CN116205458A - Method for establishing comprehensive energy system optimization scheduling model of hydroelectric hydrogen production by considering uncertainty - Google Patents

Method for establishing comprehensive energy system optimization scheduling model of hydroelectric hydrogen production by considering uncertainty Download PDF

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CN116205458A
CN116205458A CN202310201445.5A CN202310201445A CN116205458A CN 116205458 A CN116205458 A CN 116205458A CN 202310201445 A CN202310201445 A CN 202310201445A CN 116205458 A CN116205458 A CN 116205458A
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骆钊
贾芸睿
刘德文
王钢
李钊
喻品钦
雷元庆
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Kunming University of Science and Technology
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Abstract

The invention belongs to the technical field of comprehensive energy system research, and provides a comprehensive energy system optimization scheduling model building method for hydrogen production by water and electricity with uncertainty being considered, which comprises the following steps: s1, analyzing feasibility of an information gap decision theory on uncertainty treatment, S2, establishing an information gap decision theory model, S3, constructing an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model, S4, and performing calculation analysis; the invention builds an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model based on the information gap decision theory and combining the characteristics of uncertain water and electricity output and load demands, and mainly comprises an opportunity model under an opportunity seeking strategy and a robust model under a risk avoidance strategy; and finally, carrying out calculation analysis on the proposed model.

Description

Method for establishing comprehensive energy system optimization scheduling model of hydroelectric hydrogen production by considering uncertainty
Technical Field
The invention relates to an uncertainty-considered method for establishing an optimal scheduling model of a comprehensive energy system for hydrogen production by water and electricity, belonging to the technical field of comprehensive energy system research.
Background
Energy is a major source of carbon emissions, and in recent years, global warming is caused by massive consumption of fossil energy, and huge changes in climate, land utilization, biodiversity and natural disasters are caused, which become serious threats for human survival and development. In the large background of global climate change, environmental protection and energy safety issues are relevant to sustainable development of human society.
Along with the deep promotion of clean low-carbon transformation of energy, china gets rid of fossil energy dependence gradually. The wind and the light are representative of new energy sources for power generation of the main body of China. From the endowment of resources, wind and light are unlimited and inexhaustible, but because of low energy density and reverse distribution of resources and loads, cross-regional centralized digestion is an important development way, the intermittence, randomness and volatility of the energy storage system are required to be huge in flexible resource balance, and if no matched energy storage facilities are provided, the power grid is difficult to solve the problems of electric power and electricity balance and new energy digestion, and the problems of regulating and controlling the frequency of the power grid, guaranteeing the voltage stability and the safety of the power grid are very outstanding. Unlike other world energy structure transformed countries, china has the most abundant hydropower resources in the world, and theoretical reserve is 676GW. Through construction of western electric east-asian engineering for 20 years, china has built the world maximum scale hydropower system, and hydropower installation capacity successively spans steps of 100GW, 200GW and 300GW, and is always stable in the first world. Considering the scale matching of the Chinese hydropower and the current and future wind and light energy sources and the characteristics of the flexibility of the hydropower, how to construct a novel power system taking the new energy source as the main body by utilizing the unique favorable condition of the hydropower resources in China becomes an important research direction in China. The water and electricity are used as the main force of clean renewable energy sources, which is the key of green energy source transformation in China, but the uncertainty and fluctuation of the water and electricity output and load demand in actual conditions are larger, so that the scheduling operation of the system generates a certain degree of deviation, and the requirements of the system on the economy and the robustness of the system cannot be met.
Disclosure of Invention
In order to solve one of the technical defects, the invention provides a method for establishing an integrated energy system optimization scheduling model for hydroelectric hydrogen production by taking uncertainty into consideration.
In order to solve the technical problems, the invention adopts the following technical scheme: the method for establishing the comprehensive energy system optimization scheduling model for the hydro-electric hydrogen production by considering uncertainty comprises the following steps:
s1, analyzing feasibility of an information gap decision theory on uncertainty treatment;
s2, establishing an information gap decision theory model;
s3, constructing an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model;
s4, analyzing an example.
The invention builds an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model based on the information gap decision theory and combining the characteristics of uncertain water and electricity output and load demands, and mainly comprises an opportunity model under an opportunity seeking strategy and a robust model under a risk avoidance strategy; finally, carrying out calculation analysis on the provided model, wherein the calculation result shows that in the deterministic model, the hydrogen storage device is considered to promote the water and electricity digestion capacity, so that the system operation economy is improved; in the uncertainty problem, the opportunity model scheduling result can ensure that the daily profit of the system is greater than the expected profit when the uncertainty of the water power output and the load demand is minimized; the robust model scheduling result can ensure that the uncertainty of the water power output and the load demand reaches the maximum under the condition of meeting the daily profit of the system, and ensure the robustness of the system; in addition, the system in the IGDT robust model is greatly influenced by the uncertainty of the water power at the same time, and the system in the IGDT opportunity model is greatly influenced by the uncertainty of the load.
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The invention is described in further detail below with reference to the accompanying drawings;
FIG. 1 is a flow chart of the IGDT scheduling model solution of the present invention;
FIG. 2 is a graph of time-of-use electricity price and hydrogen price data in an example analysis of the present invention;
FIG. 3 is a graph showing the results of deterministic system power scheduling in an example analysis of the present invention;
FIG. 4 is a graph showing the results of deterministic system thermal power scheduling in an example analysis of the present invention;
FIG. 5 is a graph showing the result of deterministic system cold power scheduling in the analysis of the present invention;
FIG. 6 is a graph of the scheduling results for consideration of water power uncertainty in the analysis of the present example;
FIG. 7 is a graph of the load demand uncertainty scheduling results considered in the example analysis of the present invention;
FIG. 8 is a graph showing the results of the water power and load demand uncertainty scheduling in the example analysis of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention relates to a method for establishing an integrated energy system optimization scheduling model for hydrogen production by hydroelectric power, which takes uncertainty into consideration, and comprises the following steps:
s1, analyzing feasibility of an information gap decision theory on uncertainty treatment;
s2, establishing an information gap decision theory model;
s3, constructing an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model;
s4, analyzing an example.
