CN117254502A - Multi-objective optimization scheduling method based on electric-hydrogen hybrid energy storage comprehensive energy system - Google Patents

Multi-objective optimization scheduling method based on electric-hydrogen hybrid energy storage comprehensive energy system Download PDF

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CN117254502A
CN117254502A CN202311239188.0A CN202311239188A CN117254502A CN 117254502 A CN117254502 A CN 117254502A CN 202311239188 A CN202311239188 A CN 202311239188A CN 117254502 A CN117254502 A CN 117254502A
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杨玲
邱存勇
宋潞贇
张琦琦
罗树林
王云龙
杨轶航
邓鑫隆
陈梓桐
刘海悦
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Southwest Petroleum University
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Abstract

The invention provides a multi-objective optimization scheduling method based on an electric-hydrogen hybrid energy storage comprehensive energy system, which considers the feasibility of combining the electric-hydrogen hybrid energy storage operation efficiency, economic scheduling, graded hydrogen price and electric-hydrogen thermal flexible load at a user side; firstly, an electric hydrogen hybrid energy storage based comprehensive energy system model is constructed, and the electric hydrogen hybrid energy storage based comprehensive energy system model is divided into transferable, translatable and load-reducing loads according to electric hydrogen thermal flexible load characteristics of a user side, so that utilization and development of hydrogen energy are promoted by classifying hydrogen price, renewable energy consumption is improved, and waste wind and waste light are reduced; the efficiency maximization of the electro-hydrogen hybrid energy storage device is considered, the system cost is reduced, and the power grid pressure is relieved; the introduction of various energy conversion devices increases the flexibility of the system; the optimization model of the multi-objective gray wolf algorithm established by considering economy and environment and the data of the predicted wind-light output, the time-sharing electric price, the grading hydrogen price and the like are used for solving and optimizing the model, so that the efficiency maximization capability of the hybrid energy storage device is coordinated to a great extent, and the near-zero carbon emission of the system is realized.

Description

Multi-objective optimization scheduling method based on electric-hydrogen hybrid energy storage comprehensive energy system
Technical Field
The invention relates to the technical field of comprehensive energy scheduling, in particular to a multi-objective optimized scheduling method based on an electric-hydrogen hybrid energy storage comprehensive energy system.
Background
In recent years, with the continuous development of renewable energy sources, hydrogen energy has been widely focused and studied as a green energy source with high calorific value, multiple sources and zero pollution. The generation of green hydrogen by renewable energy sources will help solve renewable energy consumption problems. The electro-hydrogen energy system is an ideal low pollution energy system, and can combine different energy conversion and storage technologies to improve the stability of renewable energy supply. However, the access to renewable energy sources using the power grid is subject to varying degrees of fluctuation, the unpredictable intermittent supply of renewable energy sources (e.g., during nighttime or cloudy days) and seasonal variations present significant challenges to the energy supply of the system. Hydrogen is an efficient energy carrier, and the amount and the utilization efficiency of wind and light can be improved by converting wind and light into hydrogen through electrolysis, electrodialysis or other methods, but the production and transportation are difficult, and decarburization of energy departments limited by technical development is attracting more and more attention. The battery and the hydrogen storage can improve the reliability of solar energy and wind power utilization. The battery storage has the characteristics of low energy density and quick response, and is suitable for short-term storage and adjustment. The hydrogen energy is used as a long-term energy storage carrier, so that the defects of photovoltaic fluctuation, intermittence and the like can be overcome, and the large-scale development of the photovoltaic energy can be promoted. The utilization of this heat is critical to improving the energy efficiency of the multi-energy coupling system. In addition, the capacity scale of the hybrid energy storage is influenced by load distribution and multi-energy conversion in different periods, and strategies such as power grid interaction, energy flow paths and the like in the operation stage also influence the economy and reliability. Most of the existing researches integrate hydrogen energy into an electrothermal system to improve the running economy of the system, and researches are carried out simultaneously by considering the electric hydrogen hybrid energy storage and the fluctuation of renewable energy sources and loads. In the electric-hydrogen hybrid energy storage comprehensive energy system, renewable energy, fluctuation information of user load and energy equipment can be monitored and scheduled through an energy management system, meanwhile, the electric-hydrogen hybrid energy storage influence is predicted and controlled by considering energy prices and related influence factors (such as meteorological factors and the like), and the output of the energy equipment is coordinated in time to meet the requirement of the user load, so that an optimal scheduling strategy is obtained.
At present, most of researches on an electric-hydrogen hybrid energy storage system focus on capacity configuration optimization, and economic operation optimization scheduling which basically aims at the lowest daily operation cost in the aspect of operation optimization scheduling is achieved. The operation optimization scheduling in the aspects of environmental protection, wind and light abandoning and energy conversion equipment efficiency maximization are rarely considered, and the flexible load of electricity-hydrogen-heat is optimized by not completely and accurately considering the equipment model in the electricity-hydrogen hybrid energy storage system and less considering the comfort of users.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-objective optimization scheduling method for an electric-hydrogen hybrid energy storage comprehensive energy system considering electric-hydrogen-heat flexible load, which carries out fine modeling on the electric-hydrogen-heat load of a user side, establishes an electric-hydrogen hybrid energy storage system model, fully considers the coordination and optimization of equipment among different energy sources, and coordinates the flexibility and economy of the output of each equipment through an energy management system so as to achieve the optimal system operation.
