Low-carbon transformation treatment method and device for park comprehensive energy system

Abstract

The invention discloses a park comprehensive energy system low-carbon transformation processing method and device, and the method achieves the coupling utilization of fossil energy, renewable energy and hydrogen energy through a hydrogen energy equipment model and a stepped carbon transaction mechanism. Meanwhile, taking source and load time sequence characteristics and uncertainty into consideration, and adopting an ordered clustering and scene generation method based on Fisher optimal segmentation to obtain a typical scene; on this basis, a system evaluation model is constructed to identify weak links of the existing PIES in energy supply and consumption, a low-carbon transformation stochastic optimization model is constructed, a low-carbon transformation strategy including five operations of addition, extension, replacement, decommissioning and hydrogen doping transformation is obtained, and the transformed PIES has lower comprehensive annual cost due to lower operation and carbon transaction cost; the modified PIES does not use CB with high carbon emission any more, and the adequacy of heat energy supply is improved through heat charging and discharging of HS; the difference of photovoltaic output in different seasons is made up; and hydrogen energy utilization is promoted.

Description

Technical Field

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

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

[0003] In the existing technology, the research on low-carbon transformation of energy production and utilization links is mainly focused on the equipment and operation mechanism level. At the equipment level, hydrogen blending transformation is carried out on gas turbines (GT) and gas boilers (GB); new energy and electrolysis hydrogen production equipment are used to increase the penetration rate of renewable energy. At the operation mechanism level, a carbon trading mechanism is introduced. However, the current method does not take into account the replacement and retirement of old equipment, carbon trading mechanisms, hydrogen energy utilization, renewable energy power generation and other methods and their synergistic effects on low-carbon transformation. Therefore, the transformation measures and mechanisms are relatively single, and the potential of transformation to reduce carbon emissions of existing PIES has not been fully explored. Summary of the invention

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

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for low-carbon transformation of a comprehensive energy system in a park, comprising:

[0006] Construct a hydrogen energy equipment model and a ladder carbon trading model, and use the hydrogen energy equipment model and the ladder carbon trading model to couple fossil energy and renewable energy and guide the park's comprehensive energy system to reduce carbon emissions, so as to achieve a low-carbon transformation of the park's comprehensive 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 PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading;

[0007] The ordered clustering method based on Fisher's optimal segmentation is used to segment the historical data of photovoltaic output and electric and thermal loads respectively. Then, according to the corresponding relationship in time between the segmentation points of each group of data, the long time series samples are segmented again. Each segment after segmentation corresponds to a typical day. The photovoltaic output and electric and thermal load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. Latin hypercube sampling is used to generate the uncertainty scenarios corresponding to each typical day based on the segmentation results. And according to the determined time series, the uncertainty scenarios corresponding to each typical day are arranged to obtain the time series scenarios for the whole year.

[0008] A system evaluation model and a low-carbon transformation model are constructed. The system evaluation model uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES and obtain a PIES evaluation strategy. The constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints. 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 additions, expansions, replacements, retirements and hydrogen blending transformation operations is obtained.

[0009] As a preferred solution for the low-carbon transformation of the park's comprehensive energy system, the formula of the electrolytic cell sub-model is:

[0010]

[0011] Where P EZ inputting electrical power to the electrolyzer; is the calorific value of hydrogen; Q P It is the conversion value between kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the 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 state in the h period of the dth day, Δh and Δd are the scheduling periods in hours and days respectively; Seasonal hydrogen storage status for the next hour; The seasonal hydrogen storage state during the l-th period on the d-th day; Seasonal hydrogen storage status for the next day; It is the seasonal hydrogen storage state at the first moment of the year; It is a seasonal hydrogen storage state for the last moment of the year; are the efficiency of hydrogen charging and discharging respectively; Q is the mass of hydrogen charged and discharged respectively; SHS is the seasonal hydrogen storage capacity; D is the number of days in a year;

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

[0016]

[0017] Where, X∈{HGT,HGB}, are the electrical power and thermal power output of the X-type hydrogen blending equipment, HGT is the hydrogen blending gas turbine, and HGB is the hydrogen blending 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; is the calorific value of natural gas; are the masses of natural gas and hydrogen consumed, respectively;

[0018] The formula of the hydrogen fuel cell submodel is:

[0019]

[0020] Where P HFC , H HFC are the electrical power and thermal power output of the hydrogen fuel cell, They are the electrical and thermal conversion efficiencies of hydrogen fuel cells; is the mass of hydrogen consumed.

[0021] As the preferred solution for the low-carbon transformation of the park's comprehensive energy system, the formula of the step-by-step 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 set of short time series Φ m The i-th group of short time series in ; Represents a set of short time series Φ m The jth data sample in, i∈[1,n+1],j∈[1,j i ];

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

[0032]

[0033] 4) Determine the segmentation method that minimizes the 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 indicator S n Evaluate the segmentation effect:

[0037]

[0038] In the formula, a w is the average Euclidean distance from the wth data to other data in its group; b w The minimum value of the average Euclidean distance from the w-th data to all data in the adjacent groups of the group to which it belongs 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 a preferred solution for the low-carbon transformation of the park's comprehensive energy system, the objective function of the low-carbon transformation model is:

[0041]

[0042]

[0043]

[0044] Where N s is the number of scenes, p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod The annualized investment costs for new additions, expansions, replacements, decommissioning and hydrogen blending modifications are respectively; are the unit investment costs of the corresponding operations; N CM is the number of equipment types; They represent the equipment capacity changes caused by the five transformation operations of new addition, expansion, replacement, decommissioning 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 residual value of the k-th type of equipment; is the capacity of the k-type equipment before transformation; δ k is the net residual value rate of the k-th type of equipment; L k is the number of years of operation of the k-type equipment from initial configuration to modification.

