CN114243694B - Grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response - Google Patents
Grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response Download PDFInfo
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Abstract
The grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response is characterized by comprising the following steps of: constructing a grid-connected micro-grid containing hydrogen for energy storage, and establishing a mathematical model of each device in the micro-grid; introducing a ladder carbon transaction mechanism, and establishing a ladder carbon transaction cost calculation model; establishing an excitation type demand response model by taking the minimum sum of absolute values of differences of renewable energy power generation power and load power as a target; establishing a grid-connected micro-grid double-layer optimization configuration model, wherein the upper-layer optimization model optimizes the equipment capacity by taking the minimum annual value comprehensive cost of the micro-grid and the like as a target, and the lower-layer optimization model takes step carbon transaction and demand response into consideration and performs operation optimization by taking the minimum sum of annual operation cost and annual carbon transaction cost as a target; the model is solved by a method combining genetic algorithm with mixed integer linear programming. The method has the advantages of being scientific and reasonable, strong in applicability, good in effect and capable of enabling economical efficiency and low carbon to be optimally matched while meeting reliability requirements.
Description
Technical Field
The invention relates to the field of planning and design of grid-connected micro-grids, in particular to a grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response.
Background
The micro-grid is constructed by means of renewable energy sources, energy storage and other technologies, and energy conservation and emission reduction of the power industry are guided through mechanisms such as carbon transaction, demand response and the like, so that the micro-grid is recognized as an important measure for promoting sustainable development in the field. Since reasonable configuration of the capacity of each device in the micro-grid is a precondition for ensuring good operation efficiency, the research of introducing a carbon transaction mechanism and a demand response in the micro-grid capacity configuration process has great significance. And secondly, the energy storage device in the micro-grid is mostly a storage battery, the price of the energy storage device is very high, long-term electricity storage is difficult, along with the gradual maturity of a hydrogen storage technology, the hydrogen energy storage is used as a completely clean energy storage mode, the electrochemical energy storage device has the characteristic of high electrochemical energy storage speed, and the energy storage device has the advantages of large physical energy storage scale and capability of storing in seasons, so that the application of the energy storage device is wider and wider.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and establish a grid-connected micro-grid optimal configuration method which is scientific, reasonable, high in applicability and good in effect, and can comprehensively consider the economical efficiency and the low carbon of a system and realize the best matching of the economical efficiency and the low carbon and takes the stepped carbon transaction and the demand response into consideration on the premise of meeting the reliability requirement, the non-renewable energy power generation duty ratio requirement and the renewable energy permeability requirement.
The technical scheme adopted for realizing the purpose of the invention is that a grid-connected micro-grid optimizing configuration method considering ladder carbon transaction and demand response is characterized by comprising the following steps:
1) Constructing a hydrogen-containing energy storage grid-connected micro-grid
The grid-connected micro-grid comprises a wind driven generator, a photovoltaic array, a diesel generator, a load and a hydrogen energy storage system, wherein the photovoltaic array, the wind driven generator and the hydrogen energy storage system are connected to an alternating-current micro-grid through respective converters or inverters respectively, and the load of a user is divided into a time-variable load and a rigid load according to the electricity consumption condition of the user;
the hydrogen energy storage system comprises an electrolytic tank, a hydrogen storage tank and a fuel cell, and in the period that the wind-solar output power is larger than the load demand, the electrolytic tank equipment consumes surplus electric energy to generate hydrogen through electrolysis of water, and the hydrogen is stored in the hydrogen storage tank, which is equivalent to increasing the electric load; when the wind-solar output power is smaller than the load demand, the fuel cell takes hydrogen and oxygen as raw materials to generate electric energy through chemical reaction so as to meet the load demand;
the mathematical model of each device of the hydrogen energy storage system is respectively established as follows:
(1) establishing a mathematical model of the electrolytic cell
The output power of the electrolytic cell is shown in formula (1):
P el-out ＝η el P el-in (1)
wherein ,Pel-out The output power of the electrolytic cell; η (eta) el Is the efficiency of the electrolyzer; p (P) el-in The input power of the electrolytic cell;
the maximum input power of the electrolyzer is related to the rated capacity of the electrolyzer and is influenced by the residual hydrogen storage capacity of the hydrogen storage tank, and the maximum input power of the electrolyzer is shown in the formulas (2) and (3):
E ht,max ＝SOC ht,max P ht,N (3)
wherein ,Pel-in,max (t) is the maximum input power of the electrolyzer; p (P) el,N Is the rated capacity of the electrolytic cell; e (E) ht,max Maximum energy storage capacity of the hydrogen storage tank; e (E) ht (t) is the energy stored in the hydrogen storage tank at time t; p (P) ht,N Is the rated capacity of the hydrogen storage tank; defining the state of charge of the hydrogen storage tank as SOC in analogy to the state of charge of the storage battery ht ，SOC ht,max Is the maximum state of charge of the hydrogen storage tank; Δt is the time interval;
(2) establishing a mathematical model of a fuel cell
The output power of the fuel cell is shown in formula (4);
P fc-out ＝η fc P fc-in (4)
wherein ,Pfc-out Is the output power of the fuel cell; η (eta) fc Is the operating efficiency of the fuel cell; p (P) fc-in The input power of the fuel cell, namely the output power of the hydrogen storage tank;
the maximum output power of the fuel cell is limited by the capacity of the fuel cell and the residual capacity of the hydrogen storage tank, as shown in formulas (5) and (6);
E ht,min ＝SOC ht,min P ht,N (6)
wherein ,Pfc-out,max Is the maximum output power of the fuel cell; p (P) fc,N Is the rated capacity of the fuel cell; e (E) ht,min Is the minimum energy storage capacity of the hydrogen storage tank; SOC (State of Charge) ht,min Is the minimum state of charge of the hydrogen storage tank;