The multi-objective optimization scheduling method for the electric-hydrogen hybrid energy storage comprehensive energy system comprises the following steps:
s1: the electric-hydrogen hybrid energy storage comprehensive energy system comprises a wind power and photovoltaic power generation system, an electric-hydrogen energy storage system, a load management system and energy conversion equipment, wherein wind energy and photovoltaic prediction data are input into the energy management system, and the operation parameters and the output constraint of the energy equipment, time-of-use electricity price, gas price, graded hydrogen price and load prediction data are input into the energy management system;
s2: according to the load prediction data of the user side, considering the electric-hydrogen-thermal load parameter characteristics, and respectively carrying out fine modeling on the electric-hydrogen-thermal load parameter characteristics; establishing energy equipment models based on an electric-hydrogen hybrid energy storage system, wherein each energy equipment model comprises a hydrogen fuel cell model, an electrolytic cell model, an electric energy storage model, a hydrogen energy storage model, a cogeneration unit model and a hydrogen-to-methane model;
s3: setting operation condition constraints on the energy models, including electric-hydrogen-thermal load balance constraints, energy equipment output constraints, climbing constraints and electric-hydrogen networking interaction constraints;
s4: establishing a multi-objective optimization model based on the operation economic cost and equipment efficiency maximization and carbon emission cost of an electric-hydrogen hybrid energy storage comprehensive energy system;
s5, solving and calculating the constructed electricity-hydrogen hybrid energy storage comprehensive energy system optimization model to obtain an optimal operation scheme;
s6, adjusting the comprehensive energy system through the energy management system;
further, for the step S1, it includes:
the user side flexible load is further divided into translatable load, reducible load,
translatable load: wherein the energy supply time can be changed according to the planTranslational load scheduling may span multiple periods, but requires the load to translate in its entirety. To ensure the rationality of load translation, a translatable period constraint must be set. Setting the unit scheduling time interval to be 1h, and for a certain translatable loadThe power distribution vector before the participation in the scheduling is:
wherein x is the type of electric, hydrogen, heat load, t s For the initial period, t d For duration, P shift To transfer power;
assume that the translatable period interval is [ t ] sh- ,t sh+ ]Due to the integral translation, consideration is given toIs represented by the variable 0-1 +.>A translational state of a certain period τ, α=1, representing +.>Starting from the τ period; alpha τ =0 means +.>The load does not translate, the set of start periods is:
if τ=t s The load is unchanged if T.epsilon.t sh- ,t sh+ -t d +1]And τ+.t s ThenFrom the start period t s Shift to start period τ +.>The power distribution vector of (2) is:
load can be transferred: in transferable loadsThe transition period interval of (a) is [ t ] tr- ,t tr+ ]Represented by variables 0-1Transition state of a certain period of time τ, β τ =1 means +.>Power of->The transfer takes place and,
if there is no limitation in load transfer, load transfer to a plurality of single time periods occurs, and the external appearance is that the equipment is frequently started and stopped, so that the minimum continuous operation time for transferring the load is required to be constrained:
in the method, in the process of the invention,for the minimum, maximum, kW, +.>Is the smallest linkContinuous operation time, h;
load can be reduced: unlike the characteristics of translatable and transferable loads, which do not change the user's usage, the load can be reduced by reducing the user's usage, represented by the variable gamma of 0-1The state of reduction of a certain period of time τ, γ τ =1 means->And when the tau period is cut down, the power in the tau period after participating in the scheduling is as follows:
considering user satisfaction, the minimum and maximum continuous reduction time and reduction times are also required to be constrained:
in the formulas (5) and (6), θ τ Load shedding coefficient, θ, for τ period τ ∈[0,1]；Participation and scheduling of power for pre- τ period, KW; />For minimum continuous reduction time, h, < >>The maximum continuous reduction time; n (N) max Is the maximum reduction number;
the reasonable hydrogen price model can effectively help a manager to reduce peak electricity consumption and save cost, simultaneously stimulates market development hydrogen energy utilization rate and helps the user to save cost, at present, hydrogen price establishment is based on fuel of a thermal power plant, along with the development of renewable energy sources, hydrogen energy generation and consumption have great correlation with an electrolytic cell and a hydrogen fuel cell, so that the constant hydrogen price limits the development of the hydrogen market, due to the limitation of various aspects, real-time hydrogen price establishment is difficult to directly carry out, excessive fragmentation pricing strategies reversely generate adverse reactions, according to the adopted distributed load control architecture, the functions and characteristics of an intelligent switch are combined, reasonable pricing is ensured, the pricing strategies are not excessively fragmented, a small-level grading hydrogen price model is designed, the peak time, the low peak time and the silence time are respectively graded, and the grading hydrogen price model is established as follows:
wherein: g h (C h ) For a pricing function, h is the time between 0 and 24h, C h For the total hydrogen consumption of all electric equipment of a certain user, the calculation method is as follows:
wherein: n represents the number of hydrogen devices on the user side;the hydrogen demand at a certain moment is represented; />And->Is a graded hydrogen demand threshold; m is m h ，n h And g h Three levels of hydrogen prices, respectively. In addition, the staged hydrogen price cannot be switched seamlessly, i.e. there is a part of error, and the error model is:
in the method, in the process of the invention,for the delay coefficient of each device at the time H, lambda is more than or equal to 0,
a complete hydrogen valence model can be obtained:
in summary, a complete model is constructed;
further, the step S2 includes:
establishing an electric energy storage model according to the following steps:
where ess represents the storage of electrical energy,is the battery capacity of the ess device at time t; />Andbattery capacity at the initial and final moments of the optimization cycle; />And->Electric power of essMinimum and maximum cell capacity; />And->Is the charge/discharge power at high amperage; η (eta) ess,cha,high And eta ess,dis,high Is responsive to charge/discharge efficiency; />And->Is the charge/discharge power at high amperage; η (eta) ess,cha,low And eta ess,dis,low Is responsive to charge/discharge efficiency; />Is the self-discharge rate; s is S ess Is the installed capacity of ess;is the capacity of ess to operate in a low amperage state; notably, the ess operating in the high amperage state has two capacities +.>And->Because if the high current intensity state has only one capacity variable, ess will charge and discharge simultaneously; kappa (kappa) ess Refers to the break point of the current intensity; />Is the power to capacity ratio of the device;
