Two-stage distribution robust optimization method for polar field observation station

Abstract

The invention belongs to the technical field of coordinated operation of new energy and an energy storage system, particularly relates to a polar region field observation station two-stage distribution robust optimization method considering wind and light uncertainty, and solves the technical problem of how to realize coordinated operation of the new energy and the energy storage system through the two-stage distribution robust optimization method under an extreme climate condition. Based on thermal dynamic characteristics of a polar field observation station building and adjustability of the equipment operation environment temperature, an observation station model considering the equipment operation environment temperature is constructed, and the observation station model aims at minimizing carbon emission; secondly, considering the uncertainty of wind and light output and outdoor temperature, providing a polar region field observation station two-stage distribution robust optimization method considering the wind and light uncertainty, and flexibly adjusting and optimizing the conservative degree by setting uncertainty adjustment parameters; in addition, a hydrogen energy recovery model is added in research, and the comprehensive energy utilization rate of the observation station is further improved. Then, Camp is adopted; and the optimal solution of the original problem is obtained through CG algorithm iteration solution.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated operation of new energy and energy storage systems, and specifically to a two-stage distributed robust optimization method for polar field observation stations taking into account the uncertainty of wind and solar power. Background Art

[0002] With the increase of Antarctic scientific research activities, the number of Antarctic field observation stations has continued to increase, posing a severe challenge to the region's energy supply. Currently, 31 countries in the world have built more than 150 scientific research bases and nearly 80 Antarctic scientific research stations in Antarctica. In order to reduce dependence on fossil fuels and improve the energy independence of observation stations, more and more Antarctic scientific research stations have begun to use renewable energy such as wind power and photovoltaic power generation.

[0003] However, wind and solar resources have significant volatility and uncertainty, and extreme climates are more likely to lead to intermittent and unstable energy supply, which poses potential risks to the continuous operation of the observatory. In order to cope with this problem, energy storage technology has become an important means to balance energy supply and demand. It can store electricity when wind and solar resources are sufficient and release electricity when they are insufficient. However, the special environment of the Antarctic region and the continuous demand for scientific research activities make traditional energy storage configuration and scheduling schemes unable to fully cope with the drastic fluctuations in the supply of new energy. Existing research focuses on energy management optimization under fixed conditions, ignoring the complexity and real-time scheduling requirements brought by the uncertainty of renewable energy generation in extreme environments. In recent years, the distributed robust optimization method has achieved good application results in solving uncertainty problems, especially in the field of uncertainty in wind and photovoltaic power generation. Through the distributed robust optimization method, the system can be robustly scheduled and planned in the worst case scenario to ensure that the observatory can still obtain a stable energy supply under severe climate conditions. In addition, hydrogen energy systems have gradually been used in Antarctic observatories as part of energy storage and energy conversion. However, hydrogen energy systems will generate a lot of heat loss during operation. If this part of energy is not used, it will lead to energy waste and reduce the overall efficiency of the system. By rationally designing the heat recovery system, the waste heat generated during the operation of the hydrogen energy system can be effectively recovered and utilized, which can significantly improve the overall energy utilization rate. Based on this, the two-stage distributed robust optimization method considering the uncertainty of wind and solar power has begun to attract attention.

[0004] This method can not only effectively deal with the uncertainty of wind and solar power generation in the long-term planning stage, but also flexibly adjust the charging and discharging strategy of the energy storage system in the real-time scheduling stage to ensure the efficient use of energy and the continuity of supply. This research direction can not only improve the energy management level of Antarctic scientific research observatory, but also provide a reference for the optimization of renewable energy systems in other extreme environments. How to achieve the coordinated operation of new energy and energy storage systems under extreme climate conditions through a two-stage distributed robust optimization method is one of the important directions of current technological development. Summary of the invention

[0005] In order to overcome the technical defects of how to achieve the coordinated operation of new energy and energy storage systems through a two-stage distributed blue-robust optimization method under extreme climatic conditions, the present invention provides a two-stage distributed blue-robust optimization method for polar field observation stations.

