CN113221325B - Multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion - Google Patents
Multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion Download PDFInfo
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Abstract
The invention discloses a multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion gas. The invention can improve the on-site consumption of wind power, reduce the carbon emission of the power grid side and promote the CO generated by the power grid side 2 And provides a sustainable development direction for environmental protection.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy, and particularly relates to a design of a multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion.
Background
With the rapid development of modern construction of the country, the energy exhaustion and the greenhouse effect are gradually increased. Wherein, the power industry has been the largest air pollution source in China, producing about 40% CO 2 Discharge amount. The huge environmental pollution from the power industry promotes the China to explore and develop renewable energy sources greatly and carry out low-carbon energy conversion. Among them, wind power is considered to be a potential huge renewable, clean, low cost and the likeAnd (5) regenerating energy sources. Therefore, the deployment of wind power is increased, the supply of stable energy sources can bring great environmental benefit. However, as the permeability of wind power in a power grid is improved, the performance requirement on the power grid for receiving renewable energy sources is gradually improved, the serious 'wind abandoning' phenomenon is caused by the mismatch of the large-scale construction of wind power and the power grid construction capacity, and the intermittence, the fluctuation and the anti-peak shaving characteristic of wind power also weaken the controllability of the wind power. In particular, in northern areas of China, the rich wind area is highly overlapped with the heating area, and the higher proportion of the thermal power generating unit has adverse effects on the wind power consumption. Therefore, analysis is carried out between multiple benefits of wind power penetration in comprehensive energy low-carbon operation optimization based on wind power, and the method has important significance for realizing sustainable development of energy.
At present, with the rapid development of the energy internet, a multi-energy coordination and complementation operation mode of the comprehensive energy system has become a direction of future energy utilization, but most of the construction of the comprehensive energy system model is not comprehensive, for example, the functions of the carbon capturing, cogeneration, electricity-to-gas conversion, energy storage and other systems are not comprehensively considered. Therefore, in the utilization level of renewable energy sources, the multi-energy source complementation and low-carbon operation level are still to be improved.
Disclosure of Invention
The invention aims to solve the problems of the existing comprehensive energy low-carbon operation method, and provides a multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion gas, so as to realize low-carbon operation optimization of an electric-thermal-gas interconnected comprehensive energy system.
The technical scheme of the invention is as follows: the multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion comprises the following steps of:
s1, according to a natural gas network and an electric power network structure, a comprehensive electric conversion gas model, a cogeneration model, an electric boiler model, a carbon capture system model and an energy storage model are combined, and an electric-thermal-gas interconnected comprehensive energy system model is established, so that electric-thermal-gas energy closed loop coordination optimization complementation is realized.
S2, establishing an objective function and a constraint condition corresponding to the comprehensive energy system model of the electric-thermal-gas interconnection.
S3, establishing a single-target continuous optimization pulse neural membrane system, and solving an objective function by adopting the single-target continuous optimization pulse neural membrane system to obtain the minimum energy scheduling cost and the output condition of each energy unit system.
Further, the electric transfer model in step S1 includes a first stage and a second stage; the first stage uses the surplus wind power in the water electrolysis process to generate hydrogen and oxygen; and in the second stage, the generated hydrogen and carbon dioxide generated by the power grid side thermal power generating unit are combined at high temperature to generate natural gas.
The coupling relation between the electric energy and the natural gas in the electric conversion gas model is expressed as follows:
wherein 
Represents the natural gas quantity generated by the electric conversion gas at the time t, < >>
Represents the electric energy participating in electric conversion at the moment t, eta e→g Indicating the electrical energy efficiency.
Constraint conditions of the electric conversion model are as follows:
wherein 
Is the maximum input power of the electric conversion system.
Further, in the cogeneration model in step S1, the coupling relationship between the natural gas and the electric energy and the heat energy under the cogeneration effect is expressed as:
wherein ,
represents the electric energy generated by cogeneration at time t, < >>
Represents heat energy, eta generated by cogeneration at time t g→e For air-to-electricity energy efficiency, eta g→h For converting qi into heat, add>
And the natural gas energy participating in the cogeneration at the time t is shown.
Constraint conditions of the cogeneration model are as follows:
wherein ,
is the maximum input power of the cogeneration system.
Further, in the electric boiler model in step S1, the coupling relationship between electric energy and thermal energy under the action of the electric boiler is expressed as:
wherein ,
represents the heat energy generated by the electric boiler at the time t +.>
Representing the electric energy of the electric boiler system participating in the moment t, eta e→h Indicating the electrical to thermal energy efficiency.
Constraint conditions of the electric boiler model are as follows:
wherein ,
is the maximum input power of the electric boiler system.
Further, the energy consumption of the carbon capture system model in step S1 is expressed as:
P c (t)＝P oc (t)+P mc (t)
wherein ,Pc (t) represents the total energy consumption of the carbon capture system model at the moment t, P mc (t) represents the maintenance energy consumption of the carbon capture system model at the moment t, P oc And (t) representing the carbon capture energy consumption of the carbon capture system model at the moment t, wherein the calculation formula is as follows:
P oc (t)＝η c1 W c (t)
wherein ,ηc1 Represents the unit carbon capture energy consumption, W, of a carbon capture system model c (t) represents CO captured at time t 2 The calculation formula of the quantity is as follows:
W c (t)＝η c2 η f P p (t)
wherein ,ηc2 Is CO 2 Capture rate, eta f Representing CO generation by thermal power of unit power 2 Amount of gas, P p And (t) represents the electric power generated by the power grid side thermal power generating unit at the moment t.
