CN116050585A - Nash negotiation-based hydrogen-containing gas comprehensive energy multi-microgrid optimal scheduling method - Google Patents
Nash negotiation-based hydrogen-containing gas comprehensive energy multi-microgrid optimal scheduling method Download PDFInfo
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Abstract
The invention discloses a comprehensive energy multi-microgrid optimizing and scheduling method for hydrogen-containing fuel gas based on Nash negotiation, which is characterized in that the fuel gas is fed into a fuel gas unit after being hydrogen-loaded, so that a hydrogen energy storage model containing the fuel gas is established, and a calculation method based on outsourcing electric power carbon emission is supplemented in a ladder-type carbon transaction mechanism; calculating condition risk values through net interaction cost of the multiple micro-networks and the power distribution network to measure uncertainty of the multiple micro-networks, and performing two-stage optimization on the multiple micro-networks by utilizing a Nash negotiation method, wherein the total cost of the multiple micro-networks is minimized in the first stage, and benefits are distributed and benefits of the micro-networks are maximized in the second stage; and carrying out distributed solving on the two-stage optimization model by improving an alternate direction multiplier method. The method can realize the maximization of benefits of each micro-grid on the premise of ensuring the lowest total cost of the multi-micro-grid, improves the new energy consumption level, reduces the carbon emission and provides a reference for low-carbon economic dispatch of the comprehensive energy multi-micro-grid in an uncertain environment.
Description
Technical Field
The invention belongs to the technical field of new energy scheduling, and particularly relates to a comprehensive energy multi-microgrid optimal scheduling method for hydrogen-containing fuel gas based on Nash negotiation.
Background
The comprehensive energy system improves the efficiency and the cleanliness level of terminal energy consumption, and is an important means for realizing clean energy substitution and low-carbon sustainable development of energy. With the increase of the number of the comprehensive energy microgrids in the same power distribution area, adjacent microgrids are coupled with each other to form a comprehensive energy multi-microgrid. When the electric energy interaction is carried out between the micro networks, the micro networks can sell electric quantity to other micro networks or purchase electric quantity from other micro networks so as to improve the running economy of the micro networks. However, each micro-network belongs to different interest bodies, and the complex coupling of interest interaction and multi-energy flows among a plurality of micro-networks makes the traditional single micro-network scheduling method difficult to apply.
The existing dispatching method for the multi-microgrid only considers source load uncertainty, a carbon transaction mechanism focuses on carbon emission generated by the output of equipment of the microgrid, and equivalent carbon emission generated by outsourcing electric power is not considered. In the hydrogen-using link, power generation is generally performed using hydrogen fuel cell technology. The hydrogen fuel cell power generation equipment is relatively simple, has a wide power range, can well cope with the fluctuation of renewable energy sources, but has high price and low power generation efficiency of only 30-60 percent.
Disclosure of Invention
The invention aims to provide a comprehensive energy multi-microgrid optimal scheduling method for hydrogen-containing fuel gas based on Nash negotiation, which can improve the new energy consumption level and reduce the carbon emission.
The technical scheme adopted by the invention is that the integrated energy multi-microgrid optimizing and scheduling method for hydrogen-doped gas based on Nash negotiation is implemented according to the following steps:
step 1, carrying out low-carbon economical improvement on a comprehensive energy multi-microgrid, feeding the gas after hydrogen loading into a gas unit, and establishing a comprehensive energy microgrid model containing hydrogen loading gas;
step 2, calculating a condition risk value through net interaction cost of the multi-microgrid and the power distribution network, and implementing two-stage optimization on the multi-microgrid by utilizing Nash negotiation theory, wherein the first stage minimizes the total cost of the multi-microgrid, the second stage distributes benefits and maximizes benefits of each microgrid, and a comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation is constructed;
and 3, carrying out distributed optimization solution on the comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation by improving an alternate direction multiplier algorithm, wherein the obtained parameters are scheduling results.
The hydrogen energy storage model containing the fuel gas and hydrogen in the step 1 comprises an output model of integrated energy micro-grid equipment, an energy efficiency lifting mechanism model of the integrated energy micro-grid, a single micro-grid cost model and constraint conditions, and the output model of the integrated energy micro-grid equipment comprises a carbon capture power plant model and a hydrogen energy storage system model containing the hydrogen-doped fuel gas.
The carbon capture power plant model construction process is as follows: output P of thermal power unit in t period in carbon capture power plant TU,e,i (t) supplying a portion to the carbon capture apparatus and another portion to the grid for supplying an electrical load; electric power P input by the carbon trapping device in t period CCS,e,i (t) is further divided into two parts: self-fixed electric power consumption P CCS,e1,i (t) and treatment of CO 2 Electric power consumption P of (2) CCS,e2,i (t)：
CO captured by carbon capture device 2 One part is sealed and the other part is supplied to the electric power conversion equipment, and the carbon capture power plant model is as follows:
wherein P is TU,e,i (t) is the electric power output by the thermal power generating unit in the micro-grid i (i=1, 2,3, …) in the period t, and kW; e, e TU Is the carbon emission intensity in unit time, m 3 /(kW·h)；V TU,CO2,i (t) CO emission of thermal power generating unit in period t 2 Volume flow, m 3 /h；λ CCS Treatment of unit volume flow CO for carbon capture plants 2 Energy consumption of (kW.h)/m 3 ；μ CCS Is carbon capture efficiency; v (V) CCS1,CO2,i (t) sequestering CO for carbon capture devices 2 Is a volume flow of (1); v (V) CCS2,CO2,i (t) supplying an electric gas conversion plant CO for carbon capture 2 V of volume flow of (2) CCS,CO2,i (t) capturing CO for carbon capture 2 Is provided).
The construction process of the hydrogen energy storage system model containing the hydrogen-doped fuel gas comprises the following steps:
1) Unified cell model of dimension:
the electric energy is converted into hydrogen energy by the electrolytic tank, the hydrogen is produced by proton exchange electrolysis, and the constructed variable efficiency mathematical model is as follows:
Wherein n is EL,H2,i (t) is the amount of hydrogen-generating material in the cells in the microgrid i during period t; n is n ELN Is the rated capacity of the electrolytic cell; p (P) EL,e,i (t) is the electric power input by the electrolytic cell during the period t; p (P) EL,eN Inputting rated value of electric power for the electrolytic tank; f (P) EL,e,i (t)) is an electrolyzer efficiency function; a, a EL 、b EL 、c EL Is an efficiency function coefficient;
the electrolytic tank model with unified dimensions is as follows:
wherein n is EL,H2,i (t) is the amount of hydrogen-generating substances in the electrolysis cell in the micro-grid i in the period t, kmol; p (P) EL,e,i (t) is the electric power input by the electrolytic cell in the period t, kW; m is m EL,H2,i (t) is the mass of hydrogen produced by the electrolytic cell in the period t, kg; m is M H2 Is the molar mass of hydrogen; ρ H2 Is the density of hydrogen; Δt is the scheduling unit time, P EL,H2,i (t) is the hydrogen production power of the electrolyzer in the period t;
2) Methane reactor model with unified dimension
4H 2 +CO 2 →CH 4 +2H 2 O (5)
Since chemical reaction equations are commonly usedThe amount of molecular species is calculated, thus capturing the carbon to the CO of the electrical gas converting apparatus 2 The volume flow (formula (2)) is converted into the amount of substance as follows:
wherein m is CCS2,CO2,i (t) supplying electric gas conversion equipment CO for capturing carbon in micro-grid i in period t 2 Mass of (3) kg; n is n CCS2,CO2,i,i (t) supplying an electric gas conversion plant CO for carbon capture 2 Kmol; m is M CO2 Is CO 2 Molar mass of (c); v (V) CCS2,CO2,i (t) supplying an electric gas conversion plant CO for carbon capture 2 Is a volume flow of (1); ρ CO2 Is CO 2 Is a density of (3);
methane reactor CH generation 4 The amount of (2) depends on CO 2 And H is 2 The amount of the substance (B) and the reaction amount and the production amount can be determined by the following formula:
wherein n is MR1,H2,i (t) feeding the electrolyzer with methane reactor H during period t 2 The amount of the substance; n is n CCS2,CO2,i (t) supplying methane reactor CO for carbon capture 2 The amount of the substance; n is n MR,H2,i (t) reference to methane reaction H during period t 2 The amount of the substance; n is n MR,CH4,i (t) CH generation for methane reactor during period t 4 The amount of the substance;
will be fed into methane reactor H 2 The amount of the substance and the generation of CH by a methane reactor 4 The mass rate of the substance is further converted to obtain input H 2 And generate CH 4 Is defined by the following formula:
wherein P is MR,H2,i (t) H is fed to the methane reactor during period t 2 Power, kW; p (P) MR,g,i (t) is CH output from the methane reactor during period t 4 A power; η (eta) MR,g Output efficiency of the methane reactor in the period t; m is M CH4 Is CH 4 Molar mass of (c); ρ CH4 Is CH 4 Is a density of (3);
3) Hydrogen storage tank model
The hydrogen energy loss generated in the process of filling and discharging the hydrogen storage tank is approximately represented by the filling and discharging efficiency, so that the model is as follows:
in the method, in the process of the invention,
the charging and discharging power, efficiency and capacity of the hydrogen storage tank in the micro-grid i in the period t are respectively;
4) Hydrogen-doped gas unit model
Natural gas is coupled with hydrogen generated in the formula (4) in a certain hydrogen adding proportion range and is supplied to a gas unit, and the gas unit has small influence on the operation of the gas unit under the hydrogen adding proportion of 10% -20%, so that the electrothermal conversion efficiency of the gas unit is set as a constant, and the mathematical model of the hydrogen adding gas turbine is as follows:
Wherein R is GT,i (t) is the hydrogen loading ratio of the gas turbine; v (V) GT,H2,i (t)、V GT,g,i (t) the gas volumes of hydrogen and natural gas input into the gas turbine in the period t, namely the gas volume flowing in the conveying pipeline in unit time, m 3 /h；HHV CH4 Is methaneA heating value; p (P) GT,g,i (t)、P GT,H2,i (t) natural gas and hydrogen power input to the gas turbine during time period t, respectively; p (P) GT,e,i (t) is the electrical power output by the gas turbine engine during period t; η (eta) GT,e Electrical conversion efficiency for a gas turbine; p (P) GT,h,i (t) is the thermal power output by the micro-grid i gas turbine in the period t, kW; η (eta) GT,h Heat conversion efficiency for the gas turbine;
the mathematical model of the hydrogen-doped gas boiler is as follows:
P GB,h,i (t)＝η GB,h (P GB,g,i (t)+P GB,H2,i (t)) (14)
wherein R is GB,i (t) is the hydrogen loading ratio of the gas boiler; v (V) GB,H2,i (t)、V GB,g,i (t) hydrogen and natural gas volumetric flows to the gas turbine during time period t, respectively; p (P) GB,g,i (t) and P GB,H2,i (t) natural gas and hydrogen power which are respectively input into a gas boiler in a period t, and kW; p (P) GB,h,i (t) is the thermal power output by the gas boiler in the period t, kW; η (eta) GB,h Is the heat conversion efficiency of the gas boiler.