[0006] The present invention provides a two-stage distributed robust optimization method for polar field observation stations considering wind and solar uncertainty, the steps are as follows:

[0007] Step 1: Based on the thermal dynamic characteristics of polar field observation station buildings and the adjustable equipment operating environment temperature, an observation station model considering the equipment operating environment temperature is constructed. The sub-steps are:

[0008] Step 1.1, construct the objective function;

[0009] The objective function of the polar field observation station model considering wind and solar uncertainty is to minimize carbon emissions, then:

[0010]

[0011] In formula (1), f co2 is the carbon emission; En(t) is the energy requirement in the period t; Fen is the energy released per unit of fuel; Eff(t) is the equipment efficiency in the period t; E fac is the carbon emission factor for a specific fuel;

[0012] Step 1.2: Determine the constraints;

[0013] The constraints satisfied by the polar field observation station model include battery energy storage constraints, hydrogen energy system constraints, wall heat balance constraints, indoor heat balance constraints and electric power balance constraints. The battery energy storage constraints are:

[0014]

[0015] In formula (2), (3), (4): E ess (t) is the energy stored in the energy storage power station; η abs and η relea is the charging efficiency and discharging efficiency of the energy storage device; P ess,abs (t) and P ess,relea (t) is the charging power and discharging power of the energy storage power station; U abs (t) and U relea (t) is the charging and discharging status of the energy storage power station, that is, a 0-1 variable;

[0016] The constraints of the hydrogen energy system are:

[0017]

[0018]

[0019] In formulas (5) and (6): α ec,on,t , α ec,off,t are 0-1 variables representing the electrolysis startup and shutdown actions respectively; M ec,on,min 、M ec,off,min are the shortest working time and shortest downtime of electrolysis respectively; α ec,on,max , α ec,off,max are the upper limits of the number of daily startup and shutdown actions set considering the life of the electrolyzer; T is the total period of daily optimization scheduling of the electric hydrogen energy system; k is any time of the day;

[0020] The wall thermal balance constraint is:

[0021]

[0022] In formula (7): C wall,ij is the heat capacity of the wall; is the wall temperature; is the temperature in the IBs room; is the temperature in the adjacent room; R wall,ij is the thermal resistance of the wall; when the wall is exposed to external solar radiation, q ij Take 1 otherwise take 0; v ij is the wall heat absorption rate; A wall,ij is the wall area; Q rad,ij is the light intensity in the corresponding direction of the wall; is the outdoor temperature;

[0023] The indoor heat balance constraint is:

[0024]

[0025] The electric power balance constraint is:

[0026] P w +P s +P c +P relea +P fc =P h +P abs +P pem +P load (9)

[0027] Step 1.3: Build a polar field observation station model;

[0028] The polar field observation station model includes a wind turbine output model, a photovoltaic output model, and a hydrogen energy heat recovery model;

[0029] The fan output model is:

[0030]

[0031] In formula (10): g WT (t) is the actual output of the fan; g is the rated power of the fan; v in is the cut-in wind speed of the fan; v R is the rated wind speed of the fan; v out is the cut-out wind speed of the fan; v t is the real-time wind speed of the fan;

[0032] The photovoltaic output model is:

[0033] g PV (t) = η PV S PV θ(t) (11)

[0034] In formula (11), η PV is the solar radiation efficiency; S PV is the daylighting area; θ(t) is the solar radiation intensity at time t;

[0035] The hydrogen heat recovery model is:

[0036]

[0037] In formula (12), Q in The heat input to the system; Q useful The useful heat output of the system; Q loss The heat lost by the system, which cannot be recovered; Q recovered is the heat recovered by the waste heat recovery equipment. U is the heat transfer coefficient of the heat exchanger, in W / m 2 K; A is the effective heat exchange area of ​​the heat exchanger, in m; ΔT log is the logarithmic mean temperature difference of the heat exchanger; η thermal is the waste heat recovery efficiency, which indicates the percentage of recovered heat to the total input heat;

[0038] Step 2: Construct an optimization model for polar field observation stations;

[0039] The operating goal of the polar field observation station is to minimize carbon emissions. The constraints that the polar field observation station needs to meet are the battery energy storage constraint, hydrogen energy system constraint, wall heat balance constraint, indoor heat balance constraint and electric power balance constraint in step 1;

[0040] First, construct the box type uncertainty set U to which the wind and solar power output and outdoor temperature belong:

[0041]

