scenario s; is the power generation of the gas turbine; is the power generation of the hydrogen-blended gas turbine; is the power generation of the photovoltaic; is the power discharge of the electrical energy storage; is the electrical power of the hydrogen fuel cell; is the electrical load power; is the power charge of the electrical energy storage; is the electrical power of the electrolyzer; is the thermal power of the gas turbine; is the thermal power of the gas boiler; is the thermal power of the hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; is the heat release power of the thermal energy storage; is the thermal power of the coal-fired boiler; is the thermal power of the hydrogen fuel cell; is the thermal load power; is the heat storage power of HS; is the hydrogen production mass of the electrolyzer; is the hydrogen release mass of seasonal hydrogen storage; is the hydrogen consumption mass of the hydrogen-blended gas turbine; is the hydrogen consumption mass of the hydrogen-blended gas boiler; is the hydrogen storage mass of seasonal hydrogen storage.

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