establishing a hydrogen energy storage model according to the following formula:
a hydrogen storage model of formula (13) (14):
wherein hs is the hydrogen storage energy type;is hydrogen stored during time t; />And->Time t is the hydrogen storage/release rate; η (eta) hs,cha And eta hs,sid Is response efficiency; />Refers to the self-discharge rate of hs; />Is the power to capacity ratio of the device; s is S hs Is the installed capacity;
equation (15) is an electrolytic cell model:
from the above equation, the hydrogen production rate of the electrolytic cell of the following formula (16) can be deduced:
in U el Is the voltage of the electrolytic cell; n (N) el Is the number of series connections in the stack;is the battery voltage; />Is a reversible voltage; />Is an active and ohmic polarization overvoltage; />Is a reversible voltage at a temperature of 50 ℃ and one atmosphere; t (T) el ，p el Indicating cell temperature and pressure; p is p ae Is ambient pressure; i.e el Is the current density in the cell; zeta type toy n For activating the empirical parameters of the polarization voltage, n=1, 2,3; iota (iota) n ，ο n Is an empirical parameter of ohmic voltage with temperature and pressure, n=1, 2; mu (mu) F Is Faraday efficiency;
equation (17) is a hydrogen fuel cell model:
in U fc Refers to the voltage of the hydrogen fuel cell; n (N) fc Is the number of series connections in the stack;is the battery voltage; />Is a thermodynamic potential; />Is active, ohmic, differential polarization loss; Δg is the change in gibbs energy; Δs is the change in entropy; t (T) fc Is the temperature of the battery; g is the gas constant; />Refers to the partial pressure of hydrogen at the anode-catalyst interface; />Is the partial pressure of oxygen at the cathode-catalyst interface; v refers to the number of electrons transferred per reaction; f is Faraday constant; zeta type toy n For the empirical parameters of the activation overvoltage, n=1, 2,3,4; i.e fc Is the current density;
conversion relation of hydrogen methane production device:
in the method, in the process of the invention,for CO consumed in the process of preparing methane from hydrogen 2 Is a flow rate of (1); />Hydrogen flow for methane production; k is a molar volume conversion coefficient; />To the methane flow rate produced; />The efficiency of the methane production equipment for hydrogen;
further, the constraint conditions of the operation of the integrated energy system in the step S3 include:
electric energy balance constraint:
in the method, in the process of the invention,for t period of energy systemElectric quantity purchased or sold by power grid, < >>The output electric power of the photovoltaic cell panel and the wind power generation is t time periods; />The total user side electrical load is t time periods;
hydrogen energy balance constraint:
thermal energy balance constraint:
because the environmental benefit of preparing methane from hydrogen is considered, the design considers the combustion of hydrogen-natural gas mixed gas, so the gas energy balance constraint of the heat combined supply unit is required to be added:
wherein the hydrogen-natural gas ratioIs an important parameter, representing the ratio of hydrogen to the total gas volume in the pipeline:
heat value L of mixed gas mix The method comprises the following steps:
typically no more than 20%;
further, in the step S4, the multi-objective operation optimization objective function based on the electric-hydrogen hybrid energy storage integrated energy system is as follows:
wherein f 1 For the daily operation economic cost of the system, f 2 Maximizing an index for system efficiency; c (C) com For daily operation and maintenance cost of each equipment of the system, C eg For the daily purchase energy costs of the system,daily carbon emission costs for the system; η (eta) hess Is the hybrid energy storage efficiency;
wherein f 1 ：
Wherein omega represents each energy device (such as an electrolytic cell, a hydrogen fuel cell, an electric energy storage device, a hydrogen energy storage device, a heat pump, a methane-making device by hydrogen, a cogeneration unit and the like); t is the number of time sampling intervals in a day; c e 、c gas Electricity and gas prices at time t respectively; x represents the user-side flexible load divided into three (translatable, reducible); c cep Penalty factors for carbon emissions; q grid The carbon emission intensity of the power grid; q CHP To the degree of emission pollutants; q MR Carbon fixation degree;
wherein f 2 ：
Wherein R is ess Is the battery resistance, U ESS Is a batteryA voltage; y is n N=1, 2,3,4, being an empirical constant;the original efficiency of the electrolytic cell; t is t el The unit is h for the actual running time; />Running the reduced power per unit time;
further, in the step S5, the multi-objective operation optimization problem based on the electric-hydrogen hybrid energy storage integrated energy system is constructed, in the design, a multi-objective wolf algorithm is adopted to perform optimization solution, the electric-hydrogen hybrid energy storage charging and discharging power and the initial efficiency are initialized, namely, an initial population in the multi-objective wolf optimization algorithm is obtained, each objective function value is calculated according to the initialized population, non-dominant solutions in the initial population are found out to form an initial elite library, and three optimal solutions are selected from the initial elite library to serve as first wolves, namely Alpha (Alpha) wolves, beta (Beta) wolves and Delta (Delta) wolves, and the rest wolves serve as Omega wolves; updating elite library according to a wolf group hunting mechanism and a head wolf selection mechanism of a multi-target wolf optimization algorithm; judging whether the maximum iteration times are reached, if the maximum iteration times are reached, ending the calculation and outputting the pareto solution set, otherwise, returning to the step of updating the elite library by using a wolf group hunting mechanism and a head wolf selection mechanism of the multi-target gray wolf optimization algorithm; after the pareto solution set is obtained, an optimal compromise solution is selected as an optimal scheme for system energy management;
further, in the step S6, scheduling of each device of the comprehensive energy system based on the electric-hydrogen hybrid energy storage is performed:
according to the operation optimization scheme obtained by solving the model, the energy management system is used for adjusting the output power and output time of each energy device, dividing and adjusting flexible load and purchasing electricity and hydrogen quantity and natural gas requirements by predicting wind and light output and load fluctuation information, so that the low-carbon economic operation of the comprehensive energy system based on electricity and hydrogen energy storage is realized.
