CN110555595B  Biogaswindlight allrenewable energy system based on energy hub and method thereof  Google Patents
Biogaswindlight allrenewable energy system based on energy hub and method thereof Download PDFInfo
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Abstract
The invention discloses a biogaswindlight allrenewable energy system based on an energy hub and a method thereof. The invention can realize the promotion effect on the consumption of renewable energy sources, promote the consumption of biomass energy, realize the heat preservation and heat supply of the methane tank in winter and ensure the stable supply of methane; the thermoelectric coupling degree is weakened, the consumption of wind and light renewable energy sources is effectively promoted, and meanwhile, the comprehensive scheduling cost of the system is reduced.
Description
Technical Field
The invention belongs to the technical field of optimization scheduling of an integrated energy system, and particularly relates to a biogaswindlight fullrenewable energy system based on an energy hub and a method thereof.
Background
Currently, uncertainty in renewable energy output presents a significant challenge to renewable energy system development. Therefore, the method has important practical significance in comprehensively considering uncertain factors, constructing the renewable energy system and researching the optimal scheduling model of the renewable energy system. At the present stage, a plurality of scholars research a comprehensive energy system containing renewable energy sources based on an energy hub concept, but an energy hub coupling matrix is not constructed aiming at specific problems, so that the validity and rationality of the energy operation optimization problem of energy hub analysis cannot be fully explained, and in addition, the system usually contains a conventional unit and does not further research on a fully renewable energy system; in recent years, a great deal of research has been conducted by domestic and foreign scholars on a solution method for a system collaborative optimization problem considering uncertainty of renewable energy output. Besides the traditional random optimization and robust optimization, some scholars try to combine the two methods to process uncertain quantity by using a distributed robust optimization method, and the solution process is too complex although the method effectively balances the economy and the conservation of the system.
Disclosure of Invention
The present invention is directed to solve or improve the abovementioned problems by providing a biogaswindlight fully renewable energy system based on an energy hub and a method thereof.
In order to achieve the purpose, the invention adopts the technical scheme that:
a marshwindlight full renewable energy system based on energy hub and method thereof, which comprises:
the methanewind energysolar energy hub model is used for feeding back part of heat energy and electric energy generated in the energy hub to the methane tank, increasing the reaction temperature and increasing the methane yield; the marsh gas can be directly supplied to a gas load and can also be converted into electric energy, and meanwhile, the waste heat of the generator in the CHP, the heat generated by the marsh gas furnace and the electric boiler can be directly supplied to a heat load;
the renewable energy system optimization scheduling model is used for controlling the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes with minimized cost and realizing the optimal coordination and utilization among multiple energies;
the uncertaintyconsidering dayaheadrealtime twostage distributed robust optimization scheduling model is used for optimizing CHP startup and shutdown cost and other cost under a windlight basic prediction scene in a dayahead scheduling stage, and the realtime scheduling stage adjusts the output of each device in a hub according to the actual output of windlight on the basis that the output of dayahead scheduling is known.
Preferably, the coupling relationship between the output of the methane tank and the temperature in the methanewind energysolar energy hub model is as follows:
E _{bio} ＝aT _{Z} T _{O} +b
wherein, E _{bio} Is the output per unit time of the methane tank, T _{Z} And T _{O} Respectively the actual reaction temperature and the optimum reaction temperature, the time T is calculated _{O} Taking the temperature of 35 ℃, and taking a and b as coefficients obtained by data fitting;
the heat transfer model of the methane tank considering the electric heat feedback is as follows:
Q _{R} ＝η _{B} S _{ef} +S _{hf}
wherein Q is _{R} Energy, eta, injected into the biogas digester for the energy hub _{B} For the conversion efficiency of the electric boiler, S _{ef} And S _{hf} Respectively representing residual electricity and residual heat feedback, R, in an energy hub _{in} 、R _{out} 、R _{w} Internal and external convective heat transfer resistances and pool wall conductive heat transfer resistances, T, respectively _{in} 、T _{out} 、T _{w} The temperature of the inner part, the outer part and the wall of the methane tank respectively, and the T can be known from the reaction principle of the methane tank _{in} ＝T _{Z} ；C _{Z} 、C _{W} The heat capacities of the interior and the wall of the methane tank are respectively, and t represents unit time;
the energy pivot coupling model is as follows:
wherein L is _{e} 、L _{h} 、L _{g} Respectively the electric load, the gas load and the heat load of the energy hub, namely the output quantity of the energy hub model; q _{W} 、Q _{PVT} 、E _{bio} The power generation power of the fan, the total power output of the PVT system and the biogas output in unit time are respectively the input quantity of the energy hub model; p _{e} 、P _{h} 、V _{bio} Respectively the net output electricity and the heat power of the electricity storage device and the heat storage device and the net output quantity of the gas storage device in unit time;and/or>The power generation and heating efficiency of the PVT system; />And/or>Is the gastoelectricity and gastoheat efficiency of the CHP; eta _{B} And η _{F} The energy conversion efficiency of the electric boiler and the energy conversion efficiency of the methane furnace are respectively; q. q of _{bio} The heat value of the biogas is; v is _{B} 、ν _{CHP} 、ν _{F} The dispatching factors of the electric boiler, the CHP and the methane furnace are respectively;