(3) establishing mathematical model of hydrogen storage tank
The hydrogen storage tank can store hydrogen generated by the electrolytic tank and can also provide hydrogen for the fuel cell, and mathematical models of the hydrogen storage tank are shown in formulas (7) and (8);
when the hydrogen storage tank stores hydrogen:
E ht (t)＝E ht (t-1)+P el-in (t-1)η el Δt (7)
when the hydrogen storage tank discharges hydrogen:
wherein ,ηht The working efficiency of the hydrogen storage tank is;
2) Construction of carbon transaction mechanism model
The carbon trade is a trade mechanism for realizing carbon emission reduction by buying and selling carbon emission quota, according to the built grid-connected micro-grid, the carbon emission in the micro-grid is determined to be derived from a diesel generator and electric power purchased to an upper-level power grid, and the electric power purchased to the upper-level power grid by default is all derived from thermal power, so that the gratuitous carbon emission quota of the carbon trade is shown as a formula (9):
wherein ,DG Is carbon emission quota; alpha de Carbon emission quota for unit electric quantity of the diesel generator; alpha grid Carbon emission allowance for outsourcing unit electric quantity; p (P) de (t) is the output power of the diesel generator in period t; p (P) grid,buy (t) power purchased to the upper grid for the micro grid t period;t is the settlement period of the carbon transaction fee;
the step carbon trade calculation model is adopted, namely the carbon emission is divided into a plurality of sections, the more the carbon emission is, the higher the carbon trade price is, the larger the carbon trade cost is, and the step carbon trade cost calculation model is shown as formulas (10) and (11):
wherein ,cost for carbon trade;A trade price for carbon; e (E) G Carbon emissions for the system; l is the carbon emission interval length; lambda is the increase in carbon trade price; beta de Carbon emission intensity for unit electric quantity of the diesel generator; beta grid,buy Carbon emission intensity for outsourcing unit electric quantity;Positive values indicate that the system needs to purchase carbon emissions rights;Negative values indicate that the system sells carbon emissions rights to gain a benefit;
3) Constructing a demand response model
(1) Establishing an objective function
The demand response is a mechanism of load participation power adjustment, and an excitation type demand response is adopted, wherein the optimization target is that the sum of the absolute values of the difference between the renewable energy generated power and the load power at each moment in a scheduling period is minimum, as shown in a formula (12):
wherein ,Pload,before (t) is the load power for a period t before demand response; p (P) load,after (t) is the load power for a period t after demand response; ΔP load (t) is the load transfer amount of the t period, when it is greater than 0, it is the transfer-in load, otherwise it is the transfer-out load;
(2) establishing constraint conditions
a. Establishing transfer period constraints
The load can only be transferred in or out in the same scheduling period, and the constraint of the formula (13) is satisfied:
t∈T n ,t'∈T n (13) Wherein t is the load transfer period; t' is the load roll-out period; t (T) n For the nth scheduling period;
b. establishing transfer volume constraints
The load transfer amount should satisfy the total load demand before and after the demand response in one scheduling period, and the load transfer amount in each period should not exceed the maximum required transfer amount, as shown in the formula (14):
wherein ,ΔPload,max (t) is the maximum load transfer amount for period t;
4) Construction of micro-grid double-layer optimization configuration model and solving thereof
The built double-layer optimization model comprises two optimization tasks, wherein the upper-layer optimization is capacity configuration optimization, and aims at minimizing annual comprehensive cost of a micro-grid and the like; the lower layer is optimized for system operation, and aims at minimizing the sum of annual operation cost and annual carbon transaction cost of the micro-grid; the upper layer capacity configuration result is transmitted to the lower layer, the lower layer transmits the solved optimal operation result to the upper layer according to the known equipment capacity, and the upper layer and the lower layer iterate to obtain the optimal configuration result;
(1) establishing an upper layer optimization model:
a. establishing an objective function of an upper-layer optimization model
The upper layer aims at meeting the annual value integrated cost Fmin of the operator for constructing the micro-grid under the requirements of the micro-grid construction and operation; the decision variables are the installed capacity of each device, and the objective functions are equations (15) - (18):
wherein F is the annual value comprehensive cost; f (F) inv Investment cost is equal to the annual value; f (F) main Maintenance costs for the year; f (F) om The annual running cost; omega shape k A set of build devices; comprises a photovoltaic array, a wind driven generator, a diesel generator, an electrolytic tank, a hydrogen storage tank and a fuel cell; c (C) k Investment cost per unit capacity for the kth class of equipment; p (P) k,N Is the installed capacity of the k-th equipment; r is (r) CR Is a fund withdrawal factor; gamma is the rate of occurrence; y is k The operation time of the k-th set is; c (C) main,k Annual maintenance costs for a unit capacity of a class k device;
b. establishing constraint conditions of upper-layer optimization model
Because of the limited area of construction within the area for installing the various types of equipment, the configurable capacity of each equipment should satisfy the constraint of formula (19):
wherein ：minimum and maximum installation capacity of k-th equipment respectively;
(2) step of establishing lower optimization model
a. Establishing objective function of lower optimization model
The lower layer considers a ladder carbon transaction mechanism and a demand response, and aims at minimizing the sum of the annual running cost and the annual carbon transaction cost of the system; the objective function is equation (20):
wherein ,F1 Optimizing a target value for the lower layer; f (F) fuel For annual fuel costs, only the fuel consumption of the diesel generator is considered; f (F) dr Annual load transfer compensation costs for users participating in demand response; f (F) grid Annual power interaction cost with large power grids; f (F) waste Punishment costs for annual renewable energy waste; omega shape D Is a typical day set comprising three typical days of summer, winter and spring and autumn;days taken in one year for typical day d; c (C) fuel Is diesel unit price;Power for the diesel generator during a typical time period t of day d; a. b is the slope and intercept coefficient in the fuel consumption-power curve respectively; c (C) dr The transfer compensation cost of the load of the unit electric quantity is calculated; c (C) grid,buy (t) is the electricity purchase price in the t period; c (C) grid,sell (t) electricity selling price for period t;Purchasing and selling electricity of micro-grid at time period t in typical day dA power; c (C) waste Penalty fees are wasted for the unit renewable energy sources;Wasting power for renewable energy sources of time period t in typical day d;