Advantageous effects
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a multi-objective optimization scheduling method based on an electric-hydrogen hybrid energy storage comprehensive energy system, which comprehensively considers the feasibility and the potential of combining the electric-hydrogen hybrid energy storage operation efficiency, economic scheduling, the market stimulation effect of graded hydrogen price and the electric-hydrogen thermal flexible load of a user side; firstly, an electric-hydrogen hybrid energy storage-based comprehensive energy system model is constructed according to the actual condition of comprehensive energy, the electric-hydrogen hybrid energy storage-based comprehensive energy system model is classified into transferable, translatable and load-reducible type modeling according to the electric-hydrogen thermal flexibility coincidence characteristic of a user side, and a grading hydrogen price is established according to the system model so as to stimulate the utilization and development of hydrogen energy in the market, so that the consumption of renewable energy sources is increased, the wind and light abandoning and environmental pollution are greatly reduced, and the like; secondly, the efficiency maximization of each energy conversion device in the electro-hydrogen hybrid energy storage is considered, so that the energy consumption cost and the investment replacement cost of the system are reduced, and meanwhile, the fluctuation of the power grid in high-proportion renewable energy sources is reduced and the power grid pressure is relieved; meanwhile, various energy conversion devices are introduced, so that the flexibility of the comprehensive energy system is improved; and finally, solving and optimizing the model according to an optimized model of a multi-target gray wolf algorithm established according to the economical efficiency and the environmental performance, and predicting data such as wind-light output, time-of-use electrical price, grading hydrogen price and the like, so that the economic benefit and the environmental benefit of the system are improved, the efficiency maximization capability of the hybrid energy storage device is coordinated to a great extent, the wind-discarding and light-discarding capability of the system is greatly reduced, and the near-zero carbon emission of the system is realized.
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FIG. 1 is a flow chart of an embodiment of the present invention
FIG. 2 is a diagram showing a model structure of an electro-hydrogen hybrid energy storage integrated energy system according to the present invention
Detailed Description
The multi-objective optimization scheduling method for the electric-hydrogen hybrid energy storage comprehensive energy system comprises the following steps:
s1: the electric-hydrogen hybrid energy storage comprehensive energy system comprises a wind power and photovoltaic power generation system, an electric-hydrogen energy storage system, a load management system and energy conversion equipment, wherein wind energy and photovoltaic prediction data are input into the energy management system, and the operation parameters and the output constraint of the energy equipment, time-of-use electricity price, gas price, graded hydrogen price and load prediction data are input into the energy management system;
s2: according to the load prediction data of the user side, considering the electric-hydrogen-thermal load parameter characteristics, and respectively carrying out fine modeling on the electric-hydrogen-thermal load parameter characteristics; establishing energy equipment models based on an electric-hydrogen hybrid energy storage system, wherein each energy equipment model comprises a hydrogen fuel cell model, an electrolytic cell model, an electric energy storage model, a hydrogen energy storage model, a cogeneration unit model and a hydrogen-to-methane model;
s3: setting operation condition constraints on the energy models, including electric-hydrogen-thermal load balance constraints, energy equipment output constraints, climbing constraints and electric-hydrogen networking interaction constraints;
s4: establishing a multi-objective optimization model based on the operation economic cost and equipment efficiency maximization and carbon emission cost of an electric-hydrogen hybrid energy storage comprehensive energy system;
s5, solving and calculating the constructed electricity-hydrogen hybrid energy storage comprehensive energy system optimization model to obtain an optimal operation scheme;
s6, adjusting the comprehensive energy system through the energy management system;
further, for the step S1, it includes:
the user side flexible load is further divided into translatable load, reducible load,
translatable load: wherein the energizable time may be changed according to a schedule, the translatable load schedule may span multiple periods, but the load is translated in its entirety. To ensure the rationality of load translation, a translatable period constraint must be set. Let the unit dispatch period be 1h, for a translatable load, the power distribution vector before its participation in dispatch is:
wherein x is the type of electric, hydrogen, heat load, t s For the initial period, t d For duration, P shift To transfer power;
hypothesis canThe shift time interval is [ t ] sh- ,t sh+ ]Due to the integral translation, consideration is given toIs represented by the variable 0-1 +.>A translational state of a certain period τ, α=1, representing +.>Starting from the τ period; indicating that the load is not translating, the set of start periods is:
if τ=t s The load is unchanged if T.epsilon.t sh- ,t sh+ -t d +1]And τ+.t s ThenFrom the start period t s Shift to start period τ +.>The power distribution vector of (2) is:
load can be transferred: in transferable loadsThe transition period interval of (a) is [ t ] tr- ,t tr+ ]Represented by variables 0-1Transition state of a certain period of time τ, β τ =1 indicates that in the periodTau (tau) in->Power of->The transfer takes place and,
if there is no limitation in load transfer, load transfer to a plurality of single time periods occurs, and the external appearance is that the equipment is frequently started and stopped, so that the minimum continuous operation time for transferring the load is required to be constrained:
in the method, in the process of the invention,for the minimum, maximum, kW, +.>H is the minimum continuous running time;