Further, the energy storage model in step S1 includes an electric energy storage system, a thermal energy storage system and a gas energy storage system, and the general charge and discharge conditions are expressed as follows:
P ss (t)＝εP ss,out (t)-(1-ε)P ss,in (t)
wherein ,Pss (t) is t moment energy storage modelThe transmitted electric quantity, epsilon is a charging and discharging variable for controlling the energy storage model, epsilon=1 indicates that the energy storage model is in a discharging state, epsilon=0 indicates that the energy storage model is in a charging state, and P ss,out (t) is the discharge power of the energy storage model at the moment t, P ss,in And (t) is the charging power of the energy storage model at the moment t.
The general energy storage capacity of the energy storage model is expressed as:
Q ss (t)＝Q ss (t-1)-P ss (t)
wherein ,Qss And (t) represents the stored energy of the energy storage model at the moment t.
The constraint conditions of the energy storage model are as follows:
Q s_min ≤Q ss (t)≤Q s_max
wherein ,
and />
Respectively representing the maximum stored power and the maximum output power of the energy storage model at the time t, Q s_min and Qs_max Representing the minimum and maximum energy storage capacities of the energy storage model, respectively.
Further, the comprehensive energy system model of the electric-thermal-gas interconnection in the step S1 adopts an electric conversion model, a cogeneration model and an electric boiler model as an electric-thermal-gas multidirectional energy conversion model to realize closed loop coordination optimization complementation of electric-thermal-gas energy, adopts an energy storage model to further realize the absorption of renewable energy, and adopts a carbon capture system model to further realize the low-carbon operation target of the comprehensive energy.
Further, the objective function in step S2 is:
f min ＝C p_c +C g_c +C s_d +C c
wherein ,fmin C, representing the lowest cost of current energy scheduling of the comprehensive energy system model p_c Representing the interaction cost of the integrated energy system model and the power network, C g_c Representing natural gas cost of integrated energy system model, C s_d Representing the running cost of the energy storage model, C c Representing the cost of the environment.
Interaction cost C of comprehensive energy system model and power network p_c Expressed as:
wherein ,λp Represents the purchase price of the comprehensive energy system model to the power network, lambda up Indicating the residual electricity surfing unit price, P p→c (t) represents the electricity purchasing quantity of the comprehensive energy system model at the moment t to the power network, P c→p And (T) represents the residual electricity on-line electric quantity at the moment T, and T represents the running period of the comprehensive energy system model.
Natural gas cost C of integrated energy system model g_c Expressed as:
wherein ,λg Representing the gas purchase unit price of the comprehensive energy system model to the natural gas network, and P g→c And (t) represents the gas purchase amount of the comprehensive energy system model to the natural gas network at the moment t.
Running cost C of energy storage model s_d Expressed as:
wherein ,μe ,μ h ,μ g Respectively represent an electric energy storage systemCoefficient of operation cost, P, of system, thermal energy storage system and gas energy storage system ess,out (t),P hss,out (t),P gss,out (t) represents the discharge power of the electric energy storage system, the thermal energy storage system and the gas energy storage system at the moment t respectively, P ess,in (t),P hss,in (t),P gss,in And (t) respectively representing the charging power of the electric energy storage system, the thermal energy storage system and the gas energy storage system at the moment t.
Environmental cost C c Expressed as:
wherein ,τp Represents the environmental cost of unit power generation of a conventional thermal power plant, and tau oc CO representing unit power generation of conventional thermal power plant 2 Cost τ c Represents the cost of outsourcing carbon dioxide, lambda f Represents the electricity cost of a conventional thermal power plant, W c (t) represents CO captured at time t 2 Amount, P c (t) represents the total energy consumption of the carbon capture system model at the moment t, Q c (t) represents outsourcing CO at time t 2 The calculation formula of the quantity is as follows:
wherein ,ηc Representing natural gas consumption CO per unit power 2 The coefficient of the,
represents the natural gas quantity generated by electric conversion gas at the time t, if Q c (t)>0 represents CO 2 Manufacturer purchases Q c kg of CO 2 Otherwise, redundant CO is needed to be added on the power grid side 2 And (5) performing sealing and storing treatment.
Further, the constraint conditions in step S2 include an electric power balance constraint, a thermal power balance constraint, and a natural gas power balance constraint.
The electric power balance constraint is expressed as:
wherein ,Pp (t) represents the electric power generated by the thermal power generating unit at the power grid side at the moment t, P wind (t) outputting electric energy for the wind power plant at the moment t, P ess (t) represents the power transfer of the electrical energy storage system,
for the t moment zone electrical load,/->
Represents the electric energy generated by cogeneration at time t, < >>
Indicating the electric energy of the electric boiler system participating at time t, < >>
Representing the electric energy involved in electric conversion at time t, P c (t) represents the total energy consumption of the carbon capture system model at the moment t, P c→p And (t) represents the residual electricity on-line electricity quantity at the moment t.
The thermal power balance constraint is expressed as:
wherein ,
represents the heat energy generated by cogeneration at time t, < >>
Representing heat energy generated by an electric boiler at time t, P hss (t) represents the thermal energy transfer of the thermal energy storage system, ">
Is t time zoneAnd (3) heat load.
The natural gas power balance constraint is expressed as:
wherein ,Pg→c (t) represents the gas purchase amount of the comprehensive energy system model to the natural gas network at the moment t,
represents the natural gas quantity generated by electric conversion gas at the time t, P gss (t) represents the gas energy transfer condition of the gas energy storage system,>
for the regional gas load at time t,/->
And the natural gas energy participating in the cogeneration at the time t is shown.