b. establishing constraint conditions of lower optimization model
Constraint 1: power balance constraint: as shown in formula (21):
wherein ,output power of the photovoltaic array in a t period in a typical day d;The output power of the wind driven generator in the t period of the typical day d is given;Load power for a period t within a typical day d;
constraint 2: distributed power supply output constraint: as shown in formula (22):
constraint 3: hydrogen energy storage system constraints: the upper and lower limit constraint of the power of the electrolytic tank, the fuel cell and the hydrogen storage tank in the hydrogen energy storage are shown in formulas (23) - (25), the upper and lower limit constraint of the charge state of the hydrogen storage tank is shown in formula (26), and in order to ensure that the hydrogen energy storage can continuously and effectively work, the charge state of the hydrogen energy storage is ensured to be equal at the beginning and the end of a dispatching period, and is shown in equation constraint of formula (27);
SOC ht (t 0 )＝SOC ht (t N ) (27)
wherein ,the state of charge of the hydrogen storage tank is the period t in a typical day d; t is t 0 、t N Respectively starting and ending moments of a scheduling period; SOC (State of Charge) ht (t 0) and SOCht (t N ) The charge states of the hydrogen storage tanks are respectively the beginning and the end of the dispatching period;
constraint 4: power exchange constraints: the grid-connected micro-grid is connected with the large power grid, and electricity purchasing and selling can be performed, but because the power limit of a circuit and the excessive electricity selling power can influence the large power grid, the electricity purchasing and selling power of the micro-grid should meet the inequality constraint shown in the formula (28);
wherein , andThe maximum power value of the micro-grid for purchasing and selling electricity to the large power grid is respectively;
constraint 5: discarding capacity constraint of renewable energy sources: in order to ensure the utilization rate of renewable energy sources, the renewable energy source waste capacity of the micro-grid should meet the inequality constraint shown in the formula (29);
wherein ,discarding the maximum renewable energy source electricity quantity in a typical day d;
(3) solving the constructed double-layer optimal configuration model
Adopting a genetic algorithm and a CPLEX solver to carry out joint solving, firstly, reading annual meteorological data, annual load data and related parameters, and clustering wind speed, illumination and load according to summer, winter, spring and autumn by using a k-means algorithm to obtain a typical scene; secondly, the upper model adopts a genetic algorithm to randomly generate a population, generates a capacity configuration scheme and transmits the capacity configuration scheme to the lower layer; then, the lower model calculates and obtains the load after the demand response on the basis of the equipment capacity output by the upper model, carries out micro-grid operation simulation according to the load, takes the minimum sum of annual operation cost and annual carbon transaction cost as a target, adopts a CPLEX solver to obtain an optimal operation scheme, and transmits the optimal operation scheme to the upper layer; and finally, calculating fitness values by the upper model according to the capacity configuration scheme and the optimal operation scheme of the lower model, selecting, crossing and mutating the population, transmitting the generated new population to the lower optimizing model for cyclic iterative calculation, stopping the circulation after the required cyclic times are reached, and outputting the population of the last generation.
According to the grid-connected micro-grid capacity optimization configuration method considering ladder carbon transaction and demand response, firstly, a hydrogen-containing energy storage grid-connected micro-grid is constructed, and a mathematical model of the output of each device in the micro-grid is built; secondly, introducing a ladder carbon transaction mechanism for reducing carbon emission in the micro-grid, and establishing a ladder carbon transaction cost calculation model; thirdly, according to the supply-demand relation between the renewable energy source and the load, taking the minimum sum of the absolute values of the difference values of the renewable energy source power generation power and the load power as a target, and establishing an excitation type demand response model; then, a grid-connected micro-grid double-layer optimization configuration model comprising a wind driven generator, a photovoltaic array, a diesel generator, an electrolytic tank, a hydrogen storage tank and a fuel cell is established, the upper-layer optimization model optimally configures the capacity of each device in the micro-grid by taking the minimum annual value comprehensive cost of the micro-grid and the like as a target, and the lower-layer optimization model performs operation optimization by taking stepped carbon transaction and demand response into consideration on the basis of the device capacity output by the upper-layer model and taking the minimum sum of annual operation cost and annual carbon transaction cost as a target; finally, solving the model by a method combining a genetic algorithm and mixed integer linear programming. Has the advantages of science, reasonability, strong applicability and good effect. On the premise of meeting the reliability requirement, the non-renewable energy power generation duty ratio requirement and the renewable energy permeability requirement, the economical efficiency and the low carbon property of the system are comprehensively considered, and the best matching of the economical efficiency and the low carbon property is realized.
Drawings
FIG. 1 is a basic block diagram of a grid-connected micro-grid;
FIG. 2 is a graph of annual wind speed over time within a grid-connected microgrid;
FIG. 3 is a graph of annual solar radiation intensity over time within a grid-tied microgrid;
FIG. 4 is a graph of annual load change over time for a grid-connected microgrid;
FIG. 5 is a graph of wind speed within a grid-connected microgrid over various typical times of day;
FIG. 6 is a graph of solar radiation intensity of a grid-connected microgrid over various typical times of day;
FIG. 7 is a graph of load of a grid-connected microgrid over various typical times of day;
FIG. 8 is a solution flow chart of a grid-connected micro-grid double-layer optimization configuration model;
FIG. 9 is a graph comparing annual energy production of each unit under various scenarios;
FIG. 10 is a graph of renewable energy output, raw load, and demand response afterload for a typical day in winter;
FIG. 11 is a graph of renewable energy output, raw load, and demand response afterload for a typical day of the summer season.
