CN112290533A - Method for scheduling comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage - Google Patents
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Abstract
The invention discloses a method for scheduling a hydrogen energy-natural gas hybrid energy storage comprehensive energy microgrid, which comprises the steps of modeling energy equipment of each unit in the hydrogen energy-natural gas hybrid energy storage-based comprehensive energy microgrid, and obtaining initial operation information of each unit; determining the energy flow relation of each unit; establishing an optimized scheduling model based on the environment cost and the operation cost; converting multiple targets into a single target by adopting a weight coefficient method; setting safety constraint conditions; and (4) combining a difference operator in a random fractal search algorithm, optimizing and solving the established optimized scheduling model, and obtaining an optimal scheduling strategy. The optimal scheduling scheme with environmental protection and economy can be obtained for the comprehensive energy micro-grid system for hydrogen-natural gas hybrid energy storage, and stable and efficient operation of the comprehensive energy micro-grid for hydrogen-natural gas hybrid energy storage is guaranteed; the convergence rate can be effectively improved, the algorithm is prevented from falling into local optimum, and the running cost of the system is reduced.
Description
Technical Field
The invention belongs to the technical field of microgrid scheduling, and particularly relates to a microgrid scheduling method for comprehensive energy sources of hydrogen energy-natural gas mixed energy storage.
Background
In recent years, with the situation of global warming and environmental pollution being aggravated, the concept of energy conservation and emission reduction is getting more and more popular, and the development of low-carbon and environment-friendly clean new energy becomes a great trend. The micro-grid is an organic whole consisting of distributed energy, load, an energy storage system and a central control device, has the characteristics of small scale and rapid control and adjustment, can be flexibly switched between an independent operation mode and a grid-connected operation mode, and is an effective way for solving the problem of development of the distribution network by merging the distributed renewable energy into the distribution network.
In recent years, the existing comprehensive energy microgrid mostly adopts new energy such as photovoltaic energy, wind power and the like to construct a microgrid, but both the photovoltaic energy and the wind power are output energy and cannot effectively store electric energy. The storage battery is adopted for storing energy in the conventional method, but the method has limitation, cannot meet the requirements of storage and transportation, cannot ensure the effective utilization of energy, and causes energy waste. In addition, the existing scheduling method of the comprehensive energy microgrid cannot acquire the comprehensive energy microgrid system for hydrogen-natural gas mixed energy storage to obtain optimal scheduling with environmental protection and economy, and cannot guarantee stable and efficient operation of the comprehensive energy microgrid for hydrogen energy-natural gas mixed energy storage. And the convergence becomes slow and the precision is reduced in the calculation.
Disclosure of Invention
In order to solve the problems, the invention provides a comprehensive energy microgrid scheduling method for hydrogen energy-natural gas mixed energy storage, which can obtain an optimal scheduling scheme with environmental protection and economy for a comprehensive energy microgrid system for hydrogen energy-natural gas mixed energy storage, and ensure the stable and efficient operation of the comprehensive energy microgrid for hydrogen energy-natural gas mixed energy storage; the convergence rate can be effectively improved, the algorithm is prevented from falling into local optimum, and the running cost of the system is reduced.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for scheduling a hydrogen energy-natural gas hybrid energy storage comprehensive energy microgrid comprises the following steps:
step 1: modeling energy equipment of each unit in the comprehensive energy micro-grid based on hydrogen energy-natural gas mixed energy storage, and obtaining initial operation information of each unit; determining the energy flow relationship of each unit in the comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage;
step 2: establishing an optimized scheduling model of the comprehensive energy micro-grid based on hydrogen energy-natural gas hybrid energy storage based on environmental cost and operating cost; converting multiple targets into a single target by adopting a weight coefficient method; setting safety constraint conditions of a comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage;
and step 3: and (3) combining a difference operator in a random fractal search algorithm, and optimizing and solving the established optimized scheduling model of the comprehensive energy micro-grid based on the hydrogen energy-natural gas hybrid energy storage to obtain an optimal scheduling strategy.