[0045] As the optimal solution for the low-carbon transformation of the park's comprehensive energy system, the upper limit constraint formula for equipment capacity is:

[0046]

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

[0048] After the transformation, the operating power constraints of the energy supply equipment and the energy storage equipment, and the SOC constraint formula are:

[0049]

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

[0051] The constraint formulas for electrical power, thermal power and hydrogen energy balance are:

[0052]

[0053]

[0054]

[0055] In the formula, is the off-line purchased electricity power at time h on the dth typical day under scenario s; is the power generation capacity of the gas turbine; The power generated by 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 hydrogen fuel cell; is the electrical load power; Charging power for 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 hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; Heat release power for 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; Thermal power storage for thermal energy; is the hydrogen production quality of the electrolyzer; It is the quality of seasonal hydrogen storage and release; It is the mass of hydrogen consumed by hydrogen-blended gas turbine; It is the mass of hydrogen consumed by hydrogen-blended gas boiler; It is the quality of seasonal hydrogen storage.

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

[0057] An energy coupling analysis module is used to construct a hydrogen energy equipment model and a ladder carbon trading model, through which fossil energy and renewable energy are coupled and utilized, and the park's comprehensive energy system is guided to reduce carbon emissions, so as to achieve a low-carbon transformation of the park's comprehensive 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 PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading;

[0058] 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 in time between the segmentation points of each group of data. Each segment after segmentation corresponds to a typical day. The photovoltaic output and electric and thermal load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. Latin hypercube sampling is used to generate the uncertainty scenarios corresponding to each typical day based on the segmentation results. The uncertainty scenarios corresponding to each typical day are arranged according to the determined time series to obtain the annual time series scenario.

[0059] 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 uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES and obtain a PIES evaluation strategy. The constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints. 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 additions, expansions, replacements, retirements and hydrogen blending transformation operations is obtained.

[0060] As a preferred solution for the low-carbon transformation and treatment device of the park's comprehensive energy system, in the energy coupling analysis module, the formula of the electrolytic cell sub-model is:

[0061]

[0062] Where P EZ inputting electrical power to the electrolyzer; is the calorific value of hydrogen; Q PIt is the conversion value between kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the 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 state in the h period of the dth day, Δh and Δd are the scheduling periods in hours and days respectively; Seasonal hydrogen storage status for the next hour; The seasonal hydrogen storage state during the l-th period on the d-th day; Seasonal hydrogen storage status for the next day; It is the seasonal hydrogen storage state at the first moment of the year; It is a seasonal hydrogen storage state for the last moment of the year; are the efficiency of hydrogen charging and discharging respectively; Q is the mass of hydrogen charged and discharged respectively; SHS is the seasonal hydrogen storage capacity; D is the number of days in a year;

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

[0067]

[0068] Where, X∈{HGT,HGB}, are the electrical power and thermal power output of the X-type hydrogen blending equipment, HGT is the hydrogen blending gas turbine, and HGB is the hydrogen blending 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; 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] Where P HFC , H HFC are the electrical power and thermal power output of the hydrogen fuel cell, They are the electrical and thermal conversion efficiencies of hydrogen fuel cells; 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 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.

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

[0078] The long time series sample division submodule is used to divide the historical data into 24-hour intervals to obtain a long time series sample containing 364 points to be divided;

[0079] The long time series sample segmentation submodule is used to segment 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:

[0080]

[0081] In the formula, Represents a set of short time series 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 ];

[0082] The total deviation square calculation submodule is used to calculate the total deviation square sum B(m) of all segmentation methods:

[0083]

[0084] The minimum total deviation square sum determination submodule is used to determine the segmentation method with the minimum total deviation square sum:

[0085]

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

[0087] Segmentation effect evaluation submodule, used to use the silhouette coefficient index S n Evaluate the segmentation effect:

[0088]

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

[0090] The segmentation scheme generating submodule 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 for the long time series sample.

[0091] As a preferred solution for the low-carbon transformation treatment device of the park comprehensive 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] Where N s is the number of scenes, p sis the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod The annualized investment costs for new additions, expansions, replacements, decommissioning and hydrogen blending modifications are respectively; are the unit investment costs of the corresponding operations; N CM is the number of equipment types; They represent the equipment capacity changes caused by the five transformation operations of new addition, expansion, replacement, decommissioning 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 residual value of the k-th type of equipment; is the capacity of the k-type equipment before transformation; δ k is the net residual value rate of the k-th type of equipment; L k is the number of years of operation of the k-type equipment from initial configuration to modification;

[0096] In the energy system transformation assessment 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 transformation evaluation and analysis module, the operating power constraints of the energy supply equipment and the energy storage equipment after the transformation, and the SOC constraint formula are:

[0100]

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

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

[0103]

[0104]

[0105]

[0106] In the formula, is the off-line purchased electricity power at time h on the dth typical day under scenario s; is the power generation capacity of the gas turbine; The power generated by 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 hydrogen fuel cell; is the electrical load power; Charging power for 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 hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; Heat release power for 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; Thermal power storage for thermal energy; is the hydrogen production quality of the electrolyzer; It is the quality of seasonal hydrogen storage and release; It is the mass of hydrogen consumed by hydrogen-blended gas turbine; It is the mass of hydrogen consumed by hydrogen-blended gas boiler; It is the quality of seasonal hydrogen storage.