load can be reduced: unlike the characteristics of translatable and transferable loads, which do not change the user's usage, the load can be reduced by reducing the user's usage, represented by the variable gamma of 0-1The state of reduction of a certain period of time τ, γ τ =1 means->And when the tau period is cut down, the power in the tau period after participating in the scheduling is as follows:
considering user satisfaction, the minimum and maximum continuous reduction time and reduction times are also required to be constrained:
in the formulas (5) and (6), θ τ Load shedding coefficient, θ, for τ period τ ∈[0,1]；Participation and pre-scheduling period power, KW; />For minimum continuous reduction time, h, < >>The maximum continuous reduction time; is the maximum reduction number;
the reasonable hydrogen price model can effectively help a manager to reduce peak electricity consumption and save cost, simultaneously stimulates market development hydrogen energy utilization rate and helps the user to save cost, at present, hydrogen price establishment is based on fuel of a thermal power plant, along with the development of renewable energy sources, hydrogen energy generation and consumption have great correlation with an electrolytic cell and a hydrogen fuel cell, so that the constant hydrogen price limits the development of the hydrogen market, due to the limitation of various aspects, real-time hydrogen price establishment is difficult to directly carry out, excessive fragmentation pricing strategies reversely generate adverse reactions, according to the adopted distributed load control architecture, the functions and characteristics of an intelligent switch are combined, reasonable pricing is ensured, the pricing strategies are not excessively fragmented, a small-level grading hydrogen price model is designed, the peak time, the low peak time and the silence time are respectively graded, and the grading hydrogen price model is established as follows:
wherein: g h (C h ) For pricing functions, h is the time between 0-24h,C h for the total hydrogen consumption of all electric equipment of a certain user, the calculation method is as follows:
wherein: n represents the number of hydrogen devices on the user side;the hydrogen demand at a certain moment is represented; />And->Is a graded hydrogen demand threshold; m is m h ，n h And g h Three levels of hydrogen prices, respectively. In addition, the staged hydrogen price cannot be switched seamlessly, i.e. there is a part of error, and the error model is:
in the method, in the process of the invention,for the delay coefficient of each device at the time H, lambda is more than or equal to 0,
in summary, a complete hydrogen valence model can be obtained:
in summary, a complete model is constructed;
further, the step S2 includes:
establishing an electric energy storage model according to the following steps:
where ess represents the storage of electrical energy,is the battery capacity of the ess device at time t; />Andbattery capacity at the initial and final moments of the optimization cycle; />And->Is the minimum and maximum values of the battery capacity of ess; />And->Is the charge/discharge power at high amperage; η (eta) ess,cha,high And eta ess,dis,high Is responsive to charge/discharge efficiency; />And->Is the charge/discharge power at high amperage; η (eta) ess,cha,low And eta ess,dis,low Is responsive to charge/discharge efficiency; />Is the self-discharge rate; s is S ess Is the installed capacity of ess;is the capacity of ess to operate in a low amperage state; notably, the ess operating in the high amperage state has two capacities +.>And->Because if the high current intensity state has only one capacity variable, ess will charge and discharge simultaneously; kappa (kappa) ess Refers to the break point of the current intensity; />Is the power to capacity ratio of the device;
establishing a hydrogen energy storage model according to the following formula:
hydrogen storage model:
wherein hs is the hydrogen storage energy type; hs is hydrogen stored at time t;and->Time t is the hydrogen storage/release rate; η (eta) hs,cha And eta hs,sid Is response efficiency; />Refers to the self-discharge rate; />Is the power to capacity ratio of the device; s is S hs Is the installed capacity;
electrolytic cell model:
from the above equation, the hydrogen production rate of the electrolytic cell of the following formula (16) can be deduced:
in U el Is the voltage of the electrolytic cell; n (N) el Is the number of series connections in the stack;is the battery voltage; />Is a reversible voltage; />Is an active and ohmic polarization overvoltage; />Is a reversible voltage at a temperature of 50 ℃ and one atmosphere; t (T) el ，p el Indicating cell temperature and pressure; p is p ae Is ambient pressure; i.e el Is the current density in the cell; zeta type toy n For activating the empirical parameters of the polarization voltage, n=1, 2,3; iota (iota) n ，ο n Is an empirical parameter of ohmic voltage with temperature and pressure, n=1, 2; mu (mu) F Is Faraday efficiency;
hydrogen fuel cell model:
in U fc Refers to the voltage of the hydrogen fuel cell; n (N) fc Is the number of series connections in the stack;is the battery voltage; />Is a thermodynamic potential; />Is active, ohmic, differential polarization loss; Δg is the change in gibbs energy; Δs is the change in entropy; t (T) fc Is the temperature of the battery; g is the gas constant; />Refers to the partial pressure of hydrogen at the anode-catalyst interface; />Is the partial pressure of oxygen at the cathode-catalyst interface; v refers to the number of electrons transferred per reaction; f is Faraday constant; for the empirical parameters of the activation overvoltage, n=1, 2,3,4; zeta type toy n Is the current density;
conversion relation of hydrogen methane production device:
in the method, in the process of the invention,for CO consumed in the process of preparing methane from hydrogen 2 Is a flow rate of (1); />Hydrogen flow for methane production; k is a molar volume conversion systemA number; />To the methane flow rate produced; />The efficiency of the methane production equipment for hydrogen;
further, the constraint conditions of the operation of the integrated energy system in the step S3 include:
electric energy balance constraint:
in the method, in the process of the invention,electric quantity purchased or sold to the power grid for t-period energy system, < >>The output electric power of the photovoltaic cell panel and the wind power generation is t time periods; />The total user side electrical load is t time periods;
hydrogen energy balance constraint:
thermal energy balance constraint:
/>
because the environmental benefit of preparing methane from hydrogen is considered, the design considers the combustion of hydrogen-natural gas mixed gas, so the gas energy balance constraint of the heat combined supply unit is required to be added:
wherein the hydrogen-natural gas ratio is an important parameter, representing the ratio of hydrogen to the total gas volume in the pipeline:
heat value L of mixed gas mix The method comprises the following steps:
typically no more than 20%,
further, in the step S4, the multi-objective operation optimization objective function based on the electric-hydrogen hybrid energy storage integrated energy system is as follows:
wherein f 1 For the daily operation economic cost of the system, f 2 Maximizing an index for system efficiency; c (C) com For daily operation and maintenance cost of each equipment of the system, C eg Cco is the daily purchase energy cost of the system 2 Daily carbon emission costs for the system; η (eta) hess Is the hybrid energy storage efficiency;
wherein f 1 ：
Wherein omega represents each energy device (such as an electrolytic cell, a hydrogen fuel cell, an electric energy storage device, a hydrogen energy storage device, a heat pump, a methane-making device by hydrogen, a cogeneration unit and the like); t is the number of time sampling intervals in a day; c e 、c gas Electricity and gas prices at time t respectively; x represents the user-side flexible load divided into three (translatable, reducible); c cep Penalty factors for carbon emissions; q grid The carbon emission intensity of the power grid; q CHP To the degree of emission pollutants; q MR Carbon fixation degree;
wherein f 2 ：
Wherein R is ess Is the battery resistance, U ESS Is the battery voltage; y is n N=1, 2,3,4, being an empirical constant;the original efficiency of the electrolytic cell; t is t el The unit is h for the actual running time; />Running the reduced power per unit time;
further, in the step S5, the multi-objective operation optimization problem based on the electric-hydrogen hybrid energy storage integrated energy system is constructed, in the design, a multi-objective wolf algorithm is adopted to perform optimization solution, the electric-hydrogen hybrid energy storage charging and discharging power and the initial efficiency are initialized, namely, an initial population in the multi-objective wolf optimization algorithm is obtained, each objective function value is calculated according to the initialized population, non-dominant solutions in the initial population are found out to form an initial elite library, and three optimal solutions are selected from the initial elite library to serve as first wolves, namely Alpha (Alpha) wolves, beta (Beta) wolves and Delta (Delta) wolves, and the rest wolves serve as Omega wolves; updating elite library according to a wolf group hunting mechanism and a head wolf selection mechanism of a multi-target wolf optimization algorithm; judging whether the maximum iteration times are reached, if the maximum iteration times are reached, ending the calculation and outputting the pareto solution set, otherwise, returning to the step of updating the elite library by using a wolf group hunting mechanism and a head wolf selection mechanism of the multi-target gray wolf optimization algorithm; after the pareto solution set is obtained, an optimal compromise solution is selected as an optimal scheme for system energy management;
further, in the step S6, scheduling of each device of the comprehensive energy system based on the electric-hydrogen hybrid energy storage is performed:
according to the operation optimization scheme obtained by solving the model, the energy management system is used for adjusting the output power and output time of each energy device, dividing and adjusting flexible load and purchasing electricity and hydrogen quantity and natural gas requirements by predicting wind and light output and load fluctuation information, so that the low-carbon economic operation of the comprehensive energy system based on electricity and hydrogen energy storage is realized.
The present invention is not limited to the above-mentioned embodiments, but is not limited to the above-mentioned embodiments, and any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical matters of the present invention can be made by those skilled in the art without departing from the scope of the present invention.

Claims (8)

1. The multi-objective optimization scheduling method based on the electric-hydrogen hybrid energy storage comprehensive energy system is characterized by comprising the following steps of:
s1: the electric-hydrogen hybrid energy storage comprehensive energy system comprises a wind power and photovoltaic power generation system, an electric-hydrogen energy storage system, a load management system and energy conversion equipment, wherein wind energy and photovoltaic prediction data are input into the energy management system, and the operation parameters and the output constraint of the energy equipment, time-of-use electricity price, gas price, graded hydrogen price and load prediction data are input into the energy management system;
s2: according to the load prediction data of the user side, considering the electric-hydrogen-thermal load parameter characteristics, and respectively carrying out fine modeling on the electric-hydrogen-thermal load parameter characteristics; establishing energy equipment models based on an electric-hydrogen hybrid energy storage system, wherein each energy equipment model comprises a hydrogen fuel cell model, an electrolytic cell model, an electric energy storage model, a hydrogen energy storage model, a cogeneration unit model and a hydrogen-to-methane model;
s3: setting operation condition constraints on the energy models, including electric-hydrogen-thermal load balance constraints, energy equipment output constraints, climbing constraints and electric-hydrogen networking interaction constraints;
s4: establishing a multi-objective optimization model based on the operation economic cost and equipment efficiency maximization and carbon emission cost of an electric-hydrogen hybrid energy storage comprehensive energy system;
s5, solving and calculating the constructed electricity-hydrogen hybrid energy storage comprehensive energy system optimization model to obtain an optimal operation scheme;
and S6, adjusting the comprehensive energy system through the energy management system.
2. The multi-objective optimization scheduling method based on the electric-hydrogen hybrid energy storage integrated energy system according to claim 1, wherein the electric-hydrogen hybrid energy storage integrated energy system in the step S1 is composed of a wind-solar power generation system, an electric-hydrogen hybrid energy storage system, a hydrogen methane generating device, a heat pump and a user side electric-hydrogen-heat flexible load.