The feasibility of the step S1 for analyzing the information gap decision theory model for processing uncertainty is specifically as follows:
the information gap decision theory (Information Gap Decision Theory, IGDT) is a non-probabilistic and non-fuzzy method of processing uncertainty variables/parameters based on non-probabilistic models. Information decision theory states that a decision maker can go through three main stages when facing complex and changeable information environments: (1) information collection stage: the decision maker acquires various information from the external environment and learns more knowledge and skills about the environment; (2) an information processing stage: the decision maker analyzes the association, the priority and the result of the information according to the continuously-changing information acquired from the environment; (3) decision stage: the decision maker designates a series of actions according to the information processing result, and solves the problem with minimum cost. The core idea of the information gap decision theory is to study the influence of uncertain parameters/variables on the scheduling operation of the system in a variation interval of the maximized uncertain variables under the condition of meeting the preset objective function. It is compared with several other common uncertain variable processing methods as shown in the following table:
table 1 optimization method contrast for handling uncertainty variables
Figure SMS_1
As can be seen from table 1, the stochastic programming method is an optimization of the expected target value under the confidence level of the probability of establishment of a given constraint condition, the accuracy of uncertainty handling is affected by the probability distribution and the historical data of an uncertainty amount, and the uncertainty which cannot be represented by probability or scene is difficult to optimize; the robust optimization directly considers the accurate interval range of the parameter uncertainty set in the process of establishing the optimization model, so that the robustness of an optimization solution is improved, but the economy is poor. The IGDT method has the outstanding advantages that only the upper boundary and the lower boundary of the variable are needed to be considered, the precise probability distribution and the change interval of the uncertain variable are not needed to be considered, the fluctuation degree of the uncertain parameter is set as a new objective function, the objective function of the original problem is taken as a constraint condition, the constraint condition is not limited to historical data and probability distribution, and meanwhile, the robustness of the system and the economical efficiency of the system are simultaneously considered.
The step S2 of establishing information gap decision theory model content comprises the following steps:
s21, information gap decision theory model
Information gap decision theory has two policy models, risk Avoidance (RA) and opportunity seek (opportunity seeking, OS):
the risk avoidance refers to searching a worst boundary target caused by a maximum uncertainty variable set, and ensuring that a preset target always meets the boundary, so as to avoid the risk caused by uncertainty; opportunistic searching refers to searching for the best boundary target of the minimum uncertainty variable set, thereby searching for a target of the opportunistic lifting system;
the basic model for risk avoidance and opportunity seeking is as follows:
Figure SMS_2
wherein X is an uncertain parameter, delta is a decision variable, B is an objective function, D is an equality constraint condition, and G is an inequality constraint condition;
the information gap decision theory is a non-probability and non-fuzzy method for processing uncertain variables or parameters based on a non-probability model, and the traditional non-probability model comprises an envelope model, a fraction uncertainty model and an ellipsoid model; according to the characteristic of uncertainty of the water power output and the load demand, a score uncertainty model is adopted as follows:
Figure SMS_3
where, alpha is uncertainty,
Figure SMS_4
in order to be able to predict the value,
in the middle of
Figure SMS_5
Is transformed into->
Figure SMS_6
And->
Figure SMS_7
Respectively the actual value X t Upper and lower set limits of (2);
the decision maker who considers the opportunity to seek pays more attention to the favorable influence of the uncertainty of the water power output and the load demand on the comprehensive energy system, namely, the adverse disturbance of the uncertain parameters is minimized, the higher benefit is obtained by using lower scheduling cost, and the corresponding mathematical model is as follows:
Figure SMS_8
wherein B is 0 Taking the predicted value for the uncertain parameter X
Figure SMS_9
Objective function value at time, B OS For the objective function value under the predicted value, θ OS For the deviation coefficient between the expected target and the optimal solution of the model, theta OS The larger the decision maker, the greater the preference degree of the decision maker to the risk, and the smaller the scheduling cost;
the decision maker considering risk avoidance pays more attention to the adverse effect of the uncertainty of the water power output and the load demand on the comprehensive energy system, namely, the adverse disturbance of the uncertain parameters is maximized on the basis of ensuring the dispatching cost, and the corresponding mathematical model is as follows:
Figure SMS_10
wherein B is 0 Taking the predicted value for the uncertain parameter X
Figure SMS_11
Objective function value at time, B RA For the objective function value under the predicted value, θ RA For the deviation coefficient between the expected target and the optimal solution of the model, theta RA The larger the decision maker, the greater the degree of avoidance of the risk; for example, when θ RA Taking 0.1, this indicates the most acceptable decision makerThe target value under the large uncertainty is 1.1 times of the predicted target value;
s22, objective function
The operation income of the comprehensive energy system for the water-containing hydrogen production is improved, the maximum daily operation profit of the system is taken as a design target, the daily operation profit of the system is represented by the difference between total income and total operation cost, the total income comprises the income sold by the system to users, the income sold by hydrogen and the income sold by the system to a power grid, and the total operation cost comprises electricity purchasing cost, gas purchasing cost, operation cost, water-electricity hydrogen production cost, operation maintenance cost of the electric storage equipment and operation maintenance cost of the heat storage equipment:
Figure SMS_12
wherein C is d C for daily operation profit of the system n For total system income, C x C is the total running cost of the system sell The electric power is sold into the electric network,
Figure SMS_13
for selling hydrogen and taking in C u For selling energy and income to users for the system, C e,m C for the cost of system electricity purchase gas C for the system gas purchasing cost oc For the running cost of the system, C w,hp For the cost of hydrogen production by water and electricity, C e,s For the operation and maintenance cost of the power storage equipment, C h,s The operation and maintenance cost of the heat storage equipment; />
Figure SMS_14
Figure SMS_15
Figure SMS_16
In the method, in the process of the invention,
Figure SMS_17
for selling electricity price of system to power grid, P t sell The sales power of the system to the power grid is calculated; Δt is a time variable; />
Figure SMS_18
Price for system hydrogen selling->
Figure SMS_19
The sales amount of hydrogen for the hydrogen storage tank; />
Figure SMS_20
And->
Figure SMS_21
The selling unit price of the system to the electricity, cold and heat users is>
Figure SMS_22
And->
Figure SMS_23
The electrical load, the cold load and the heat load of the user respectively;
Figure SMS_24
wherein C is e,m In order to pay for the purchase of electricity,
Figure SMS_25