[0107] The present invention has the following advantages: through the hydrogen energy equipment model and the ladder 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 sources and loads, 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 of existing PIES in energy supply and use, and a low-carbon transformation random optimization model is constructed to obtain a low-carbon transformation strategy including five operations of new addition, expansion, replacement, retirement and hydrogen blending transformation. After the transformation, the PIES has lower comprehensive annualized cost due to lower operation and carbon trading costs; after the transformation, the PIES no longer uses CB with higher carbon emissions, and improves the abundance of heat supply through the charging and discharging of HS; in the winter and autumn when the light is insufficient, the stored hydrogen energy is released and converted into electrical energy and thermal energy for utilization, thereby compensating for the difference in photovoltaic output between different seasons and promoting the utilization of hydrogen energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0109] Figure 1 A schematic diagram of a process flow of a low-carbon transformation method for a park integrated energy system provided in an embodiment of the present invention;

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

[0111] Figure 3 The uncertainty scenario and typical daytime PV output and electrical and thermal load curves provided in the embodiments of the present invention;

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

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

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

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

[0116] Figure 8 This is an architecture diagram of a low-carbon transformation processing device for a park integrated energy system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0117] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0118] Example 1

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

[0120] S1. Construct a hydrogen energy equipment model and a ladder carbon trading model, and use the hydrogen energy equipment model and the ladder carbon trading model to couple fossil energy and renewable energy and guide the park's comprehensive energy system to reduce carbon emissions, so as to achieve a low-carbon transformation of the park's comprehensive 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 PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading;

[0121] S2. The ordered clustering method based on Fisher optimal segmentation is used to segment the historical data of photovoltaic output and electric and thermal loads respectively. Then, according to the corresponding relationship in time between the segmentation points of each group of data, the long time series samples are segmented again. Each segment after segmentation corresponds to a typical day. The photovoltaic output and electric and thermal load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. Latin hypercube sampling is used to generate the uncertainty scenarios corresponding to each typical day based on the 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 scenarios.

[0122] S3. Construct a system evaluation model and a low-carbon transformation model. The system evaluation model uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES and obtain a PIES evaluation strategy. The constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints. 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 additions, expansions, replacements, retirements and hydrogen blending transformation operations is obtained.

[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] Where P EZ inputting electrical power to the electrolyzer; is the calorific value of hydrogen; Q P It is the conversion value between kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the 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 storage of hydrogen 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 state in the h period of the dth day, Δh and Δd are the scheduling periods in hours and days respectively; Seasonal hydrogen storage status for the next hour; The seasonal hydrogen storage state during the l-th period on the d-th day; Seasonal hydrogen storage status for the next day; It is the seasonal hydrogen storage state at the first moment of the year; It is a seasonal hydrogen storage state for the last moment of the year; are the efficiency of hydrogen charging and discharging respectively; Q is the mass of hydrogen charged and discharged respectively; SHS is the seasonal hydrogen storage capacity; D is the number of days in a year;

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

[0130]

[0131] Where, X∈{HGT,HGB}, are the electrical power and thermal power output of the X-type hydrogen blending equipment, HGT is the hydrogen blending gas turbine, and HGB is the hydrogen blending 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; 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 electrical energy and thermal energy by causing hydrogen to undergo an oxidation reaction on a proton exchange membrane. The formula of the hydrogen fuel cell sub-model is:

[0133]

[0134] Where P HFC , H HFCare the electrical power and thermal power output of the hydrogen fuel cell, They are the electrical and thermal conversion efficiencies of hydrogen fuel cells; is the mass of hydrogen consumed.

[0135] In this embodiment, in step S1, the formula of the ladder 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 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.

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

[0141]

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

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

[0144] 1) Divide the historical data into 24-hour intervals 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 set the number of initial segmentation points;

[0145] 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:

[0146]

[0147] In the formula, Represents a set of short time series 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 ];

[0148] 3) Calculate the total deviation square sum B(m) of all segmentation methods:

[0149]

[0150] 4) Determine the segmentation method that minimizes the total sum of squared deviations:

[0151]

[0152] In the formula, Represents the mean of the short time series of the i-th group; the smaller the value, the smaller the difference in the values ​​within the group;

[0153] 5) Use the silhouette coefficient indicator S n Evaluate the segmentation effect:

[0154]

[0155] In the formula, a w is the average Euclidean distance from the wth data to other data in its group; b w The minimum value of the average Euclidean distance from the wth data to all data in the adjacent groups of the group to which it belongs in the time period; the silhouette coefficient index S n The range is [-1,1]. The larger the value, the better the segmentation effect.

[0156] 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.

[0157] The photovoltaic (PV) output and electricity and heat load historical data are segmented respectively, and then the long time series samples are segmented again according to the time correspondence between the segmentation points of each group of data. Each segment after segmentation corresponds to a typical day. The specific PV output and electricity and heat load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. The segmentation results are shown in the figure below. Figure 2 The specific segments are shown in Table 1.