Taking the thermal power duty ratio of the electric quantity of the power distribution network in the dispatching period as
The concrete calculation model of the carbon transaction cost is as follows:
m L,i ＝m GT0,i +m GB0,i +m TU0,i (16)
wherein m is GT0,i 、m GB0,i 、m TU0,i 、m L,i Respectively scheduling period internal combustion gas turbines, gas boilers, thermal power generating units and total carbon emission quota for the micro-grid i; sigma (sigma) e Carbon emission quota for unit power generation of the gas unit; sigma (sigma) h Carbon emission quota for unit heating power of the gas unit; sigma (sigma) p Carbon emission quota of unit power generation power of the thermal power generating unit;
wherein m is P,i (t) is the total actual carbon emissions in the microgrid i scheduling period; m is m GT,i (t)、m GB,i (t)、m TU,i (t) the actual carbon emission of the micro-grid i gas turbine, the gas boiler and the thermal power unit in the period t respectively; a, a i 、b i 、c i (i=1, 2, 3) carbon emission coefficients for gas unit power supply, gas unit heat supply and thermal power unit power supply, respectively;
wherein C is CO2,i Carbon trade cost for micro-net i; c is the carbon trade unit price on the market; upsilon is the rising range of the price of each ladder-type carbon transaction; omega is the carbon emission interval length and epsilon is the carbon trade rewarding coefficient.
The construction process of the single micro-grid cost model comprises the following steps:
the goal of optimized operation of micro-net i is total cost C i Minimum:
C i ＝min(C a,i +C buy,i +C TU,i +C CO2,i +C DR,i +C ES,i -C trade,i +C c,i ) (20)
wherein C is CO2,i The calculation formulas of the rest of the carbon transaction cost are as follows:
1) The wind and light discarding punishment cost calculation formula is as follows:
in delta a Punishment coefficients for wind and light abandoning; p (P) WTI,e,i (t)、P WT,e,i (t) respectively inputting and absorbing wind power to the micro-grid in a period t; p (P) PVI,e,i (t)、P PV,e,i (t) photovoltaic power input and consumed by the microgrid during period t;
2) The calculation formula of the purchase energy cost is as follows:
in delta buy,e (t)、δ sell,e (t) electricity prices for purchase at time period t, respectively; p (P) buy,e,i (t)、P sell,e,i (t) respectively purchasing electricity from the distribution network and selling electricity to the distribution network by the micro-grid i in a period t; delta buy,g (t) is the gas price for period t; p (P) buy,g,i (t) is the gas purchase power of the micro-grid in the period t;
3) The calculation formula of the start-stop and coal consumption cost of the thermal power generating unit is as follows:
wherein P is TU,e,i (t) the electric power output by the thermal power generating unit in the micro-grid i in the period t; a, a 4 、b 4 、c 4 The coal consumption cost coefficients of the thermal power generating unit are respectively; u (U) TU,i (t) is a binary variable, U TU,i (t) =1 indicates start-up, U TU,i (t) =1 indicates shutdown; delta TU The starting and stopping cost coefficient of the thermal power generating unit;
4) The demand response cost calculation formula is:
in delta cut,e 、δ cut,h Compensating cost coefficients for reducing the electric heating load respectively; delta tran,e 、δ tran,h Compensating cost coefficients for transferring electrothermal loads respectively;
5) The energy storage operation and maintenance cost calculation formula is as follows:
in delta ES1 、δ ES2 The cost coefficients of charge and discharge operation and maintenance of the electric and hydrogen energy storage equipment are respectively element/(kW.h);
6) The calculation formula of the electric energy transaction cost is as follows:
wherein P is e,ij (t) is the electric power interacted between micro net i and micro net j in period t, P e,ij (t)>0 represents that micro-grid i sells electricity to micro-grid j, delta e,ij (t) is the electricity price of interaction between the micro-grid i and the micro-grid j, and the element/(kW.h);
7) The calculation formula of the internet fee cost is as follows:
in the formula g e And the element/(kW.h) is the cost coefficient of the net fee.
The constraint is expressed as:
The electrical, thermal, gas, hydrogen balance constraints are expressed as:
P GT,h,i (t)+P GB,h,i (t)＝P load,h,i (t) (29)
P buy,g,i (t)+P MR,g,i (t)＝P GT,g,i (t)+P GB,g,i (t) (30)
wherein P is load,e,i (t)、P load,h,i (t) represents the electrical and thermal loads of the microgrid i, respectively.
The wind-light output constraint is expressed as:
wherein P is WTI,e,i (t) is wind power input power during period t; p (P) WT,e (t) wind power consumed by the microgrid in a period t; p (P) PVI,e,i (t) wind power input power of the micro-grid in a period t; p (P) PV,e,i (t) wind power consumed by the microgrid in a period t;
the device operation constraints are expressed as:
in the method, in the process of the invention,
the upper and lower limits of the output power of the device x are respectively; />
The upper and lower limits of the output power ramp of the device x are respectively defined.
The power trade constraint is expressed as:
in the method, in the process of the invention,
an upper limit of electric power for interaction of the micro-grid i with other micro-grids;
if transmission power loss is not considered, the sum of all the micro-grid interaction electric power in the period t is zero:
if transmission power loss is not considered, the sum of all micro-grid electric energy transaction costs in the period t is zero:
the specific process of the step 2 is as follows:
2.1, for the comprehensive energy multi-micro-grid, a plurality of uncertainty factors exist to influence the scheduling result, the risk loss faced by the comprehensive energy multi-micro-grid is calculated by adopting the conditional risk value, when the actual value of wind power and photovoltaic power is lower than a predicted value or the actual value of load is higher than the predicted value, the electric energy transaction power cannot meet the daily planning and causes loss of load, and the multi-micro-grid purchases the absent electric energy from the distribution grid; when the wind power and the photovoltaic power are higher than the predicted value or the load is lower than the predicted value, the multi-microgrid sells redundant electric energy to the distribution network; the risk loss function f (ζ, X) is characterized by the net interaction cost of the multiple micro-networks with the distribution network, the conditional risk value cost X of micro-network i CVaR,β,i Expressed as:
wherein N is the number of micro-nets, beta is the confidence coefficient and alpha i The risk value cost of the micro-grid i;
the formula (37) relaxes to:
wherein y is i,s The value that the conditional risk value cost of the micro-grid i in the scene s exceeds the risk value cost;
step 2.2, implementing two-stage optimization on the multi-microgrid, specifically:
in the comprehensive energy multi-microgrid, each microgrid can be regarded as a competing negotiation unit, nash negotiation is adopted for solving, and a mathematical model is solved as follows:
wherein C is 0,i For the maximum cost possible for micro-net i, C 0,i Cost for micro-net i;
in the first stage, the expression (40) is deformed as follows:
wherein C is 0,i For the maximum cost possible for the micro-grid i, i.e. without taking into account the cost of the micro-grid i during the power interaction, p s Probability of occurrence for the s-th typical scene; s is the number of typical scenes; k is a risk preference coefficient and represents the attitude of a micro-grid investor to risks, and the value range is [0,1]A smaller k indicates that the microgrid investors are at higher risk pursuing lower costs and obtaining the optimal solution by solving the model
Due to P e,ij (t)＝-P e,ji (t) the electric energy transaction costs will cancel each other out during the summation process, so that the sum of the electric energy transaction costs of each micro-grid is 0, but the net cost (formula (27)) still contains the electric power interaction variable P e,ij (t) the objective function of the comprehensive energy multi-microgrid is the sum of the running cost of each microgrid and the minimum electricity purchasing and selling risk:
wherein C is I Cost for the comprehensive energy multi-microgrid; the optimal solution interaction power can be obtained in the model
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And a second stage: after obtaining the optimal cost and optimal electric power interaction variable except the electric energy transaction cost in the micro-grid i, the formula (42) can be further converted into:
(43) in delta e,ij,s (t) as an optimization variable, the inequality ensures that each micro-grid can obtain benefits, and the objective function of the second problem is obtained after conversion:
delta e,ij,s (t) is an optimization variable, in the formula
Is the optimal solution of the first stage;
formulas (41) - (44) are comprehensive energy multi-microgrid two-stage optimization scheduling models based on Nash negotiations.