Further, the hydrogen energy-natural gas hybrid energy storage comprehensive energy microgrid comprises an electrolytic hydrogen production unit, a fuel cell unit, a hydrogen-to-natural gas unit, a hydrogen energy storage unit, a micro gas turbine unit, a natural gas network and an electric network, wherein the natural gas network generates electric energy through the micro gas turbine unit to supply to the electric network; the electric network is also connected with a power grid, a wind turbine unit and a photovoltaic power generation unit in parallel, and the electric network supplies power to an electric load; the hydrogen energy storage unit is connected with a hydrogen load, and the natural gas network is connected with a gas load.
Further, in the step 1, an energy flow relationship of each unit in the comprehensive energy micro-grid for hydrogen energy-natural gas hybrid energy storage is constructed: the new energy in the micro-grid is output to a wind turbine unit and a photovoltaic power generation unit, and when surplus energy exists in the electric network, electric energy is converted into hydrogen energy through an electrolytic hydrogen production unit and stored in a hydrogen energy storage unit or directly supplied to a hydrogen load; in the constraint range, when the electric quantity cannot be consumed, the hydrogen is converted into natural gas for storage through the electric gas conversion unit, and meanwhile, the natural gas is supplied to a gas load; and when the micro-grid is in power shortage, the fuel cell unit and the micro gas turbine unit convert hydrogen in the hydrogen energy storage unit and/or natural gas in a natural gas network into electric energy for supplement so as to meet the power balance of the micro-grid.
Further, in the step 2, based on the operation cost f1And environmental cost f2Establishing an optimized scheduling model of the comprehensive energy system; the running cost f1The method comprises the steps of operation and maintenance cost of the micro gas turbine, interaction cost with a power distribution network, energy loss cost of hydrogen to gas and natural gas fuel profit of hydrogen to gas; the environmental cost f2From CO2Calculating the discharge amount;
the objective function of the constructed comprehensive energy system optimization scheduling model is as follows:
in the formula, CEX,tFor interaction costs with the grid, CMT,tFor the maintenance costs of gas turbines, CLOSS,tCost of energy loss for conversion of hydrogen to gas, CCH4,tNatural gas fuel yield for hydrogen to gas, CEN,tTo environmental cost, NTFor the scheduling period, 24 hours.
Further, in the step 2, a weight coefficient method is adopted to convert the multi-objective function into a single objective function, and the comprehensive operation objective function comprehensively considering the minimization of the operation cost and the minimization of the environmental cost is as follows:
in the above formula, ω1And ω2Is a weight coefficient, f1max、f2maxThe maximum values of the system operating cost and the environmental cost, respectively, can be found by weight maximization.
Further, in the step 2, the safety constraint conditions of the comprehensive energy microgrid for hybrid hydrogen energy-natural gas energy storage include: hydrogen-gas hybrid energy storage system constraints, hydrogen to gas unit constraints, micro gas turbine constraints, natural gas network constraints, interactive power constraints with the power grid, and power balance constraints.
Further, the safety constraint conditions are as follows:
1) and (3) restraining a hydrogen-gas hybrid energy storage system:
in the above formula, PEL,maxIs the maximum hydrogen production power of the electrolytic cell, PFC,maxMaximum operating power of the fuel cell, EH,tHydrogen energy storage capacity, alpha, for a period of tE2HFor the hydrogen production efficiency of the cell, betaH2EFor the efficiency of fuel cell generation, PELi,tFor hydrogen production power of cell i during time t, PFCi,tThe output of the fuel cell i during the period t, EHmax/EHminStoring maximum energy for hydrogenSmall capacity, NELAnd NFCRespectively the number of electrolyzers and fuel cells. PHG,tHydrogen power, P, flowing into the hydrogen-to-gas apparatus for a period of tHL,tIs hydrogen load power;
2) unit constraint of hydrogen to gas:
in the above formula, PH2Gi,tFor the power of the hydrogen-to-gas device i during the period t, PH2G,max/PH2G,minIs maximum/minimum hydrogen to gas power, alphaH2GFor hydrogen to gas efficiency, GP2G,tThe amount of the natural gas output by the hydrogen-to-gas system in the t period;
3) and (3) constraining the micro gas turbine:
in the above formula, PMTi,tFor the output, P, of the micro gas turbine i during time tMT,max/PMT,minMaximum/minimum output for micro gas turbine; eta is the generating efficiency coefficient of the micro gas turbine, GMTi,tThe consumption of the gas of the micro gas turbine i is shown, and H is the low heating value of the natural gas;
4) natural gas network constraints:
in the above formula, Gload,tAir load for period t, NMTNumber of micro gas turbines, Gnet,tA quantity of gas purchased from the natural gas network for a time period t;
5) interaction power constraint with the grid:
-Psell，max≤PEX，t≤Pbuy，max；
in the above formula, PEX,tFor interactive power of the microgrid with the grid, Pbuy,max/Psell,maxMaximum power purchasing/selling power from the micro-grid to the power distribution network;
6) and power balance constraint:
in the above formula, PPV,t，PWT,t，PEX,t，Pload,tPhotovoltaic, fan output, interaction power with the distribution network and user load.