Further, the single-target continuous optimization pulse neural membrane system pi in step S3 is specifically:
∏＝(S 1 ,...,S m ,G)
wherein Sl ＝(O,σ 1 ,...,σ n+2 ,syn,I out ) And the l is equal to or more than 1 and equal to or less than m, and m is the total number of the subsystems.
O= { a } represents a set of nerve impulses, and a represents one nerve impulse.
Q＝Q p ∪Q s Is a set of neurons, wherein Q p ＝{σ 1 ,...,σ n Set of neurons for pulse generation, Q s ＝{σ n+1 ,σ n+2 And is a set of impulse-fed neurons.
σ i ＝(θ i ,R i ,P i ) Represents the ith pulse generating neuron, 1.ltoreq.i.ltoreq.n, where θ i Is neuron sigma i Pulse value, R i ＝{r i ′,r i "represents a finite set of rules, r i ' representing neuron sigma i In the form of r i ′＝{a θ →a β When executing the rule, the rule consumes a pulse a θ Simultaneously generating a new pulse, designated as a β ；r i "represents neuron sigma i Forgetting rule of (a) in the form of r i ″＝{a θ -lambda, which consumes a pulse a when executing the rule θ Simultaneously generating a blank character, denoted lambda; p (P) i ＝{p i ′,p i "represents neuron sigma i An inner set of finite rule selection probabilities, where p i ' corresponds to rule r i ′，p i "correspond to rule r i ", p i ′+p i ″＝1。
Neuron sigma n+1 ,σ n+2 Is neuron sigma i Providing the pulse required for each step to execute, wherein sigma n+1 ,σ n+2 The firing rules will be executed simultaneously, supplying pulses to each other; sigma (sigma) n+2 To neuron sigma i The pulses are supplied.
syn= { (i, j) | (1+.i+.n+1) ∈ (j=n+2)) (i=n+2) ∈ (j=n+1)) } represents a directional synaptic connection between neurons.
I out ＝{σ 1 ,σ 2 ,...,σ n The } represents a set of output neurons, and the output of the single-target continuous optimized pulse neural membrane system pi is represented by neuron sigma i Is a continuous pulse train of outputs of the (c) device.
G represents a guide for regulating neuron sigma i The rule selection probability.
The beneficial effects of the invention are as follows:
(1) The comprehensive energy system model of the electric-thermal-gas interconnection provided by the invention can improve the on-site absorption of wind power, reduce the carbon emission of the power grid side and promote the CO generated by the power grid side 2 And provides a sustainable development direction for environmental protection.
(2) The objective function and the constraint condition corresponding to the electric-thermal-gas interconnected comprehensive energy system model established by the invention can improve the utilization efficiency of comprehensive energy and obtain the smallest possible energy scheduling cost.
(3) The invention applies the pulse neural membrane system to the combination optimization problem of continuous variables for the first time, namely, the single-target continuous optimization pulse neural membrane system has strong parallel processing capability, can efficiently process the problem of running scheduling among various energy sources, seeks to an optimal solution, and obtains the lowest cost of energy source scheduling and the output condition of each energy source unit system.
Drawings
Fig. 1 is a flowchart of a multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion gas according to an embodiment of the invention.
FIG. 2 is a schematic diagram showing the process of the electrotransport natural gas chemical reaction according to the embodiment of the invention.
Fig. 3 is a schematic diagram of a cogeneration process according to an embodiment of the invention.
Fig. 4 is a schematic diagram of an integrated energy system model of electric-thermal-pneumatic interconnection according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a typical daily load in winter in an area according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of the power output situation of a power supply device according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a heating apparatus output situation according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of the output situation of the air supply device in case one embodiment of the present invention.
Fig. 9 is a schematic diagram of the output situation of the case two power supply device according to the embodiment of the present invention.
Fig. 10 is a schematic diagram of the output situation of the case two heating apparatus according to the embodiment of the present invention.
Fig. 11 is a schematic diagram of the output situation of the case two air supply device according to the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It is to be understood that the embodiments shown and described in the drawings are merely illustrative of the principles and spirit of the invention and are not intended to limit the scope of the invention.
The embodiment of the invention provides a multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion, which comprises the following steps of S1-S3:
s1, according to a natural gas network and an electric power network structure, a comprehensive electric conversion gas model, a cogeneration model, an electric boiler model, a carbon capture system model and an energy storage model are combined, and an electric-thermal-gas interconnected comprehensive energy system model is established, so that electric-thermal-gas energy closed loop coordination optimization complementation is realized.
(1) The electric conversion gas technology is a technology for converting electric energy into gaseous fuel, wherein electric conversion gas can be divided into electric conversion hydrogen and electric conversion natural gas. Because natural gas has higher unit energy density than hydrogen, and can be directly injected into the existing natural gas network for large-scale storage and long-distance transmission, the embodiment of the invention mainly discusses the process of converting electricity into natural gas. In the embodiment of the invention, the electric conversion model comprises a first stage and a second stage; the first stage uses the surplus wind power in the water electrolysis process to generate hydrogen and oxygen; and in the second stage, the generated hydrogen and carbon dioxide generated by the power grid side thermal power generating unit are combined at high temperature to generate natural gas, and the chemical reaction process is shown in figure 2.
The coupling relation between the electric energy and the natural gas in the electric conversion gas model is expressed as follows:
wherein 
Represents the natural gas quantity generated by the electric conversion gas at the time t, < >>
Represents the electric energy participating in electric conversion at the moment t, eta e→g Indicating the electrical energy efficiency.