The specific process of the step 3 is as follows:
step 3.1, carrying out distributed solution on the formula (41) by utilizing an improved alternate direction multiplier algorithm, wherein the specific solution method is as follows:
1) Establishing an augmented lagrangian function for the micro-net i:
wherein lambda is ij,s (t) Lagrangian multiplier of the first stage optimization model, ρ being a penalty factor;
2) Setting the maximum iteration number l max 100, the interaction electric power P between the micro-net i and the micro-net j in the period t in the initial scene s e,ij,s (t)＝0，P e,ij,s (t) Lagrangian multiplier lambda ij,s Is passed through by iteration of (a)The process is as follows:
3) Judging whether the algorithm converges, and calculating an original residual error and a dual residual error:
in the method, in the process of the invention,
the original residual error of the electric energy transaction between the first iteration micro-grid i and the micro-grid j is the first +1st iteration micro-grid i and the micro-grid j; />
The dual residual error of the electric energy transaction of the i+1st iteration micro-grid and the micro-grid j is obtained;
the iteration stop conditions are:
wherein ε pri 、ε dual The upper limits of the original residual error and the dual residual error are respectively set;
4) Automatically updating a penalty factor through the quantitative relation between the original residual and the dual residual, wherein the dynamic penalty factor is expressed as:
where v is the proportionality coefficient of the original residual and the dual residual, θ 1 、θ 2 Respectively, accelerating convergence systemNumber and deceleration convergence factor (v, θ) 1 ,θ 2 >1)；
5) Outputting the dispatching result and the equipment output of each micro-network, and optimally solving the interaction power
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Step 3.2, the optimal solution obtained in the step 3.1
And->
In a second-stage objective function formula (44) of a comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation, the formula (44) is subjected to distributed solution by utilizing an improved alternate direction multiplier algorithm, and the calculated interactive electric quantity between the microgrids is calculated>
Electric energy trade cost->
And the scheduling result is obtained.
The beneficial effects of the invention are as follows:
1) A comprehensive energy multi-microgrid two-stage optimization scheduling model is established based on a Nash negotiation method in an uncertain environment, part of hydrogen generated in the electric hydrogen conversion process is mixed with fuel gas and then sent to a fuel gas unit, a hydrogen energy storage system comprising an electrolytic tank, a methane reactor, a hydrogen adding unit and a hydrogen storage tank is established, the system cost can be reduced, carbon emission is reduced, and electric energy sharing is performed among the microgrids.
2) After a carbon transaction mechanism is introduced, the multi-micro-grid system can realize the dual purposes of low carbon and economic operation, and is an effective means for realizing the low carbon operation of the comprehensive energy system.
3) The risk loss of the comprehensive energy multi-micro-grid is quantified by using the CVaR under the uncertain environment, the characteristic that the net interaction power of the multi-micro-grid and the power distribution network is changed due to the fact that the source load day-ahead prediction and the real-time power have errors is utilized, the risk loss function is represented by the net interaction cost of the multi-micro-grid and the power distribution network, and a proper risk coefficient is selected, so that the risk and the economical efficiency of the multi-micro-grid under the uncertain environment can be well measured.
4) The penalty factors can be dynamically corrected through the quantity relation between the original residual errors and the dual residual errors by adopting the improved alternate direction multiplier algorithm, so that the solving randomness caused by the initial penalty factors is weakened, and a more accurate and effective multi-micro-network optimized scheduling result is obtained.
Drawings
FIG. 1 is a schematic diagram of the thermal power duty cycle of the power distribution network in a scheduling period in the present invention;
FIG. 2 is a graph showing the variation of the power generation curves of the first, second and third exemplary solar photovoltaic, wind power and load prediction of the micro-grid under five scenes in the embodiment of the invention;
FIG. 3 is a schematic diagram of the result of a transaction of electrical energy between micro-grids according to an embodiment of the present invention;
Fig. 4 is a schematic diagram of a power optimization result of the micro-grid 1 according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a power optimization result of the micro-grid 2 according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a power optimization result of the micro-grid 3 according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a transaction electricity price optimization result between micro networks in an embodiment of the invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
The integrated energy micro-grid containing hydrogen-doped fuel gas comprises 4 types of energy sources of electricity, heat, gas and hydrogen, and main equipment of the integrated energy micro-grid comprises a carbon capture power plant, a hydrogen energy storage system, a photovoltaic device, a fan and a storage battery. The carbon emissions and absorption involved in the operation of each plant are ultimately traded by the carbon trade market.
The invention discloses a method for optimizing and dispatching hydrogen-containing fuel gas comprehensive energy multi-microgrid based on Nash negotiation, which is implemented according to the following steps:
step 1, carrying out low-carbon economical improvement on a comprehensive energy multi-microgrid, feeding the gas after hydrogen loading into a gas unit, and establishing a comprehensive energy microgrid model containing hydrogen loading gas;
the carbon capture power plant model construction process is as follows: output P of thermal power unit in t period in carbon capture power plant TU,e,i (t) supplying a portion to the carbon capture apparatus and another portion to the grid for supplying an electrical load; electric power P input by the carbon trapping device in t period CCS,e,i (t) is further divided into two parts: self-fixed electric power consumption P CCS,e1,i (t) and treatment of CO 2 Electric power consumption P of (2) CCS,e2,i (t)：
CO captured by carbon capture device 2 One part is sealed and the other part is supplied to the electric power conversion equipment, and the carbon capture power plant model is as follows:
wherein P is TU,e,i (t) is the electric power output by the thermal power generating unit in the micro-grid i (i=1, 2,3, …) in the period t, and kW; e, e TU Is the carbon emission intensity in unit time, m 3 /(kW·h)；V TU,CO2,i (t) CO emission of thermal power generating unit in period t 2 Volume flow, m 3 /h；λ CCS Treatment of unit volume flow CO for carbon capture plants 2 Energy consumption of (kW.h)/m 3 ；μ CCS Is carbon capture efficiency; v (V) CCS1,CO2,i (t) sequestering CO for carbon capture devices 2 Is a volume flow of (1); v (V) CCS2,CO2,i (t) supplying an electric gas conversion plant CO for carbon capture 2 V of volume flow of (2) CCS,CO2,i And (t) is the volume of carbon dioxide captured by the carbon capture.