Further, in the step 3: the optimized solution of the optimized scheduling model of the comprehensive energy micro-grid based on hydrogen energy-natural gas hybrid energy storage is formed by combining a difference operator in a random fractal search algorithm, and comprises the following steps:
3.1, setting parameters and initializing a population position;
the initialization equation for the jth individual is: pj＝LB+ε(UB-LB)；
In the formula, LB and UB are upper and lower boundaries of a problem vector to be solved; epsilon is a random number obeying uniform distribution over [0,1 ];
3.2, calculating a fitness function value of the individual in the population, namely a target value corresponding to the cost function of the comprehensive energy microgrid, and finding out the optimal individual BP in the population;
3.3, diffusion process: all individuals walk around the current position to expand the search space, the positions of new individuals are created for the individuals needing to be diffused according to a Gaussian walking mode, and the best individual in all the diffused individuals is found by executing the step 3.2;
3.4 first update: all individuals in the population are sorted, and an individual P is calculatediProbability value P ofaiIf P isaiIf < epsilon, the component of the individual is updated to obtain P'iOtherwise, keeping the state unchanged;
wherein the content of the first and second substances,
rank(Pi) For population ranking, N isThe number of the populations; p'i(j)＝Pr(j)-ε·(Pr0(j)-Pi(j))，PiIs' PiUpdated position, PrAnd Pr0J represents an updated jth variable for randomly selected individuals;
3.5 second update: reordering the individuals after the primary updating, and calculating to obtain the individuals P 'after the updating'iProbability value P'aiIt is determined again whether or not P 'is satisfied'aiIf epsilon is satisfied, the individual is updated to obtain P "iIf f (P) "i) Is better than f (P'i) If, then, P "iReplacement of P'i；
And in the second updating stage, the variation idea of the differential algorithm is introduced for improvement, the convergence speed is accelerated, the local optimum is avoided, and the algorithm performance is improved. Wherein, P'i＝Pr1+Fω·(P′r2-P′r3)，P’r1、P’r2、P’r3The method comprises the following steps that F omega is a scaling factor and is randomly selected from a population after one-time updating, the scaling factor is used for scaling a differential vector, the search step length is controlled, and F omega is taken to be 0.5;
6) and (4) judging whether the maximum iteration number is reached, if so, outputting an optimization result, otherwise, returning to the step 3.3 to continue execution.
Further, the gaussian walking mode is as follows:
in which epsilon' is the interval [0,1]]Random numbers, BP and P obeying uniform distributioniThe position of the best individual and the ith individual in the population; mu.sBPAnd σ is a Gaussian parameter, wherein
g is the number of iterations.
The beneficial effects of the technical scheme are as follows:
aiming at the comprehensive energy micro-grid system for hydrogen-natural gas mixed energy storage, the safe operation constraint of each unit of the system is comprehensively considered, and a scheduling model is established with the aim of minimizing the operation cost and the environment cost. Converting a multi-target problem into a single-target problem by a linear weighting method, introducing a concept of differential variation, improving a random fractal search algorithm, and finally solving by the improved algorithm to obtain an optimized scheduling scheme. The optimal scheduling scheme with environmental protection and economy can be obtained for the comprehensive energy micro-grid system for hydrogen-natural gas hybrid energy storage, and stable and efficient operation of the comprehensive energy micro-grid for hydrogen-natural gas hybrid energy storage is guaranteed.