the conversion relationship between the scheduling factor and the efficiency is as follows:
ν _{B} ＝P _{B} /η _{B}
ν _{F} ＝P _{F} /η _{F}
wherein, P _{B} 、P _{F} Heat output, P, of electric boiler and methane furnace respectively _{CHP} An electrical output that is CHP;
the deformed energy pivot coupling model is as follows:
preferably, the renewable energy system optimization scheduling model under the deterministic condition is represented in a matrix form:
s.t.Ax≤b
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
wherein x represents a 01 variable of the equipment operation state in the energy hub, y represents a continuous variable of the output of each equipment in the hub, and xi represents a continuous variable of the output of the fan and the PVT system;
representing a model objective function with an optimization objective of comprehensive scheduling cost minimization, wherein a ^{T} x represents the cost only related to the variable 01, and the cost is not influenced by the output of the fan and the PVT, particularly the starting/stopping cost of the CHP unit; b ^{T} y+c ^{T} Xi represents the cost related to continuous variables, including energy conversion, chargedischarge loss cost, wind abandoning cost and light abandoning penalty cost;
ax is less than or equal to b represents 01 variable inequality constraint, including charge and discharge state constraint of the electricity storage and heat storage device;
cy is less than or equal to D xi represents continuous variable inequality constraint, including fan, PVT system, electric boiler, biogas furnace output constraint and biogas digester state constraint;
gx + Hy ≦ g and Jx + Ky = h represent the coupling relationship between the variables in the two phases, wherein Gx + Hy ≦ g includes CHP equipment constraints and gas storage device constraints, and Jx + Ky = h is energy balance constraints.
Preferably, the renewable energy system optimization scheduling model objective function is:
min(C _{CHP} +C _{T} +C _{L} +C _{P} )
wherein, C _{CHP} For the startup/shutdown costs of the CHP units, C _{T} For energy conversion loss cost, C _{L} For energy charge and discharge loss cost, C _{P} Penalizing cost for wind abandoning and light abandoning; t is an operation period, and T is an operation period,λ _{loss} 、λ _{w} and λ _{p} The unit cost of CHP starting, stopping, energy loss and wind and light abandoning is respectively; u. u _{t} Is a variable from 0 to 1, and represents that the CHP is in an opening or closing state at the time t; Δ t represents the length of each period; />And/or>And/or>The charging and discharging power of the battery and the charging and discharging power of the heat storage device in the time period t are respectively; />And &>And/or>And/or>The charging and discharging efficiencies of the electricity storage equipment, the gas storage equipment and the heat storage equipment are respectively obtained; />And &>The predicted and actual output of the fan is obtained; />And/or>The predicted and actual total contribution for the PVT system.
Preferably, based on the matrix description of the deterministic renewable energy system optimization scheduling model, when the uncertainty of the wind power and photovoltaic output is described by using a datadriven distributed robust optimization method, the objective function of the dayaheadrealtime twostage optimization scheduling model considering the uncertainty is as follows:
wherein,representing the output adjustment cost in the k scene of the realtime scheduling stage; p is a radical of _{k} Representing the value of the probability distribution in the k scene, { p } _{k} And omega is a feasible domain of scene probability values. />
Preferably, a probability distribution set which is based on the initial probability distribution value of each discrete scene and is subjected to fluctuation constraint by adopting a 1norm and an infinitenorm set is constructed, namely, omega = omega _{1} ∩Ω _{∞} The results are as follows:
wherein, theta _{1} 、θ _{∞} And (4) representing the probability fluctuation allowable deviation limit under the constraints of 1norm and infinitynorm.
Preferably, a CCG algorithm is adopted to solve the dayaheadrealtime twostage optimization scheduling model, and the model is decomposed into a main problem and a subproblem to be repeatedly solved in an iterative manner;
solving the optimal solution meeting the constraint condition under the known severe probability distribution, realizing the dayahead scheduling of the energy hub, and providing a lower bound value for the twostage robust model:
wherein m represents the number of iterations;
the subproblem is that under the condition that the main problem gives a variable x in the first stage, the worst probability distribution under realtime operation is searched and returned to the main problem for next iteration, and the subproblem provides an upper bound value for the twostage robust model:
because the probability scene set omega is not associated with the constraint set Y of the secondstage variable Y in each scene, the innerlayer min optimization problems in each scene are independent of each other and can be processed by adopting a parallel solving method, and the subproblems can be rewritten as follows:
and repeating iteration of the main problem and the rewritten subproblems until the difference between the upper bound value and the lower bound value reaches the required precision, stopping iteration, and returning to the optimal solution.