In order to ensure the stable operation of the electric conversion gas model, the constraint conditions of the electric conversion gas model are as follows:
wherein 
Is the maximum input power of the electric conversion system.
(2) The cogeneration technology utilizes a gas turbine to convert natural gas into electric energy, and utilizes a waste heat recovery device to recover heat energy generated along with the electric energy, so that deep coupling of electricity, heat and gas is realized, and multiple guarantee supply of regional energy supply is achieved. The technology has the comprehensive benefits of saving energy, improving environment, improving heat supply quality, increasing power supply and the like, and the cogeneration process is shown in figure 3.
The output capacity of cogeneration depends on the energy scheduling situation, the energy efficiency of gas-to-electricity and the energy efficiency of gas-to-heat. Therefore, in the cogeneration model provided by the embodiment of the invention, the coupling relation between the natural gas and the electric energy and the heat energy under the cogeneration effect is expressed as follows:
wherein ,
represents the electric energy generated by cogeneration at time t, < >>
Represents heat energy, eta generated by cogeneration at time t g→e For air-to-electricity energy efficiency, eta g→h For converting qi into heat, add>
And the natural gas energy participating in the cogeneration at the time t is shown.
In order to ensure the stable operation of the cogeneration model, the constraint conditions of the cogeneration model are as follows:
wherein ,
is the maximum input power of the cogeneration system.
(3) The electric boiler converts electric energy into heat energy to act on regional heat load, so that the electric boiler can promote the absorption of wind power, and meanwhile, the mode of heat fixation of the cogeneration unit in the traditional sense is broken. Therefore, in the electric boiler model provided by the embodiment of the invention, the coupling relation between the electric energy and the heat energy under the action of the electric boiler is expressed as follows:
wherein ,
represents the heat energy generated by the electric boiler at the time t +.>
Representing the electric energy of the electric boiler system participating in the moment t, eta e→h Indicating the electrical to thermal energy efficiency.
In order to ensure the stable operation of the electric boiler model, the constraint conditions of the electric boiler model are as follows:
wherein ,
is the maximum input power of the electric boiler system.
(4) The carbon capture system is generally built in a thermal power plant to capture CO generated by a thermal power unit 2 Collected, stored and operated by a carbon capture system. The energy consumption of the carbon capture system model is expressed as:
P c (t)＝P oc (t)+P mc (t)
wherein ,Pc (t) represents the total energy consumption of the carbon capture system model at the moment t, P mc (t) represents the maintenance energy consumption of the carbon capture system model at the moment t, which is a fixed consumption constant and takes on the value of 80kW; p (P) oc And (t) representing the carbon capture energy consumption of the carbon capture system model at the moment t, wherein the calculation formula is as follows:
P oc (t)＝η c1 W c (t)
wherein ,ηc1 Represents the unit carbon capture energy consumption, W, of a carbon capture system model c (t) represents CO captured at time t 2 The calculation formula of the quantity is as follows:
W c (t)＝η c2 η f P p (t)
wherein ,ηc2 Is CO 2 Capture rate, eta f Representing CO generation by thermal power of unit power 2 Amount of gas, P p And (t) represents the electric power generated by the power grid side thermal power generating unit at the moment t.
(5) The application of the energy storage model in the comprehensive energy system has important significance for improving the system reliability and optimizing the system structure, can realize energy time shift, and provides a way for transferring redundant energy to other energy forms. The energy storage model provided by the embodiment of the invention comprises an electric energy storage system, a thermal energy storage system and a gas energy storage system, and the general charge and discharge conditions are expressed as follows:
P ss (t)＝εP ss,out (t)-(1-ε)P ss,in (t)
wherein ,Pss (t) is the electric quantity transmitted by the energy storage model at the moment t, epsilon is a variable for controlling the charge and discharge of the energy storage model, epsilon=1 represents that the energy storage model is in a discharge state, epsilon=0 represents that the energy storage model is in a charge state, and P ss,out (t) discharge power of the energy storage model at the moment t，P ss,in And (t) is the charging power of the energy storage model at the moment t.
The general energy storage capacity of the energy storage model is expressed as:
Q ss (t)＝Q ss (t-1)-P ss (t)
wherein ,Qss And (t) represents the stored energy of the energy storage model at the moment t.
The constraint conditions of the energy storage model are as follows:
Q s_min ≤Q ss (t)≤Q s_max
wherein ,
and />
Respectively representing the maximum stored power and the maximum output power of the energy storage model at the time t, Q s_min and Qs_max Representing the minimum and maximum energy storage capacities of the energy storage model, respectively.
(6) The integrated energy system model for electric-thermal-gas interconnection, which is built by the embodiment of the invention, is used for polymerizing clean energy mainly based on wind power, adopts an electric conversion gas model, a cogeneration model and an electric boiler model as an electric-thermal-gas multidirectional energy conversion model, realizes closed loop coordination optimization complementation of electric-thermal-gas energy, further realizes the absorption of renewable energy by adopting an energy storage model, and further realizes the low-carbon operation target of the integrated energy by adopting a carbon capture system model. An integrated energy system model of an electrical-thermal-gas interconnection is shown in fig. 4, where the different arrows represent the transmission of different energy sources. Meanwhile, in order to facilitate understanding, in the embodiment of the present invention, all units of energy are unified as power.
S2, establishing an objective function and a constraint condition corresponding to the comprehensive energy system model of the electric-thermal-gas interconnection.