Detailed Description
The invention relates to a grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response, which comprises the following steps:
1) Constructing a hydrogen-containing energy storage grid-connected micro-grid
The grid-connected micro-grid comprises a wind power generator (WT), a photovoltaic array (photovoltaic cell, PV), a diesel generator (DEG), a load and hydrogen energy storage (hydrogen energy storage, HES) system, wherein the photovoltaic array, the wind power generator and the hydrogen energy storage system are respectively connected to an alternating-current micro-grid through respective converters or inverters, and the user load is divided into a time-shifting load and a fixed load according to the power consumption condition of the user;
the HES system comprises an electrolytic tank, a hydrogen storage tank and a fuel cell, and in the period that the wind-solar output power is larger than the load demand, the electrolytic tank equipment consumes surplus electric energy to generate hydrogen through electrolysis of water, and the hydrogen is stored in the hydrogen storage tank, so that the electric load is increased, and the level of the renewable energy sources is improved; when the wind-solar output power is smaller than the load demand, the fuel cell takes hydrogen and oxygen as raw materials to generate electric energy through chemical reaction so as to meet the load demand, and the reliability of the system is improved, wherein the process is the reverse process of electrolysis of water;
the mathematical model of each device of the HES system is respectively established as follows:
(1) establishing a mathematical model of the electrolytic cell
The output power of the electrolytic cell is shown in formula (1):
P el-out ＝η el P el-in (30)
wherein ,Pel-out The output power of the electrolytic cell; η (eta) el Is the efficiency of the electrolyzer; p (P) el-in The input power of the electrolytic cell;
the maximum input power of the electrolyzer is related to the rated capacity of the electrolyzer and is influenced by the residual hydrogen storage capacity of the hydrogen storage tank, and the maximum input power of the electrolyzer is shown in the formulas (2) and (3):
E ht,max ＝SOC ht,max P ht,N (32)
wherein ,Pel-in,max (t) is the maximum input power of the electrolyzer; p (P) el,N Is the rated capacity of the electrolytic cell; e (E) ht,max Maximum energy storage capacity of the hydrogen storage tank; e (E) ht (t) is the energy stored in the hydrogen storage tank at time t; p (P) ht,N Is the rated capacity of the hydrogen storage tank; defining the state of charge of the hydrogen storage tank as SOC in analogy to the state of charge of the storage battery ht ，SOC ht,max Is the maximum state of charge of the hydrogen storage tank; Δt is the time interval;
(2) establishing a mathematical model of a fuel cell
The output power of the fuel cell is shown in formula (4);
P fc-out ＝η fc P fc-in (33)
wherein ,Pfc-out Is the output power of the fuel cell; η (eta) fc Is the operating efficiency of the fuel cell; p (P) fc-in The input power of the fuel cell, namely the output power of the hydrogen storage tank;
the maximum output power of the fuel cell is limited by the capacity of the fuel cell and the residual capacity of the hydrogen storage tank, as shown in formulas (5) and (6);
E ht,min ＝SOC ht,min P ht,N (35)
wherein ,Pfc-out,max Is the maximum output power of the fuel cell; p (P) fc,N Is the rated capacity of the fuel cell; e (E) ht,min Is the minimum energy storage capacity of the hydrogen storage tank; SOC (State of Charge) ht,min Is the minimum state of charge of the hydrogen storage tank;
(3) establishing mathematical model of hydrogen storage tank
The hydrogen storage tank can store hydrogen generated by the electrolytic tank and also can provide hydrogen for the fuel cell, and the mathematical model of the hydrogen storage tank is shown in formulas (7) and (8);
when the hydrogen storage tank stores hydrogen:
E ht (t)＝E ht (t-1)+P el-in (t-1)η el Δt (36)
when the hydrogen storage tank discharges hydrogen:
wherein ,ηht The working efficiency of the hydrogen storage tank is;
2) Construction of carbon transaction mechanism model
The carbon trade is a trade mechanism for realizing carbon emission reduction by buying and selling carbon emission quota, the carbon trade market in China is still in an initial stage, the carbon emission quota is freely distributed to power generation enterprises participating in the carbon trade mechanism, according to the built grid-connected micro-grid, the carbon emission in the micro-grid is determined to be derived from a diesel generator and electric power purchased to an upper-level power grid, and the electric power purchased to the upper-level power grid is determined to be derived from thermal power by default, so that the gratuitous carbon emission quota of the carbon trade is shown in a formula (9):
wherein ,DG Is carbon emission quota; alpha de Carbon emission quota for unit electric quantity of the diesel generator; alpha grid Carbon emission allowance for outsourcing unit electric quantity; p (P) de (t) is the output power of the diesel generator in period t; p (P) grid,buy (t) power purchased to the upper grid for the micro grid t period; t is the settlement period of the carbon transaction fee;
The invention adopts a step carbon trade calculation model, namely, the carbon emission is divided into a plurality of intervals, the higher the carbon trade price is, the larger the carbon trade cost is, and the step carbon trade cost calculation model is shown as formulas (10) and (11):
wherein ,cost for carbon trade;A trade price for carbon; e (E) G Carbon emissions for the system; l is the carbon emission interval length; lambda is the increase in carbon trade price; beta de Carbon emission intensity for unit electric quantity of the diesel generator; beta grid,buy The carbon emission intensity for outsourcing unit electric quantity is shown as formula (10)>Positive values indicate that the system needs to purchase carbon emissions rights;Negative values indicate that the system sells carbon emissions rights to gain a benefit;
3) Constructing a demand response model
(1) Establishing an objective function
The demand response is a mechanism of the load to participate in the power adjustment, the invention adopts an excitation type demand response, and the optimization target is that the sum of the absolute value of the difference between the renewable energy power generation power and the load power at each moment in the scheduling period is minimum, as shown in a formula (12):
wherein ,Pload,before (t) is the load power for a period t before demand response; p (P) load,after (t) is the load power for a period t after demand response; ΔP load (t) is the load transfer amount of the t period, when it is greater than 0, it is the transfer-in load, otherwise it is the transfer-out load;
(2) Establishing constraint conditions
a. Establishing transfer period constraints
The load can only be transferred in or out in the same scheduling period, and the constraint of the formula (13) is satisfied:
t∈T n ,t'∈T n (42)
wherein t is the load transfer period; t' is the load roll-out period; t (T) n For the nth scheduling period;
b. establishing transfer volume constraints
The load transfer amount should satisfy the total load demand before and after the demand response in one scheduling period, and the load transfer amount in each period should not exceed the maximum required transfer amount, as shown in the formula (14):
wherein ,ΔPload,max (t) is the maximum load transfer amount for period t;
4) Construction of micro-grid double-layer optimization configuration model and solving thereof