The invention aims at minimizing the operation cost and maximizing the environmental cost, combines the difference operator variation with the random fractal search algorithm, effectively improves the calculation convergence speed, prevents the algorithm from falling into local optimization, reduces the system operation cost, and optimizes to obtain an operation scheme with environmental protection and economy. The invention introduces the random fractal algorithm to solve the model, and introduces the concept of differential variation in the secondary updating stage of the algorithm, thereby effectively avoiding the solution process from falling into local optimization, greatly improving the operation speed and effectively reducing the operation cost.
According to the invention, natural gas has strong storage and transportation capacity, and the natural gas and the micro-grid are combined with each other to construct the electricity-gas comprehensive energy micro-grid, so that the utilization rate of new energy can be effectively improved, the emission of carbon dioxide can be reduced, and energy conservation and emission reduction can be realized. And the electric energy is directly converted into natural gas, the electric network is connected with the natural gas network, and the power fluctuation of the renewable energy source can bring great impact influence to the natural gas network. The combustion product of hydrogen energy is water, the heat value and the energy density are high, and the hydrogen energy has obvious effect on the dual requirements of replacing the traditional fossil energy to realize environmental protection and relieve the energy crisis.
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FIG. 1 is a schematic flow chart of a hydrogen energy-natural gas hybrid energy storage comprehensive energy microgrid scheduling method of the invention;
fig. 2 is a schematic topological structure diagram of a comprehensive energy microgrid system for hydrogen energy-natural gas hybrid energy storage in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings.
In this embodiment, referring to fig. 1, the present invention provides a method for scheduling a microgrid for integrated energy of hydrogen energy-natural gas hybrid energy storage, including:
step 1: modeling energy equipment of each unit in the comprehensive energy micro-grid based on hydrogen energy-natural gas mixed energy storage, and obtaining initial operation information of each unit; determining the energy flow relationship of each unit in the comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage;
step 2: establishing an optimized scheduling model of the comprehensive energy micro-grid based on hydrogen energy-natural gas hybrid energy storage based on environmental cost and operating cost; converting multiple targets into a single target by adopting a weight coefficient method; setting safety constraint conditions of a comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage;
and step 3: and (3) combining a difference operator in a random fractal search algorithm, and optimizing and solving the established optimized scheduling model of the comprehensive energy micro-grid based on the hydrogen energy-natural gas hybrid energy storage to obtain an optimal scheduling strategy.
As shown in fig. 2, the hydrogen energy-natural gas hybrid energy storage comprehensive energy microgrid comprises an electrolytic hydrogen production unit, a fuel cell unit, a hydrogen-to-natural gas unit, a hydrogen energy storage unit, a micro gas turbine unit, a natural gas network and an electrical network, wherein the natural gas network generates electric energy through the micro gas turbine unit and supplies the electric energy to the electrical network; the electric network is also connected with a power grid, a wind turbine unit and a photovoltaic power generation unit in parallel, and the electric network supplies power to an electric load; the hydrogen energy storage unit is connected with a hydrogen load, and the natural gas network is connected with a gas load.
As an optimization scheme of the above embodiment, in the step 1, an energy flow relationship of each unit in the comprehensive energy microgrid for hydrogen energy-natural gas hybrid energy storage is constructed: the new energy in the micro-grid is output to a wind turbine unit and a photovoltaic power generation unit, and when surplus energy exists in the electric network, electric energy is converted into hydrogen energy through an electrolytic hydrogen production unit and stored in a hydrogen energy storage unit or directly supplied to a hydrogen load; in the constraint range, when the electric quantity cannot be consumed, the hydrogen is converted into natural gas for storage through the electric gas conversion unit, and meanwhile, the natural gas is supplied to a gas load; and when the micro-grid is in power shortage, the fuel cell unit and the micro gas turbine unit convert hydrogen in the hydrogen energy storage unit and/or natural gas in a natural gas network into electric energy for supplement so as to meet the power balance of the micro-grid.