A biogaswindlight full renewable energy method based on an energy hub comprises the following steps:
s1, constructing a methanewind energysolar energy pivot model;
s2, constructing a renewable energy system optimization scheduling model according to the aim of minimizing the comprehensive scheduling cost;
s3, considering the output uncertainty of the wind and light renewable energy sources, and constructing a datadriven dayaheadrealtime twostage distributed robust optimization scheduling model;
and S4, dividing the model into a main problem and a sub problem by adopting a CCG algorithm, repeatedly carrying out iterative solution, stopping iteration when the difference between the upper bound value and the lower bound value of the model reaches the required precision, and returning to the optimal solution.
The biogaswindlight allrenewable energy system based on the energy hub and the method thereof have the following beneficial effects:
the invention constructs a full renewable energy system based on a biogaswind energysolar energy hub, performs detailed modeling on the energy hub containing an energy storage element, processes the energy hub into a linearized matrix more suitable for system optimization scheduling, further considers the uncertainty of windlight renewable energy output, and constructs a dayaheadrealtime twostage distributed robust optimization scheduling model based on data driving.
The invention can realize the promotion effect on the consumption of renewable energy sources, promote the consumption of biomass energy, realize the heat preservation and heat supply of the methane tank in winter and ensure the stable supply of methane; the thermoelectric coupling degree is weakened, the consumption of wind and light renewable energy sources is effectively promoted, and the comprehensive scheduling cost of the system is reduced.
Drawings
Fig. 1 is a schematic structural diagram of a biogaswindlight energy hub of a biogaswindlight allrenewable energy system based on an energy hub and a method thereof.
Fig. 2 is a diagram of a heat transfer network of a methane tank considering electric heat feedback in an energy hub of a methanewindlight fullyrenewable energy system based on the energy hub and a method thereof.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
According to an embodiment of the present application, referring to fig. 1, the present solution of the energy hubbased biogaswindlight fully renewable energy system and the method thereof includes:
construction of methanewindsolar pivot model
The model feeds back part of heat energy and electric energy generated in the energy hub to the methane tank so as to improve the reaction temperature and increase the methane yield; the biogas can be directly supplied to a gas load, and can also be converted into electric energy by a biogas generator in the biomass cogeneration unit, and meanwhile, the waste heat of the generator in the CHP and the heat generated by a biogas furnace and an electric boiler can be directly supplied to the heat load; in addition, the electric boiler, the methane furnace and the electricity and heat storage device can break through the traditional operation mode of 'fixing electricity by heat' of CHP, and realize thermoelectric decoupling.
The device can realize the coupling connection and the optimized scheduling of 3 energy sources jointly, provide certain flexibility and synergistic effect for regional multienergy supply, and promote regional renewable energy consumption, energy conservation and emission reduction.
The constructed biogaswind energysolar energy hub inputs energy sources of wind energy, solar energy and biomass energy, and the renewable energy sources can be converted and consumed through a wind driven generator, a PVT system and a biogas reaction tank to generate usable secondary energy sources such as electric energy, heat energy and biogas; still contained multiple energy conversion equipment (CHP, electric boiler, biogas stove) in the pivot, energy conversion principle in the pivot: the biogas obtained from the biomass can be directly supplied to a gas load, or can be converted into electric energy by using a CHP gas engine, and in addition, the waste heat of the CHP engine and the heat generated by a biogas furnace and an electric boiler can be directly supplied to a heat load or used for feedback heating of a biogas digester; an energy storage device is additionally arranged in the energy hub, and can provide largecapacity storage for electric power, heat and methane.
The coupling relation between the output of the methane tank and the temperature in the energy hub is as follows:
E _{bio} ＝aT _{Z} T _{O} +b
wherein E is _{bio} The yield of the biogas digester in unit time; t is _{Z} And T _{O} Respectively the actual reaction temperature and the optimum reaction temperature, the time T is calculated _{O} Taking the mixture to 35 ℃; and a and b are coefficients obtained by data fitting.
The heat transfer model of the methane tank considering the electric heat feedback is as follows:
Q _{R} ＝η _{B} S _{ef} +S _{hf}
wherein Q _{R} Energy injected into the methane tank for the energy hub; eta _{B} Is the conversion efficiency of the electric boiler; s _{ef} And S _{hf} Respectively representing residual electricity and residual heat feedback in the energy hub; r is _{in} ，R _{out} ，R _{w} The thermal resistance of internal and external convection heat transfer and the thermal resistance of pool wall conduction heat transfer are respectively; t is _{in} ，T _{out} ，T _{w} The temperature of the inner part, the outer part and the wall of the methane tank respectively, and the T can be known from the reaction principle of the methane tank _{in} ＝T _{Z} ；C _{Z} ，C _{W} Respectively the heat capacity of the inner part and the wall of the methane tank; t represents a unit time.