[0158] Table 1 Results of time period division

[0159]

[0160] Among them, based on the obtained segmentation results, assuming that PV output and electrical and thermal loads obey Beta and normal distributions respectively, Latin hypercube sampling is used to generate 2000 uncertainty scenarios corresponding to each typical day. Further, according to the determined time series, each typical day scenario is arranged to obtain the annual time series scenario. In order to improve the calculation efficiency, K-means clustering is used to reduce the scenarios, and finally 5 annual time series scenarios are obtained. The results are as follows 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 PIES. In order to comprehensively analyze the status of the existing PIES before transformation, and obtain the weak links that restrict the existing PIES in energy conservation and emission reduction, and improve operational efficiency. First, the initial planning data and the actual operation data of the existing PIES are obtained, and the original data is processed. Secondly, the equipment configuration, system operation status, and system carbon emissions are qualitatively and quantitatively analyzed, and the indicators in Table 2 are calculated. Find out the problem links that cause high carbon emissions, low energy utilization efficiency, and insufficient energy supply in energy supply, conversion, and storage. The results of the evaluation strategy can help formulate targeted transformation plans.

[0162] Table 2 Evaluation index definitions and descriptions

[0163]

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

[0165] In this embodiment, in step S3, the low-carbon transformation of existing PIES includes not only the expansion, replacement, decommissioning and hydrogen-doping 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] Where N s is the number of scenes, p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod The annualized investment costs for new additions, expansions, replacements, decommissioning and hydrogen blending modifications are respectively; are the unit investment costs of the corresponding operations; N CM is the number of equipment types; They represent the equipment capacity changes caused by the five transformation operations of new addition, expansion, replacement, decommissioning 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 residual value of the k-th type of equipment; is the capacity of the k-type equipment before transformation; δ k is the net residual value rate of the k-th type of equipment; L k is the number of years of operation of the k-type equipment from initial configuration to modification.

[0170] Among them, operation and maintenance costs Specifically:

[0171]

[0172] In the formula, is the number of typical days represented by the s-type scenario; p s,d is the probability of scenario s occurring on the dth typical day; and is the natural gas and coal consumption of GB, GT and CB at time h on the dth typical day under scenario s; c grid ,h is the electricity price at time h; Purchase electricity from the external network.

[0173] In this embodiment, in step S3, the upper limit constraint formula of the equipment capacity 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 operating power constraints of the energy supply equipment and the energy storage equipment after the transformation, the SOC constraint formula is:

[0177]

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

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

[0180]

[0181]

[0182]

[0183]

[0184] In the formula, is the off-line purchased electricity power at time h on the dth typical day under scenario s; is the power generation capacity of the gas turbine; The power generated by 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 hydrogen fuel cell; is the electrical load power; Charging power for 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 hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; Heat release power for 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; Thermal power storage for thermal energy; is the hydrogen production quality of the electrolyzer; It is the quality of seasonal hydrogen storage and release; It is the mass of hydrogen consumed by hydrogen-blended gas turbine; It is the mass of hydrogen consumed by hydrogen-blended gas boiler; It is the quality of seasonal hydrogen storage.

[0185] The effect of the embodiment of the present invention is verified by the following examples:

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

[0187] Table 3 Equipment configuration results before transformation and integrated solution

[0188]

[0189] The power balance of the existing PIES before the transformation is as follows: Figure 4 As shown in the figure. In terms of power supply, due to the degradation of ES capacity, the energy stored and released is reduced, which limits the ability to smooth PV output and load fluctuations. In terms of heating, the capacity of GT and GB cannot adapt to the growing load demand and is often fully loaded, which restricts the sufficient heating supply of the system.

[0190] Among them, the evaluation indicators of existing PIES are shown in Table 4. The existing PIES has less PV capacity, resulting in a low PV penetration rate in the system. Secondly, the existing PIES has high carbon emissions. On the one hand, the use of CB and external power purchase are the main sources of carbon emissions. Their low energy conversion efficiency leads to low primary energy utilization of existing PIES; on the other hand, the system makes less use of renewable energy. GT and GB also generate high carbon emissions. The high carbon emissions have led to a significant increase in carbon trading costs, and the excessive external power purchase has also increased the cost of purchasing electricity. These two factors jointly restrict the economic operation of the system.

[0191] Table 4 Evaluation indicators before transformation and integrated solution

[0192]

[0193] Therefore, in the low-carbon transformation of PIES, consideration is given to expanding PV to increase PV penetration, and replacing and expanding degraded ES to smooth out PV output fluctuations; in addition, consideration is given to adding high-efficiency power supply and heating equipment and hydrogen-blending transformation of existing gas equipment to improve the environmental protection and economy of energy supply; finally, consideration is given to retiring CB early to further reduce system carbon emissions and recover costs.

[0194] Different schemes were adopted to transform the existing PIES, and the equipment configuration of each device is shown in Table 3. Compared with the existing PIES, the transformed PIES expanded the PV and GT to improve the system's self-sufficiency in electricity and reduce the dependence on external grid electricity purchases. At the same time, the ES was replaced and expanded to smooth the output fluctuation of PV; the CB with high carbon emissions was retired, and the HS was added. One GB and two GTs were transformed with hydrogen blending, which increased the system's hydrogen demand to produce more hydrogen and promote the use of renewable energy.