and P t e,m Respectively is t The electricity purchase price and the electricity purchase power at the moment;
Figure SMS_26
wherein C is gas For gas purchase expense c gas Is the price of natural gas; p (P) t g Is that t Generating power of the gas turbine at the moment;
Figure SMS_27
represents the heat generation power (kW) of the gas boiler; η (eta) g And eta o Respectively gas turbine and combustionEfficiency of the gas boiler;
Figure SMS_28
wherein C is oc For hydropower operation cost, C h,s Maintenance cost for hydropower operation C h,pu Punishment costs for water disposal; r is R h Cost factor for unit power of hydroelectric field generation, f h Punishment coefficient for water electric field, P t h Is that t Generating power of water electric field at moment, P t sh Is that t The power of surfing the net of the hydropower plant at any moment;
Figure SMS_29
wherein, c hp In order to produce the price of hydrogen,
Figure SMS_30
is that t Hydrogen production amount of the electrolytic tank at any time;
Figure SMS_31
wherein C is capacity Is the capacity of the electricity storage device; c r Is the charge and discharge cost; p (P) t es,c And P t es,d Respectively is t Charging and discharging power of the electricity storage device at the moment;
Figure SMS_32
in the method, in the process of the invention,
Figure SMS_33
is that t Operation and maintenance cost of the heat storage equipment at any time; />
Figure SMS_34
And->
Figure SMS_35
Respectively is t The heat absorption and release power of the heat storage device at the moment;
s23, constraint conditions
In order to distinguish the predicted value and the actual value of the water power output and the load demand, the water discarding punishment cost and the power balance constraint of the electric power system are shown in formulas (15) and (16):
1) Cost constraint for water disposal punishment
Figure SMS_36
Wherein C is h,pu To discard the water punishment cost, f h In order to discard the water and punish the cost coefficient,
Figure SMS_37
for the predicted power of the hydroelectric generating set in the period t, P t h The power of surfing the net of the hydroelectric generating set at the moment t;
2) Electrical balance constraint
Figure SMS_38
Purchase power constraint:
Figure SMS_39
gas turbine power constraint:
Figure SMS_40
hydropower unit power constraint:
Figure SMS_41
wherein P is t grid Is that t Generating power of the power grid at any moment; p (P) t g Is that t Time of day gas turbine power; p (P) t es,c And P t es,d Respectively is t Charging and discharging power of the electricity storage device at the moment; p (P) t ec Is that t Power of the electric refrigerator at moment; p (P) t EL Is that t The power of the electrolytic cell at the moment;
Figure SMS_42
is a predictive value of electrical load demand; p (P) t g Is that t Time of day gas turbine power; p (P) t sh Is that t Generating power of the hydroelectric generating set at any moment; f (F) t g For fuel consumption, eta of fuel gas turbine input at time t GT Is gas turbine efficiency;
3) Thermal equilibrium constraint:
Figure SMS_43
in the method, in the process of the invention,
Figure SMS_44
and->
Figure SMS_45
Respectively is t Time gas boiler power and waste heat recovery boiler power, eta GB And eta RB Respectively is t Time gas efficiency and waste heat boiler efficiency; />
Figure SMS_46
Is that t Moment heat load; p (P) t GB The power generation power at the time t of the gas boiler; p (P) t RB The power generation power at the moment t of the waste heat recovery boiler is;
4) Cold balance power constraint:
Figure SMS_47
wherein eta is EC Is that t Refrigerating coefficients of the electric refrigerator at moment; p (P) t EC Is that t Power of the electric refrigerator at moment;
Figure SMS_48
is that t Time-of-day cold load;
5) Power storage device operation constraint:
Figure SMS_49
wherein:
Figure SMS_51
and->
Figure SMS_55
The upper limit and the lower limit of the charging power of the storage battery at the moment t are respectively set; />
Figure SMS_56
And->
Figure SMS_52
The upper limit and the lower limit of the discharge power of the storage battery at the moment t are respectively set; />
Figure SMS_53
And->
Figure SMS_54
A storage battery charging and discharging state marking bit at the time t respectively, wherein the value of the storage battery charging and discharging state marking bit is 0 to respectively stop charging and discharging, and 1 to respectively charge and discharge; w (W) t es The electric energy stored in the storage battery at the moment t; sigma (sigma) es The self-discharge rate of the storage battery is set; η (eta) es,c And eta es,d Charging and discharging efficiencies of the storage battery respectively; />
Figure SMS_57
And->
Figure SMS_50
Respectively the upper limit and the lower limit of the energy storage of the storage battery;
6) Heat storage device operation constraints:
Figure SMS_58
in the method, in the process of the invention,
Figure SMS_61
and->
Figure SMS_62
Respectively is t The upper limit and the lower limit of the heat absorption power of the heat storage equipment at any moment; />
Figure SMS_64
And->
Figure SMS_60
The upper limit and the lower limit of the heat release power of the heat storage equipment at the moment t are respectively set; />
Figure SMS_63
And->
Figure SMS_65
The heat storage equipment absorbs and releases heat at the moment t respectively, wherein the value of the heat storage equipment absorbs and releases heat at the moment t is 0, and 1 represents absorbing and releasing heat; w (W) t hs The electric energy stored by the heat storage equipment at the moment t; sigma (sigma) hs The self-heat release rate of the heat storage equipment is achieved; η (eta) hs,c And eta hs,d The heat absorption efficiency and the heat release efficiency of the heat storage equipment are respectively; />
Figure SMS_66
And->
Figure SMS_59
The upper limit and the lower limit of the power of the heat storage equipment are respectively;
7) Hydrogen storage device operating constraints:
Figure SMS_67
in the method, in the process of the invention,
Figure SMS_68
and->
Figure SMS_69
The contents of hydrogen in the hydrogen storage tank at the time t and t-1 are respectively; />
Figure SMS_70
And->
Figure SMS_71
The hydrogen input amount and the hydrogen output amount of the hydrogen storage tank are respectively; e (E) in,max And E is out,max The maximum hydrogen quantity is respectively input and emitted to the hydrogen storage tank; e (E) h,min And E is h,max An upper limit and a lower limit of the hydrogen storage capacity, respectively; />
Figure SMS_72
The hydrogen storage amount at the beginning and the end of the dispatching are respectively;
8) Operating condition constraints for IES various devices:
Figure SMS_73
wherein P is t GT The electric power of the gas turbine at the time t; s is S GT Is the rated capacity of the gas turbine; η (eta) GT Rated power generation efficiency of the gas turbine;
Figure SMS_74
the heat efficiency of the gas boiler at the time t; s is S GB Is the rated capacity of the gas boiler; η (eta) GB The rated heat supply efficiency of the gas boiler is achieved; />
Figure SMS_75
The refrigerating power of the electric refrigerator at the time t; s is S EC Is the rated capacity of the electric refrigerator; η (eta) EC The refrigerating efficiency of the electric refrigerator;
“0.25S GT η GT ≤P t GT ≤S GT η GT "the lower limit of the output power is the product of the lowest load rate (25%) of the gas turbine and the configuration capacity and the energy conversion efficiency thereof, and the upper limit is the product of the configuration capacity of the gas turbine and the energy conversion efficiency thereof;
Figure SMS_76
the lower limit of the output power of the gas boiler is the product of the lowest load rate (30%) of the gas boiler and the configuration capacity and the energy conversion efficiency of the gas boiler, and the upper limit is the product of the configuration capacity of the gas boiler and the energy conversion efficiency of the gas boiler; />
Figure SMS_77
The lower input limit of the electric refrigerator is set to 0, and the upper limit is the product of the configuration capacity of the electric refrigerator and the energy conversion efficiency of the electric refrigerator.