In the embodiment of the invention, the energy interaction cost, the natural gas purchase cost, the environment cost and the energy storage system operation cost of the comprehensive energy system model and the power network are comprehensively considered, and then the corresponding objective functions are as follows:
f min ＝C p_c +C g_c +C s_d +C c
wherein ,fmin C, representing the lowest cost of current energy scheduling of the comprehensive energy system model p_c Representing the interaction cost of the integrated energy system model and the power network, C g_c Representing natural gas cost of integrated energy system model, C s_d Representing the running cost of the energy storage model, C c Representing the cost of the environment.
Interaction cost C of comprehensive energy system model and power network p_c Expressed as:
wherein ,λp Represents the purchase price of the comprehensive energy system model to the power network, lambda up Indicating the residual electricity surfing unit price, P p→c (t) represents the electricity purchasing quantity of the comprehensive energy system model at the moment t to the power network, P c→p And (T) represents the residual electricity on-line electric quantity at the moment T, and T represents the running period of the comprehensive energy system model.
Natural gas cost C of integrated energy system model g_c Expressed as:
wherein ,λg Representing the gas purchase unit price of the comprehensive energy system model to the natural gas network, and P g→c And (t) represents the gas purchase amount of the comprehensive energy system model to the natural gas network at the moment t.
Cost of operation of energy storage modelC s_d Expressed as:
wherein ,μe ,μ h ,μ g Representing the running cost coefficients, P, of the electric energy storage system, the thermal energy storage system and the gas energy storage system, respectively ess,out (t),P hss,out (t),P gss,out (t) represents the discharge power of the electric energy storage system, the thermal energy storage system and the gas energy storage system at the moment t respectively, P ess,in (t),P hss,in (t),P gss,in And (t) respectively representing the charging power of the electric energy storage system, the thermal energy storage system and the gas energy storage system at the moment t.
Environmental cost C c Expressed as:
wherein ,τp Represents the environmental cost of unit power generation of a conventional thermal power plant, and tau oc CO representing unit power generation of conventional thermal power plant 2 Cost τ c Represents the cost of outsourcing carbon dioxide, lambda f Represents the electricity cost of a conventional thermal power plant, W c (t) represents CO captured at time t 2 Amount, P c (t) represents the total energy consumption of the carbon capture system model at the moment t, Q c (t) represents outsourcing CO at time t 2 The calculation formula of the quantity is as follows:
wherein ,ηc Representing natural gas consumption CO per unit power 2 The coefficient of the,
represents the natural gas quantity generated by electric conversion gas at the time t, if Q c (t)>0 represents CO 2 Manufacturer purchases Q c kg of CO 2 Otherwise, the grid side needs to be connected withExcess CO 2 And (5) performing sealing and storing treatment.
In the embodiment of the invention, constraint conditions of the comprehensive energy system model comprise electric power balance constraint, thermal power balance constraint and natural gas power balance constraint.
(1) The electric power balance constraint is expressed as:
wherein ,Pp (t) represents the electric power generated by the thermal power generating unit at the power grid side at the moment t, P wind (t) outputting electric energy for the wind power plant at the moment t, P ess (t) represents the power transfer of the electrical energy storage system,
for the t moment zone electrical load,/->
Represents the electric energy generated by cogeneration at time t, < >>
Indicating the electric energy of the electric boiler system participating at time t, < >>
Representing the electric energy involved in electric conversion at time t, P c (t) represents the total energy consumption of the carbon capture system model at the moment t, P c→p And (t) represents the residual electricity on-line electricity quantity at the moment t.
(2) The thermal power balance constraint is expressed as:
wherein ,
represents the heat energy generated by cogeneration at time t, < >>
Representing heat energy generated by an electric boiler at time t, P hss (t) represents the thermal energy transfer of the thermal energy storage system, ">
Is the zone heat load at time t.
(3) The natural gas power balance constraint is expressed as:
wherein ,Pg→c (t) represents the gas purchase amount of the comprehensive energy system model to the natural gas network at the moment t,
represents the natural gas quantity generated by electric conversion gas at the time t, P gss (t) represents the gas energy transfer condition of the gas energy storage system,>
for the regional gas load at time t,/->
And the natural gas energy participating in the cogeneration at the time t is shown.
S3, establishing a single-target continuous optimization pulse neural membrane system (Single Objective Continuous Optimization Spiking Neural P System, SOCOSNPS), and solving an objective function by adopting the single-target continuous optimization pulse neural membrane system to obtain the lowest energy scheduling cost and the output condition of each energy unit system.
In the embodiment of the invention, the single-target continuous optimization pulse neural membrane system pi is specifically as follows:
∏＝(S 1 ,...,S m ,G)
wherein Sl ＝(O,σ 1 ,...,σ n+2 ,syn,I out ) And the l is equal to or more than 1 and equal to or less than m, and m is the total number of the subsystems.
O= { a } represents a set of nerve impulses, and a represents one nerve impulse.
Q＝Q p ∪Q s Is a set of neurons, wherein Q p ＝{σ 1 ,...,σ n Set of neurons for pulse generation, Q s ＝{σ n+1 ,σ n+2 And is a set of impulse-fed neurons.