The double-layer optimization model constructed by the invention comprises two optimization tasks, wherein the upper-layer optimization is capacity configuration optimization, and aims at minimizing annual comprehensive cost of a micro-grid and the like; the lower layer is optimized for system operation, and aims at minimizing the sum of annual operation cost and annual carbon transaction cost of the micro-grid; the upper layer capacity configuration result is transmitted to the lower layer, the lower layer transmits the solved optimal operation result to the upper layer according to the known equipment capacity, and the upper layer and the lower layer iterate to obtain the optimal configuration result;
(1) establishing an upper layer optimization model:
a. establishing an objective function of an upper-layer optimization model
The upper layer aims at meeting the annual value integrated cost Fmin of the operator for constructing the micro-grid under the requirements of the micro-grid construction and operation; the decision variables are the installed capacity of each device, and the objective functions are equations (15) - (18):
wherein F is the annual value comprehensive cost; f (F) inv Investment cost is equal to the annual value; f (F) main Maintenance costs for the year; f (F) om The annual running cost; omega shape k A set of build devices; comprises a photovoltaic, a fan, a diesel generator, an electrolytic tank, a hydrogen storage tank and a fuel cell; c (C) k Investment cost per unit capacity for the kth class of equipment; p (P) k,N Is the installed capacity of the k-th equipment; r is (r) CR Is a fund withdrawal factor; gamma is the rate of occurrence; y is k The operation time of the k-th set is; c (C) main,k Annual maintenance costs for a unit capacity of a class k device;
b. establishing constraint conditions of upper-layer optimization model
Because of the limited area of construction within the area for installing the various types of equipment, the configurable capacity of each equipment should satisfy the constraint of formula (19):
wherein ：minimum and maximum installation capacity of k-th equipment respectively;
(2) step of establishing lower optimization model
a. Establishing objective function of lower optimization model
The lower layer considers a ladder carbon transaction mechanism and a demand response, and aims at minimizing the sum of the annual running cost and the annual carbon transaction cost of the system; the objective function is equation (20):
wherein ,F1 Optimizing a target value for the lower layer; f (F) fuel For annual fuel costs, only the fuel consumption of the diesel generator is considered; f (F) dr Annual load transfer compensation costs for users participating in demand response; f (F) grid Annual power interaction cost with large power grids; f (F) waste Punishment costs for annual renewable energy waste; omega shape D Is a typical day set comprising three typical days of summer, winter and spring and autumn;days taken in one year for typical day d; c (C) fuel Is diesel unit price;Power for the diesel generator during a typical time period t of day d; a. b is the slope and intercept coefficient in the fuel consumption-power curve respectively; c (C) dr The transfer compensation cost of the load of the unit electric quantity is calculated; c (C) grid,buy (t) is the electricity purchase price in the t period; c (C) grid,sell (t) electricity selling price for period t;The power of purchasing and selling of the micro-grid at the time period t in the typical day d is respectively; c (C) waste Penalty fees are wasted for the unit renewable energy sources;Wasting power for renewable energy sources of time period t in typical day d;
b. establishing constraint conditions of lower optimization model
Constraint 1: power balance constraint: as shown in formula (21):
wherein ,output power of the photovoltaic array in a t period in a typical day d;The output power of the wind driven generator in the t period of the typical day d is given;Load power for a period t within a typical day d;
Constraint 2: distributed power supply output constraint: as shown in formula (22):
constraint 3: hydrogen energy storage system constraints: the upper and lower limit constraint of the power of the electrolytic tank, the fuel cell and the hydrogen storage tank in the hydrogen energy storage are shown in formulas (23) - (25), the upper and lower limit constraint of the charge state of the hydrogen storage tank is shown in formula (26), and in order to ensure that the hydrogen energy storage can continuously and effectively work, the charge state of the hydrogen energy storage is ensured to be equal at the beginning and the end of a dispatching period, and is shown in equation constraint of formula (27);
SOC ht (t 0 )＝SOC ht (t N ) (56)
wherein ,the state of charge of the hydrogen storage tank is the period t in a typical day d; t is t 0 、t N Respectively starting and ending moments of a scheduling period; SOC (State of Charge) ht (t 0) and SOCht (t N ) The charge states of the hydrogen storage tanks are respectively the beginning and the end of the dispatching period;
constraint 4: power exchange constraints: the grid-connected micro-grid is connected with the large power grid, and electricity purchasing and selling can be performed, but because the power limit of a circuit and the excessive electricity selling power can influence the large power grid, the electricity purchasing and selling power of the micro-grid should meet the inequality constraint shown in the formula (28);
wherein , andThe maximum power value of the micro-grid for purchasing and selling electricity to the large power grid is respectively;
constraint 5: discarding capacity constraint of renewable energy sources: in order to ensure the utilization rate of renewable energy sources, the renewable energy source waste capacity of the micro-grid should meet the inequality constraint shown in the formula (29);
wherein ,discarding the maximum renewable energy source electricity quantity in a typical day d;
(3) solving the double-layer optimal configuration model constructed by the invention
Adopting a genetic algorithm and a CPLEX solver to carry out joint solving, firstly, reading annual meteorological data, annual load data and related parameters, and clustering wind speed, illumination and load according to summer, winter, spring and autumn by using a k-means algorithm to obtain a typical scene; secondly, the upper model adopts a genetic algorithm to randomly generate a population, generates a capacity configuration scheme and transmits the capacity configuration scheme to the lower layer; then, the lower model calculates and obtains the load after the demand response on the basis of the equipment capacity output by the upper model, carries out micro-grid operation simulation according to the load, takes the minimum sum of annual operation cost and annual carbon transaction cost as a target, adopts a CPLEX solver to obtain an optimal operation scheme, and transmits the optimal operation scheme to the upper layer; and finally, calculating fitness values by the upper model according to the capacity configuration scheme and the optimal operation scheme of the lower model, selecting, crossing and mutating the population, transmitting the generated new population to the lower optimizing model for cyclic iterative calculation, stopping the circulation after the required cyclic times are reached, and outputting the population of the last generation.
The grid-connected micro-grid optimization configuration method considering ladder carbon transaction and demand response is described with reference to the accompanying drawings.