The step S3 is to construct an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model content, and comprises the following steps:
considering that there is uncertainty in the water power and load demands, the fluctuation range is available according to the conversion of formula (2), as shown in formulas (26), (27):
Figure SMS_78
in the method, in the process of the invention,
Figure SMS_79
P t sh respectively a predicted value and an actual value of the output of the hydroelectric generating set in a t period; alpha sh For uncertainty in the water power, alpha E Uncertainty as load demand; />
Figure SMS_80
Respectively a predicted value and an actual value of the output force of the load demand in the t period;
when the water power and load demand uncertainty is set to zero, i.e. α=α sh +α E When the value is=0, the predicted value in the water and electricity hydrogen production comprehensive energy system scheduling model is equal to the actual value, and the predicted value is regarded as a deterministic scheduling model, so that the water and electricity predicted value, the load predicted value and the rest parameters are brought into the model to solve the reference value of the obtained objective function, and the reference value is marked as B 0 ；
According to the two strategy models, from two different angles of a decision maker, an opportunity model and a robust model based on opportunity seeking and risk avoiding are established:
s31, opportunity IGDT scheduling model based on opportunity seeking
Considering the opportunity seeking and establishing a model to consider that the uncertainty of the output and load demands of the hydroelectric generating set can bring greater economic benefits to the comprehensive energy system, and setting the scheduling cost deviation coefficient of the opportunity model to be theta under the opportunity seeking scheduling decision OS In order to ensure that the daily profit of the system obtained in the objective function is greater than the expected profit, the minimum uncertainty of the uncertainty scheduling model needs to be obtained, and the smaller the uncertainty is, the smaller the load demand constraint is indicated under the corresponding opportunity seeking decision, the higher the profit can be obtained by the system, and the opportunity model under the corresponding opportunity seeking strategy is shown as a formula (28):
Figure SMS_81
in the method, in the process of the invention,
Figure SMS_82
to find the profit threshold under opportunity, C d0 The method comprises the steps of (1) taking a value of a deterministic hydroelectric hydrogen production comprehensive energy system scheduling model;
equation (28) is a two-layer planning model, the objective function in the upper layer model being minimizing uncertainty; the objective function in the underlying model is to maximize profit under the opportunity seeking scheduling decision, resulting in the maximized profit of the underlying model being greater than the profit value C of the deterministic model due to the existence of the opportunity seek d0 ；
When the water power and load requirements are that
Figure SMS_83
When the lower limit of the uncertainty set is taken as the output value, the daily profit of the lower model reaches the maximum value in the acceptable range, so that the solution of the lower objective function can be ignored, the double-layer planning model (28) which is difficult to solve is converted into a single-layer planning model shown as a formula (29), the solving cost objective function is converted into the minimum uncertainty alpha, and the complexity of the model is reduced: />
Figure SMS_84
S32, robust IGDT scheduling model based on risk avoidance
Considering a model established by risk avoidance to consider that the uncertainty of the output and load demands of the hydroelectric generating set influences the dispatching result of the comprehensive energy system, and setting the dispatching cost deviation coefficient of the robust model as theta under the decision of risk avoidance dispatching RA In order to ensure that the objective function meets the expected value range, the maximum uncertainty of the uncertainty scheduling model needs to be calculated, and the larger the uncertainty is, the larger the variable uncertainty disturbance is indicated under the corresponding risk avoidance decision, the better the robustness of the system is, and the opportunity model under the corresponding opportunity seeking strategy is shown as a formula (30):
Figure SMS_85
in the method, in the process of the invention,
Figure SMS_86
for the benefit threshold under risk avoidance, similar to equation (29), equation (30) is also a two-layer planning model, and the objective function in the upper layer model is maximizing uncertainty; the objective function in the lower model is the scheduling cost under the risk avoidance scheduling decision;
when the water power and load requirements are that
Figure SMS_87
In this case, the upper limit of the uncertainty set is taken as the output value, and the scheduling cost of the lower model is the highest, so that the solution of the lower objective function can be ignored, and in the same way, the equation (30) can be converted into the equation (31), and the objective function for solving the scheduling cost becomes the maximum uncertainty α:
Figure SMS_88
s33, IGDT scheduling model solving
The solution flow is as shown in fig. 1:
step one: taking unit parameters, electricity price, water and electricity output predicted values, load predicted values and the like as data input;
step two: solving a deterministic scheduling model according to the input data of the first step to obtain an optimal value of an objective function, and taking the optimal value as a reference value of an IGDT model;
step three: according to the requirement, selecting an IGDT strategy as required, and respectively setting a robust model and an opportunity model scheduling cost deviation coefficient as theta RA 、θ OS ；
Step four: on the premise of ensuring that the values of all decision variables meet constraint conditions, respectively solving an IGDT robust model under a risk avoidance strategy and an IGDT opportunity model under an opportunity seeking strategy to obtain uncertainty alpha and scheduling day profit C under the two strategies d 。
The following performs the example analysis of step S4 with specific data.
To verify the validity of the proposed model, the deterministic system scheduling results and the system scheduling results taking IGDT into account will be analyzed, and the relevant device parameters and capacities are shown in table 2. And converting the mixed integer nonlinear programming problem of the constructed model into a mixed integer linear programming problem, carrying out a simulation experiment on a MATLAB simulation platform by combining with a Yalmip tool box, and calling a CPLEX solver to solve.
S41, deterministic system scheduling result analysis
In the deterministic hydroelectric hydrogen production comprehensive energy system optimization scheduling model, both the hydroelectric power output and the load demand are accurate predicted values. In order to better simulate the optimization condition, a time-of-use electricity price strategy and a fixed hydrogen price are adopted according to the current situation of the novel electric power system, as shown in fig. 2.
(1) Scene overview
And setting two deterministic scenes for comparison analysis. Scene 1: irrespective of the optimization mode of the hydrogen storage device; scene 2: consider an optimized mode of the hydrogen storage device.
(2) Basic parameters
The plant operating parameters are shown in table 2.
Table 2 device capacities and parameters
Figure SMS_89
The energy storage parameters are shown in table 3.
TABLE 3 energy storage parameters
Figure SMS_90
(3) Calculation result analysis
1) System operation condition analysis
Fig. 3 is a deterministic system electrical power scheduling result. As can be seen from fig. 3 (a), regardless of the scheduling result of the hydrogen storage device, during the peak period of the water power output, the water power output in the system preferentially meets the system power demand. And the system purchases electric energy to complement the deficiency in the period of low water and electricity output so as to achieve electric load balance. In the time periods 07:00-11:00 and 19:00-23:00, the electricity price is at a peak value, so that the output of the gas turbine is increased to complement the electricity purchasing quantity reduced due to the increase of the electricity price, the running cost of the system is reduced, and the daily profit of the system is improved. As can be seen from the scheduling result of the hydrogen storage device in fig. 3 (b), in the high water period, since the hydroelectric power generation prediction amount is high, a large amount of water is discarded, so that the electrolyzer device is added to convert the surplus hydroelectric power into hydrogen and store the hydrogen in the hydrogen storage tank, thereby reducing the water discarding penalty cost.