The energy pivot coupling model is as follows:
wherein L is _{e} 、L _{h} 、L _{g} The electric, gas and heat loads of the energy hub are the output quantity of the energy hub model; q _{W} 、Q _{PVT} 、E _{bio} Respectively the power generation power of the fan, the total power output of the PVT system and the biogas output in unit time, namely the input quantity of the energy hub model；P _{e} 、P _{h} 、V _{bio} Respectively the net output electricity and the heat power of the electricity storage device and the heat storage device, and the net output quantity of the gas storage device per unit time (net output quantity = output quantityinput quantity);and/or>The power generation and heating efficiency of the PVT system; />And/or>Is the gastoelectricity and gastoheat efficiency of the CHP; eta _{B} And η _{F} The energy conversion efficiency of the electric boiler and the energy conversion efficiency of the methane furnace are respectively; q. q.s _{bio} Is the heat value (1 m) of the marsh gas ^{3} Energy that can be released when the biogas is completely combusted); v is _{B} 、ν _{CHP} 、ν _{F} The electric boiler, the CHP and the methane furnace are respectively used as scheduling factors.
The conversion relation between the scheduling factor and the efficiency in the model is as follows:
ν _{B} ＝P _{B} /η _{B}
ν _{F} ＝P _{F} /η _{F}
wherein, P _{B} 、P _{F} Heat output, P, of electric boiler and methane furnace respectively _{CHP} Is the electrical output of the CHP.
The deformed energy pivot coupling model is as follows:
referring to fig. 2, the network modeling is performed on the heat conduction relationship between the inside and the outside of the methane tank considering the electric heat feedback, and the actual reaction temperature of the methane tank can be solved by using a node thermodynamic equilibrium equation, knowing that the output of the methane per unit time is determined by the temperature. The heat transfer and storage are respectively expressed by means of thermal resistance and thermal capacity, the temperature inside the methane tank, the temperature of the methane wall and the temperature of the outside can be expressed by nodes with potential energy, and finally, a methane tank node thermodynamic equilibrium equation is derived according to a network structure.
According to one embodiment of the application, a renewable energy system optimization scheduling model is constructed.
The renewable energy system scheduling optimization aims to control the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes with minimized cost and realize the optimal coordination and utilization among multiple energies.
The renewable energy system optimization scheduling model aims to control the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes at the minimized cost and realize the optimal coordination utilization among multiple energies.
Firstly, a renewable energy system optimization scheduling model under a deterministic condition is represented in a matrix form:
s.t.Ax≤b
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
wherein x represents a variable of 01 of the running state of equipment in the energy junction; y represents a continuous variable of the output of each device in the hub (excluding a fan and a PVT system); ξ is a continuous variable representing the fan to PVT system output.
Representing a model objective function with an optimization objective of comprehensive scheduling cost minimization, wherein a ^{T} x represents the cost associated with the variable of only 01, which is not affected by the fan and PVT output, and specifically the CHP unit activation @The cost of shutdown; b ^{T} y+c ^{T} ξ represents the cost related to continuous variables, and specifically comprises energy conversion, chargedischarge loss cost, wind abandoning cost and light abandoning penalty cost.
And Ax is less than or equal to b represents a variable inequality constraint of 01, in particular to a chargedischarge energy state constraint of the electricity storage and heat storage device.
And D xi which is less than or equal to Cy represents continuous variable inequality constraint, and the continuous variable inequality constraint specifically comprises a fan, a PVT system, an electric boiler, a biogas furnace output constraint and a biogas digester state constraint.
The coupling relation between variables in two stages is represented by Gx + Hy ≦ g and Jx + Ky = h, wherein Gx + Hy ≦ g specifically refers to CHP equipment constraint and gas storage device constraint, and Jx + Ky = h refers to energy balance constraint. The objective function and the constraint are specifically described below.
The optimization scheduling model objective function of the renewable energy system is as follows:
min(C _{CHP} +C _{T} +C _{L} +C _{P} )
wherein, C _{CHP} For the startup/shutdown costs of the CHP units, C _{T} For energy conversion loss cost, C _{L} For energy charge and discharge loss cost, C _{P} Penalizing cost for wind abandoning and light abandoning; t is an operation period, and T is an operation period,λ _{loss} 、λ _{w} and λ _{p} The unit cost of CHP starting, stopping, energy loss and wind and light abandoning are respectively; u. of _{t} Is a variable from 0 to 1 and represents that the CHP is in an open or closed state at the moment t; Δ t represents the length of each period; />And &>And &>The charging and discharging power of the battery and the charging and discharging power of the heat storage device in the time period t are respectively; />And/or>And/or>And &>The charging and discharging efficiencies of the electricity storage equipment, the gas storage equipment and the heat storage equipment are respectively improved; />And/or>The predicted and actual output of the fan is obtained; />And &>Is the predicted and actual total contribution of the PVT system.
The energy balance constraint of the renewable energy system optimization scheduling model is as follows:
wherein L is _{e,t} 、L _{h,t} 、L _{g} , _{t} The electric load, the heat load and the air load are respectively in the t period; [ C 'S' D ]]And [ Q 'P' H] ^{T} The detailed expression of the method is shown in an energy pivot coupling model after deformation.
The constraint of output equipment of the renewable energy system optimization scheduling model is as follows:
reaction temperature limit of the methane tank: t is a unit of _{Z,min} ≤T _{Z,t} ≤T _{Z,max}
Note: the coupling model of the output and the temperature of the methane tank, the heat transfer model and the reaction temperature limit jointly form the state constraint of the methane tank.