The invention relates to a grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response, which comprises the following steps:
1) Constructing a hydrogen-containing energy storage grid-connected micro-grid
The grid-connected micro-grid mainly comprises a wind driven generator, a photovoltaic array, a diesel generator, a load and a hydrogen energy storage system consisting of an electrolytic tank, a hydrogen storage tank and a fuel cell, wherein the wind driven generator, the photovoltaic array and the hydrogen energy storage system are connected to an alternating current bus through different types of converters; the load considers both rigid and time-shifted types of load. The micro-grid structure is shown in fig. 1.
2) Establishing a grid-connected micro-grid operation scene
And selecting weather and load data of a certain year in a certain region for example analysis, wherein the hour-level data of the wind speed and the load are actual measurement data, the solar radiation intensity data are generated by combining the average daily total solar radiation quantity of the month in the region with Homer software, the annual wind speed time-varying curve is shown in figure 2, the annual solar radiation intensity time-varying curve is shown in figure 3, and the annual load time-varying curve is shown in figure 4.
Because the optimization calculation is carried out on 365 days all the year round, the variables to be optimized are more, and the calculation amount is larger, the method has seasonal characteristics for wind speed, solar radiation intensity and load, and the seasonal typical days of wind speed, solar radiation intensity and load are used for replacing the wind speed, solar radiation intensity and load data of each day under the season. The wind speed typical day scene is shown in fig. 5, the solar radiation intensity typical day scene is shown in fig. 6, and the load typical day scene is shown in fig. 7.
3) Construction of grid-connected micro-grid double-layer optimal configuration model
Constructing a grid-connected micro-grid double-layer optimization configuration model by using formulas (15) - (29), wherein each parameter in the model is set as follows: the economic parameters of the distributed power supply are shown in table 1; other parameters are as follows: the paste rate is taken to be 5%; the electricity purchasing price of the micro-grid is 0.49 yuan/kW.h; the electricity price of electricity selling is 0.38 yuan/kW.h; the carbon trade price is 0.2676 yuan/kg; gratuitous carbon emission quota of outsourcing power is 0.789 kg/kW.h; the gratuitous carbon emission quota of the power generation of the diesel generator is 0.5 kg/kW.h; the carbon emission of the outsourcing unit electric quantity is 0.92 kg/kW.h; the carbon emission intensity of the unit electric quantity of the diesel generator is 0.649 kg/kW.h; diesel unit price is 6.13 yuan/liter; the user participates in the demand response load transfer by 0.4 yuan per kW.h; the penalty cost per renewable energy waste is 0.6 yuan/kW.h. The time interval can be shifted by 10% of the time interval load; the initial state of charge of the energy storage is 0.5, the maximum state of charge is 0.8, and the minimum state of charge is 0.2.
Table 1 economic parameters of distributed power supply
4) Solving double-layer optimization configuration model of grid-connected micro-grid
The genetic algorithm and the CPLEX solver are adopted to carry out joint solving, and the solving flow is shown in figure 8. Firstly, annual meteorological data, annual load data and related parameters are read, and wind speed, illumination and load are clustered according to summer, winter, spring and autumn by using a k-means algorithm to obtain a typical daily operation scene; secondly, the upper model adopts a genetic algorithm to randomly generate a population, generates a capacity configuration scheme and transmits the capacity configuration scheme to the lower layer; then, the lower model calculates and obtains the load after the demand response on the basis of the equipment capacity output by the upper model, carries out micro-grid operation simulation according to the load, takes the minimum sum of annual operation cost and annual carbon transaction cost as a target, adopts a CPLEX solver to obtain an optimal operation scheme, and transmits the optimal operation scheme to the upper layer; and finally, calculating the fitness value of the upper model according to the capacity configuration scheme and the optimal operation scheme of the lower model, selecting, crossing and mutating the population, and transmitting the generated new population to the lower optimizing model for cyclic iterative calculation. And stopping circulation after the required circulation times are reached, and outputting the population of the last generation.
In order to fully analyze and consider the influence of ladder carbon transaction and demand response on configuration results, the invention sets the following 5 schemes for comparison analysis.
Scheme 1: the optimal configuration of carbon trade and demand response is not considered.
Scheme 2: consider conventional carbon transactions but do not consider the optimal configuration of demand response.
Scheme 3: the ladder carbon transaction is considered, but the optimal configuration of the demand response is not considered.
Scheme 4: consider an optimized configuration of conventional carbon trade and demand response.
Scheme 5: the invention provides an optimal configuration considering ladder carbon transaction and demand response.
The capacity configuration results are shown in table 2, and the operation results are shown in table 3. The configuration results are analyzed from different aspects as follows.
Table 2 capacity configuration results under different schemes
TABLE 3 economic calculation results under different scenarios
1) And comparing the scheme 1, the scheme 2 and the scheme 3, and analyzing the influence of carbon transaction on the optimal configuration of the micro-grid.
As can be seen from the optimal configuration results of table 2, compared with the scheme 1, after considering carbon transaction, the configuration capacity of the fan and the photovoltaic is increased by 75kW and 255kW respectively, and the configuration capacity of the diesel generator is reduced by 259kW, because the carbon transaction mechanism converts the carbon emission of the high-carbon emission unit into the carbon emission cost of the unit, the power generation cost of the high-carbon emission unit is indirectly increased, the overall cost of the fan and the photovoltaic unit is relatively reduced, and the capacity of the fan and the photovoltaic can be significantly increased. Compared with the scheme 2, the configuration capacity of the fan and the photovoltaic unit in the scheme 3 is further increased, and the configuration capacity of the diesel generator is reduced again. In addition, as the capacity of the blower and the photovoltaic capacity are increased, the waste wind waste light is increased, and in order to reduce the waste wind waste light, the arrangement capacity of the electrolytic tank and the hydrogen storage tank is increased along with the increase of the photovoltaic arrangement capacity of the blower.