σ i ＝(θ i ,R i ,P i ) Represents the ith pulse generating neuron, 1.ltoreq.i.ltoreq.n, where θ i Is neuron sigma i Pulse value, R i ＝{r i ′,r i "represents a finite set of rules, r i ' representing neuron sigma i In the form of r i ′＝{a θ →a β When executing the rule, the rule consumes a pulse a θ Simultaneously generating a new pulse, designated as a β ；r i "represents neuron sigma i Forgetting rule of (a) in the form of r i ″＝{a θ -lambda, which consumes a pulse a when executing the rule θ Simultaneously generating a blank character, denoted lambda; p (P) i ＝{p i ′,p i "represents neuron sigma i An inner set of finite rule selection probabilities, where p i ' corresponds to rule r i ′，p i "correspond to rule r i ", p i ′+p i ″＝1。
Neuron sigma n+1 ,σ n+2 Is neuron sigma i Providing the pulse required for each step to execute, wherein sigma n+1 ,σ n+2 The firing rules will be executed simultaneously, supplying pulses to each other; sigma (sigma) n+2 To neuron sigma i The pulses are supplied.
syn= { (i, j) | (1+.i+.n+1) ∈ (j=n+2)) (i=n+2) ∈ (j=n+1)) } represents a directional synaptic connection between neurons.
I out ＝{σ 1 ,σ 2 ,…,σ n The } represents a set of output neurons, and the output of the single-target continuous optimized pulse neural membrane system pi is represented by neuron sigma i Is a continuous pulse train of outputs of the (c) device.
G represents a guide for regulating neuron sigma i The rule selection probability.
The comprehensive energy low-carbon optimal scheduling process is further described below by taking a certain northern area as an example and taking a specific experimental example. Dividing 8:00-20:00 into peak sections in the ground heating period; the 20:00-next day 8:00 was divided into valley segments, the prices of which are shown in Table 1, in kWh/yuan.
Table 1 related prices
The load data is selected from the winter typical days, which comprise electric load, gas load and thermal load, and the load characteristics are shown in fig. 5.
The values of the parameters in the integrated energy system model of the electric-thermal-gas interconnection are shown in table 2.
Table 2 integrated energy system model parameter values
| Parameters (parameters) | Numerical value |
| τ p | 0.11 |
| τ oc | 0.02 |
| τ c | 0.4 |
| λ f | 0.33 |
| η f | 0.841 |
| η c | 22 |
| η e→h | 95％ |
| η e→g | 60％ |
| η g→e | 35％ |
| η g→h | 50％ |
| η c2 | 90％ |
| η c1 | 0.23 |
| P mc | 80 |
| μ e | 0.2 |
| μ h | 0.16 |
| μ g | 0.16 |
In order to verify the superiority of the method of the invention, the experimental example sets two cases for comparison analysis:
case one: the carbon capture system model and the electric conversion gas model are not needed.
Case two: the embodiment of the invention establishes an integrated energy system model of electric-thermal-gas interconnection.
The optimization results of the first case are shown in fig. 6 to 8, the optimization results of the second case are shown in fig. 9 to 11, and the cost comparison analysis of the two cases is shown in table 3.
Table 3 case cost comparative analysis
| Case (B) | Total cost of | Environmental cost | CO2 production amount of thermal power plant | CO2 reuse amount |
| A first part | 2.5584*10 4 | 1.5354*10 3 | 1.1739*10 4 | 0 |
| Two (II) | 3.5542*10 4 | 1.3779*10 4 | 9.7640*10 3 | 8.7876*10 3 |
As can be seen from the analysis of fig. 6 to 11 and table 3, in fig. 6 and 9, the electric load is low in the region of 0:00 to 10:00 period, and the wind power is high. In the first case, larger wind power can not be consumed in situ, and in the second case, the P2G is added, so that the in-situ consumption rate of the wind power is improved.
In fig. 7 and 10, the heat load energy consumption is higher in the period of 8:00-20:00, the introduction of the electric boiler and the heat energy storage system breaks through the traditional CHP mode of heat fixation and electricity utilization, and the heat utilization is more flexible. In the first case, CHP is mainly purchased from natural gas network, and does not promote the wind power absorption. However, in the second case, the CHP can consume the natural gas converted by the electric conversion gas model as much as possible, and indirectly provides an assistance for the in-situ consumption of wind power.
In fig. 8 and 11, the gas load energy consumption is higher in the periods 7:00-8:00, 16:00-22:00, but the purchase amount of the comprehensive energy system to the natural gas network is reduced due to the additional natural gas generated by the P2G in the second case.