[0042] In formula (13), u P,t is the uncertain variable of wind and solar power output, u T,t is the outdoor temperature uncertain variable; is the maximum fluctuation deviation allowed by the uncertain variable of wind and solar power output, The maximum fluctuation deviation allowed for the outdoor temperature uncertainty variable;

[0043] The observation station model considering wind and solar uncertainty constructed by the two-stage distributed robust optimization method is:

[0044]

[0045] In formula (14), the first stage problem is the outermost min, and the optimization variable is x; the second stage problem is the inner max-min, and the optimization variables are u and y. The min problem is equivalent to the objective function, which represents the minimization of carbon emissions of the observation station; Ω(x,u) means the feasible domain of the optimization variable y when the value of (x,u) is given; the expression of Ω(x,u) is:

[0046]

[0047] In formula (15), γ, λ, ν, and π are dual variables used to deal with the minimization problem in the second stage;

[0048] Step 3: Use the column and constraint algorithm to solve the two-stage distributed robust optimization problem of the polar field observation station considering the uncertainty of wind and solar. The column and constraint algorithm divides the original problem of the polar field observation station into a main problem and a sub-problem. By alternately solving the main problem and the sub-problem, the carbon emission difference between the two problems is made less than a given threshold, thereby obtaining the optimal solution to the original problem. Decompose the observation station model of formula (14) to obtain the main problem in the form of:

[0049]

[0050] In formula (16), k is the number of iterations; y l is the solution of the observation station subproblem after the lth iteration; is the value of the wind and solar power output and the outdoor temperature at the first iteration;

[0051] The sub-problems obtained after decomposition are in the form of:

[0052]

[0053] When (x,u) is given, the inner minimization of formula (17) is a linear problem. According to the duality theory, min is converted into max form and then merged with the outer max problem. The resulting dual problem is:

[0054]

[0055] In formula (18), there is a bilinear term u T π, when the wind and solar power output and outdoor temperature are taken as the right boundary of formula (13), the carbon emissions generated by the polar field observation station are higher; therefore, the uncertainty set U to which the wind and solar power output and outdoor temperature belong is restated as follows:

[0056]

[0057] In formula (19): B = [B P,t ,B T,t ] T is a binary variable, and the value of 1 indicates that the wind power output and outdoor temperature reach the maximum value of the uncertainty set; Γ P,t and Γ T,t are the uncertainty adjustment parameters corresponding to wind and solar power output and outdoor temperature, respectively. The smaller the Γ value, the more risky the model is, so it can be used to adjust the conservativeness of the scheme. However, when the uncertain variable expression in formula (19) is substituted into formula (18), the product of continuous variables and binary variables will appear. Therefore, it is linearized by the big M method to obtain the following expression:

[0058]

[0059] In formula (20), Δu=[Δu P,t ,Δu T,t ] T ,B′=[B′ P,t ,B′ T,t ] T is a continuous auxiliary variable, is a positive real number;

[0060] Step 4: Use the column and constraint algorithm to solve the polar field observation station model considering the uncertainty of wind and light. The sub-steps are:

[0061] Step 4.1: Given a set of wind and solar output and outdoor temperature as the worst scenario u, set the lower bound of carbon emissions of the observation station provided by the main problem LB = -∞, the upper bound UB provided by the sub-problem UB = +∞, and the convergence threshold ε;

[0062] Step 4.2: The worst scenario obtained by the observation station for the first time Solve the main problem and get Update the Nether

[0063] Step 4.3: Solve Substitute as a fixed value to obtain the objective function value of the subproblem and the value of the uncertain variable u And the obtained objective function value is used as the new upper bound, that is,

[0064] Step 4.4: Determine whether the upper and lower bounds meet the convergence conditions. If UB-LB≤ε, the iteration ends and the optimal solution is returned. and Otherwise increase the variable y k+1 And the following constraints:

[0065]

[0066] Let k = k + 1, and re-solve the main problem and sub-problems until the algorithm converges or reaches the maximum number of iterations.

[0067] Compared with the prior art, the technical solution provided by the present invention has the following technical effects:

[0068] 1) Based on the synergistic effect of the equipment operating environment temperature and the thermal dynamic characteristics of the building envelope of the polar field observation station, the flexibility potential of the heating system is fully utilized under the premise of maintaining the normal temperature of the equipment, further improving the consumption rate of new energy and the energy efficiency of the observation station;

[0069] 2) Considering the uncertainty of wind and solar power output and outdoor temperature, a two-stage distributed robust optimization method for polar field observation stations considering wind and solar power uncertainty is proposed. Under the condition of uncertainty of wind and solar power output and outdoor temperature, the needs of the observation station can be met while reducing carbon emissions.