As can be seen from table 4, both the carbon trade cost and the investment cost of the system increase after considering the carbon trade, and conversely the carbon emissions of the system decrease. Compared with scheme 1, the total cost of scheme 2 is increased by 5.5 ten thousand yuan, namely by 1.34%, but the power grid output is reduced by 272.3 MW.h compared with scheme 1, and the diesel engine power generation power is also reduced, so that the carbon emission of the system is reduced by 274.9t compared with scheme 1, namely by 17.02%. The overall cost of scheme 3 is increased by 13.66 ten thousand yuan, i.e., by 3.33%, and the system carbon emissions are reduced by 540.7t, i.e., by 33.48%, as compared to scheme 1. Overall, while the overall cost of scheme 3 is 1.96% higher than scheme 2, the system carbon emissions are reduced by 19.84%, which demonstrates that the step carbon trade is more effective in reducing carbon emissions. In combination, the introduction of carbon trade makes the system economy less than optimal, but the benefits obtained in terms of low carbon can far from compensate for the economic losses.
2) Comparing the scheme 2 with the scheme 4, the scheme 3 with the scheme 5, and analyzing the influence of the adding demand response on the capacity allocation of the micro-grid on the basis of the carbon transaction.
As can be seen from table 2, after further considering the demand response on the basis of considering the carbon trade, the capacities of the electrolytic cell, the fuel cell and the hydrogen storage tank of scheme 4 are reduced by 278kW, 121kW, 905kW, respectively, than that of scheme 2; the hydrogen storage capacity of scheme 5 is reduced by 290kW, 136kW, 2400kW, respectively, compared to scheme 3. Therefore, when the capacity of the micro-grid is configured, the introduction of the demand response can reduce the configuration capacity of the energy storage of the system, and resources are saved.
As can be seen from Table 3, compared with scheme 2, the diesel cost of scheme 4 is reduced by 0.5 ten thousand yuan, and the power grid output is reduced by 138 MW.h, so that the carbon emission of scheme 4 is reduced by 129.1t; compared with scheme 3, the diesel oil cost of scheme 5 is reduced, the power grid output of scheme 5 is reduced by 71.5 MW-h, and the carbon emission is reduced by 65.2t. The invention can make the load curve more consistent with the wind-light output curve by the demand response strategy, and reduce the diesel generator output and the power grid output required for stabilizing the wind-light output fluctuation. In addition, due to the reduction of the output of the power grid and the reduction of the capacity of the energy storage configuration, the total cost of the scheme 4 is reduced by 18.33 ten thousand yuan compared with the scheme 2, and the total cost of the scheme 5 is reduced by 7.47 ten thousand yuan compared with the scheme 3.
Of the 5 configurations, from an economic standpoint, the overall cost of scheme 4 is the lowest; from the standpoint of carbon emissions, the carbon emissions of scheme 5 are the lowest. Of these, scheme 5 has 19.02 ten thousand yuan more than scheme 4, i.e., 4.79%, and scheme 5 has a 201.9t less carbon emissions, i.e., 16.67%, than scheme 4. From the perspective of comprehensive micro-grid economy and carbon emission, the carbon emission benefit of the scheme 5 is far greater than the economic loss, and the scheme 5 is an optimal configuration scheme under the background of low-carbon development.
Fig. 9 is a graph showing the results of the arrangement according to 5 schemes, and the annual energy production of each generator set is counted, from which it is known that scheme 3 and scheme 5, which consider the ladder carbon trade, generate more renewable energy than the other 3 schemes. In comparison with scenario 3, scenario 5 allows for a reduction in energy storage capacity after the demand response is considered, which results in a sacrifice in renewable energy utilization, but the renewable energy utilization loss is negligible compared to its economy and environmental friendliness.
FIGS. 10 and 11 show the renewable energy generation and load change before and after the demand response is implemented in scheme 2 and on a typical day in summer. From the graph, the load is transferred from the period of high output of the renewable energy source to the period of low output of the renewable energy source, so that the load curve and the output curve of the renewable energy source are more consistent in time sequence, the absolute value of the difference between the load demand power and the renewable energy source power generation power in the scheduling period is reduced, and the consumption rate of the renewable energy source is improved.
The particular examples used in the present invention have been described in detail with respect to the present invention and are not limited thereto, but rather are intended to be within the scope of the appended claims.
Claims (1)
1. A grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response is characterized by comprising the following steps:
1) Constructing a hydrogen-containing energy storage grid-connected micro-grid
The grid-connected micro-grid comprises a wind driven generator, a photovoltaic array, a diesel generator, a load and a hydrogen energy storage system, wherein the photovoltaic array, the wind driven generator and the hydrogen energy storage system are connected to an alternating-current micro-grid through respective converters or inverters respectively, and the load of a user is divided into a time-variable load and a rigid load according to the electricity consumption condition of the user;
the hydrogen energy storage system comprises an electrolytic tank, a hydrogen storage tank and a fuel cell, and in the period that the wind-solar output power is larger than the load demand, the electrolytic tank equipment consumes surplus electric energy to generate hydrogen through electrolysis of water, and the hydrogen is stored in the hydrogen storage tank, which is equivalent to increasing the electric load; when the wind-solar output power is smaller than the load demand, the fuel cell takes hydrogen and oxygen as raw materials to generate electric energy through chemical reaction so as to meet the load demand;
the mathematical model of each device of the hydrogen energy storage system is respectively established as follows:
(1) establishing a mathematical model of the electrolytic cell
The output power of the electrolytic cell is shown in formula (1):
P el-out ＝η el P el-in (1)
wherein ,Pel-out The output power of the electrolytic cell; η (eta) el Is the efficiency of the electrolyzer; p (P) el-in The input power of the electrolytic cell;
the maximum input power of the electrolyzer is related to the rated capacity of the electrolyzer and is influenced by the residual hydrogen storage capacity of the hydrogen storage tank, and the maximum input power of the electrolyzer is shown in the formulas (2) and (3):
E ht,max ＝SOC ht,max P ht,N (3)