[0195] The evaluation indicators of the existing PIES before and after the transformation are shown in Table 4. In terms of PV penetration rate improvement, the transformed PIES has diversified hydrogen energy utilization links, which promotes PV consumption. In terms of annual carbon emissions, the emission reduction effect of the transformed PIES is significantly improved compared with other solutions because it is equipped with more PV, applies more hydrogen energy equipment, and realizes hydrogen-blended combustion of gas equipment. In terms of comprehensive annualized cost, the transformed PIES has lower comprehensive annualized cost due to lower operation and carbon trading costs.

[0196] The typical day scenes corresponding to time periods 1, 5, 8 and 14 are selected to represent winter, spring, summer and autumn respectively, and then the actual operation of each transformation scheme is analyzed. Figure 5 As shown in the figure, the modified PIES has increased the PV absorption capacity and reduced the amount of electricity purchased from the external grid. Among them, the modified PIES uses EZ to produce hydrogen during periods of sufficient sunlight, and uses HFC to supply electricity during periods of insufficient sunlight. Through the conversion and storage between solar energy, electricity and hydrogen energy, the PV absorption level and the ability to transfer energy across seasons are improved. After the hydrogen blending modification, the HGT has a greater output than the GT of the existing PIES, which reduces the system's carbon emissions and carbon trading costs, while also reducing gas purchase costs. The thermal power balance of the existing PIES before and after the modification is shown in the figure. Figure 5 As shown in the figure, after the transformation, PIES no longer uses CB with higher carbon emissions, and improves the adequacy of heat energy supply through the charging and releasing heat of HS.

[0197] The hydrogen energy balance in a typical PIES scenario after low-carbon transformation is as follows: Figure 6 As shown in the figure, after the existing PIES is modified, it can use EZ to produce hydrogen for consumption by HFC and other equipment, and store excess hydrogen in SHS. By transferring hydrogen energy at different times, the energy supply flexibility of PIES during the day and between days is enhanced and its dependence on fossil fuels is reduced, significantly reducing the carbon emissions of the system. Figure 7 The hydrogen energy balance throughout the year after the transformation of the existing PIES is demonstrated. PIES uses PV to produce and store hydrogen in the spring and summer when the sunlight is abundant, and releases the stored hydrogen energy in the winter and autumn when the sunlight is insufficient, converting it into electricity and heat for use, thus making up for the difference in PV output between seasons and promoting the use of hydrogen energy.

[0198] In summary, the present invention constructs a hydrogen energy equipment model and a ladder carbon trading model, through which the hydrogen energy equipment model and the ladder carbon trading model are used to couple fossil energy and renewable energy and guide the park's comprehensive energy system to reduce carbon emissions, so as to achieve a low-carbon transformation of the park's comprehensive 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 PIES carbon emission quotas, actual carbon emissions and carbon emissions participating in carbon trading; the ordered clustering method based on Fisher optimal segmentation is used to segment the photovoltaic output and the electricity and heat load historical data respectively, and then the long time series samples are re-segmented according to the temporal correspondence between the segmentation points of each group of data, and each segment after segmentation corresponds to a typical day. The photovoltaic output and the electricity and heat load values ​​in the typical day are calculated by the corresponding segmentation data. The historical data is clustered; Latin hypercube sampling is used to generate uncertainty scenarios corresponding to each typical day based on the obtained segmentation results; and the uncertainty scenarios corresponding to each typical day are arranged according to the determined time series to obtain the annual time series scenarios; a system evaluation model and a low-carbon transformation model are constructed, and the system evaluation model uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES to obtain a PIES evaluation strategy; the constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints, and the low-carbon transformation strategy including new addition, expansion, replacement, retirement and hydrogen blending transformation operations is obtained through the low-carbon transformation model according to the hydrogen energy equipment model, the ladder carbon trading model and the annual time series scenarios. The present invention realizes the coupled utilization of fossil energy, renewable energy and hydrogen energy through a hydrogen energy equipment model and a ladder carbon trading mechanism. Meanwhile, considering the time series characteristics and uncertainties of sources and loads, 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 of existing PIES in energy supply and utilization, and a low-carbon transformation random optimization model is constructed to obtain a low-carbon transformation strategy including five operations of new addition, expansion, replacement, decommissioning 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 CB with higher carbon emissions, and improves the abundance of heat supply through the charging and discharging of HS. In the winter and autumn when the sunlight is insufficient, the stored hydrogen energy is released and converted into electrical energy and thermal energy for utilization, thereby compensating for the difference in photovoltaic output between different seasons and promoting the utilization of hydrogen energy.

[0199] Example 2

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

[0201] The energy coupling analysis module 100 is used to construct a hydrogen energy equipment model and a ladder carbon trading model, and the coupled utilization of fossil energy and renewable energy is carried out through the hydrogen energy equipment model and the ladder carbon trading model, and the carbon emission of the park comprehensive energy system is guided to be reduced, so as to realize the low-carbon transformation of the park comprehensive energy system; the hydrogen energy equipment model includes an electrolyzer sub-model, a seasonal hydrogen storage sub-model, a hydrogen blending combustion equipment sub-model and a hydrogen fuel cell sub-model; the parameters of the ladder carbon trading model include PIES carbon emission quota, actual carbon emissions and carbon emissions participating in carbon trading;

[0202] The annual time series scenario analysis module 200 is used to segment the photovoltaic output and the electricity and heat load historical data 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 in time between the segmentation points of each group of data. Each segment after segmentation corresponds to a typical day. The photovoltaic output and the electricity and heat load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. Latin hypercube sampling is used to generate the uncertainty scenario corresponding to each typical day according to the 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.