[0070] 3) An algorithm is proposed to efficiently solve the two-stage distributed robust optimization problem of polar field observation stations considering the uncertainty of wind and solar power, and the optimal scheduling plan for carbon emissions of the observation station is obtained when the wind and solar power output is minimum and the outdoor temperature is lowest. DETAILED DESCRIPTION

[0071] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all of the embodiments.

[0073] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0074] In one embodiment, a two-stage distributed robust optimization method for polar field observation stations considering wind and solar uncertainty is disclosed, and the steps are as follows:

[0075] Step 1: Based on the thermal dynamic characteristics of polar field observation station buildings and the adjustable equipment operating environment temperature, an observation station model considering the equipment operating environment temperature is constructed. The sub-steps are:

[0076] Step 1.1, construct the objective function;

[0077] The objective function of the polar field observation station model considering wind and solar uncertainty is to minimize carbon emissions, then:

[0078]

[0079] In formula (1), f co2 is the carbon emission; En(t) is the energy requirement in the period t; Fen is the energy released per unit of fuel; Eff(t) is the equipment efficiency in the period t; E fac is the carbon emission factor for a specific fuel;

[0080] Step 1.2: Determine the constraints;

[0081] The constraints satisfied by the polar field observation station model include battery energy storage constraints, hydrogen energy system constraints, wall heat balance constraints, indoor heat balance constraints and electric power balance constraints. The battery energy storage constraints are:

[0082]

[0083] In formula (2), (3), (4): E ess (t) is the energy stored in the energy storage power station; η abs and η relea is the charging efficiency and discharging efficiency of the energy storage device; P ess,abs (t) and P ess,relea (t) is the charging power and discharging power of the energy storage power station; U abs (t) and U relea (t) is the charging and discharging status of the energy storage power station, that is, a 0-1 variable;

[0084] The constraints of the hydrogen energy system are:

[0085]

[0086]

[0087] In formulas (5) and (6): α ec,on,t , α ec,off,t are 0-1 variables representing the electrolysis startup and shutdown actions respectively; M ec,on,min、M ec,off,min are the shortest working time and shortest downtime of electrolysis respectively; α ec,on,max , α ec,off,max are the upper limits of the number of daily startup and shutdown actions set considering the life of the electrolyzer; T is the total period of daily optimization scheduling of the electric hydrogen energy system; k is any time of the day;

[0088] The wall thermal balance constraint is:

[0089]

[0090] In formula (7): C wall,ij is the heat capacity of the wall; is the wall temperature; is the temperature in the IBs room; is the temperature in the adjacent room; R wall,ij is the thermal resistance of the wall; when the wall is exposed to external solar radiation, q ij Take 1 otherwise take 0; v ij is the wall heat absorption rate; A wall,ij is the wall area; Q rad,ij is the light intensity in the corresponding direction of the wall; is the outdoor temperature;

[0091] The indoor heat balance constraint is:

[0092]

[0093] The electric power balance constraint is:

[0094] P w +P s +P c +P relea +P fc =P h +P abs +P pem +P load (9)

[0095] Step 1.3: Build a polar field observation station model;

[0096] The polar field observation station model includes a wind turbine output model, a photovoltaic output model, and a hydrogen energy heat recovery model;

[0097] The fan output model is:

[0098]

[0099] In formula (10): g WT (t) is the actual output of the fan; g is the rated power of the fan; v in is the cut-in wind speed of the fan; vR is the rated wind speed of the fan; v out is the cut-out wind speed of the fan; v t is the real-time wind speed of the fan;

[0100] The photovoltaic output model is:

[0101] g PV (t) = η PV S PV θ(t) (11)

[0102] In formula (11), η PV is the solar radiation efficiency; S PV is the daylighting area; θ(t) is the solar radiation intensity at time t;

[0103] The hydrogen heat recovery model is:

[0104]