3. The multi-objective optimized scheduling method based on the electric-hydrogen hybrid energy storage comprehensive energy system as claimed in claim 1, wherein the method comprises the following steps: in the step S1, classification modeling is performed by considering the flexible load of the user side, and a classification hydrogen price model is constructed, and the method specifically comprises the following steps:
the user side flexible load is further divided into translatable load, reducible load,
translatable load: wherein the energizable time may be changed according to a schedule, the translatable load schedule may span multiple periods, but the load is translated in its entirety. To ensure the rationality of load translation, a translatable period constraint must be set. Setting the unit scheduling time interval to be 1h, and for a certain translatable loadThe power distribution vector before the participation in the scheduling is:
wherein x is the type of electric, hydrogen, heat load, t s For the initial period, t d For duration, P shift To transfer power;
assume that the translatable period interval is [ t ] sh- ,t sh+ ]Due to the integral translation, consideration is given toIs represented by the variable 0-1 +.>A translational state of a certain period τ, α=1, representing +.>Starting from the τ period; alpha τ =0 meansThe load does not translate, the set of start periods is:
if τ=t s The load is unchanged if T.epsilon.t sh- ,t sh+ -t d +1]And τ+.t s ThenFrom the start period t s Shift to start period τ +.>The power distribution vector of (2) is:
load can be transferred: in transferable loadsThe transition period interval of (a) is [ t ] tr- ,t tr+ ]Expressed by 0-1 variable +.>Transition state of a certain period of time τ, β τ =1 means +.>Power of->The transfer takes place and,
if there is no limitation in load transfer, load transfer to a plurality of single time periods occurs, and the external appearance is that the equipment is frequently started and stopped, so that the minimum continuous operation time for transferring the load is required to be constrained:
in the method, in the process of the invention,for the minimum, maximum, kW, +.>H is the minimum continuous running time;
load can be reduced: unlike the characteristics of translatable and transferable loads, which do not change the user's usage, the load can be reduced by reducing the user's usage, represented by the variable gamma of 0-1The state of reduction of a certain period of time τ, γ τ =1 means->And when the tau period is cut down, the power in the tau period after participating in the scheduling is as follows:
considering user satisfaction, the minimum and maximum continuous reduction time and reduction times are also required to be constrained:
in θ τ Load shedding coefficient, θ, for τ period τ ∈[0,1]；Participation and scheduling of power for pre- τ period, KW; />For minimum continuous reduction time, h, < >>The maximum continuous reduction time; n (N) max Is the maximum reduction number;
the reasonable hydrogen price model can effectively help a manager to reduce peak electricity consumption and save cost, simultaneously stimulates market development hydrogen energy utilization rate and helps the user to save cost, at present, hydrogen price establishment is based on fuel of a thermal power plant, along with the development of renewable energy sources, hydrogen energy generation and consumption have great correlation with an electrolytic cell and a hydrogen fuel cell, so that the constant hydrogen price limits the development of the hydrogen market, due to the limitation of various aspects, real-time hydrogen price establishment is difficult to directly carry out, excessive fragmentation pricing strategies reversely generate adverse reactions, according to the adopted distributed load control architecture, the functions and characteristics of an intelligent switch are combined, reasonable pricing is ensured, the pricing strategies are not excessively fragmented, a small-level grading hydrogen price model is designed, the peak time, the low peak time and the silence time are respectively graded, and the grading hydrogen price model is established as follows:
wherein: g h (C h ) For a pricing function, h is the time between 0 and 24h, C h For the total hydrogen consumption of all electric equipment of a certain user, the calculation method is as follows:
wherein: n represents the number of hydrogen devices on the user side;the hydrogen demand at a certain moment is represented; />And->Is a graded hydrogen demand threshold; m is m h ，n h And g h Three levels of hydrogen prices, respectively. In addition, the staged hydrogen price cannot be switched seamlessly, i.e. there is a part of error, and the error model is:
in the method, in the process of the invention,for the delay coefficient of each device at the time H, lambda is more than or equal to 0,
a complete hydrogen valence model can be obtained:
in summary, a complete model is constructed.
4. The multi-objective optimized scheduling method based on the electric-hydrogen hybrid energy storage comprehensive energy system as claimed in claim 1, wherein the method comprises the following steps: in the step S2, various energy equipment models are built, which specifically includes the following steps:
establishing an electric energy storage model according to the following steps:
where ess represents the storage of electrical energy,is the battery capacity of the ess device at time t; />Andbattery capacity at the initial and final moments of the optimization cycle; />And->Is the minimum and maximum values of the battery capacity of ess; />And->Is the charge/discharge power at high amperage; η (eta) ess,cha,high And eta ess,dis,high Is responsive to charge/discharge efficiency; />And->Is the charge/discharge power at high amperage; η (eta) ess,cha,low And eta ess,dis,low Is responsive to charge/discharge efficiency; />Is the self-discharge rate; s is S ess Is the installed capacity of ess;is the capacity of ess to operate in a low amperage state; notably, the ess operating in the high amperage state has two capacities +.>And->Because if the high current intensity state has only one capacity variable, ess will charge and discharge simultaneously; kappa (kappa) ess Refers to the break point of the current intensity; />Is the power to capacity ratio of the device;
establishing a hydrogen energy storage model according to the following formula:
hydrogen storage model:
wherein hs is the hydrogen storage energy type;is hydrogen stored during time t; />And->Time t is the hydrogen storage/release rate; η (eta) hs,cha And eta hs,sid Is response efficiency; />Refers to the self-discharge rate of hs; />Is the power to capacity ratio of the device; s is S hs Is the installed capacity;
electrolytic cell model:
from the above formula, the hydrogen production rate of the electrolytic cell of the following formula can be deduced:
in U el Is the voltage of the electrolytic cell; n (N) el Is the number of series connections in the stack;is the battery voltage; />Is a reversible voltage; />Is an active and ohmic polarization overvoltage; />Is a reversible voltage at a temperature of 50 ℃ and one atmosphere; t (T) el ，p el Indicating cell temperature and pressure; p is p ae Is ambient pressure; i.e el Is the current density in the cell; zeta type toy n For activating the empirical parameters of the polarization voltage, n=1, 2,3; iota (iota) n ，ο n Is an empirical parameter of ohmic voltage with temperature and pressure, n=1, 2; mu (mu) F Is Faraday efficiency;
hydrogen fuel cell model:
in U fc Refers to the voltage of the hydrogen fuel cell; n (N) fc Is the number of series connections in the stack;is the battery voltage;is a thermodynamic potential; />Is active, ohmic, differential polarization loss; Δg is the change in gibbs energy; Δs is the change in entropy; t (T) fc Is the temperature of the battery; g is the gas constant; />Refers to the partial pressure of hydrogen at the anode-catalyst interface; />Is the partial pressure of oxygen at the cathode-catalyst interface; v refers to the number of electrons transferred per reaction; f is Faraday constant; zeta type toy n For the empirical parameters of the activation overvoltage, n=1, 2,3,4; i.e fc Is the current density;
conversion relation of hydrogen methane production device:
in the method, in the process of the invention,for CO consumed in the process of preparing methane from hydrogen 2 Is a flow rate of (1); />Hydrogen flow for methane production; k is a molar volume conversion coefficient; />To the methane flow rate produced; />Is the efficiency of the hydrogen-to-methane equipment.