As is clear from fig. 4 and 5, since the natural gas is relatively expensive in the electricity price valley period, the gas turbine does not generate electricity and generates heat, the heat load is satisfied by the gas boiler, and the cooling load is satisfied by the electric refrigerator. In the level period and the peak period, the price of the natural gas is relatively low, and the gas turbine generates electricity and generates heat at the moment, so that the heat load is met by the gas turbine. The cold load is supplied by an electric refrigerator and an absorption refrigerator. The hydrogen storage device has almost negligible effect on hot and cold power scheduling, so the scenario discussion is not divided.
2) IES economic optimization scheduling analysis
TABLE 4IES economic optimization scheduling results
Figure SMS_91
As can be seen from table 4: the water power plant in scenario 1 does not include water reject hydrogen production. Assuming that the water and electricity can be consumed for sale, the daily profit obtainable is about 31.544 ten thousand yuan. And in the scene 2, water is added for hydrogen production, when the electricity price reaches a peak value which is larger than the hydrogen price, surplus water and electricity are sold to a power grid preferentially to obtain high income, when the electricity price is smaller than the hydrogen price, the surplus water and electricity are electrolyzed for hydrogen production preferentially, the high income is obtained by producing and selling hydrogen, and the daily profit obtained by the law comprises three parts of hydrogen selling, electricity selling and energy selling, namely about 45.860 ten thousand yuan, and the daily profit of increased income is about 14.316 ten thousand yuan.
S42, analyzing the system scheduling result considering the IGDT
(1) Scene overview
And setting two uncertainty scenes based on the deterministic hydroelectric hydrogen production comprehensive energy system for comparison analysis. Scene 1: only hydro-electric power uncertainty is considered. Scene 2: only load demand uncertainty is considered.
(2) Calculation result analysis
1) Single-target scheduling result analysis considering only uncertainty of hydro-electric output
When only the uncertainty of the hydroelectric power output is considered, the predicted value of the load demand can be regarded as a fixed and unchanged accurate value, the fluctuation range of the hydroelectric power output is regarded as uncertainty, and the opportunity model and the robust model under opportunity seeking and risk avoidance are respectively solved by setting the opportunity and robust deviation factor range.
Table 5 corresponds to the water power uncertainty scheduling results
Figure SMS_92
/>
Figure SMS_93
Table 5 shows θ in the case of uncertainty in the water electrical output OS ＝0,0.01，0.02,0.03,0.04,0.05 and θ RA When= 0,0.01,0.02,0.03,0.04,0.05, the hydro-electric hydrogen production integrated energy system adopts system uncertainty and daily profit when adopting Opportunity Seeking (OS) and Risk Avoidance (RA) 2 risk coping strategies. In the opportunity model, as shown in FIG. 6 (a), the deviation factor θ follows OS Increase in uncertainty alpha sh The increase of the water and electricity output uncertainty positively affects the daily profit, the daily profit is increased, the water and electricity absorption rate of the system is increased, and the water and electricity absorption rate is increased at theta OS Taking 0.05, the corresponding uncertainty alpha sh Up to 0.247, daily profit increases to 49.642 ten thousand yuan. In the robust model, as shown in FIG. 6 (b), the deviation factor θ follows RA Increase in uncertainty alpha sh The uncertainty of the water and electricity output is increased to bring negative influence to the daily profit, the daily profit is reduced, the power generation plan is more conservative, and the power generation plan is more conservative at theta RA Taking 0.05, the corresponding uncertainty alpha sh The daily profit is reduced to 42.078 ten thousand yuan when reaching 0.149.
2) Single-target scheduling result analysis considering only load demand uncertainty
When only the uncertainty of the load demand is considered, the predicted value of the water and electricity output can be regarded as a fixed and unchanged accurate value, the fluctuation range of the load demand is regarded as uncertainty, and the opportunity model and the robust model under opportunity seeking and risk avoidance are respectively solved by setting the opportunity and robust deviation factor range.
Table 6 corresponds to load demand uncertainty scheduling results
Figure SMS_94
Figure SMS_95
Table 6 shows θ in the case of uncertainty in load demand OS = 0,0.01,0.02,0.03,0.04,0.05 and θ RA When= 0,0.01,0.02,0.03,0.04,0.05, the system of the integrated hydrogen production by water and electricity is not adopted when 2 risk coping strategies of Opportunity Seeking (OS) and Risk Avoiding (RA) are adoptedCertainty, and daily profit. In the opportunity model, as shown in FIG. 7 (a), the deviation factor θ follows OS Increase, load uncertainty alpha E The increase of the load uncertainty positively affects the daily profit, and the daily profit is increased along with the increase of the load uncertainty, and the load uncertainty is expressed in theta OS Taking 0.05, the corresponding uncertainty alpha E Up to 0.076, daily profit increases to 49.642 ten thousand yuan. In the robust model, as shown in FIG. 7 (b), the deviation factor θ follows RA Increase, load uncertainty alpha E Increasing the load uncertainty negatively affects daily profit, decreasing daily profit, at θ RA Taking 0.05, the corresponding uncertainty alpha E The daily profit is reduced to 42.078 ten thousand yuan when reaching 0.078.
3) Comparing and respectively considering the water power output and the load uncertainty scheduling result
Under the opportunity seeking strategy, as shown in fig. 8 (a), when pursuing higher daily profit, the uncertainty of the load demand is smaller, and the corresponding scheduling scheme has a larger possibility of producing favorable results, for example, when taking uncertainty, the daily profit of the load demand is larger than the daily profit of the water power, so that compared with the situation, the influence of the uncertainty of the load demand on the opportunity model is larger; under the risk avoidance strategy, as shown in fig. 8 (b), on the basis of meeting a certain daily profit, the fluctuation range of the uncertain parameters of the hydropower output is larger, and the less sensitive the corresponding scheduling scheme is to the fluctuation of the parameters, the stronger the robustness is. For example, when daily profit= 43.5908 ten thousand yuan, the uncertainty of the hydropower output is larger than the uncertainty of the load demand, so the uncertainty of the hydropower output has a larger influence on the robust model than the uncertainty.
The invention firstly expounds the feasibility of the information gap decision theory for processing uncertainty; secondly, based on an information gap decision theory and combining the characteristic of uncertain water power output and load demand, an IGDT-based water power hydrogen production comprehensive energy system optimization scheduling model is constructed, and the model mainly comprises an opportunity model under an opportunity seeking strategy and a robust model under a risk avoidance strategy; finally, calling a Cplex solver to solve the proposed model, wherein the calculation result shows that in the deterministic model, the hydrogen storage device is considered to promote the water and electricity digestion capacity, so that the running economy of the system is improved; in the uncertainty problem, the opportunity model scheduling result can ensure that the daily profit of the system is greater than the expected profit when the uncertainty of the water power output and the load demand is minimized; the robust model scheduling result can ensure that the uncertainty of the water power output and the load demand reaches the maximum under the condition of meeting the daily profit of the system, and ensure the robustness of the system. In addition, the system in the IGDT robust model is greatly influenced by the uncertainty of the water power at the same time, and the system in the IGDT opportunity model is greatly influenced by the uncertainty of the load.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (5)

1. The method for establishing the comprehensive energy system optimization scheduling model for the hydro-electric hydrogen production by considering uncertainty is characterized by comprising the following steps of:
s1, analyzing feasibility of an information gap decision theory on uncertainty treatment;
s2, establishing an information gap decision theory model;
s3, constructing an IGDT-based water and electricity hydrogen production comprehensive energy system optimization scheduling model;
s4, analyzing an example.