[0203] The energy system transformation evaluation and analysis module 300 is used to construct a system evaluation model and a low-carbon transformation model. The system evaluation model uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES and obtain a PIES evaluation strategy; the constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints. 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 additions, expansions, replacements, retirements and hydrogen blending transformation operations is obtained.

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

[0205]

[0206] Where P EZ inputting electrical power to the electrolyzer; is the calorific value of hydrogen; Q P It is the conversion value between kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZis the 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, Seasonal hydrogen storage status for the next hour; The seasonal hydrogen storage state during the l-th period on the d-th day; Seasonal hydrogen storage status for the next day; It is the seasonal hydrogen storage state at the first moment of the year; It is a seasonal hydrogen storage state for the last moment of the year; are the efficiency of hydrogen charging and discharging respectively; Q is the mass of hydrogen charged and discharged respectively; SHS is the seasonal hydrogen storage capacity; D is the number of days in a year;

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

[0211]

[0212] Where, X∈{HGT,HGB}, are the electrical power and thermal power output of the X-type hydrogen blending equipment, HGT is the hydrogen blending gas turbine, and HGB is the hydrogen blending 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; 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] Where P HFC , H HFC are the electrical power and thermal power output of the hydrogen fuel cell, They are the electrical and thermal conversion efficiencies of hydrogen fuel cells; is the mass of hydrogen consumed;

[0216] In the energy coupling analysis module 100, the formula of the ladder 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 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.

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

[0222] The long time series sample division submodule 201 is used to divide the historical data into 24-hour intervals to obtain a long time series sample containing 364 points to be divided;

[0223] The long time series sample segmentation submodule 202 is used to segment the long time series sample 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:

[0224]

[0225] 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 Φm The jth data sample in, i∈[1,n+1],j∈[1,j i ];

[0226] The total deviation square calculation submodule 203 is used to calculate the total deviation square sum B(m) of all segmentation modes:

[0227]

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

[0229]

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

[0231] The segmentation effect evaluation submodule 205 is used to use the silhouette coefficient index S n Evaluate the segmentation effect:

[0232]

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

[0234] The segmentation scheme generating submodule 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 for the long time series samples.

[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] Where N s is the number of scenes, p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod The annualized investment costs for new additions, expansions, replacements, decommissioning and hydrogen blending modifications are respectively; are the unit investment costs of the corresponding operations; N CMis the number of equipment types; They represent the equipment capacity changes caused by the five transformation operations of new addition, expansion, replacement, decommissioning 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 residual value of the k-th type of equipment; is the capacity of the k-type equipment before transformation; δ k is the net residual value rate of the k-th type of equipment; L k is the number of years of operation of the k-type equipment from initial configuration to modification;

[0239] In the energy system transformation assessment 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 transformation evaluation and analysis module 300, 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:

[0243]

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

[0245] In the energy system transformation assessment and analysis module, the electric power, thermal power and hydrogen energy balance constraint formula is:

[0246]

[0247]

[0248]

[0249] In the formula, is the off-line purchased electricity power at time h on the dth typical day under scenario s; is the power generation capacity of the gas turbine; The power generated by 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 hydrogen fuel cell; is the electrical load power; Charging power for 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 hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; Heat release power for 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; Thermal power storage for thermal energy;

[0250] is the hydrogen production quality of the electrolyzer; It is the quality of seasonal hydrogen storage and release; It is the mass of hydrogen consumed by hydrogen-blended gas turbine; It is the mass of hydrogen consumed by hydrogen-blended gas boiler; It is the quality of seasonal hydrogen storage.

[0251] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0252] Example 3

[0253] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for low-carbon transformation of an integrated energy system in a park is stored. The program code includes instructions for executing the method for low-carbon transformation of an integrated energy system in a park of Embodiment 1 or any possible implementation thereof.

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

[0255] Example 4

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

[0257] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the low-carbon transformation and processing method of the park integrated energy system of Example 1 or any possible implementation method thereof.

[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 implemented by reading software codes stored in a 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 process or function described in the embodiment of the present invention is 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 a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0260] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a 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, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, 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 by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for low-carbon transformation of a park integrated energy system, characterized in that: include: Construct a hydrogen energy equipment model and a ladder carbon trading model, and use the hydrogen energy equipment model and the ladder carbon trading model to couple fossil energy and renewable energy and guide the park's comprehensive energy system to reduce carbon emissions, so as to achieve a low-carbon transformation of the park's comprehensive 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 PIES carbon emission quotas, actual carbon emissions, and carbon emissions participating in carbon trading; The ordered clustering method based on Fisher's optimal segmentation is used to segment the historical data of photovoltaic output and electric and thermal loads respectively. Then, according to the corresponding relationship in time between the segmentation points of each group of data, the long time series samples are segmented again. Each segment after segmentation corresponds to a typical day. The photovoltaic output and electric and thermal load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. Latin hypercube sampling is used to generate the uncertainty scenarios corresponding to each typical day based on the segmentation results. And according to the determined time series, the uncertainty scenarios corresponding to each typical day are arranged to obtain the time series scenarios for the whole year. A system evaluation model and a low-carbon transformation model are constructed. The system evaluation model uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES and obtain a PIES evaluation strategy. The constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints. 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 additions, expansions, replacements, retirements and hydrogen blending transformation operations is obtained.