As an optimization scheme of the above embodiment, in the step 2, the operation cost f is based on1And environmental cost f2Establishing an optimized scheduling model of the comprehensive energy system; the running cost f1The method comprises the steps of operation and maintenance cost of the micro gas turbine, interaction cost with a power distribution network, energy loss cost of hydrogen to gas and natural gas fuel profit of hydrogen to gas; the environmental cost f2From CO2Calculating the discharge amount;
the objective function of the constructed comprehensive energy system optimization scheduling model is as follows:
in the formula, CEX,tFor interaction costs with the grid, CMT,tFor the maintenance costs of gas turbines, CLOSS,tCost of energy loss for conversion of hydrogen to gas, CCH4,tNatural gas fuel yield for hydrogen to gas, CEN,tTo environmental cost, NTFor the scheduling period, 24 hours.
In the step 2, a weight coefficient method is adopted to convert the multi-objective function into a single objective function, and the comprehensive operation objective function comprehensively considering the minimization of the operation cost and the minimization of the environmental cost is as follows:
in the above formula, ω1And ω2Is a weight coefficient, f1max、f2maxThe maximum values of the system operating cost and the environmental cost, respectively, can be found by weight maximization.
In the step 2, the safety constraint conditions of the comprehensive energy microgrid for hybrid hydrogen energy-natural gas energy storage comprise: hydrogen-gas hybrid energy storage system constraints, hydrogen to gas unit constraints, micro gas turbine constraints, natural gas network constraints, interactive power constraints with the power grid, and power balance constraints.
Among the safety constraints:
1) and (3) restraining a hydrogen-gas hybrid energy storage system:
in the above formula, PEL,maxIs the maximum hydrogen production power of the electrolytic cell, PFC,maxMaximum operating power of the fuel cell, EH,tHydrogen energy storage capacity, alpha, for a period of tE2HFor the hydrogen production efficiency of the cell, betaH2EFor the efficiency of fuel cell generation, PELi,tFor hydrogen production power of cell i during time t, PFCi,tThe output of the fuel cell i during the period t, EHmax/EHminStoring maximum minimum capacity, N, for hydrogenELAnd NFCRespectively the number of electrolyzers and fuel cells. PHG,tHydrogen power, P, flowing into the hydrogen-to-gas apparatus for a period of tHL,tIs hydrogen load power;
2) unit constraint of hydrogen to gas:
in the above formula，PH2Gi,tFor the power of the hydrogen-to-gas device i during the period t, PH2G,max/PH2G,minIs maximum/minimum hydrogen to gas power, alphaH2GFor hydrogen to gas efficiency, GP2G,tThe amount of the natural gas output by the hydrogen-to-gas system in the t period;
3) and (3) constraining the micro gas turbine:
in the above formula, PMTi,tFor the output, P, of the micro gas turbine i during time tMT,max/PMT,minMaximum/minimum output for micro gas turbine; eta is the generating efficiency coefficient of the micro gas turbine, GMTi,tThe consumption of the gas of the micro gas turbine i is shown, and H is the low heating value of the natural gas;
4) natural gas network constraints:
in the above formula, Gload,tAir load for period t, NMTNumber of micro gas turbines, Gnet,tA quantity of gas purchased from the natural gas network for a time period t;
5) interaction power constraint with the grid:
-Psell，max≤PEX，t≤Pbuy，max；
in the above formula, PEX,tFor interactive power of the microgrid with the grid, Pbuy,max/Psell,maxMaximum power purchasing/selling power from the micro-grid to the power distribution network;
6) and power balance constraint:
in the above formula, PPV,t，PWT,t，PEX,t，Pload,tRespectively photovoltaic power, fan output power,Interactive power with the distribution network and user load.