2. The method for establishing the comprehensive energy system optimization scheduling model for the hydro-electric hydrogen production by considering uncertainty as claimed in claim 1, wherein the feasibility of the step S1 analysis information gap decision theory model for the uncertainty treatment is specifically as follows:
the key idea of the information gap decision theory is to study the influence of uncertain parameters or variables on the scheduling operation of the system in a change interval of the maximized uncertain variables under the condition of meeting the preset objective function, and the method has the outstanding advantages that only the upper and lower boundaries of the variables are needed to be considered, and the accurate probability distribution and the change interval of the uncertain variables are not needed to be considered.
3. The method for establishing an integrated energy system optimization scheduling model for hydrogen production by hydroelectric generation with consideration of uncertainty as claimed in claim 1, wherein the step S2 of establishing information gap decision theory model content comprises the following steps:
s21, information gap decision theory model
Information gap decision theory includes risk avoidance and opportunity seeking two strategy models:
the risk avoidance refers to searching a worst boundary target caused by a maximum uncertainty variable set, and ensuring that a preset target always meets the boundary, so as to avoid the risk caused by uncertainty; opportunistic searching refers to searching for the best boundary target of the minimum uncertainty variable set, thereby searching for a target of the opportunistic lifting system;
the basic model for risk avoidance and opportunity seeking is as follows:
Figure FDA0004109158290000011
wherein X is an uncertain parameter, delta is a decision variable, B is an objective function, D is an equality constraint condition, and G is an inequality constraint condition;
the information gap decision theory is a non-probability and non-fuzzy method for processing uncertain variables or parameters based on a non-probability model, and the traditional non-probability model comprises an envelope model, a fraction uncertainty model and an ellipsoid model; according to the characteristic of uncertainty of the water power output and the load demand, a score uncertainty model is adopted as follows:
Figure FDA0004109158290000021
where, alpha is uncertainty,
Figure FDA0004109158290000022
in order to be able to predict the value,
in the middle of
Figure FDA0004109158290000023
Is transformed into->
Figure FDA0004109158290000024
Figure FDA0004109158290000025
And->
Figure FDA0004109158290000026
Respectively the actual value X t Upper and lower set limits of (2);
the decision maker who considers the opportunity to seek pays more attention to the favorable influence of the uncertainty of the water power output and the load demand on the comprehensive energy system, namely, the adverse disturbance of the uncertain parameters is minimized, the higher benefit is obtained by using lower scheduling cost, and the corresponding mathematical model is as follows:
Figure FDA0004109158290000027
wherein B is 0 Taking the predicted value for the uncertain parameter X
Figure FDA0004109158290000028
Objective function value at time, B OS For the objective function value under the predicted value, θ OS For the deviation coefficient between the expected target and the optimal solution of the model, theta OS The larger the decision maker, the greater the preference degree of the decision maker to the risk, and the smaller the scheduling cost;
the decision maker considering risk avoidance pays more attention to the adverse effect of the uncertainty of the water power output and the load demand on the comprehensive energy system, namely, the adverse disturbance of the uncertain parameters is maximized on the basis of ensuring the dispatching cost, and the corresponding mathematical model is as follows:
Figure FDA0004109158290000029
wherein B is 0 Taking the predicted value for the uncertain parameter X
Figure FDA0004109158290000031
Objective function value at time, B RA For the objective function value under the predicted value, θ RA For the deviation coefficient between the expected target and the optimal solution of the model, theta RA The larger the decision maker, the greater the degree of avoidance of the risk;
s22, objective function
The operation income of the comprehensive energy system for the water-containing hydrogen production is improved, the maximum daily operation profit of the system is taken as a design target, the daily operation profit of the system is represented by the difference between total income and total operation cost, the total income comprises the income sold by the system to users, the income sold by hydrogen and the income sold by the system to a power grid, and the total operation cost comprises electricity purchasing cost, gas purchasing cost, operation cost, water-electricity hydrogen production cost, operation maintenance cost of the electric storage equipment and operation maintenance cost of the heat storage equipment:
Figure FDA0004109158290000032
wherein C is d C for daily operation profit of the system n For total system income, C x C is the total running cost of the system sell The electric power is sold into the electric network,
Figure FDA0004109158290000033
for selling hydrogen and taking in C u Selling energy revenue to users for a system，C e,m C for the cost of system electricity purchase gas C for the system gas purchasing cost oc For the running cost of the system, C w,hp For the cost of hydrogen production by water and electricity, C e,s For the operation and maintenance cost of the power storage equipment, C h,s The operation and maintenance cost of the heat storage equipment;
Figure FDA0004109158290000034
Figure FDA0004109158290000035
Figure FDA0004109158290000036
/>
in the method, in the process of the invention,
Figure FDA0004109158290000037
for selling electricity price of system to power grid, P t sell The sales power of the system to the power grid is calculated; Δt is a time variable; />
Figure FDA0004109158290000038
Price for system hydrogen selling->
Figure FDA0004109158290000039
The sales amount of hydrogen for the hydrogen storage tank; />
Figure FDA00041091582900000310
And->
Figure FDA00041091582900000311
The selling unit price of the system to the electricity, cold and heat users is>
Figure FDA00041091582900000312
And->
Figure FDA00041091582900000313
The electrical load, the cold load and the heat load of the user respectively;
Figure FDA00041091582900000314
wherein C is e,m In order to pay for the purchase of electricity,
Figure FDA00041091582900000315
and P t e,m The electricity purchase price and the electricity purchase power at the time t are respectively;
Figure FDA0004109158290000041
wherein C is gas For gas purchase expense c gas Is the price of natural gas; p (P) t g The power generated by the gas turbine at the time t;
Figure FDA0004109158290000042
represents the heat generation power (kW) of the gas boiler; η (eta) g And eta b The efficiency of the gas turbine and the gas boiler respectively;
Figure FDA0004109158290000043
wherein C is oc For hydropower operation cost, C h,s Maintenance cost for hydropower operation C h,pu Punishment costs for water disposal; r is R h Cost factor for unit power of hydroelectric field generation, f h Punishment coefficient for water electric field, P t h The power of the water electric field at the time t is P t sh The power of surfing the net of the hydropower plant at the moment t;
Figure FDA0004109158290000044
wherein, c hp In order to produce the price of hydrogen,
Figure FDA0004109158290000045
the hydrogen production amount of the electrolytic tank at the moment t;
Figure FDA0004109158290000046