Refining the electricity to gasFor the electric hydrogen conversion and methanation process and the realization of dimension unification, a hydrogen energy storage system comprising an electrolytic tank, a methane reactor, a hydrogen adding unit and a hydrogen storage tank is established. The electrolytic tank utilizes electrolytic water to produce hydrogen, realizes hydrogen storage through the hydrogen storage tank, and part of the produced hydrogen is reacted with CO in the methane reactor 2 The reaction is converted into methane, the methane is injected into a natural gas system, and the other part of methane is mixed with natural gas in a certain proportion to form hydrogen-doped fuel gas, and the hydrogen-doped fuel gas is sent into a gas turbine and a gas boiler to generate electric heat energy to supply load, so that hydrogen utilization is realized. Based on the above, the hydrogen energy storage system model construction process of the hydrogen-doped fuel gas comprises the following steps:
1) Unified cell model of dimension:
the electric energy is converted into hydrogen energy by the electrolytic tank, the hydrogen is produced by proton exchange electrolysis, and the constructed variable efficiency mathematical model is as follows:
wherein n is EL,H2,i (t) is the amount of hydrogen-generating material in the cells in the microgrid i during period t; n is n ELN Is the rated capacity of the electrolytic cell; p (P) EL,e,i (t) is the electric power input by the electrolytic cell during the period t; p (P) EL,eN Inputting rated value of electric power for the electrolytic tank; f (P) EL,e,i (t)) is an electrolyzer efficiency function; a, a EL 、b EL 、c EL Is an efficiency function coefficient;
according to the hydrogen production quality, the relation between the hydrogen production power and the hydrogen production material quantity can be deduced through the calculation of the material quantity and the molar mass, the gas volume flow and the density, the cost calculation and the constraint balance are carried out by uniformly using the power, the dimension uniformity is realized, and the electrolytic tank model with the uniform dimension is obtained as follows:
wherein n is EL,H2,i (t) is the amount of hydrogen-generating substances in the electrolysis cell in the micro-grid i in the period t, kmol; p (P) EL,e,i (t) is the electric power input by the electrolytic tank in the period tRate, kW; m is m EL,H2,i (t) is the mass of hydrogen produced by the electrolytic cell in the period t, kg; m is M H2 Is the molar mass of hydrogen; ρ H2 Is the density of hydrogen; Δt is the scheduling unit time, P EL,H2,i (t) is the hydrogen production power of the electrolyzer in the period t;
2) Methane reactor model with unified dimension
4H 2 +CO 2 →CH 4 +2H 2 O (5)
Since the chemical reaction equation is typically calculated using the amount of molecular species, carbon is captured of the CO supplied to the electrical conversion apparatus 2 The volume flow (formula (2)) is converted into the amount of substance as follows:
wherein m is CCS2,CO2,i (t) supplying electric gas conversion equipment CO for capturing carbon in micro-grid i in period t 2 Mass of (3) kg; n is n CCS2,CO2,i,i (t) supplying an electric gas conversion plant CO for carbon capture 2 Kmol; m is M CO2 Is CO 2 Molar mass of (c); v (V) CCS2,CO2,i (t) supplying an electric gas conversion plant CO for carbon capture 2 Is a volume flow of (1); ρ CO2 Is CO 2 Is a density of (3);
methane reactor CH generation 4 The amount of (2) depends on CO 2 And H is 2 The amount of the substance (B) and the reaction amount and the production amount can be determined by the following formula:
wherein n is MR1,H2,i (t) feeding the electrolyzer with methane reactor H during period t 2 The amount of the substance; n is n CCS2,CO2,i (t) supplying methane reactor CO for carbon capture 2 The amount of the substance; n is n MR,H2,i (t) reference to methane reaction H during period t 2 The amount of the substance; n is n MR,CH4,i (t) CH generation for methane reactor during period t 4 The amount of the substance;
similarly to formula (4), methane reactor H is fed 2 The amount of the substance and the generation of CH by a methane reactor 4 The mass rate of the substance is further converted to obtain input H 2 And generate CH 4 Is defined by the following formula:
wherein P is MR,H2,i (t) H is fed to the methane reactor during period t 2 Power, kW; p (P) MR,g,i (t) is CH output from the methane reactor during period t 4 A power; η (eta) MR,g Output efficiency of the methane reactor in the period t; m is M CH4 Is CH 4 Molar mass of (c); ρ CH4 Is CH 4 Is a density of (3);
3) Hydrogen storage tank model
The hydrogen energy loss generated in the process of filling and discharging the hydrogen storage tank is approximately represented by the filling and discharging efficiency, so that the model is as follows:
in the method, in the process of the invention,
E ES,H2,i (t) respectively the hydrogen charging and discharging power, efficiency and capacity of the hydrogen storage tank in the micro-grid i in the period t;
4) Hydrogen-doped gas unit model
Natural gas is coupled with hydrogen generated in the formula (4) in a certain hydrogen adding proportion range and is supplied to a gas unit, and the gas unit has small influence on the operation of the gas unit under the hydrogen adding proportion of 10% -20%, so that the electrothermal conversion efficiency of the gas unit is set as a constant, and the mathematical model of the hydrogen adding gas turbine is as follows:
wherein R is GT,i (t) is the hydrogen loading ratio of the gas turbine; v (V) GT,H2,i (t)、V GT,g,i (t) the gas volumes of hydrogen and natural gas input into the gas turbine in the period t, namely the gas volume flowing in the conveying pipeline in unit time, m 3 /h；HHV CH4 Is the calorific value of methane; p (P) GT,g,i (t)、P GT,H2,i (t) natural gas and hydrogen power input to the gas turbine during time period t, respectively; p (P) GT,e,i (t) is the electrical power output by the gas turbine engine during period t; η (eta) GT,e Electrical conversion efficiency for a gas turbine; p (P) GT,h,i (t) is the thermal power output by the micro-grid i gas turbine in the period t, kW; η (eta) GT,h Heat conversion efficiency for the gas turbine;
the mathematical model of the hydrogen-doped gas boiler is as follows:
P GB,h,i (t)＝η GB,h (P GB,g,i (t)+P GB,H2,i (t)) (14)
wherein R is GB,i (t) is the hydrogen loading ratio of the gas boiler; v (V) GB,H2,i (t)、V GB,g,i (t) hydrogen and natural gas volumetric flows to the gas turbine during time period t, respectively; p (P) GB,g,i (t) and P GB,H2,i (t) natural gas and hydrogen power which are respectively input into a gas boiler in a period t, and kW; p (P) GB,h,i (t) is the thermal power output by the gas boiler in the period t, kW; η (eta) GB,h Is the heat conversion efficiency of the gas boiler.
Introducing a carbon transaction machine to manufacture a micro-grid energy efficiency lifting mechanism, wherein a conventional model is adopted for demand response; the construction process of the comprehensive energy efficiency improving mechanism model of the energy micro-grid comprises the following steps:
since outsourcing electricity can be sourced from a thermal power generating unit, distribution is calculatedAnd the thermal power duty ratio in the network is used for obtaining the equivalent thermal power unit power in electricity purchasing, so as to obtain the carbon emission generated by external electricity purchasing, thereby finely quantifying the carbon emission of the multi-micro-network comprehensive energy system. The electric quantity of the power distribution network in the researched area mainly comes from thermal power, wind power and hydropower, and the thermal power duty ratio in the electric quantity of the power distribution network in the dispatching cycle is obtained by consulting the installed capacity homonymy data of the area
As shown in fig. 1, the concrete calculation model of the carbon transaction cost is as follows:
m L,i ＝m GT0,i +m GB0,i +m TU0,i (16)
wherein m is GT0,i 、m GB0,i 、m TU0,i 、m L,i Respectively scheduling period internal combustion gas turbines, gas boilers, thermal power generating units and total carbon emission quota for the micro-grid i; sigma (sigma) e Carbon emission quota for unit power generation of the gas unit; sigma (sigma) h Carbon emission quota for unit heating power of the gas unit; sigma (sigma) p Carbon emission quota of unit power generation power of the thermal power generating unit;
wherein m is P,i (t) is the total actual carbon emissions in the microgrid i scheduling period; m is m GT,i (t)、m GB,i (t)、m TU,i (t) the actual carbon emission of the micro-grid i gas turbine, the gas boiler and the thermal power unit in the period t respectively; a, a i 、b i 、c i (i=1, 2, 3) carbon emission coefficients for gas unit power supply, gas unit heat supply and thermal power unit power supply, respectively;
wherein C is CO2,i Carbon trade cost for micro-net i; c is the carbon trade unit price on the market; upsilon is the rising range of the price of each ladder-type carbon transaction; omega is the carbon emission interval length and epsilon is the carbon trade rewarding coefficient.
The construction process of the single micro-grid cost model comprises the following steps:
the goal of optimized operation of micro-net i is total cost C i Minimum:
C i ＝min(C a,i +C buy,i +C TU,i +C CO2,i +C DR,i +C ES,i -C trade,i +C c,i ) (20)
wherein C is CO2,i The calculation formulas of the rest of the carbon transaction cost are as follows:
1) The wind and light discarding punishment cost calculation formula is as follows:
in delta a Punishment coefficients for wind and light abandoning; p (P) WTI,e,i (t)、P WT,e,i (t) respectively inputting and absorbing wind power to the micro-grid in a period t; p (P) PVI,e,i (t)、P PV,e,i (t) photovoltaic power input and consumed by the microgrid during period t;
2) The calculation formula of the purchase energy cost is as follows:
in delta buy,e (t)、δ sell,e (t) electricity prices for purchase at time period t, respectively; p (P) buy,e,i (t)、P sell,e,i (t) respectively purchasing electricity from the distribution network and selling electricity to the distribution network by the micro-grid i in a period t; delta buy,g (t) is the gas price for period t; p (P) buy,g,i (t) is the gas purchase power of the micro-grid in the period t;
3) The calculation formula of the start-stop and coal consumption cost of the thermal power generating unit is as follows:
wherein P is TU,e,i (t) the electric power output by the thermal power generating unit in the micro-grid i in the period t; a, a 4 、b 4 、c 4 The coal consumption cost coefficients of the thermal power generating unit are respectively; u (U) TU,i (t) is a binary variable, U TU,i (t) =1 indicates start-up, U TU,i (t) =1 indicates shutdown; delta TU The starting and stopping cost coefficient of the thermal power generating unit;
4) The demand response cost calculation formula is:
in delta cut,e 、δ cut,h Compensating cost coefficients for reducing the electric heating load respectively; delta tran,e 、δ tran,h Compensating cost coefficients for transferring electrothermal loads respectively;
5) The energy storage operation and maintenance cost calculation formula is as follows:
in delta ES1 、δ ES2 The cost coefficients of charge and discharge operation and maintenance of the electric and hydrogen energy storage equipment are respectively element/(kW.h);
6) The calculation formula of the electric energy transaction cost is as follows:
wherein P is e,ij (t) is the electric power interacted between micro net i and micro net j in period t, P e,ij (t)>0 represents that micro-grid i sells electricity to micro-grid j, delta e,ij (t) is the electricity price of interaction between the micro-grid i and the micro-grid j, and the element/(kW.h);
7) The calculation formula of the internet fee cost is as follows:
electric energy interaction is carried out among the micro networks through the energy routers, certain network fee is required to be paid to the power distribution network, and the model is as follows:
in the formula g e And the element/(kW.h) is the cost coefficient of the net fee.