As an optimization scheme of the above embodiment, in the step 3: the optimized solution of the optimized scheduling model of the comprehensive energy micro-grid based on hydrogen energy-natural gas hybrid energy storage is formed by combining a difference operator in a random fractal search algorithm, and comprises the following steps:
3.1, setting parameters and initializing a population position;
the initialization equation for the jth individual is: pj＝LB+ε(UB-LB)；
In the formula, LB and UB are upper and lower boundaries of a problem vector to be solved; epsilon is a random number obeying uniform distribution over [0,1 ];
3.2, calculating a fitness function value of the individual in the population, namely a target value corresponding to the cost function of the comprehensive energy microgrid, and finding out the optimal individual BP in the population;
3.3, diffusion process: all individuals walk around the current position to expand the search space, the positions of new individuals are created for the individuals needing to be diffused according to a Gaussian walking mode, and the best individual in all the diffused individuals is found by executing the step 3.2;
3.4 first update: all individuals in the population are sorted, and an individual P is calculatediProbability value P ofaiIf P isaiIf < epsilon, the component of the individual is updated to obtain P'iOtherwise, keeping the state unchanged;
wherein the content of the first and second substances,
rank(Pi) Ranking the population, wherein N is the number of the population; p'i(j)＝Pr(j)-ε·(Pr0(j)-Pi(j))，PiIs' PiUpdated position, PrAnd Pr0J represents an updated jth variable for randomly selected individuals;
3.5 second update: reordering the individuals after the primary updating, and calculating to obtain the individuals P 'after the updating'iProbability value P'aiIt is determined again whether or not P 'is satisfied'aiIf epsilon is satisfied, the individual is updated to obtain P "iIf f (P) "i) Is better than f (P'i) If, then, P "iReplacement of P'i；
And in the second updating stage, the variation idea of the differential algorithm is introduced for improvement, the convergence speed is accelerated, the local optimum is avoided, and the algorithm performance is improved. Wherein, P'i＝Pr1+Fω·(P′r2-P′r3)，P’r1、P’r2、P’r3The method comprises the following steps that F omega is a scaling factor and is randomly selected from a population after one-time updating, the scaling factor is used for scaling a differential vector, the search step length is controlled, and F omega is taken to be 0.5;
7) and (4) judging whether the maximum iteration number is reached, if so, outputting an optimization result, otherwise, returning to the step 3.3 to continue execution.
The Gaussian walking mode is as follows:
in which epsilon' is the interval [0,1]]Random numbers, BP and P obeying uniform distributioniThe position of the best individual and the ith individual in the population; mu.sBPAnd σ is a Gaussian parameter, wherein
g is the number of iterations.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A method for scheduling a hydrogen energy-natural gas hybrid energy storage comprehensive energy microgrid is characterized by comprising the following steps:
step 1: modeling energy equipment of each unit in the comprehensive energy micro-grid based on hydrogen energy-natural gas mixed energy storage, and obtaining initial operation information of each unit; determining the energy flow relationship of each unit in the comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage;
step 2: establishing an optimized scheduling model of the comprehensive energy micro-grid based on hydrogen energy-natural gas hybrid energy storage based on environmental cost and operating cost; converting multiple targets into a single target by adopting a weight coefficient method; setting safety constraint conditions of a comprehensive energy micro-grid for hydrogen energy-natural gas mixed energy storage;
and step 3: and (3) combining a difference operator in a random fractal search algorithm, and optimizing and solving the established optimized scheduling model of the comprehensive energy micro-grid based on the hydrogen energy-natural gas hybrid energy storage to obtain an optimal scheduling strategy.
2. The method for scheduling the microgrid for the comprehensive energy of the mixed energy storage of hydrogen energy and natural gas as claimed in claim 1, wherein the microgrid for the comprehensive energy of the mixed energy storage of hydrogen energy and natural gas comprises an electrolytic hydrogen production unit, a fuel cell unit, a hydrogen-to-natural gas unit, a hydrogen energy storage unit, a micro gas turbine unit, a natural gas network and an electric network, the natural gas network generates electric energy through the micro gas turbine unit to supply to the electric network, the electrolytic hydrogen production unit takes electricity from the electric network to produce hydrogen and supplies the hydrogen energy to the hydrogen energy storage unit, the hydrogen energy storage unit supplies hydrogen energy to the fuel cell unit to supply the electric energy to the fuel cell unit, and the hydrogen energy storage unit converts the hydrogen energy into natural gas energy through the hydrogen-to-natural gas unit to supply to the natural gas network; the electric network is also connected with a power grid, a wind turbine unit and a photovoltaic power generation unit in parallel, and the electric network supplies power to an electric load; the hydrogen energy storage unit is connected with a hydrogen load, and the natural gas network is connected with a gas load.