The coupling element constraint of the renewable energy system optimization scheduling model is as follows:
CHP equipment:
P _{CHP,t} P _{CHP,tΔt} ≤r _{CHP}
wherein,and &>Respectively representing the lower limit and the upper limit of the CHP power generation output; />And &>Respectively representing the lower and upper limits of CHP heat output; r is a radical of hydrogen _{CHP} Is the ramp rate of CHP.
Electric boiler output: p is more than or equal to 0 _{B,t} ≤P _{B,max}
In the formula, P _{B,max} Is the upper limit of the output of the electric boiler.
The output of the methane furnace: p is more than or equal to 0 _{F,t} ≤P _{F,max}
In the formula: p _{F,max} Is the upper limit of the output of the methane furnace.
Constraint of energy storage devices of the renewable energy system optimization scheduling model:
an electricity storage device:
E _{e,min} ≤E _{e,t} ≤E _{e,max}
wherein E is _{e,t} The battery power storage amount at the time t; e _{e,min} And E _{e,max} Lower and upper limits of the capacity of the accumulator, respectivelyLimiting;the variables are 01 and respectively represent the charge and discharge states of the device at the time t; />And/or>And/or>Respectively minimum and maximum charge and discharge power.
The gas storage device:
R _{bio,t} ＝R _{bio,tΔt} V _{bio,t}
R _{bio,min} ≤R _{bio,t} ≤R _{bio,max}
k _{t} ·V _{bio,min} ≤V _{bio,t} ≤k _{t} ·V _{bio,max}
wherein k is _{t} Is a variable of 0 to 1 and indicates that the methane tank is in an input or output state at the moment t; r _{bio,t} The biogas storage capacity is t time period; r _{bio,min} And R _{bio,max} Respectively the lower limit and the upper limit of the capacity of the gas storage device; v _{bio,t} Is the net output of the methane tank in the period of t, when V _{bio,t} >When 0, the biogas output is shown in the period of time, and when V _{bio,t} <And 0 represents input. V _{bio,min} And V _{bio,max} Respectively the lower limit and the upper limit of the input and the output of the marsh gas.
A heat storage device:
E _{h,min} ≤E _{h,t} ≤E _{h,max}
wherein E is _{h,t} The battery power storage amount at the time t; e _{h,min} And E _{h,max} Respectively, a lower limit and an upper limit of the capacity of the electric storage device;the variables are 01, and respectively represent the charge and discharge states of the device at the time t; />And/or>And/or>Respectively minimum and maximum charge and discharge power.
According to one embodiment of the application, a dayaheadrealtime twophase distributed robust optimization scheduling model considering uncertainty is constructed.
Because the wind speed and the illumination intensity can be influenced by factors such as weather and seasons, uncertainty exists between the output of the fan and the PVT system.
By combining the above considerations, a dayaheadrealtime twostage distributed robust optimization scheduling model of the renewable energy system is constructed, and the CHP startup and shutdown cost and other costs under the scene of windlight basic prediction are optimized in the dayahead scheduling stage; and in the realtime scheduling stage, the output of each device in the hub is adjusted according to the actual wind and light output on the basis of the known dayahead scheduling output.
In a dayaheadrealtime twostage distributed robust optimization scheduling model considering uncertainty, optimizing CHP starting and stopping cost and other costs under a scene of windlight basic prediction in a dayahead scheduling stage; and in the realtime scheduling stage, the output of each device in the hub is adjusted according to the actual wind and light output on the basis of the known dayahead scheduling output.
Based on the matrix description of the deterministic renewable energy system optimization scheduling model, when the uncertainty of wind power and photovoltaic output is described by adopting a distributed robust optimization method based on data driving, the objective function of the dayaheadrealtime twostage optimization scheduling model can be expressed in the following form:
wherein,representing the output adjustment cost in the k scene of the realtime scheduling stage; p is a radical of _{k} Representing the value of the probability distribution in the k scene, { p } _{k} And omega is a feasible domain of scene probability values. />
Theoretically, the probability distribution can be valued in any range, however, in order to make the scene probability distribution more appropriate to the actual operation data and fluctuate in a reasonable range, a probability distribution set which uses the initial probability distribution value of each discrete scene as a reference and adopts a 1norm and an infinitynorm set to constrain fluctuation is constructed, namely, Ω = Ω _{1} ∩Ω _{∞} The results are as follows:
in the formula, theta _{1} 、θ _{∞} And (4) representing the probability fluctuation allowable deviation limit under the constraints of 1norm and infinitynorm.
And further adopting a CCG algorithm to solve the dayaheadrealtime twostage optimization scheduling model, and decomposing the model into a Main Problem (MP) and a Sub Problem (SP) for repeated iterative solution. The main problem is to solve the optimal solution meeting constraint conditions under the known severe probability distribution, realize the dayahead scheduling of the energy hub and provide a lower bound value for the twostage robust model:
in the formula, m represents the number of iterations.