[0105] In formula (12), Q in The heat input to the system; Q useful The useful heat output of the system; Q loss The heat lost by the system, which cannot be recovered; Q recovered is the heat recovered by the waste heat recovery equipment. U is the heat transfer coefficient of the heat exchanger, in W / m 2 K; A is the effective heat exchange area of ​​the heat exchanger, in m; ΔT log is the logarithmic mean temperature difference of the heat exchanger; η thermal is the waste heat recovery efficiency, which indicates the percentage of recovered heat to the total input heat;

[0106] Step 2: Construct an optimization model for polar field observation stations;

[0107] The operating goal of the polar field observation station is to minimize carbon emissions. The constraints that the polar field observation station needs to meet are the battery energy storage constraint, hydrogen energy system constraint, wall heat balance constraint, indoor heat balance constraint and electric power balance constraint in step 1;

[0108] First, construct the box type uncertainty set U to which the wind and solar power output and outdoor temperature belong:

[0109]

[0110] In formula (13), u P,t is the uncertain variable of wind and solar power output, u T,t is the outdoor temperature uncertain variable; is the maximum fluctuation deviation allowed by the uncertain variable of wind and solar power output, The maximum fluctuation deviation allowed for the outdoor temperature uncertainty variable;

[0111] The observation station model considering wind and solar uncertainty constructed by the two-stage distributed robust optimization method is:

[0112]

[0113] In formula (14), the first stage problem is the outermost min, and the optimization variable is x; the second stage problem is the inner max-min, and the optimization variables are u and y. The min problem is equivalent to the objective function, which represents the minimization of carbon emissions of the observation station; Ω(x,u) means the feasible domain of the optimization variable y when the value of (x,u) is given; the expression of Ω(x,u) is:

[0114]

[0115] In formula (15), γ, λ, ν, and π are dual variables used to deal with the minimization problem in the second stage;

[0116] Step 3: Use the column and constraint algorithm to solve the two-stage distributed robust optimization problem of the polar field observation station considering the uncertainty of wind and solar. The column and constraint algorithm divides the original problem of the polar field observation station into a main problem and a sub-problem. By alternately solving the main problem and the sub-problem, the carbon emission difference between the two problems is made less than a given threshold, thereby obtaining the optimal solution to the original problem. Decompose the observation station model of formula (14) to obtain the main problem in the form of:

[0117]

[0118] In formula (16), k is the number of iterations; y l is the solution of the observation station subproblem after the lth iteration; is the value of the wind and solar power output and the outdoor temperature at the first iteration;

[0119] The sub-problems obtained after decomposition are in the form of:

[0120]

[0121] When (x,u) is given, the inner minimization of formula (17) is a linear problem. According to the duality theory, min is converted into max form and then merged with the outer max problem. The resulting dual problem is:

[0122]

[0123] In formula (18), there is a bilinear term u T π, when the wind and solar power output and outdoor temperature are taken as the right boundary of formula (13), the carbon emissions generated by the polar field observation station are higher; therefore, the uncertainty set U to which the wind and solar power output and outdoor temperature belong is restated as follows:

[0124]

[0125] In formula (19): B = [B P,t ,B T,t ] T is a binary variable, and the value of 1 indicates that the wind power output and outdoor temperature reach the maximum value of the uncertainty set; Γ P,t and Γ T,t are the uncertainty adjustment parameters corresponding to wind and solar power output and outdoor temperature, respectively. The smaller the Γ value, the more risky the model is, so it can be used to adjust the conservativeness of the scheme. However, when the uncertain variable expression in formula (19) is substituted into formula (18), the product of continuous variables and binary variables will appear. Therefore, it is linearized by the big M method to obtain the following expression:

[0126]

[0127] In formula (20), Δu=[Δu P,t ,Δu T,t ] T ,,B′=[B′ P,t ,B′ T,t ] T is a continuous auxiliary variable, is a positive real number;

[0128] Step 4: Use the column and constraint algorithm to solve the polar field observation station model considering the uncertainty of wind and light. The sub-steps are:

[0129] Step 4.1: Given a set of wind and solar output and outdoor temperature as the worst scenario u, set the lower bound of carbon emissions of the observation station provided by the main problem LB = -∞, the upper bound UB provided by the sub-problem UB = +∞, and the convergence threshold ε;

[0130] Step 4.2: The worst scenario obtained by the observation station for the first time Solve the main problem and get Update the Nether

[0131] Step 4.3: Solve Substitute as a fixed value to obtain the objective function value of the subproblem and the value of the uncertain variable u And the obtained objective function value is used as the new upper bound, that is,

[0132] Step 4.4: Determine whether the upper and lower bounds meet the convergence conditions. If UB-LB≤ε, the iteration ends and the optimal solution is returned. and Otherwise increase the variabley k+1 And the following constraints:

[0133]

[0134] Let k = k + 1, and re-solve the main problem and sub-problems until the algorithm converges or reaches the maximum number of iterations.