3. The method for scheduling the comprehensive energy microgrid for hydrogen energy-natural gas hybrid energy storage according to claim 2, characterized in that in the step 1, the energy flow relationship of each unit in the comprehensive energy microgrid for hydrogen energy-natural gas hybrid energy storage is constructed: the new energy in the micro-grid is output to a wind turbine unit and a photovoltaic power generation unit, and when surplus energy exists in the electric network, electric energy is converted into hydrogen energy through an electrolytic hydrogen production unit and stored in a hydrogen energy storage unit or directly supplied to a hydrogen load; in the constraint range, when the electric quantity cannot be consumed, the hydrogen is converted into natural gas for storage through the electric gas conversion unit, and meanwhile, the natural gas is supplied to a gas load; and when the micro-grid is in power shortage, the fuel cell unit and the micro gas turbine unit convert hydrogen in the hydrogen energy storage unit and/or natural gas in a natural gas network into electric energy for supplement so as to meet the power balance of the micro-grid.
4. The method for scheduling the microgrid for comprehensive energy source of hydrogen energy-natural gas hybrid energy storage according to claim 3, characterized in that in the step 2, the operation cost f is based on1And environmental cost f2Establishing an optimized scheduling model of the comprehensive energy system; the running cost f1The method comprises the steps of operation and maintenance cost of the micro gas turbine, interaction cost with a power distribution network, energy loss cost of hydrogen to gas and natural gas fuel profit of hydrogen to gas; the environmental cost f2From CO2Calculating the discharge amount;
the objective function of the constructed comprehensive energy system optimization scheduling model is as follows:
in the formula, CEX,tFor interaction costs with the grid, CMT,tFor the maintenance costs of gas turbines, CLOSS,tCost of energy loss for conversion of hydrogen to gas, CCH4,tNatural gas fuel yield for hydrogen to gas, CEN,tTo environmental cost, NTFor the scheduling period, 24 hours.
5. The method for scheduling the microgrid for the comprehensive energy source of the hydrogen energy-natural gas hybrid energy storage according to claim 4, characterized in that in the step 2, a multi-objective function is converted into a single objective function by a weight coefficient method, and the comprehensive operation objective function comprehensively considering the minimization of the operation cost and the minimization of the environmental cost is as follows:
in the above formula, ω1And ω2Is a weight coefficient, f1max、f2maxThe maximum values of the system operating cost and the environmental cost, respectively, can be found by weight maximization.
6. The method for scheduling the microgrid for hydrogen energy-natural gas hybrid energy storage according to claim 5, wherein in the step 2, the safety constraint conditions of the microgrid for hydrogen energy-natural gas hybrid energy storage comprise: hydrogen-gas hybrid energy storage system constraints, hydrogen to gas unit constraints, micro gas turbine constraints, natural gas network constraints, interactive power constraints with the power grid, and power balance constraints.