The subproblem is to find the worst probability distribution under realtime operation given the firststage variable x by the main problem and return to the main problem for the next iteration, and the subproblem provides an upper bound for equation (26):
because the probability scene set omega is not associated with the constraint set Y of the secondstage variable Y in each scene, the innerlayer min optimization problems in each scene are independent of each other and can be processed by adopting a parallel solving method, and the subproblems can be rewritten as follows:
and repeatedly iterating the main problem and the rewritten subproblems until the difference between the upper bound value and the lower bound value reaches the required precision, stopping iteration, and returning to the optimal solution.
According to one embodiment of the application, a marshwindlight full renewable energy method based on an energy hub comprises the following steps:
s1, constructing a methanewind energysolar energy pivot model;
s2, constructing a renewable energy system optimization scheduling model according to the aim of minimizing the comprehensive scheduling cost;
the deterministic model in the step S1 and the step S2 can effectively ensure that the temperature of the methane tank is within a reaction range, improve the methane yield, ensure the energy supply under extreme weather conditions and promote the digestion and utilization of biomass energy in an energy hub; in addition, the energy hub is additionally provided with an electric boiler, a methane furnace and an electricity storage and heat storage device, so that the consumption of wind and light renewable energy sources can be effectively promoted, and the comprehensive scheduling cost of the system is reduced.
S3, considering the output uncertainty of the wind and light renewable energy sources, and constructing a datadriven dayaheadrealtime twostage distributed robust optimization scheduling model;
deciding the starting and stopping cost of the unit and other costs under the scene of wind and light basic prediction in the dayahead scheduling stage; in the realtime scheduling stage, on the basis that the dayahead scheduling output is known, the uncertain scene probability distribution set under the constraint of the 1norm and the infinitynorm is comprehensively considered, so that a realtime cost decision result under the worst probability distribution is obtained.
And S4, dividing the model into a main problem and a sub problem by adopting a CCG algorithm, repeatedly iterating and solving, stopping iteration when the difference between the upper bound value and the lower bound value of the model reaches the required precision, and returning to the optimal solution.
The CCG algorithm can quickly and effectively solve the distributed robust model provided by the method; in addition, the confidence degree α _{1} And alpha _{∞} The increase of the total scheduling cost of the optimized target system increases the uncertain probability distribution range and the uncertainty.
The biogaswindlight full renewable energy system dayaheadrealtime twostage optimization scheduling model based on the energy hub can realize the promotion effect on the consumption of renewable energy sources, such as:
1. the biomass energy can be promoted to be consumed by adopting a mode of supplying heat to the methane tank by using the residual heat and the residual electricity energy, and particularly, the heat preservation and the heat supply of the methane tank can be realized in winter, so that the stable supply of the methane is ensured;
2. the arrangement of the electric boiler, the methane furnace and the electricity storage and heat storage devices can weaken the thermoelectric coupling degree, effectively promote the absorption of wind and light renewable energy sources and simultaneously reduce the comprehensive scheduling cost of the system.
In addition, the distributed robust model can be solved quickly and effectively by the CCG algorithm, the distributed robust algorithm can process uncertainty problems and can appropriately consider the economic and robust requirements of the target, and the superiority of the distributed robust optimization algorithm is verified.
While the embodiments of this invention have been described in detail, it should not be considered limited to such details. Various modifications and changes may be made by those skilled in the art without inventive step within the scope of the appended claims.
Claims (2)
1. A biogaswindlight allrenewable energy system based on an energy hub is characterized by comprising:
the methanewind energysolar energy hub model is used for feeding back part of heat energy and electric energy generated in the energy hub to the methane tank, increasing the reaction temperature and increasing the methane yield; the biogas can be directly supplied to a gas load and can also be converted into electric energy, and meanwhile, the waste heat of the generator in the CHP and the heat generated by the biogas furnace and the electric boiler can be directly supplied to a heat load;
the renewable energy system optimization scheduling model is used for controlling the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes with minimized cost and realizing the optimal coordination and utilization among multiple energies;
the uncertaintyconsidering dayaheadrealtime twostage distributed robust optimization scheduling model is used for optimizing CHP starting and stopping cost and other cost under a windlight basic prediction scene in a dayahead scheduling stage, and the realtime scheduling stage adjusts the output of each device in a hub according to the actual windlight output on the basis of known dayahead scheduling output;
the coupling relation between the output of the methane tank and the temperature in the methanewind energysolar energy pivot model is as follows:
E _{bio} ＝aT _{Z} T _{O} +b
wherein E is _{bio} Is the output per unit time of the methane tank, T _{Z} And T _{O} Respectively the actual reaction temperature and the optimum reaction temperature, the time T is calculated _{O} Taking 35 ℃, a and b as coefficients obtained by data fitting;
the heat transfer model of the methane tank considering the electric heat feedback is as follows:
Q _{R} ＝η _{B} S _{ef} +S _{hf}
wherein Q is _{R} Energy, eta, injected into the biogas digester for the energy hub _{B} For the conversion efficiency of the electric boiler, S _{ef} And S _{hf} Respectively representing residual electricity and residual heat feedback, R, in an energy hub _{in} 、R _{out} 、R _{w} Internal and external convective heat transfer resistances and pool wall conductive heat transfer resistances, T, respectively _{in} 、T _{out} 、T _{w} The temperature of the inner part, the outer part and the wall of the methane tank respectively, and the T can be known from the reaction principle of the methane tank _{in} ＝T _{Z} ；C _{Z} 、C _{W} The heat capacities of the interior and the wall of the methane tank are respectively, and t represents unit time;
the energy junction coupling model is as follows:
wherein L is _{e} 、L _{h} 、L _{g} Respectively the electric load, the gas load and the heat load of the energy hub, namely the output quantity of the energy hub model; q _{W} 、Q _{PVT} 、E _{bio} The power generation power of the fan, the total power output of the PVT system and the biogas output in unit time are respectively the input quantity of the energy hub model; p _{e} 、P _{h} 、V _{bio} Respectively the net output electricity and the heat power of the electricity storage device and the heat storage device and the net output quantity of the gas storage device in unit time;and/or>The power generation and heating efficiency of the PVT system; />And/or>Is the gastoelectricity and gastoheat efficiency of the CHP; eta _{B} And η _{F} The energy conversion efficiency of the electric boiler and the energy conversion efficiency of the methane furnace are respectively; q. q.s _{bio} The heat value of the biogas is; v is _{B} 、ν _{CHP} 、ν _{F} The scheduling factors of the electric boiler, the CHP and the biogas furnace are respectively;
the conversion relation between the scheduling factor and the efficiency is as follows:
ν _{B} ＝P _{B} /η _{B}
ν _{F} ＝P _{F} /η _{F}
wherein, P _{B} 、P _{F} Heat output, P, of electric boiler and methane furnace, respectively _{CHP} An electrical output that is CHP;
the deformed energy pivot coupling model is as follows:
the renewable energy system optimization scheduling model under the deterministic condition is represented in a matrix form:
s.t.Ax≤b
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
wherein x represents a 01 variable of the equipment operation state in the energy hub, y represents a continuous variable of the output of each equipment in the hub, and xi represents a continuous variable of the output of the fan and the PVT system;
representing a model objective function with an optimization objective of comprehensive scheduling cost minimization, wherein a ^{T} x represents the cost only related to the variable 01, and the cost is not influenced by the output of the fan and the PVT, particularly the starting/stopping cost of the CHP unit; b ^{T} y+c ^{T} Xi represents the cost related to continuous variables, including energy conversion, chargedischarge loss cost, wind abandoning cost and light abandoning penalty cost;
ax is less than or equal to b represents 01 variable inequality constraint, including charge and discharge state constraint of the electricity storage and heat storage device;
cy is less than or equal to D xi represents continuous variable inequality constraint, including fan, PVT system, electric boiler, biogas furnace output constraint and biogas digester state constraint;
gx + Hy ≤ g and Jx + Ky = h represent the coupling relation between twostage variables, wherein Gx + Hy ≤ g comprises CHP equipment constraint and gas storage device constraint, and Jx + Ky = h is energy balance constraint;
the renewable energy system optimization scheduling model objective function is as follows:
min(C _{CHP} +C _{T} +C _{L} +C _{P} )
wherein, C _{CHP} For the startup/shutdown costs of the CHP units, C _{T} For energy conversion loss cost, C _{L} For energy charge and discharge loss cost, C _{P} Penalizing cost for wind abandoning and light abandoning; t is an operation period, and T is an operation period,λ _{loss} 、λ _{w} and λ _{p} The unit cost of CHP starting, stopping, energy loss and wind and light abandoning is respectively; u. u _{t} Is a variable from 0 to 1, and represents that the CHP is in an opening or closing state at the time t; Δ t represents the length of each period; />And/or>And/or>The charging and discharging power of the battery and the charging and discharging power of the heat storage device are respectively in a time t; />And &>And &>And &>The charging and discharging efficiencies of the electricity storage equipment, the heat storage equipment and the gas storage equipment are respectively obtained;and/or>The predicted and actual output of the fan is obtained; />And/or>The predicted and actual total contribution for the PVT system;
when the uncertainty of wind power and photovoltaic output is described by adopting a distributed robust optimization method based on data driving, the objective function of the dayaheadrealtime twostage optimization scheduling model considering the uncertainty is as follows:
wherein,representing the output adjustment cost in the k scene of the realtime scheduling stage; p is a radical of _{k} Representing the value of the probability distribution in the k scene, { p } _{k} The method comprises the following steps that (1) a set of probability distribution values under each discrete scene is defined, and omega is a feasible domain of scene probability values;