Constraint conditions comprise electricity, heat, gas, hydrogen balance constraint, wind-light output constraint, equipment operation constraint, energy purchasing upper and lower limits and electric energy transaction constraint; the power trade constraint is thus examined as follows:
the electrical, thermal, gas, hydrogen balance constraints are expressed as:
P GT,h,i (t)+P GB,h,i (t)＝P load,h,i (t) (29)
P buy,g,i (t)+P MR,g,i (t)＝P GT,g,i (t)+P GB,g,i (t) (30)
wherein P is load,e,i (t)、P load,h,i (t) represents the electrical and thermal loads of the microgrid i, respectively;
the wind-light output constraint is expressed as:
wherein P is WTI,e,i (t) is wind power input power during period t; p (P) WT,e (t) wind power consumed by the microgrid in a period t; p (P) PVI,e,i (t) wind power input power of the micro-grid in a period t; p (P) PV,e,i (t) wind power consumed by the microgrid in a period t;
the device operation constraints are expressed as:
in the method, in the process of the invention,
the upper and lower limits of the output power of the device x are respectively; />
The upper and lower limits of the output power ramp of the device x are respectively defined.
The power trade constraint is expressed as:
in the method, in the process of the invention,
an upper limit of electric power for interaction of the micro-grid i with other micro-grids;
if transmission power loss is not considered, the sum of all the micro-grid interaction electric power in the period t is zero:
If transmission power loss is not considered, the sum of all micro-grid electric energy transaction costs in the period t is zero:
step 2, calculating a condition risk value through net interaction cost of the multi-microgrid and the power distribution network, and implementing two-stage optimization on the multi-microgrid by utilizing Nash negotiation theory, wherein the first stage minimizes the total cost of the multi-microgrid, the second stage distributes benefits and maximizes benefits of each microgrid, and a comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation is constructed; the specific process is as follows:
step 2.1,In the comprehensive energy multi-microgrid, through electric energy interaction of each microgrid, electricity purchasing from a power distribution network is reduced, the running cost of the multi-microgrid is reduced, the renewable energy consumption rate is improved, and carbon emission is reduced. When the actual values of wind power and photovoltaic power are lower than the predicted value or the actual value of load is higher than the predicted value, the loss of load caused by the fact that the electric energy transaction power cannot meet the daily planning can be avoided, and the multi-microgrid can purchase the absent electric energy from the distribution network; when the wind power and the photovoltaic power are higher than the predicted value or the load is lower than the predicted value, the multi-microgrid sells redundant electric energy to the distribution network; the risk loss function f (ζ, X) is characterized by the net interaction cost of the multiple micro-networks with the distribution network, the conditional risk value cost X of micro-network i CVaR,β,i Expressed as:
wherein N is the number of micro-nets, beta is the confidence coefficient and alpha i The risk value cost of the micro-grid i;
for ease of calculation, the formula (29) is relaxed as:
wherein y is i,s The value that the conditional risk value cost of the micro-grid i in the scene s exceeds the risk value cost;
and 2.2, modeling by utilizing Nash negotiation theory and converting the modeling into two easily solved sub-problems, namely, the cost minimization of multiple micro-networks and the income maximization of each micro-network, and carrying out optimal scheduling in two stages. Two-stage optimization is implemented on the multi-microgrid, specifically:
in the comprehensive energy multi-microgrid, each microgrid can be regarded as a competing negotiation unit, nash negotiation is adopted for solving, and a mathematical model is solved as follows:
wherein C is 0,i For the maximum cost possible for micro-net i, C 0,i Cost for micro-net i;
the first stage calculation model is as follows:
wherein C is 0,i For the maximum cost possible for the micro-grid i, i.e. without taking into account the cost of the micro-grid i during the power interaction, p s Probability of occurrence for the s-th typical scene; s is the number of typical scenes; k is a risk preference coefficient and represents the attitude of a micro-grid investor to risks, and the value range is [0,1]A smaller k indicates that the microgrid investors are at higher risk pursuing lower costs and obtaining the optimal solution by solving the model 
Due to P e,ij (t)＝-P e,ji (t) the electric energy transaction costs will cancel each other out during the summation process, so that the sum of the electric energy transaction costs of each micro-grid is 0, but the net cost (formula (27)) still contains the electric power interaction variable P e,ij (t) the objective function of the comprehensive energy multi-microgrid is the sum of the running cost of each microgrid and the minimum electricity purchasing and selling risk:
wherein C is I Cost for the comprehensive energy multi-microgrid; the objective function of the first stage is now a non-negative weighted sum of the convex functions, still being the convex function. The optimal solution interaction power can be obtained in the model
Internet crossing fee->
Condition risk value->
And a second stage: after obtaining the optimal cost and optimal electric power interaction variable except the electric energy transaction cost in the micro-grid i, the formula (42) can be further converted into:
(43) in delta e,ij,s (t) as an optimization variable, the inequality ensures that each micro-grid can obtain benefits, and the objective function of the second problem is obtained after conversion:
delta e,ij,s (t) is an optimization variable, in the formula
Is the optimal solution of the first stage;
formulas (41) - (44) are comprehensive energy multi-microgrid two-stage optimization scheduling models based on Nash negotiations.
And 3, carrying out distributed optimization solution on the comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation by improving an alternate direction multiplier algorithm, wherein the obtained parameters are scheduling results. The specific process is as follows:
Step 3.1, carrying out distributed solution on the formula (44) by utilizing an improved alternate direction multiplier algorithm, wherein the specific solution method is as follows:
considering that the multi-microgrid cost minimization sub-problem and the individual microgrid benefit maximization sub-problem have separable convex functions and constraints, the formula (44) is solved in a distributed manner by using the improved alternate direction multiplier algorithm, and the specific solving method is as follows:
1) Establishing an augmented lagrangian function for the micro-net i:
wherein lambda is ij,s (t) Lagrangian multiplier of the first stage optimization model, ρ being a penalty factor;
2) Setting the maximum iteration number l max 100, the interaction electric power P between the micro-net i and the micro-net j in the period t in the initial scene s e,ij,s (t)＝0，P e,ij,s (t) Lagrangian multiplier lambda ij,s The iterative process of (a) is as follows:
3) Judging whether the algorithm converges, and calculating an original residual error and a dual residual error:
in the method, in the process of the invention,
the original residual error of the electric energy transaction between the first iteration micro-grid i and the micro-grid j is the first +1st iteration micro-grid i and the micro-grid j; />
The dual residual error of the electric energy transaction of the i+1st iteration micro-grid and the micro-grid j is obtained;
the iteration stop conditions are:
wherein ε pri 、ε dual The upper limits of the original residual error and the dual residual error are respectively set;
4) The penalty factor ρ in the conventional ADMM algorithm is an artificially given empirical constant, and if the setting is unreasonable, the convergence speed of the whole algorithm may be affected. Automatically updating a penalty factor through the quantitative relation between the original residual and the dual residual, wherein the dynamic penalty factor is expressed as:
Where v is the proportionality coefficient of the original residual and the dual residual, θ 1 、θ 2 Acceleration and deceleration convergence coefficients (v, θ), respectively 1 ,θ 2 >1)；
5) Outputting the dispatching result and the equipment output of each micro-network, and optimally solving the interaction power
Internet crossing fee->
Condition risk value->
Step 3.2, the optimal solution obtained in the step 3.1
And->
In a second-stage objective function formula (44) of a comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation, the formula (44) is subjected to distributed solution by utilizing an improved alternate direction multiplier algorithm, and the calculated interactive electric quantity between the microgrids is calculated>
Electric energy trade cost->
And the scheduling result is obtained.
Description of the preferred embodiments
Selecting a comprehensive energy multi-microgrid as an example test system, wherein the system comprises three microgrids MG1, MG2 and MG3, and parameters of equipment in the microgrid are shown in a table 1;
TABLE 1
The wind-light power prediction curve and the electric heating load curve of each micro-grid on a typical day are shown in fig. 2; the electricity purchasing price and the gas purchasing price of each micro-grid are shown in tables 2 and 3; the carbon emission coefficients are shown in Table 4.