7. The method for scheduling the microgrid for comprehensive energy source of hydrogen energy-natural gas hybrid energy storage according to claim 2, characterized in that in the safety constraint condition:
1) and (3) restraining a hydrogen-gas hybrid energy storage system:
in the above formula, PEL,maxIs the maximum hydrogen production power of the electrolytic cell, PFC,maxMaximum operating power of the fuel cell, EH,tHydrogen energy storage capacity, alpha, for a period of tE2HFor the hydrogen production efficiency of the cell, betaH2EFor the efficiency of fuel cell generation, PELi,tFor hydrogen production power of cell i during time t, PFCi,tThe output of the fuel cell i during the period t, EHmax/EHminStoring maximum minimum capacity, N, for hydrogenELAnd NFCRespectively the number of electrolyzers and fuel cells. PHG,tHydrogen power, P, flowing into the hydrogen-to-gas apparatus for a period of tHL,tIs hydrogen load power;
2) unit constraint of hydrogen to gas:
in the above formula, PH2Gi,tFor the power of the hydrogen-to-gas device i during the period t, PH2G,max/PH2G,minIs maximum/minimum hydrogen to gas power, alphaH2GFor hydrogen to gas efficiency, GP2G,tThe amount of the natural gas output by the hydrogen-to-gas system in the t period;
3) and (3) constraining the micro gas turbine:
in the above formula, PMTi,tFor the output, P, of the micro gas turbine i during time tMT,max/PMT,minMaximum/minimum output for micro gas turbine; eta is the generating efficiency coefficient of the micro gas turbine, GMTi,tThe consumption of the gas of the micro gas turbine i is shown, and H is the low heating value of the natural gas;
4) natural gas network constraints:
in the above formula, Gload,tAir load for period t, NMTNumber of micro gas turbines, Gnet,tA quantity of gas purchased from the natural gas network for a time period t;
5) interaction power constraint with the grid:
-Psell，max≤PEX，t≤Pbuy，max；
in the above formula, PEX,tFor interactive power of the microgrid with the grid, Pbuy,max/Psell,maxMaximum power purchasing/selling power from the micro-grid to the power distribution network;
6) and power balance constraint:
in the above formula, PPV,t，PWT,t，PEX,t，Pload,tPhotovoltaic, fan output, interaction power with the distribution network and user load.
8. The method for scheduling the micro-grid for the comprehensive energy source of the hydrogen energy-natural gas hybrid energy storage according to any one of claims 1 to 7, wherein in the step 3: the optimized solution of the optimized scheduling model of the comprehensive energy micro-grid based on hydrogen energy-natural gas hybrid energy storage is formed by combining a difference operator in a random fractal search algorithm, and comprises the following steps:
3.1, setting parameters and initializing a population position;
the initialization equation for the jth individual is: pj＝LB+ε(UB-LB)；
In the formula, LB and UB are upper and lower boundaries of a problem vector to be solved; epsilon is a random number obeying uniform distribution over [0,1 ];
3.2, calculating a fitness function value of the individual in the population, namely a target value corresponding to the cost function of the comprehensive energy microgrid, and finding out the optimal individual BP in the population;
3.3, diffusion process: all individuals walk around the current position to expand the search space, the positions of new individuals are created for the individuals needing to be diffused according to a Gaussian walking mode, and the best individual in all the diffused individuals is found by executing the step 3.2;
3.4 first update: all individuals in the population are sorted, and an individual P is calculatediProbability value P ofaiIf P isaiIf < epsilon, the component of the individual is updated to obtain P'iOtherwise, keeping the state unchanged;
wherein the content of the first and second substances,
rank(Pi) Ranking the population, wherein N is the number of the population; p'i(j)＝Pr(j)-ε·(Pr0(j)-Pi(j))，PiIs' PiUpdated position, PrAnd Pr0J represents an updated jth variable for randomly selected individuals;
3.5 second update: reordering the individuals after the primary updating, and calculating to obtain the individuals P 'after the updating'iProbability value P'aiIt is determined again whether or not P 'is satisfied'aiIf epsilon is satisfied, the individual is updated to obtain P "iIf f (P) "i) Is better than f (P'i) If, then, P "iReplacement of P'i；
Wherein, P'i＝Pr1+Fω·(P′r2-P′r3)，P’r1、P’r2、P’r3The method comprises the following steps that F omega is a scaling factor and is randomly selected from a population after one-time updating, the scaling factor is used for scaling a differential vector, the search step length is controlled, and F omega is taken to be 0.5;
6) and (4) judging whether the maximum iteration number is reached, if so, outputting an optimization result, otherwise, returning to the step 3.3 to continue execution.
9. The method for scheduling the microgrid for the comprehensive energy source of the hydrogen energy-natural gas hybrid energy storage is characterized in that the Gaussian migration mode is as follows:
in which epsilon' is the interval [0,1]]Random numbers, BP and P obeying uniform distributioniThe position of the best individual and the ith individual in the population; mu.sBPAnd σ is highA parameter of, wherein
g is the number of iterations.
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