Finally, the cost analysis of the first case and the second case proves that the second case considers the CO generated by the thermal power plant 2 Capturing and applying the catalyst in the generation of natural gas, and extremely efficiently converting CO 2 Reuse is performed, but the environmental cost is limited by the carbon capture cost and outsourced CO 2 The cost impact increases and the overall cost increases accordingly. Although the total cost of case two is higher than that of case one, the CO in case two 2 The capture utilization amount of (C) is higher than that of the first case, and CO 2 Provides a powerful support for sustainable development of the environment.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (9)
1. The multi-source energy storage type regional comprehensive energy low-carbon operation optimization method considering electric conversion is characterized by comprising the following steps of:
s1, according to a natural gas network and an electric power network structure, a comprehensive electric conversion gas model, a cogeneration model, an electric boiler model, a carbon capture system model and an energy storage model are integrated, and an electric-thermal-gas interconnected comprehensive energy system model is established, so that electric-thermal-gas energy closed loop coordination optimization complementation is realized;
s2, establishing an objective function and a constraint condition corresponding to the comprehensive energy system model of the electric-thermal-gas interconnection;
s3, establishing a single-target continuous optimization pulse neural membrane system, and solving an objective function by adopting the single-target continuous optimization pulse neural membrane system to obtain the lowest energy scheduling cost and the output condition of each energy unit system;
the single-target continuous optimization pulse neural membrane system pi in the step S3 is specifically as follows:
∏＝(S 1 ,...,S m ,G)
wherein Sl ＝(O,σ 1 ,...,σ n+2 ,syn,I out ) Representing the ith subsystem, wherein l is more than or equal to 1 and less than or equal to m, and m represents the total number of subsystems;
o= { a } represents a set of nerve impulses, a represents one nerve impulse;
Q＝Q p ∪Q s is a set of neurons, wherein Q p ＝{σ 1 ,...,σ n Set of neurons for pulse generation, Q s ＝{σ n+1 ,σ n+2 -impulse supply neuron set;
σ i ＝(θ i ,R i ,P i ) Representing the ith pulse generating nerveElement, i is more than or equal to 1 and less than or equal to n, wherein theta i Is neuron sigma i Pulse value, R i ＝{r i ′,r i "represents a finite set of rules, r i ' representing neuron sigma i In the form of r i ′＝{a θ →a β When executing the rule, the rule consumes a pulse a θ Simultaneously generating a new pulse, designated as a β ；r i "represents neuron sigma i Forgetting rule of (a) in the form of r i ″＝{a θ -lambda, which consumes a pulse a when executing the rule θ Simultaneously generating a blank character, denoted lambda; p (P) i ＝{p i ′,p i "represents neuron sigma i An inner set of finite rule selection probabilities, where p i ' corresponds to rule r i ′，p i "correspond to rule r i ", p i ′+p i ″＝1；
Neuron sigma n+1 ,σ n+2 Is neuron sigma i Providing the pulse required for each step to execute, wherein sigma n+1 ,σ n+2 The firing rules will be executed simultaneously, supplying pulses to each other; sigma (sigma) n+2 To neuron sigma i Supplying a pulse;
syn= { (i, j) | (1+.i+.n+1)/(j=n+2)) (i=n+2)/(j=n+1)) } represents a directional synaptic connection between neurons;
I out ＝{σ 1 ,σ 2 ,...,σ n the } represents a set of output neurons, and the output of the single-target continuous optimized pulse neural membrane system pi is represented by neuron sigma i A continuous pulse train of outputs of (a);
g represents a guide for regulating neuron sigma i The rule selection probability.
2. The multi-source energy storage type regional comprehensive energy low-carbon operation optimization method according to claim 1, wherein the electric conversion model in the step S1 comprises a first stage and a second stage;
the first stage uses the surplus wind power in the water electrolysis process to generate hydrogen and oxygen;
the second stage combines the generated hydrogen with carbon dioxide generated by the power grid side thermal power unit at high temperature to generate natural gas;
the coupling relation between the electric energy and the natural gas in the electric conversion gas model is expressed as follows:
wherein 
Represents the natural gas quantity generated by the electric conversion gas at the time t, < >>
Represents the electric energy participating in electric conversion at the moment t, eta e→g Representing the energy efficiency of electric conversion;
constraint conditions of the electric conversion model are as follows:
wherein 
Is the maximum input power of the electric conversion system.
3. The method for optimizing low-carbon operation of multi-source energy storage type regional integrated energy according to claim 1, wherein in the cogeneration model in step S1, the coupling relationship between natural gas and electric energy and heat energy under the cogeneration effect is expressed as:
wherein ,
represents the electric energy generated by cogeneration at time t, < >>
Represents heat energy, eta generated by cogeneration at time t g→e For air-to-electricity energy efficiency, eta g→h For converting qi into heat, add>
The natural gas energy participating in the cogeneration at the moment t is represented;
constraint conditions of the cogeneration model are as follows:
wherein ,
is the maximum input power of the cogeneration system.
4. The method for optimizing the low-carbon operation of the multi-source energy storage type regional comprehensive energy according to claim 1, wherein in the electric boiler model in the step S1, the coupling relationship between the electric energy and the heat energy under the action of the electric boiler is expressed as:
wherein ,
represents the heat energy generated by the electric boiler at the time t +.>
Representing the electric energy of the electric boiler system participating in the moment t, eta e→h Representing the electric-to-thermal energy efficiency;
constraint conditions of the electric boiler model are as follows:
wherein ,
is the maximum input power of the electric boiler system.
5. The method for optimizing the low-carbon operation of the multi-source energy storage type regional comprehensive energy according to claim 1, wherein the energy consumption of the carbon capture system model in the step S1 is expressed as follows:
P c (t)＝P oc (t)+P mc (t)
wherein ,Pc (t) represents the total energy consumption of the carbon capture system model at the moment t, P mc (t) represents the maintenance energy consumption of the carbon capture system model at the moment t, P oc And (t) representing the carbon capture energy consumption of the carbon capture system model at the moment t, wherein the calculation formula is as follows:
P oc (t)＝η c1 W c (t)
wherein ,ηc1 Represents the unit carbon capture energy consumption, W, of a carbon capture system model c (t) represents CO captured at time t 2 The calculation formula of the quantity is as follows:
W c (t)＝η c2 η f P p (t)
wherein ,ηc2 Is CO 2 Capture rate, eta f Representing CO generation by thermal power of unit power 2 Amount of gas, P p And (t) represents the electric power generated by the power grid side thermal power generating unit at the moment t.