TABLE 2
TABLE 4 Table 4
Assuming that the prediction errors of wind power, photovoltaic and load are respectively subjected to normal distribution with the mean value of 0 and the standard deviation of 0.1,0.08,0.03, the confidence coefficient is 0.95, and the risk coefficient is initially set to be 0.5. And 5 groups of typical scenes and the occurrence probability of the corresponding scenes are obtained by using a first-stage solving method. And (3) modeling the calculation example simulation by adopting a Yalmip optimizing tool under the MATLAB R2018b compiling environment, and solving by using a solver Gurobi.
In order to verify the effectiveness of the multi-micro-grid two-stage optimization scheduling model, according to the independent operation or the cooperative operation of each micro-grid, a hydrogen energy storage system comprises a gas unit or a conventional gas unit with the hydrogen loading ratio of 15%, four scenes are set for comparison analysis, and the comparison analysis is shown in a table 5:
TABLE 5
In the model provided by the invention, the solving result of the scene 1 is the negotiating breaking point of the scene 2, and the solving result of the scene 3 is the negotiating breaking point of the scene 4. Based on the solving result, comparing each cost and equipment output of each micro-grid in four scenes, as shown in table 6:
TABLE 6
The data from table 6 results in the following:
1) As can be obtained by comparing scenes 1 and 2 and scenes 3 and 4, the cost of each micro-grid in scene 2 is reduced by 3516.18, 3584.86 and 3040.13 yuan compared with scene 1, the wind and light absorption rate is improved by 11.6%, 10.4% and 10.8%, and the total cost of multiple micro-grids is reduced by 10141.17 yuan. Compared with the scene 3, the cost of each micro-grid in the scene 4 is respectively reduced by 4944.49, 4652.05 and 2680.08 yuan, the wind and light absorption rate is respectively improved by 11.9 percent, 7.5 percent and 7.2 percent, the total cost of the multi-micro-grid is reduced by 12276.62 yuan, and therefore, the distributed scheduling model based on Nash negotiation can preferentially eliminate new energy sources through electric energy sharing by taking uncertainty measurement into consideration, and the power generation of a thermal power unit and the output of a gas unit are reduced, so that gas and electricity purchasing are reduced, the running cost of each micro-grid is reduced, and the reciprocal win-win of each micro-grid is realized.
2) As can be obtained by comparing scenes 1 and 3 and scenes 2 and 4, the cost of each micro-grid in scene 3 is reduced by 817.15, 1298.75 and 1316.77 yuan compared with scene 1, the wind and light absorption rate is improved by 1.1%, 3.7% and 3.8%, and the total cost of multiple micro-grids is reduced by 3432.67 yuan. Compared with the scene 2, the cost of each micro-grid in the scene 4 is respectively reduced by 2245.46, 2365.94 and 956.72 yuan, the wind and light absorption rate is respectively improved by 1.4%, 0.8% and 0.2%, and the total cost of the multi-micro-grid is reduced by 5568.12 yuan. The method is characterized in that after the gas is taken into account, the waste wind and the waste light are converted into hydrogen and natural gas by electric conversion and sent into a gas unit to supply electric heating load, so that the wind and solar energy absorption is promoted and the recycling of hydrogen energy is realized. Because the electric energy of electricity-to-hydrogen conversion is from the wind-light power of the self, electricity purchasing from a power distribution network is reduced, and therefore the cost of the micro-grid is saved.
3) The actual carbon emission of each micro-grid in the scene 2 is respectively reduced by 1026.65, 1156.56 and 1305.52kg compared with the scene 1, and the total carbon emission of the multi-micro-grid is reduced by 3488.73kg. The actual carbon emission of each micro-grid in the scene 4 is respectively reduced by 1043.65, 1197.56 and 1265.75kg compared with the scene 3, and the total carbon emission of the multi-micro-grid is reduced by 3506.96kg. The method is characterized in that after each micro-grid participates in electric energy transaction, through reasonable negotiation, each micro-grid exchanges electric energy in real time, new energy sources are preferentially consumed, and power generation of a thermal power unit and output of a gas unit are reduced, so that carbon emission is effectively reduced, and low-carbon and environment-friendly operation of multiple micro-grids is promoted.
4) Compared with the scene 1, the actual carbon emission of each micro-grid in the scene 3 is respectively reduced by 811.30, 415.33 and 552.87kg, and the total carbon emission of the multi-micro-grid is reduced by 1779.5kg; the actual carbon emission of each micro-grid in the scene 4 is reduced by 828.30, 456.33 and 513.10kg respectively compared with the scene 2, and the total carbon emission of the multi-micro-grid is reduced by 1797.73kg. After the gas unit is doped with hydrogen, the waste wind and waste light are converted into hydrogen and natural gas by electric conversion and are mixed and sent into the gas unit, so that the output of the thermal power unit is reduced, the purchase power of the distribution network is reduced, and the carbon emission of the equivalent thermal power unit is reduced. After hydrogen is added, the gas purchasing is increased, the hydrogen demand is increased due to the fact that the hydrogen adding ratio is fixed, the output of an electrolytic tank is increased, the power generation power of a gas turbine is correspondingly increased, and then the carbon emission of the gas turbine is increased, the heat generation power of the gas turbine is also increased due to the fact that the thermoelectric ratio of the gas turbine is fixed, the heat generation power of a gas boiler is correspondingly reduced, the carbon emission of the gas boiler is reduced, and the actual carbon emission total amount of each micro-grid is still reduced due to the fact that the carbon emission of the unit power generation power of the gas turbine is lower and the new energy consumption is increased.
Based on the scenario 4, taking the first uncertain typical scenario as an example for analysis, the electric energy transaction result between micro-networks is shown in fig. 3, and the electric energy optimization result inside each micro-network is shown in fig. 4, 5 and 6.
As can be seen from table 6, fig. 4, fig. 5 and fig. 6, the three micro-grids all preferentially consume new energy, the hydrogen-doped gas turbine realizes carbon and hydrogen circulation, the dispatching cost is saved, and the carbon emission per unit power generation is low, so that the output priority of the hydrogen-doped gas turbine is highest, and the output is all in the dispatching period. The three micro-grids only purchase electricity from the power distribution network in certain time periods, but do not sell electricity to the power distribution network, because the multi-micro-grid electric energy transaction in the first-stage optimization only considers the network charge, and though the electricity is sold to the power distribution network for profit, the electric energy interaction cost with other micro-grids is lower than that with the power distribution network in the whole, so that the micro-grids preferentially interact with the electric energy with other micro-grids, but the constraint that the sum of the electric power interaction of all micro-grids is zero in the scheduling time period is required to be met, and only in the scheduling time period when the sum of new energy, a gas turbine and electricity purchase from other micro-grids still does not meet the electric load, the micro-grids purchase electricity from the power distribution network.
The trading electricity prices among the micro-grids are shown in fig. 7, and the electricity trading prices among the micro-grids are optimized and formulated through the second stage, so that the electricity price of a certain micro-grid selling electricity to other micro-grids is higher than the electricity price of selling electricity to a power grid in the whole dispatching period, and the electricity price of purchasing electricity from other micro-grids is lower than the electricity price of purchasing electricity from the power grid, so that the economy of each micro-grid is improved.
Claims (9)
1. The integrated energy multi-microgrid optimal scheduling method for hydrogen-containing fuel gas based on Nash negotiation is characterized by comprising the following steps of:
step 1, carrying out low-carbon economical improvement on a comprehensive energy multi-microgrid, feeding the gas after hydrogen loading into a gas unit, and establishing a comprehensive energy microgrid model containing hydrogen loading gas;
step 2, calculating a condition risk value through net interaction cost of the multi-microgrid and the power distribution network, and implementing two-stage optimization on the multi-microgrid by utilizing Nash negotiation theory, wherein the first stage minimizes the total cost of the multi-microgrid, the second stage distributes benefits and maximizes benefits of each microgrid, and a comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation is constructed;
and 3, carrying out distributed optimization solution on the comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation by improving an alternate direction multiplier algorithm, wherein the obtained parameters are scheduling results.
2. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 1, wherein the hydrogen-containing energy storage model for hydrogen-containing fuel gas in step 1 comprises an integrated energy microgrid equipment output model, an integrated energy microgrid energy efficiency lifting mechanism model, a single microgrid cost model and constraint conditions, and the integrated energy microgrid equipment output model comprises a carbon capture power plant model and a hydrogen energy storage system model for hydrogen-containing fuel gas.
3. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 2, wherein the carbon capture power plant model construction process is as follows: output P of thermal power unit in t period in carbon capture power plant TU,e,i (t) supplying a portion to the carbon capture apparatus and another portion to the grid for supplying an electrical load; electric power P input by the carbon trapping device in t period CCS,e,i (t) is further divided into two parts: self-fixed electric power consumption P CCS,e1,i (t) and treatment of CO 2 Electric power consumption P of (2) CCS,e2,i (t)：
CO captured by carbon capture device 2 One part is sealed and the other part is supplied to the electric power conversion equipment, and the carbon capture power plant model is as follows:
wherein P is TU,e,i (t) is the electric power output by the thermal power generating unit in the micro-grid i (i=1, 2,3, …) in the period t, and kW; e, e TU Is the carbon emission intensity in unit time, m 3 /(kW·h)；V TU,CO2,i (t) CO emission of thermal power generating unit in period t 2 Volume flow, m 3 /h；λ CCS Treating a unit volume stream for a carbon capture plantQuantity of CO 2 Energy consumption of (kW.h)/m 3 ；μ CCS Is carbon capture efficiency; v (V) CCS1,CO2,i (t) sequestering CO for carbon capture devices 2 Is a volume flow of (1); v (V) CCS2,CO2,i (t) supplying an electric gas conversion plant CO for carbon capture 2 V of volume flow of (2) CCS,CO2,i (t) capturing CO for carbon capture 2 Is provided).
4. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 2, wherein the hydrogen energy storage system model construction process of the hydrogen-containing fuel gas is as follows:
1) Unified cell model of dimension:
the electric energy is converted into hydrogen energy by the electrolytic tank, the hydrogen is produced by proton exchange electrolysis, and the constructed variable efficiency mathematical model is as follows:
wherein n is EL,H2,i (t) is the amount of hydrogen-generating material in the cells in the microgrid i during period t; n is n ELN Is the rated capacity of the electrolytic cell; p (P) EL,e,i (t) is the electric power input by the electrolytic cell during the period t; p (P) EL,eN Inputting rated value of electric power for the electrolytic tank; f (P) EL,e,i (t)) is an electrolyzer efficiency function; a, a EL 、b EL 、c EL Is an efficiency function coefficient;
the electrolytic tank model with unified dimensions is as follows:
wherein n is EL,H2,i (t) is the amount of hydrogen-generating substances in the electrolysis cell in the micro-grid i in the period t, kmol; p (P) EL,e,i (t) is the electric power input by the electrolytic cell in the period t, kW; m is m EL,H2,i (t) is the mass of hydrogen produced by the electrolytic cell in the period t, kg; m is M H2 Is the molar mass of hydrogen; ρ H2 Is the density of hydrogenThe method comprises the steps of carrying out a first treatment on the surface of the Δt is the scheduling unit time, P EL,H2,i (t) is the hydrogen production power of the electrolyzer in the period t;
2) Methane reactor model with unified dimension
4H 2 +CO 2 →CH 4 +2H 2 O (5) since the chemical reaction equation is generally calculated from the amount of molecular species, carbon is captured and supplied to the CO of the electric conversion apparatus 2 The volume flow (formula (2)) is converted into the amount of substance as follows:
wherein m is CCS2,CO2,i (t) supplying electric gas conversion equipment CO for capturing carbon in micro-grid i in period t 2 Mass of (3) kg; n is n CCS2,CO2,i,i (t) supplying an electric gas conversion plant CO for carbon capture 2 Kmol; m is M CO2 Is CO 2 Molar mass of (c); v (V) CCS2,CO2,i (t) supplying an electric gas conversion plant CO for carbon capture 2 Is a volume flow of (1); ρ CO2 Is CO 2 Is a density of (3);
methane reactor CH generation 4 The amount of (2) depends on CO 2 And H is 2 The amount of the substance (B) and the reaction amount and the production amount can be determined by the following formula:
wherein n is MR1,H2,i (t) feeding the electrolyzer with methane reactor H during period t 2 The amount of the substance; n is n CCS2,CO2,i (t) supplying methane reactor CO for carbon capture 2 The amount of the substance; n is n MR,H2,i (t) reference to methane reaction H during period t 2 The amount of the substance; n is n MR,CH4,i (t) CH generation for methane reactor during period t 4 The amount of the substance;
will be fed into methane reactor H 2 The amount of the substance and the generation of CH by a methane reactor 4 The mass rate of the substance is further converted intoTo input H 2 And generate CH 4 Is defined by the following formula:
wherein P is MR,H2,i (t) H is fed to the methane reactor during period t 2 Power, kW; p (P) MR,g,i (t) is CH output from the methane reactor during period t 4 A power; η (eta) MR,g Output efficiency of the methane reactor in the period t; m is M CH4 Is CH 4 Molar mass of (c); ρ CH4 Is CH 4 Is a density of (3);
3) Hydrogen storage tank model
The hydrogen energy loss generated in the process of filling and discharging the hydrogen storage tank is approximately represented by the filling and discharging efficiency, so that the model is as follows:
In the method, in the process of the invention,
the charging and discharging power, efficiency and capacity of the hydrogen storage tank in the micro-grid i in the period t are respectively;
4) Hydrogen-doped gas unit model
Natural gas is coupled with hydrogen generated in the formula (4) in a certain hydrogen adding proportion range and is supplied to a gas unit, and the gas unit has small influence on the operation of the gas unit under the hydrogen adding proportion of 10% -20%, so that the electrothermal conversion efficiency of the gas unit is set as a constant, and the mathematical model of the hydrogen adding gas turbine is as follows:
wherein R is GT,i (t) is the hydrogen loading ratio of the gas turbine; v (V) GT,H2,i (t)、V GT,g,i (t) the gas volumes of hydrogen and natural gas input into the gas turbine in the period t, namely the gas volume flowing in the conveying pipeline in unit time, m 3 /h；HHV CH4 Is the calorific value of methane; p (P) GT,g,i (t)、P GT,H2,i (t) natural gas and hydrogen power input to the gas turbine during time period t, respectively; p (P) GT,e,i (t) is the electrical power output by the gas turbine engine during period t; η (eta) GT,e Electrical conversion efficiency for a gas turbine; p (P) GT,h,i (t) is the thermal power output by the micro-grid i gas turbine in the period t, kW; η (eta) GT,h Heat conversion efficiency for the gas turbine;
the mathematical model of the hydrogen-doped gas boiler is as follows:
wherein R is GB,i (t) is the hydrogen loading ratio of the gas boiler; v (V) GB,H2,i (t)、V GB,g,i (t) hydrogen and natural gas volumetric flows to the gas turbine during time period t, respectively; p (P) GB,g,i (t) and P GB,H2,i (t) natural gas and hydrogen power which are respectively input into a gas boiler in a period t, and kW; p (P) GB,h,i (t) is the thermal power output by the gas boiler in the period t, kW; η (eta) GB,h Is the heat conversion efficiency of the gas boiler.
5. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 2, wherein the integrated energy microgrid energy efficiency improvement mechanism model construction process is as follows:
taking the thermal power duty ratio of the electric quantity of the power distribution network in the dispatching period as
The concrete calculation model of the carbon transaction cost is as follows:
m L,i ＝m GT0,i +m GB0,i +m TU0,i (16)
wherein m is GT0,i 、m GB0,i 、m TU0,i 、m L,i Respectively scheduling period internal combustion gas turbines, gas boilers, thermal power generating units and total carbon emission quota for the micro-grid i; sigma (sigma) e Carbon emission quota for unit power generation of the gas unit; sigma (sigma) h Carbon emission quota for unit heating power of the gas unit; sigma (sigma) p Carbon emission quota of unit power generation power of the thermal power generating unit;
wherein m is P,i (t) is the total actual carbon emissions in the microgrid i scheduling period; m is m GT,i (t)、m GB,i (t)、m TU,i (t) the actual carbon emission of the micro-grid i gas turbine, the gas boiler and the thermal power unit in the period t respectively; a, a i 、b i 、c i (i=1, 2, 3) carbon emission coefficients for gas unit power supply, gas unit heat supply and thermal power unit power supply, respectively;
wherein C is CO2,i Carbon trade cost for micro-net i; c is the carbon trade unit price on the market; gamma is the price rising range of each ladder-type carbon transaction; omega is the carbon emission interval length and epsilon is the carbon trade rewarding coefficient.
6. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 2, wherein the single-microgrid cost model construction process is as follows:
the goal of optimized operation of micro-net i is total cost C i Minimum:
C i ＝min(C a,i +C buy,i +C TU,i +C CO2,i +C DR,i +C ES,i -C trade,i +C c,i ) (20)
wherein C is CO2,i The calculation formulas of the rest of the carbon transaction cost are as follows:
1) The wind and light discarding punishment cost calculation formula is as follows:
in delta a Punishment coefficients for wind and light abandoning; p (P) WTI,e,i (t)、P WT,e,i (t) respectively inputting and absorbing wind power to the micro-grid in a period t; p (P) PVI,e,i (t)、P PV,e,i (t) photovoltaic power input and consumed by the microgrid during period t;
2) The calculation formula of the purchase energy cost is as follows:
in delta buy,e (t)、δ sell,e (t) electricity prices for purchase at time period t, respectively; p (P) buy,e,i (t)、P sell,e,i (t) scoreThe power of the micro-grid i purchasing electricity from the power distribution network and selling electricity to the power distribution network in the period t are respectively; delta buy,g (t) is the gas price for period t; p (P) buy,g,i (t) is the gas purchase power of the micro-grid in the period t;
3) The calculation formula of the start-stop and coal consumption cost of the thermal power generating unit is as follows:
wherein P is TU,e,i (t) the electric power output by the thermal power generating unit in the micro-grid i in the period t; a, a 4 、b 4 、c 4 The coal consumption cost coefficients of the thermal power generating unit are respectively; u (U) TU,i (t) is a binary variable, U TU,i (t) =1 indicates start-up, U TU,i (t) =1 indicates shutdown; delta TU The starting and stopping cost coefficient of the thermal power generating unit;
4) The demand response cost calculation formula is:
In delta cut,e 、δ cut,h Compensating cost coefficients for reducing the electric heating load respectively; delta tran,e 、δ tran,h Compensating cost coefficients for transferring electrothermal loads respectively;
5) The energy storage operation and maintenance cost calculation formula is as follows:
in delta ES1 、δ ES2 The cost coefficients of charge and discharge operation and maintenance of the electric and hydrogen energy storage equipment are respectively element/(kW.h);
6) The calculation formula of the electric energy transaction cost is as follows:
wherein P is e,ij (t) is the electric power interacted between micro net i and micro net j in period t, P e,ij (t)>0 represents that micro-grid i sells electricity to micro-grid j, delta e,ij (t) is the electricity price of interaction between the micro-grid i and the micro-grid j, and the element/(kW.h);
7) The calculation formula of the internet fee cost is as follows:
in the formula g e And the element/(kW.h) is the cost coefficient of the net fee.
7. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method of hydrogen-containing fuel gas of claim 6, wherein the constraint condition is expressed as:
the electrical, thermal, gas, hydrogen balance constraints are expressed as:
P GT,h,i (t)+P GB,h,i (t)＝P load,h,i (t) (29)
P buy,g,i (t)+P MR,g,i (t)＝P GT,g,i (t)+P GB,g,i (t) (30)
wherein P is load,e,i (t)、P load,h,i (t) represents the electrical and thermal loads of the microgrid i, respectively;
the wind-light output constraint is expressed as:
wherein P is WTI,e,i (t) is wind power input power during period t; p (P) WT,e (t) wind power consumed by the microgrid in a period t; p (P) PVI,e,i (t) wind power input power of the micro-grid in a period t; p (P) PV,e,i (t) wind power consumed by the microgrid in a period t;
the device operation constraints are expressed as:
In the method, in the process of the invention,
the upper and lower limits of the output power of the device x are respectively; />
The upper limit and the lower limit of the climbing of the output power of the equipment x are respectively set;
the power trade constraint is expressed as:
in the method, in the process of the invention,
an upper limit of electric power for interaction of the micro-grid i with other micro-grids;
if transmission power loss is not considered, the sum of all the micro-grid interaction electric power in the period t is zero:
if transmission power loss is not considered, the sum of all micro-grid electric energy transaction costs in the period t is zero:
8. the nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 2, wherein the specific process of step 2 is as follows:
2.1, for the comprehensive energy multi-micro-grid, a plurality of uncertainty factors exist to influence the scheduling result, the risk loss faced by the comprehensive energy multi-micro-grid is calculated by adopting the conditional risk value, when the actual value of wind power and photovoltaic power is lower than a predicted value or the actual value of load is higher than the predicted value, the electric energy transaction power cannot meet the daily planning and causes loss of load, and the multi-micro-grid purchases the absent electric energy from the distribution grid; when the wind power and the photovoltaic power are higher than the predicted value or the load is lower than the predicted value, the multi-microgrid sells redundant electric energy to the distribution network; the risk loss function f (ζ, X) is characterized by the net interaction cost of the multiple micro-networks with the distribution network, the conditional risk value cost X of micro-network i CVaR,β,i Expressed as:
wherein N is the number of micro-nets, beta is the confidence coefficient and alpha i The risk value cost of the micro-grid i;
the formula (37) relaxes to:
wherein y is i,s The value that the conditional risk value cost of the micro-grid i in the scene s exceeds the risk value cost;
step 2.2, implementing two-stage optimization on the multi-microgrid, specifically:
in the comprehensive energy multi-microgrid, each microgrid can be regarded as a competing negotiation unit, nash negotiation is adopted for solving, and a mathematical model is solved as follows:
wherein C is 0,i For the maximum cost possible for micro-net i, C 0,i Cost for micro-net i;
in the first stage, the expression (40) is deformed as follows:
wherein C is 0,i For the maximum cost possible for the micro-grid i, i.e. without taking into account the cost of the micro-grid i during the power interaction, p s Probability of occurrence for the s-th typical scene; s is the number of typical scenes; k is a risk preference coefficient and represents the attitude of a micro-grid investor to risks, and the value range is [0,1]A smaller k indicates that the microgrid investors are at higher risk pursuing lower costs and obtaining the optimal solution by solving the model
Due to P e,ij (t)＝-P e,ji (t) the electric energy transaction costs will cancel each other out during the summation process, so that the sum of the electric energy transaction costs of each micro-grid is 0, but the net cost (formula (27)) still contains the electric power interaction variable P e,ij (t) the objective function of the comprehensive energy multi-microgrid is the sum of the running cost of each microgrid and the minimum electricity purchasing and selling risk:
wherein C is I Cost for the comprehensive energy multi-microgrid; can be in a modelThe optimal solution interaction power is obtained
Internet crossing fee->
Condition risk value->
And a second stage: after obtaining the optimal cost and optimal electric power interaction variable except the electric energy transaction cost in the micro-grid i, the formula (42) can be further converted into:
(43) in delta e,ij,s (t) as an optimization variable, the inequality ensures that each micro-grid can obtain benefits, and the objective function of the second problem is obtained after conversion:
delta e,ij,s (t) is an optimization variable, in the formula
Is the optimal solution of the first stage;
formulas (41) - (44) are comprehensive energy multi-microgrid two-stage optimization scheduling models based on Nash negotiations.
9. The nash negotiation-based integrated energy multi-microgrid optimization scheduling method for hydrogen-containing fuel gas, according to claim 2, wherein the specific process of step 3 is as follows:
step 3.1, carrying out distributed solution on the formula (41) by utilizing an improved alternate direction multiplier algorithm, wherein the specific solution method is as follows:
1) Establishing an augmented lagrangian function for the micro-net i:
wherein lambda is ij,s (t) Lagrangian multiplier of the first stage optimization model, ρ being a penalty factor;
2) Setting the maximum iteration number l max 100, the interaction electric power P between the micro-net i and the micro-net j in the period t in the initial scene s e,ij,s (t)＝0，P e,ij,s (t) Lagrangian multiplier lambda ij,s The iterative process of (a) is as follows:
3) Judging whether the algorithm converges, and calculating an original residual error and a dual residual error:
in the method, in the process of the invention,
the original residual error of the electric energy transaction between the first iteration micro-grid i and the micro-grid j is the first +1st iteration micro-grid i and the micro-grid j; />
Pairs for electric energy transactions of i+1st iteration micro-net and jCoupling residual errors;
the iteration stop conditions are:
wherein ε pri 、ε dual The upper limits of the original residual error and the dual residual error are respectively set;
4) Automatically updating a penalty factor through the quantitative relation between the original residual and the dual residual, wherein the dynamic penalty factor is expressed as:
where v is the proportionality coefficient of the original residual and the dual residual, θ 1 、θ 2 Acceleration and deceleration convergence coefficients (v, θ), respectively 1 ,θ 2 >1)；
5) Outputting the dispatching result and the equipment output of each micro-network, and optimally solving the interaction power
Internet crossing fee->
Condition risk value->
Step 3.2, the optimal solution obtained in the step 3.1
And->
In a second-stage objective function formula (44) of a comprehensive energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation, the formula (44) is subjected to distributed solution by utilizing an improved alternate direction multiplier algorithm, and the calculated interactive electric quantity between the microgrids is calculated >
Electric energy trade cost->
And the scheduling result is obtained. />
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| CN116957855A (en) * | 2023-09-18 | 2023-10-27 | 南京师范大学 | Comprehensive energy service provider cooperation operation method and system based on green license negotiation transaction |
| CN117217796A (en) * | 2023-09-13 | 2023-12-12 | 港华能源创科(深圳)有限公司 | Method for processing cost information of hydrogen-doped fuel gas and related products |
| CN117689179A (en) * | 2024-01-30 | 2024-03-12 | 山东建筑大学 | Comprehensive energy system operation optimization method and system based on multi-stage robustness |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117217796A (en) * | 2023-09-13 | 2023-12-12 | 港华能源创科(深圳)有限公司 | Method for processing cost information of hydrogen-doped fuel gas and related products |
| CN117217796B (en) * | 2023-09-13 | 2024-05-03 | 港华能源创科(深圳)有限公司 | Method for processing cost information of hydrogen-doped fuel gas and related products |
| CN116957855A (en) * | 2023-09-18 | 2023-10-27 | 南京师范大学 | Comprehensive energy service provider cooperation operation method and system based on green license negotiation transaction |
| CN116957855B (en) * | 2023-09-18 | 2023-11-28 | 南京师范大学 | Comprehensive energy service provider cooperation operation method and system based on green license negotiation transaction |
| CN117689179A (en) * | 2024-01-30 | 2024-03-12 | 山东建筑大学 | Comprehensive energy system operation optimization method and system based on multi-stage robustness |
| CN117689179B (en) * | 2024-01-30 | 2024-05-03 | 山东建筑大学 | Comprehensive energy system operation optimization method and system based on multi-stage robustness |
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