wherein C is capacity Is the capacity of the electricity storage device; c r Is the charge and discharge cost; p (P) t es,c And P t es,d The charging power and the discharging power of the power storage equipment at the moment t are respectively;
Figure FDA0004109158290000047
in the method, in the process of the invention,
Figure FDA0004109158290000048
the operation and maintenance cost of the heat storage equipment at the moment t; />
Figure FDA0004109158290000049
And->
Figure FDA00041091582900000410
The heat absorption power and the heat release power of the heat storage equipment at the moment t are respectively;
s23, constraint conditions
In order to distinguish the predicted value and the actual value of the water power output and the load demand, the water discarding punishment cost and the power balance constraint of the electric power system are shown in formulas (15) and (16):
1) Cost constraint for water disposal punishment
Figure FDA00041091582900000411
Wherein C is h,pu To discard the water punishment cost, f h In order to discard the water and punish the cost coefficient,
Figure FDA0004109158290000051
for the predicted power of the hydroelectric generating set in the period t, P t h The power of surfing the net of the hydroelectric generating set at the moment t;
2) Electrical balance constraint
Figure FDA0004109158290000052
Purchase power constraint:
Figure FDA0004109158290000053
gas turbine power constraint:
Figure FDA0004109158290000054
hydropower unit power constraint:
Figure FDA0004109158290000055
wherein P is t grid The power generation power of the power grid at the moment t; p (P) t g The power of the gas turbine at the moment t; p (P) t es,c And P t es,d The charging power and the discharging power of the power storage equipment at the moment t are respectively; p (P) t ec The power of the electric refrigerator at the moment t; p (P) t EL The power of the electrolytic cell at the moment t;
Figure FDA0004109158290000056
prediction of demand for electrical loadsA value; p (P) t g The power of the gas turbine at the moment t; p (P) t sh Generating power for the hydroelectric generating set at the moment t; f (F) t g For fuel consumption, eta of fuel gas turbine input at time t GT Is gas turbine efficiency;
3) Thermal equilibrium constraint:
Figure FDA0004109158290000057
in the method, in the process of the invention,
Figure FDA0004109158290000058
and->
Figure FDA0004109158290000059
Respectively the power of the gas boiler and the power of the waste heat recovery boiler at the moment t, eta GB And eta RB The efficiency of the gas boiler and the efficiency of the waste heat boiler at the moment t are respectively; />
Figure FDA00041091582900000510
The thermal load is t time; p (P) t GB The power generation power at the time t of the gas boiler; p (P) t RB The power generation power at the moment t of the waste heat recovery boiler is;
4) Cold balance power constraint:
Figure FDA00041091582900000511
wherein eta is EC Is that t Refrigerating coefficients of the electric refrigerator at moment; p (P) t EC Is that t Power of the electric refrigerator at moment;
Figure FDA0004109158290000061
is that t Time-of-day cold load;
5) Power storage device operation constraint:
Figure FDA0004109158290000062
wherein:
Figure FDA0004109158290000063
and->
Figure FDA0004109158290000064
Respectively is t An upper limit and a lower limit of the battery charging power at a moment; />
Figure FDA0004109158290000065
And->
Figure FDA0004109158290000066
Respectively is t An upper limit and a lower limit of the discharge power of the storage battery at the moment; />
Figure FDA0004109158290000067
And->
Figure FDA0004109158290000068
Respectively is t A time battery charge and discharge state flag bit, the value of which is 0 to respectively indicate stopping charge and discharge and 1 to respectively indicate performing charge and discharge; w (W) t es Is that t The electric energy stored by the storage battery at any moment; sigma (sigma) es The self-discharge rate of the storage battery is set; η (eta) es,c And eta es,d Charging and discharging efficiencies of the storage battery respectively; />
Figure FDA0004109158290000069
And->
Figure FDA00041091582900000610
Respectively the upper limit and the lower limit of the energy storage of the storage battery;
6) Heat storage device operation constraints:
Figure FDA00041091582900000611
in the method, in the process of the invention,
Figure FDA00041091582900000612
and->
Figure FDA00041091582900000613
Respectively is t The upper limit and the lower limit of the heat absorption power of the heat storage equipment at any moment; />
Figure FDA00041091582900000614
And->
Figure FDA00041091582900000615
Respectively is t An upper limit and a lower limit of heat release power of the heat storage device at any time; />
Figure FDA00041091582900000616
And->
Figure FDA00041091582900000617
Respectively is t The heat storage equipment absorbs and releases heat at the moment, the value of the heat storage equipment absorbs and releases heat at the moment is 0, and 1 represents that the heat storage equipment absorbs and releases heat; w (W) t hs Is that t The electric energy stored by the heat storage equipment at any time; sigma (sigma) hs The self-heat release rate of the heat storage equipment is achieved; η (eta) hs,c And eta hs,d The heat absorption efficiency and the heat release efficiency of the heat storage equipment are respectively; />
Figure FDA00041091582900000618
And->
Figure FDA00041091582900000619
The upper limit and the lower limit of the power of the heat storage equipment are respectively;
7) Hydrogen storage device operating constraints:
Figure FDA00041091582900000620
in the method, in the process of the invention,
Figure FDA00041091582900000621
and->
Figure FDA00041091582900000622
Respectively is t And t-1 the content of hydrogen in the hydrogen storage tank at any time; />
Figure FDA00041091582900000623
And->
Figure FDA00041091582900000624
The hydrogen input amount and the hydrogen output amount of the hydrogen storage tank are respectively; e (E) in,max And E is out,max The maximum hydrogen quantity is respectively input and emitted to the hydrogen storage tank; e (E) h,min And E is h,max An upper limit and a lower limit of the hydrogen storage capacity, respectively; />
Figure FDA0004109158290000071
The hydrogen storage amount at the beginning and the end of the dispatching are respectively;
8) Operating condition constraints for IES various devices:
Figure FDA0004109158290000072
wherein P is t GT The electric power of the gas turbine at the time t; s is S GT Is the rated capacity of the gas turbine; η (eta) GT Rated power generation efficiency of the gas turbine;
Figure FDA0004109158290000073
the heat efficiency of the gas boiler at the time t; s is S GB Is the rated capacity of the gas boiler; η (eta) GB For rated heat supply of gas boilerA rate; />
Figure FDA0004109158290000074
The refrigerating power of the electric refrigerator at the time t; s is S EC Is the rated capacity of the electric refrigerator; η (eta) EC The refrigerating efficiency of the electric refrigerator;
“0.25S GT η GT ≤P t GT ≤S GT η GT "the lower limit of the output power is the product of the lowest load rate (25%) of the gas turbine and the configuration capacity and the energy conversion efficiency thereof, and the upper limit is the product of the configuration capacity of the gas turbine and the energy conversion efficiency thereof;
Figure FDA0004109158290000075
the lower limit of the output power of the gas boiler is the product of the lowest load rate (30%) of the gas boiler and the configuration capacity and the energy conversion efficiency of the gas boiler, and the upper limit is the product of the configuration capacity of the gas boiler and the energy conversion efficiency of the gas boiler;
Figure FDA0004109158290000076
the lower input limit of the electric refrigerator is set to 0, and the upper limit is the product of the configuration capacity of the electric refrigerator and the energy conversion efficiency of the electric refrigerator.