[0135] The above is only a specific implementation of the present invention, which enables those skilled in the art to understand or implement the present invention. Although detailed descriptions are given with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A two-stage distributed robust optimization method for polar field observation stations considering wind and solar uncertainty, characterized in that: Here are the steps: Step 1: Based on the thermal dynamic characteristics of polar field observation station buildings and the adjustable equipment operating environment temperature, an observation station model considering the equipment operating environment temperature is constructed. The sub-steps are: Step 1.1, construct the objective function; The objective function of the polar field observation station model considering wind and solar uncertainty is to minimize carbon emissions, then: In formula (1), f co2 is the carbon emission; En(t) is the energy requirement in the period t; Fen is the energy released per unit of fuel; Eff(t) is the equipment efficiency in the period t; E fac is the carbon emission factor for a specific fuel; Step 1.2: Determine the constraints; The constraints satisfied by the polar field observation station model include battery energy storage constraints, hydrogen energy system constraints, wall heat balance constraints, indoor heat balance constraints and electric power balance constraints. The battery energy storage constraints are: In formula (2), (3), (4): E ess (t) is the energy stored in the energy storage power station; ηabs and ηrelea are the charging efficiency and discharging efficiency of the energy storage device; P ess,abs (t) and P ess,relea (t) is the charging power and discharging power of the energy storage power station; U abs (t) and U relea (t) is the charging and discharging status of the energy storage power station, that is, a 0-1 variable; The constraints of the hydrogen energy system are: In formulas (5) and (6): α ec,on,t , α ec,off,t are 0-1 variables representing the electrolysis startup and shutdown actions respectively; M ec,on,min 、M ec,off,min are the shortest working time and shortest downtime of electrolysis respectively; α ec,on,max , α ec,off,max They are the upper limits of the number of daily startup and shutdown actions set considering the life of the electrolyzer; T is the total period of daily optimization scheduling of the electric hydrogen energy system; k is any time of the day; The wall thermal balance constraint is: In formula (7): C wall,ij is the heat capacity of the wall; is the wall temperature; is the temperature in the IBs room; is the temperature in the adjacent room; R wall,ij is the thermal resistance of the wall; when the wall is exposed to external solar radiation, q ij Take 1 otherwise take 0; v ij is the wall heat absorption rate; A wall,ij is the wall area; Q rad,ij is the light intensity in the corresponding direction of the wall; is the outdoor temperature; The indoor heat balance constraint is: The electric power balance constraint is: P w +P s +P c +P relea +P fc =P h +P abs +P pem +P load (9) Step 1.3: Build a polar field observation station model; The polar field observation station model includes a wind turbine output model, a photovoltaic output model, and a hydrogen energy heat recovery model; The fan output model is: In formula (10): g WT (t) is the actual output of the fan; g is the rated power of the fan; v in is the cut-in wind speed of the fan; v R is the rated wind speed of the fan; v out is the cut-out wind speed of the fan; v t is the real-time wind speed of the fan; The photovoltaic output model is: g PV (t)=η PV S PV θ(t) (11) In formula (11), η PV is the solar radiation efficiency; S PV is the daylighting area; θ(t) is the solar radiation intensity at time t; The hydrogen heat recovery model is: Q in =Q useful +Q loss +Q recovered or thermal ·Q in =U·A·ΔT log (12) In formula (12), Q in The heat input to the system; Q useful The useful heat output of the system; Q loss The heat lost by the system, which cannot be recovered; Q recovered is the heat recovered by the waste heat recovery equipment. U is the heat transfer coefficient of the heat exchanger, in W / m 2 K; A is the effective heat exchange area of ​​the heat exchanger, in m; ΔT log is the logarithmic mean temperature difference of the heat exchanger; η thermal is the waste heat recovery efficiency, which indicates the percentage of recovered heat to the total input heat; Step 2: Construct an optimization model for polar field observation stations; The operating goal of the polar field observation station is to minimize carbon emissions. The constraints that the polar field observation station needs to meet are the battery energy storage constraint, hydrogen energy system constraint, wall heat balance constraint, indoor