6. The method for optimizing low-carbon operation of multi-source energy-storage type regional comprehensive energy according to claim 1, wherein the energy storage model in the step S1 includes an electric energy storage system, a thermal energy storage system and a gas energy storage system, and the general charge and discharge conditions are expressed as follows:
P ss (t)＝εP ss,out (t)-(1-ε)P ss,in (t)
wherein ,Pss (t) is the electric quantity transmitted by the energy storage model at the moment t, epsilon is a variable for controlling the charge and discharge of the energy storage model, epsilon=1 represents that the energy storage model is in a discharge state, epsilon=0 represents that the energy storage model is in a charge state, and P ss,out (t) is the discharge power of the energy storage model at the moment t, P ss,in (t) is the charging power of the energy storage model at the moment t;
the general energy storage capacity of the energy storage model is expressed as:
Q ss (t)＝Q ss (t-1)-P ss (t)
wherein ,Qss (t) represents the stored energy of the energy storage model at time t;
constraint conditions of the energy storage model are as follows:
Q s_min ≤Q ss (t)≤Q s_max
wherein ,
and />
Respectively representing the maximum stored power and the maximum output power of the energy storage model at the time t，Q s_min and Qs_max Representing the minimum and maximum energy storage capacities of the energy storage model, respectively.
7. The multi-source energy storage type regional comprehensive energy low-carbon operation optimization method according to claim 1, wherein an electric-thermal-gas interconnection comprehensive energy system model in the step S1 adopts an electric conversion gas model, a cogeneration model and an electric boiler model as an electric-thermal-gas multi-directional energy conversion model to realize closed-loop coordination optimization complementation of electric-thermal-gas energy, further realizes the absorption of renewable energy by adopting an energy storage model, and further realizes a comprehensive energy low-carbon operation target by adopting a carbon capture system model.
8. The method for optimizing low-carbon operation of multi-source energy-storage type regional comprehensive energy according to claim 1, wherein the objective function in the step S2 is:
f min ＝C p_c +C g_c +C s_d +C c
wherein ,fmin C, representing the lowest cost of current energy scheduling of the comprehensive energy system model p_c Representing the interaction cost of the integrated energy system model and the power network, C g_c Representing natural gas cost of integrated energy system model, C s_d Representing the running cost of the energy storage model, C c Representing environmental costs;
interaction cost C of comprehensive energy system model and power network p_c Expressed as:
wherein ,λp Represents the purchase price of the comprehensive energy system model to the power network, lambda up Indicating the residual electricity surfing unit price, P p→c (t) represents the electricity purchasing quantity of the comprehensive energy system model at the moment t to the power network, P c→p (T) represents the residual electricity on-line electric quantity at the moment T, and T represents the running period of the comprehensive energy system model;
natural gas cost C of integrated energy system model g_c Expressed as:
wherein ,λg Representing the gas purchase unit price of the comprehensive energy system model to the natural gas network, and P g→c (t) represents the gas purchase amount of the comprehensive energy system model to the natural gas network at the moment t;
running cost C of energy storage model s_d Expressed as:
wherein ,μe ,μ h ,μ g Representing the running cost coefficients, P, of the electric energy storage system, the thermal energy storage system and the gas energy storage system, respectively ess,out (t),P hss,out (t),P gss,out (t) represents the discharge power of the electric energy storage system, the thermal energy storage system and the gas energy storage system at the moment t respectively, P ess,in (t),P hss,in (t),P gss,in (t) respectively representing the charging power of the electric energy storage system, the thermal energy storage system and the gas energy storage system at the moment t;
environmental cost C c Expressed as:
wherein ,τp Represents the environmental cost of unit power generation of a conventional thermal power plant, and tau oc CO representing unit power generation of conventional thermal power plant 2 Cost τ c Represents the cost of outsourcing carbon dioxide, lambda f Represents the electricity cost of a conventional thermal power plant, W c (t) represents CO captured at time t 2 Amount, P c (t) represents the total energy consumption of the carbon capture system model at the moment t, Q c (t) represents outsourcing CO at time t 2 The calculation formula of the quantity is as follows:
wherein ,ηc Representing natural gas consumption CO per unit power 2 The coefficient of the,
represents the natural gas quantity generated by electric conversion gas at the time t, if Q c (t)>0 represents CO 2 Manufacturer purchases Q c kg of CO 2 Otherwise, redundant CO is needed to be added on the power grid side 2 And (5) performing sealing and storing treatment.
9. The multi-source energy storage type regional comprehensive energy low-carbon operation optimization method according to claim 1, wherein the constraint conditions in the step S2 include electric power balance constraint, thermal power balance constraint and natural gas power balance constraint;
the electric power balance constraint is expressed as:
wherein ,Pp (t) represents the electric power generated by the thermal power generating unit at the power grid side at the moment t, P wind (t) outputting electric energy for the wind power plant at the moment t, P ess (t) represents the power transfer of the electrical energy storage system,
for the t moment zone electrical load,/->
Represents the electric energy generated by cogeneration at time t, < >>
Indicating the electric energy of the electric boiler system participating at time t, < >>
Representing the electric energy involved in electric conversion at time t, P c (t) represents the total energy consumption of the carbon capture system model at the moment t, P c→p (t) represents the residual electricity on-line electricity quantity at the moment t;
the thermal power balance constraint is expressed as:
wherein ,
represents the heat energy generated by cogeneration at time t, < >>
Representing heat energy generated by an electric boiler at time t, P hss (t) represents the thermal energy transfer of the thermal energy storage system, ">
The thermal load is the zone heat load at the moment t;
the natural gas power balance constraint is expressed as:
wherein ,Pg→c (t) represents the gas purchase amount of the comprehensive energy system model to the natural gas network at the moment t,
represents the natural gas quantity generated by electric conversion gas at the time t, P gss (t) represents the gas energy transfer condition of the gas energy storage system,>
for the regional gas load at time t,/->
And the natural gas energy participating in the cogeneration at the time t is shown. />
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