4. The method for establishing the comprehensive energy system optimization scheduling model of the hydro-electric hydrogen production by considering uncertainty as claimed in claim 2, wherein the step S3 is characterized in that the content of the comprehensive energy system optimization scheduling model of the hydro-electric hydrogen production by constructing the IGDT based on the method comprises the following steps:
considering that there is uncertainty in the water power and load demands, the fluctuation range is available according to the conversion of formula (2), as shown in formulas (26), (27):
Figure FDA0004109158290000077
in the method, in the process of the invention,
Figure FDA0004109158290000078
P t sh respectively a predicted value and an actual value of the output of the hydroelectric generating set in a t period; alpha sh For uncertainty in the water power, alpha E Uncertainty as load demand; />
Figure FDA0004109158290000079
Respectively a predicted value and an actual value of the output force of the load demand in the t period;
when the hydro-power generating unit, the uncertainty of the load demand is set to zero, namely alpha=alpha sh +α E When the value is=0, the predicted value in the water and electricity hydrogen production comprehensive energy system scheduling model is equal to the actual value, and the predicted value is regarded as a deterministic scheduling model, so that the water and electricity predicted value, the load predicted value and the rest parameters are brought into the model to solve the reference value of the obtained objective function, and the reference value is marked as B 0 ；
According to the two strategy models, from two different angles of a decision maker, an opportunity model and a robust model based on opportunity seeking and risk avoiding are established:
s31, opportunity IGDT scheduling model based on opportunity seeking
Considering the opportunity seeking and establishing a model to consider that the uncertainty of the output and load demands of the hydroelectric generating set can bring greater economic benefits to the comprehensive energy system, and setting the scheduling cost deviation coefficient of the opportunity model to be theta under the opportunity seeking scheduling decision OS In order to ensure that the daily profit of the system obtained in the objective function is greater than the expected profit, the minimum uncertainty of the uncertainty scheduling model needs to be obtained, and the smaller the uncertainty is, the smaller the load demand constraint is indicated under the corresponding opportunity seeking decision, the higher the profit can be obtained by the system, and the opportunity model under the corresponding opportunity seeking strategy is shown as a formula (28):
Figure FDA0004109158290000081
in the method, in the process of the invention,
Figure FDA0004109158290000082
seeking for opportunitiesLower profit threshold, C d0 The method comprises the steps of (1) taking a value of a deterministic hydroelectric hydrogen production comprehensive energy system scheduling model;
equation (28) is a two-layer planning model, the objective function in the upper layer model being minimizing uncertainty; the objective function in the underlying model is to maximize profit under the opportunity seeking scheduling decision, resulting in the maximized profit of the underlying model being greater than the profit value C of the deterministic model due to the existence of the opportunity seek d0 ；
When the water power and load requirements are that
Figure FDA0004109158290000083
When the lower limit of the uncertainty set is taken as the output value, the daily profit of the lower model reaches the maximum value in the acceptable range, so that the solution of the lower objective function can be ignored, the double-layer planning model (28) which is difficult to solve is converted into a single-layer planning model shown as a formula (29), the solving cost objective function is converted into the minimum uncertainty alpha, and the complexity of the model is reduced: />
Figure FDA0004109158290000084
S32, robust IGDT scheduling model based on risk avoidance
Considering a model established by risk avoidance to consider that the uncertainty of the output and load demands of the hydroelectric generating set influences the dispatching result of the comprehensive energy system, and setting the dispatching cost deviation coefficient of the robust model as theta under the decision of risk avoidance dispatching RA In order to ensure that the objective function meets the expected value range, the maximum uncertainty of the uncertainty scheduling model needs to be calculated, and the larger the uncertainty is, the larger the variable uncertainty disturbance is indicated under the corresponding risk avoidance decision, the better the robustness of the system is, and the opportunity model under the corresponding opportunity seeking strategy is shown as a formula (30):
Figure FDA0004109158290000091
in the method, in the process of the invention,
Figure FDA0004109158290000092
for the benefit threshold under risk avoidance, similar to equation (29), equation (30) is also a two-layer planning model, and the objective function in the upper layer model is maximizing uncertainty; the objective function in the lower model is the scheduling cost under the risk avoidance scheduling decision;
when the water power and load requirements are that
Figure FDA0004109158290000093
In this case, the upper limit of the uncertainty set is taken as the output value, and the scheduling cost of the lower model is the highest, so that the solution of the lower objective function can be ignored, and in the same way, the equation (30) can be converted into the equation (31), and the objective function for solving the scheduling cost becomes the maximum uncertainty α:
Figure FDA0004109158290000094
s33, IGDT scheduling model solving
The method comprises the following specific steps:
step one: taking unit parameters, electricity price, water and electricity output predicted values, load predicted values and the like as data input;
step two: solving a deterministic scheduling model according to the input data of the first step to obtain an optimal value of an objective function, and taking the optimal value as a reference value of an IGDT model;
step three: according to the requirement, selecting an IGDT strategy as required, and respectively setting a robust model and an opportunity model scheduling cost deviation coefficient as theta RA 、θ OS ；
Step four: on the premise of ensuring that the values of all decision variables meet constraint conditions, respectively solving an IGDT robust model under a risk avoidance strategy and an IGDT opportunity model under an opportunity seeking strategy to obtain uncertainty alpha and scheduling day profit C under the two strategies d 。
5. The method for establishing the comprehensive energy system optimization scheduling model for the hydrogen production by the water and electricity with consideration of uncertainty as claimed in claim 1, wherein the content of the analysis of the step S4 example comprises a deterministic system scheduling result and a system scheduling result with consideration of IGDT, and the conclusion is that:
in the deterministic model, the hydrogen storage device is considered to promote the water and electricity digestion capacity, so that the system operation economy is improved; in the uncertainty problem, the opportunity model scheduling result can ensure that the daily profit of the system is greater than the expected profit when the uncertainty of the water power output and the load demand is minimized; the robust model scheduling result can ensure that the uncertainty of the water power output and the load demand reaches the maximum under the condition of meeting the daily profit of the system, and ensure the robustness of the system; in addition, the system in the IGDT robust model is greatly influenced by the uncertainty of the water power at the same time, and the system in the IGDT opportunity model is greatly influenced by the uncertainty of the load.
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CN117172389A (en) * 2023-11-01 2023-12-05 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172389A (en) * 2023-11-01 2023-12-05 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty
CN117172389B (en) * 2023-11-01 2024-02-02 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty

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