heat balance constraint and electric power balance constraint in step 1; First, construct the box type uncertainty set U to which the wind and solar power output and outdoor temperature belong: In formula (13), u P,t is the uncertain variable of wind and solar power output, u T,t is the outdoor temperature uncertain variable; is the maximum fluctuation deviation allowed by the uncertain variable of wind and solar power output, The maximum fluctuation deviation allowed for the outdoor temperature uncertainty variable; The observation station model considering wind and solar uncertainty constructed by the two-stage distributed robust optimization method is: In formula (14), the first stage problem is the outermost min, and the optimization variable is x; the second stage problem is the inner max-min, and the optimization variables are u and y. The min problem is equivalent to the objective function, which represents the minimization of carbon emissions of the observation station; Ω(x,u) means the feasible domain of the optimization variable y when the value of (x,u) is given; the expression of Ω(x,u) is: In formula (15), γ, λ, ν, and π are dual variables used to deal with the minimization problem in the second stage; Step 3: Use the column and constraint algorithm to solve the two-stage distributed robust optimization problem of the polar field observation station considering the uncertainty of wind and solar power. The column and constraint algorithm divides the original problem of the polar field observation station into a main problem and a sub-problem. By alternately solving the main problem and the sub-problem, the difference in carbon emissions obtained from the two problems is made less than a given threshold, thereby obtaining the optimal solution to the original problem. Decomposing the observation station model of formula (14), the main problem form is obtained as follows: In formula (16), k is the number of iterations; y l is the solution of the observation station subproblem after the lth iteration; is the value of the wind and solar power output and the outdoor temperature at the first iteration; The sub-problems obtained after decomposition are in the form of: When (x,u) is given, the inner minimization of formula (17) is a linear problem. According to the duality theory, min is converted into max form and then merged with the outer max problem. The resulting dual problem is: In formula (18), there is a bilinear term u T π, when the wind and solar power output and outdoor temperature are taken as the right boundary of formula (13), the carbon emissions generated by the polar field observation station are higher; therefore, the uncertainty set U to which the wind and solar power output and outdoor temperature belong is restated as follows: In formula (19): B = [B P,t ,B T,t ] T is a binary variable, and the value of 1 indicates that the wind power output and outdoor temperature reach the maximum value of the uncertainty set; Γ P,t and Γ T,t are the uncertainty adjustment parameters corresponding to wind and solar power output and outdoor temperature, respectively. The smaller the Γ value, the more risky the model is, so it can be used to adjust the conservativeness of the scheme. However, when the uncertain variable expression in formula (19) is substituted into formula (18), the product of continuous variables and binary variables will appear. Therefore, it is linearized by the big M method to obtain the following expression: In formula (20), Δu=[Δu P,t ,Δu T,t ] T ,B′=[B′ P,t ,B′ T,t ] T is a continuous auxiliary variable, is a positive real number; Step 4: Use the column and constraint algorithm to solve the polar field observation station model considering the uncertainty of wind and light. The sub-steps are: Step 4.1: Given a set of wind and solar output and outdoor temperature as the worst scenario u, set the lower bound of carbon emissions of the observation station provided by the main problem LB = -∞, the upper bound UB provided by the sub-problem UB = +∞, and the convergence threshold ε; Step 4.2: The worst scenario obtained by the observation station for the first time Solve the main problem and get Update the Nether Step 4.3: Solve Substitute as a fixed value to obtain the objective function value of the subproblem and the value of the uncertain variable u And the obtained objective function value is used as the new upper bound, that is, Step 4.4: Determine whether the upper and lower bounds meet the convergence conditions. If UB-LB≤ε, the iteration ends and the optimal solution is returned. and Otherwise increase the variable y k+1 And the following constraints: Let k = k + 1, and re-solve the main problem and sub-problems until the algorithm converges or reaches the maximum number of iterations.

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