2. A low-carbon transformation and processing method for a park integrated energy system according to claim 1, characterized in that: The formula for the electrolyzer sub-model is: Where P EZ inputting electrical power to the electrolyzer; is the calorific value of hydrogen; Q P It is the conversion value between kilowatt-hour and joule; is the hydrogen production mass of the electrolyzer; η EZ is the hydrogen production efficiency of the electrolyzer; The formula of the seasonal hydrogen storage sub-model is: In the formula, is the seasonal hydrogen storage state in the h period of the dth day, Δh and Δd are the scheduling periods in hours and days respectively; Seasonal hydrogen storage status for the next hour; The seasonal hydrogen storage state during the l-th period on the d-th day; Seasonal hydrogen storage status for the next day; It is the seasonal hydrogen storage state at the first moment of the year; It is a seasonal hydrogen storage state for the last moment of the year; are the efficiency of hydrogen charging and discharging respectively; Q is the mass of hydrogen charged and discharged respectively; SHS is the seasonal hydrogen storage capacity; D is the number of days in a year; The formula of the hydrogen-blended combustion equipment sub-model is: Where, X∈{HGT,HGB}, They are the electrical power and thermal power output of the X-type hydrogen blending equipment, HGT is the hydrogen blending gas turbine, and HGB is the hydrogen blending 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; is the calorific value of natural gas; are the masses of natural gas and hydrogen consumed, respectively; The formula of the hydrogen fuel cell submodel is: Where P HFC , H HFC are the electrical power and thermal power output of the hydrogen fuel cell, They are the electrical and thermal conversion efficiencies of hydrogen fuel cells; is the mass of hydrogen consumed.

3. A low-carbon transformation and processing method for a park integrated energy system according to claim 2, characterized in that: The formula of the ladder carbon trading model is: AND quo =And quo,grid +E quo,GT +E quo,GB +E quo,HGT +E quo,HGB +E quo,CB AND act =And act,grid +E act,gas +E act,coal AND tra =And act -AND quo In the formula, E quo 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.

4. A low-carbon transformation and processing method for a park integrated energy system according to claim 3, characterized in that: The ordered clustering method based on Fisher optimal segmentation is used to segment the photovoltaic output and the historical data of electricity and heat loads. The process includes: 1) Divide the historical data into 24-hour intervals to obtain a long time series sample containing 364 points to be divided; 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: 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 ]; 3) Calculate the total deviation square sum B(m) of all segmentation methods: 4) Determine the segmentation method with the minimum total sum of squared deviations: In the formula, represents the mean of the i-th group of short time series; 5) Use the silhouette coefficient indicator S n Evaluate the segmentation effect: In the formula, a w is the average Euclidean distance from the wth data to other data in its group; b w The minimum value of the average Euclidean distance from the w-th data to all data in the adjacent groups of the group to which it belongs in the time period; 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.

5. A low-carbon transformation and processing method for a park integrated energy system according to claim 4, characterized in that: The objective function of the low-carbon transformation model is: Where N s is the number of scenes, p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod The annualized investment costs for new additions, expansions, replacements, decommissioning and hydrogen blending modifications are respectively; are the unit investment costs of the corresponding operations; N CM is the number of equipment types; They represent the equipment capacity changes caused by the five transformation operations of new addition, expansion, replacement, decommissioning 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 residual value of the k-th type of equipment; is the capacity of the k-type equipment before transformation; δ k is the net residual value rate of the k-th type of equipment; L k is the number of years of operation of the k-type equipment from initial configuration to modification.

6. A low-carbon transformation and processing method for a park integrated energy system according to claim 5, characterized in that: The upper limit constraint formula of equipment capacity is: In the formula, represents the upper limit of the capacity of the k-th type of equipment; After the transformation, the operating power constraints of the energy supply equipment and the energy storage equipment, and the SOC constraint formula are: In the formula, are the maximum charge and discharge coefficients of the energy storage device respectively; The capacity of the k-th type equipment after transformation; are the minimum and maximum SOC state coefficients of the energy storage device respectively; The constraint formulas for electrical power, thermal power and hydrogen energy balance are: In the formula, is the off-line purchased electricity power at time h on the dth typical day under scenario s; is the power generation capacity of the gas turbine; The power generated by 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 hydrogen fuel cell; is the electrical load power; Charging power for 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 hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; Heat release power for 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; Thermal power storage for thermal energy; is the hydrogen production quality of the electrolyzer; It is the quality of seasonal hydrogen storage and release; It is the mass of hydrogen consumed by hydrogen-blended gas turbine; It is the mass of hydrogen consumed by hydrogen-blended gas boiler; It is the quality of seasonal hydrogen storage.

7. A low-carbon transformation and processing device for a park integrated energy system, characterized in that: include: An energy coupling analysis module is used to construct a hydrogen energy equipment model and a ladder carbon trading model, through which fossil energy and renewable energy are coupled and utilized, and the park's comprehensive energy system is guided to reduce carbon emissions, so as to achieve a low-carbon transformation of the park's comprehensive 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 PIES carbon emission quotas, actual carbon emissions, and carbon emissions 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 in time between the segmentation points of each group of data. Each segment after segmentation corresponds to a typical day. The photovoltaic output and electric and thermal load values ​​in the typical day are obtained by clustering the historical data corresponding to the segment. Latin hypercube sampling is used to generate the uncertainty scenarios corresponding to each typical day based on the segmentation results. The uncertainty scenarios corresponding to each typical day are arranged 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 uses annual carbon emissions, primary energy utilization rate, photovoltaic penetration rate and comprehensive annualized cost indicators to evaluate PIES and obtain a PIES evaluation strategy. The constraints of the low-carbon transformation model include equipment capacity upper limit constraints, operating power constraints of energy supply equipment after transformation, operating power constraints of energy storage equipment, SOC constraints, electric power, thermal power and hydrogen energy balance constraints. 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 additions, expansions, replacements, retirements and hydrogen blending transformation operations is obtained.