5. The multi-objective optimized scheduling method based on the electric-hydrogen hybrid energy storage comprehensive energy system as claimed in claim 1, wherein the method comprises the following steps: the constraint condition of the operation of the comprehensive energy system in the step S3 specifically comprises the following steps:
electric energy balance constraint:
in the method, in the process of the invention,electric quantity purchased or sold to the power grid for t-period energy system, < >>The output electric power of the photovoltaic cell panel and the wind power generation is t time periods; />The total user side electrical load is t time periods;
hydrogen energy balance constraint:
thermal energy balance constraint:
because the environmental benefit of preparing methane from hydrogen is considered, the design considers the combustion of hydrogen-natural gas mixed gas, so the gas energy balance constraint of the heat combined supply unit is required to be added:
wherein the hydrogen-natural gas ratioIs an important parameter, representing the ratio of hydrogen to the total gas volume in the pipeline:
heat value L of mixed gas mix The method comprises the following steps:
typically no more than 20%.
6. The multi-objective optimized scheduling method based on the electric-hydrogen hybrid energy storage comprehensive energy system as claimed in claim 1, wherein the method comprises the following steps: in the step S4, the multi-objective operation optimization objective function based on the electric-hydrogen hybrid energy storage comprehensive energy system is as follows:
wherein f 1 For the daily operation economic cost of the system, f 2 Maximizing an index for system efficiency; c (C) com For daily operation and maintenance cost of each equipment of the system, C eg For the daily purchase energy costs of the system,daily carbon emission costs for the system; η (eta) hess Is the hybrid energy storage efficiency;
wherein f 1 ：
Wherein omega represents each energy device (such as an electrolytic cell, a hydrogen fuel cell, an electric energy storage device, a hydrogen energy storage device, a heat pump, a methane-making device by hydrogen, a cogeneration unit and the like); t is the number of time sampling intervals in a day; c e 、c gas Electricity and gas prices at time t respectively; x represents the user-side flexible load divided into three (translatable, reducible); c cep Penalty factors for carbon emissions; q grid The carbon emission intensity of the power grid; q CHP To the degree of emission pollutants; q MR Carbon fixation degree;
wherein f 2 ：
Wherein R is ess Is the battery resistance, U ESS Is the battery voltage; y is n N=1, 2,3,4, being an empirical constant;the original efficiency of the electrolytic cell; t is t el The unit is h for the actual running time; />Running the reduced power per unit time.
7. The multi-objective optimization scheduling method based on the electric-hydrogen hybrid energy storage comprehensive energy system as claimed in claim 1, wherein a multi-objective gray wolf algorithm is adopted for optimization solution, electric-hydrogen hybrid energy storage charge-discharge power and initial efficiency are initialized, namely an initial population in the multi-objective gray wolf optimization algorithm, each objective function value is calculated according to the initialized population, non-dominant solution in the initial population is found out to form an initial elite library, and three optimal solutions are selected from the initial elite library to be Alpha wolves, beta wolves and Delta wolves respectively, and the rest wolves are Omega wolves; updating elite library according to a wolf group hunting mechanism and a head wolf selection mechanism of a multi-target wolf optimization algorithm; judging whether the maximum iteration times are reached, if the maximum iteration times are reached, ending the calculation and outputting the pareto solution set, otherwise, returning to the step of updating the elite library by using a wolf group hunting mechanism and a head wolf selection mechanism of the multi-target gray wolf optimization algorithm; after the pareto solution set is obtained, an optimal compromise solution is selected as an optimal scheme for system energy management.
8. The multi-objective optimization scheduling method based on the electric-hydrogen hybrid energy storage integrated energy system according to claim 1, wherein the scheduling of each device of the electric-hydrogen hybrid energy storage integrated energy system in step S6: according to the operation optimization scheme obtained by solving the model, the energy management system is used for adjusting the output power and output time of each energy device, dividing and adjusting flexible load and purchasing electricity and hydrogen quantity and natural gas requirements by predicting wind and light output and load fluctuation information, so that the low-carbon economic operation of the comprehensive energy system based on electricity and hydrogen energy storage is realized.
CN202311239188.0A 2023-09-25 2023-09-25 Multi-objective optimization scheduling method based on electric-hydrogen hybrid energy storage comprehensive energy system Pending CN117254502A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117574684A (en) * 2024-01-15 2024-02-20 华北电力大学 Sequential production simulation method and system for electric-hydrogen-carbon comprehensive energy system
CN118195178A (en) * 2024-05-17 2024-06-14 山东大学 Hydrogen energy full-link equipment combination selection method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117574684A (en) * 2024-01-15 2024-02-20 华北电力大学 Sequential production simulation method and system for electric-hydrogen-carbon comprehensive energy system
CN118195178A (en) * 2024-05-17 2024-06-14 山东大学 Hydrogen energy full-link equipment combination selection method and system

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