the energy balance constraint of the renewable energy system optimization scheduling model is as follows:
wherein L is _{e,t} 、L _{h,t} 、L _{g,t} The electric load, the heat load and the air load are respectively in the t period; [ C 'S' D ]]And [ Q 'P' H] ^{T} The detailed expression of the method is shown in an energy pivot coupling model after deformation;
the output equipment constraint of the renewable energy system optimization scheduling model is as follows:
reaction temperature limit of the methane tank: t is _{Z,min} ≤T _{Z,t} ≤T _{Z,max}
The coupling model of the output and the temperature of the methane tank, the heat transfer model and the reaction temperature limit jointly form the state constraint of the methane tank;
coupling element constraints of the renewable energy system optimization scheduling model are as follows:
CHP equipment:
P _{CHP,t} P _{CHP,tΔt} ≤r _{CHP}
wherein,and &>Respectively representing the lower limit and the upper limit of the CHP power generation output; />And &>Respectively representing the lower and upper limits of the CHP heat output; r is _{CHP} Is the ramp rate of the CHP;
the output of the electric boiler is as follows: p is more than or equal to 0 _{B,t} ≤P _{B,max}
In the formula, P _{B,max} Is the upper limit of the output of the electric boiler;
the output of the methane furnace: p is more than or equal to 0 _{F,t} ≤P _{F,max}
In the formula: p _{F,max} Is the upper limit of the output of the methane furnace;
constraint of energy storage devices of the renewable energy system optimization scheduling model:
an electricity storage device:
E _{e,min} ≤E _{e,t} ≤E _{e,max}
wherein E is _{e,t} The battery power storage amount at the time t; e _{e,min} And E _{e,max} Respectively, a lower limit and an upper limit of the capacity of the electric storage device;the variables are 01, and respectively represent the charge and discharge states of the device at the time t; />And/or>And/or>Respectively minimum and maximum chargedischarge power;
the gas storage device:
R _{bio,t} ＝R _{bio,tΔt} V _{bio,t}
R _{bio,min} ≤R _{bio,t} ≤R _{bio,max}
k _{t} ·V _{bio,min} ≤V _{bio,t} ≤k _{t} ·V _{bio,max}
wherein k is _{t} Is a variable of 0 to 1 and indicates that the methane tank is in an input or output state at the moment t; r _{bio,t} The biogas storage capacity is t time period; r _{bio,min} And R _{bio,max} Respectively the lower limit and the upper limit of the capacity of the gas storage device; v _{bio,t} Is the net output of the methane tank in the period of t, when V _{bio,t} >When 0 is shown as the biogas output in the period, when V is _{bio,t} <When 0, the input is represented; v _{bio,min} And V _{bio,max} The lower limit and the upper limit of the input and the output of the marsh gas are respectively;
a heat storage device:
r _{t} ^{ch} +r _{t} ^{dis} ≤1
E _{h,min} ≤E _{h,t} ≤E _{h,max}
wherein E is _{h,t} Storing the electric quantity of the battery at the time t; e _{h,min} And E _{h,max} Respectively, a lower limit and an upper limit of the capacity of the electric storage device; r is _{t} ^{ch} 、r _{t} ^{dis} The variables are 01 and respectively represent the charge and discharge states of the device at the time t;and/or>And/or>Respectively minimum and maximum chargedischarge power;
constructing a probability distribution set which is based on the initial probability distribution value of each discrete scene and adopts 1norm and infinitynorm set to constrain fluctuation, namely omega = omega _{1} ∩Ω _{∞} The results were as follows:
wherein, theta _{1} 、θ _{∞} Representing the probability fluctuation allowable deviation limit under the constraint of 1norm and infinity norm;
solving a dayaheadrealtime twostage optimization scheduling model by adopting a CCG algorithm, and decomposing the model into a main problem and a subproblem to carry out repeated iteration solution;
solving the optimal solution meeting the constraint condition under the known severe probability distribution, realizing the dayahead scheduling of the energy hub, and providing a lower bound value for the twostage robust model:
wherein m represents the number of iterations;
the subproblem is that under the condition that the main problem gives a firststage variable x, the worst probability distribution under realtime operation is searched and returned to the main problem for next iteration, and the subproblem provides an upper bound value for the twostage robust model:
because the probability scene set omega is not associated with the constraint set Y of the secondstage variable Y in each scene, the innerlayer min optimization problems in each scene are independent of each other and can be processed by adopting a parallel solving method, and the subproblems can be rewritten as follows:
and repeating iteration of the main problem and the rewritten subproblems until the difference between the upper bound value and the lower bound value reaches the required precision, stopping iteration, and returning to the optimal solution.
2. The renewable energy method of an energy hubbased biogaswindlight total renewable energy system of claim 1, comprising:
s1, constructing a methanewind energysolar energy pivot model;
s2, constructing a renewable energy system optimization scheduling model according to the aim of minimizing the comprehensive scheduling cost;
s3, considering the output uncertainty of the windsolar renewable energy, and constructing a datadriven dayaheadrealtime twostage distributed robust optimization scheduling model;
and S4, dividing the model into a main problem and a sub problem by adopting a CCG algorithm, repeatedly iterating and solving, stopping iteration when the difference between the upper bound value and the lower bound value of the model reaches the required precision, and returning to the optimal solution.
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