8. The low-carbon transformation and processing device for the park integrated energy system according to claim 7 is characterized in that: In the energy coupling analysis module, the formula of the electrolytic cell sub-model is: Where P EZ inputting electrical power to the electrolyzer; is the calorific value of hydrogen; Q P It is the conversion value between kilowatt-hour and joule; is the hydrogen production quality of the electrolyzer; η EZ is the hydrogen production efficiency of the electrolyzer; In the energy coupling analysis module, the formula of the seasonal hydrogen storage sub-model is: In the formula, is the seasonal hydrogen storage state in the h period of the dth day, Δh and Δd are the scheduling periods in hours and days respectively; Seasonal hydrogen storage status for the next hour; The seasonal hydrogen storage state during the l-th period on the d-th day; Seasonal hydrogen storage status for the next day; It is the seasonal hydrogen storage state at the first moment of the year; It is a seasonal hydrogen storage state for the last moment of the year; are the efficiency of hydrogen charging and discharging respectively; Q is the mass of hydrogen charged and discharged respectively; SHS is the seasonal hydrogen storage capacity; 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}, They are the electrical power and thermal power output of the X-type hydrogen blending equipment, HGT is the hydrogen blending gas turbine, and HGB is the hydrogen blending 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; is the calorific value of natural gas; are the masses of natural gas and hydrogen consumed, respectively; In the energy coupling analysis module, the formula of the hydrogen fuel cell sub-model is: Where P HFC , H HFC are the electrical power and thermal power output of the hydrogen fuel cell, They are the electrical and thermal conversion efficiencies of hydrogen fuel cells; is the mass of hydrogen consumed; In the energy coupling analysis module, the formula of the ladder carbon trading model is: AND quo =And quo,grid +E quo,GT +E quo,GB +E quo,HGT +E quo,HGB +E quo,CB AND act =And act,grid +E act,gas +E act,coal AND tra =And act -AND quo In the formula, E quo 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.

9. The low-carbon transformation and processing device for the park integrated energy system according to claim 8 is characterized in that: The annual time series scenario analysis module includes: The long time series sample division submodule is used to divide the historical data into 24-hour intervals to obtain a long time series sample containing 364 points to be divided; The long time series sample segmentation submodule is used to segment 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: 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 ]; The total deviation square calculation submodule is used to calculate the total deviation square sum B(m) of all segmentation methods: The minimum total deviation square sum determination submodule is used to determine the segmentation method with the minimum total deviation square sum: In the formula, represents the mean of the i-th group of short time series; Segmentation effect evaluation submodule, used to use the silhouette coefficient index S n Evaluate the segmentation effect: In the formula, a w is the average Euclidean distance from the wth data to other data in its group; b w The minimum value of the average Euclidean distance from the w-th data to all data in the adjacent groups of the group to which it belongs in the time period; The segmentation scheme generating submodule 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 for the long time series sample.

10. A low-carbon transformation and processing device for a park integrated energy system according to claim 9, characterized in that: In the energy system transformation assessment and analysis module, the objective function of the low-carbon transformation model is: Where N s is the number of scenes, p s is the probability of scenario s occurring; C add , C exp , C rep , C dem , C mod The annualized investment costs for new additions, expansions, replacements, decommissioning and hydrogen blending modifications are respectively; are the unit investment costs of the corresponding operations; N CM is the number of equipment types; They represent the equipment capacity changes caused by the five transformation operations of new addition, expansion, replacement, decommissioning 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 residual value of the k-th type of equipment; is the capacity of the k-type equipment before transformation; δ k is the net residual value rate of the k-th type of equipment; L k is the number of years of operation of the k-type equipment from initial configuration to modification; In the energy system transformation assessment and analysis module, the equipment capacity upper limit constraint formula is: In the formula, represents the upper limit of the capacity of the k-th type of equipment; In the energy system transformation evaluation and analysis module, the operating power constraints of the energy supply equipment and the energy storage equipment after the transformation, and the SOC constraint formula are: In the formula, are the maximum charge and discharge coefficients of the energy storage device respectively; The capacity of the k-th type equipment after transformation; are the minimum and maximum SOC state coefficients of the energy storage device respectively; In the energy system transformation assessment and analysis module, the electric power, thermal power and hydrogen energy balance constraint formula is: In the formula, is the off-line purchased electricity power at time h on the dth typical day under scenario s; is the power generation capacity of the gas turbine; The power generated by the hydrogen-blended gas turbine; is the photovoltaic power generation; is the discharge power of the electric energy storage; is the electric power of hydrogen fuel cell; is the electrical load power; Charging power for 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 hydrogen-blended gas turbine; is the thermal power of the hydrogen-blended gas boiler; Heat release power for 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 HS thermal storage power; is the hydrogen production quality of the electrolyzer; It is the quality of seasonal hydrogen storage and release; It is the mass of hydrogen consumed by hydrogen-blended gas turbine; It is the mass of hydrogen consumed by hydrogen-blended gas boiler; It is the quality of seasonal hydrogen storage.

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