wherein ,Pel-in,max (t) is the maximum input power of the electrolyzer; p (P) el,N Is the rated capacity of the electrolytic cell; e (E) ht,max Maximum energy storage capacity of the hydrogen storage tank; e (E) ht (t) is the energy stored in the hydrogen storage tank at time t; p (P) ht,N Is the rated capacity of the hydrogen storage tank; defining the state of charge of the hydrogen storage tank as SOC in analogy to the state of charge of the storage battery ht ，SOC ht,max Is the maximum state of charge of the hydrogen storage tank; Δt is the time interval;
(2) establishing a mathematical model of a fuel cell
The output power of the fuel cell is shown in formula (4);
P fc-out ＝η fc P fc-in (4)
wherein ,Pfc-out Is the output power of the fuel cell; η (eta) fc Is the operating efficiency of the fuel cell; p (P) fc-in The input power of the fuel cell, namely the output power of the hydrogen storage tank;
the maximum output power of the fuel cell is limited by the capacity of the fuel cell and the residual capacity of the hydrogen storage tank, as shown in formulas (5) and (6);
E ht,min ＝SOC ht,min P ht,N (6)
wherein ,Pfc-out,max Is the maximum output power of the fuel cell; p (P) fc,N Is the rated capacity of the fuel cell; e (E) ht,min Is the minimum energy storage capacity of the hydrogen storage tank; SOC (State of Charge) ht,min Is the minimum state of charge of the hydrogen storage tank;
(3) establishing mathematical model of hydrogen storage tank
The hydrogen storage tank can store hydrogen generated by the electrolytic tank and can also provide hydrogen for the fuel cell, and mathematical models of the hydrogen storage tank are shown in formulas (7) and (8);
when the hydrogen storage tank stores hydrogen:
E ht (t)＝E ht (t-1)+P el-in (t-1)η el Δt (7)
when the hydrogen storage tank discharges hydrogen:
wherein ,ηht The working efficiency of the hydrogen storage tank is;
2) Construction of carbon transaction mechanism model
The carbon trade is a trade mechanism for realizing carbon emission reduction by buying and selling carbon emission quota, according to the built grid-connected micro-grid, the carbon emission in the micro-grid is determined to be derived from a diesel generator and electric power purchased to an upper-level power grid, and the electric power purchased to the upper-level power grid by default is all derived from thermal power, so that the gratuitous carbon emission quota of the carbon trade is shown as a formula (9):
wherein ,DG Is carbon emission quota; alpha de Carbon emission quota for unit electric quantity of the diesel generator; alpha grid Carbon emission allowance for outsourcing unit electric quantity; p (P) de (t) is the output power of the diesel generator in period t; p (P) grid,buy (t) power purchased to the upper grid for the micro grid t period; t is the settlement period of the carbon transaction fee;
the step carbon trade calculation model is adopted, namely the carbon emission is divided into a plurality of sections, the more the carbon emission is, the higher the carbon trade price is, the larger the carbon trade cost is, and the step carbon trade cost calculation model is shown as formulas (10) and (11):
wherein ,cost for carbon trade;A trade price for carbon; e (E) G Carbon emissions for the system; l is the carbon emission interval length; lambda is the increase in carbon trade price; beta de Carbon row for unit electric quantity of diesel generatorStrength of the steel plate is released; beta grid,buy Carbon emission intensity for outsourcing unit electric quantity;Positive values indicate that the system needs to purchase carbon emissions rights;Negative values indicate that the system sells carbon emissions rights to gain a benefit;
3) Constructing a demand response model
(1) Establishing an objective function
The demand response is a mechanism of load participation power adjustment, and an excitation type demand response is adopted, wherein the optimization target is that the sum of the absolute values of the difference between the renewable energy generated power and the load power at each moment in a scheduling period is minimum, as shown in a formula (12):
wherein ,Pload,before (t) is the load power for a period t before demand response; p (P) load,after (t) is the load power for a period t after demand response; ΔP load (t) is the load transfer amount of the t period, when it is greater than 0, it is the transfer-in load, otherwise it is the transfer-out load;
(2) establishing constraint conditions
a. Establishing transfer period constraints
The load can only be transferred in or out in the same scheduling period, and the constraint of the formula (13) is satisfied:
t∈T n ,t'∈T n (13)
wherein t is the load transfer period; t' is the load roll-out period; t (T) n For the nth scheduling period;
b. establishing transfer volume constraints
The load transfer amount should satisfy the total load demand before and after the demand response in one scheduling period, and the load transfer amount in each period should not exceed the maximum required transfer amount, as shown in the formula (14):
wherein ,ΔPload,max (t) is the maximum load transfer amount for period t;
4) Construction of micro-grid double-layer optimization configuration model and solving thereof
The built double-layer optimization model comprises two optimization tasks, wherein the upper-layer optimization is capacity configuration optimization, and aims at minimizing annual comprehensive cost of a micro-grid and the like; the lower layer is optimized for system operation, and aims at minimizing the sum of annual operation cost and annual carbon transaction cost of the micro-grid; the upper layer capacity configuration result is transmitted to the lower layer, the lower layer transmits the solved optimal operation result to the upper layer according to the known equipment capacity, and the upper layer and the lower layer iterate to obtain the optimal configuration result;
(1) establishing an upper layer optimization model:
a. establishing an objective function of an upper-layer optimization model
The upper layer aims at meeting the annual value integrated cost Fmin of the operator for constructing the micro-grid under the requirements of the micro-grid construction and operation; the decision variables are the installed capacity of each device, and the objective functions are equations (15) - (18):
wherein F is the annual value comprehensive cost; f (F) inv Investment cost is equal to the annual value; f (F) main Maintenance costs for the year; f (F) om The annual running cost; omega shape k A set of build devices; comprises a photovoltaic array, a wind driven generator, a diesel generator, an electrolytic tank, a hydrogen storage tank and a fuel cell; c (C) k Investment cost per unit capacity for the kth class of equipment; p (P) k,N Is the installed capacity of the k-th equipment; r is (r) CR Is a fund withdrawal factor; gamma is the rate of occurrence; y is k The operation time of the k-th set is; c (C) main,k Annual maintenance costs for a unit capacity of a class k device;
b. establishing constraint conditions of upper-layer optimization model
Because of the limited area of construction within the area for installing the various types of equipment, the configurable capacity of each equipment should satisfy the constraint of formula (19):
wherein ：minimum and maximum installation capacity of k-th equipment respectively;
(2) step of establishing lower optimization model
a. Establishing objective function of lower optimization model
The lower layer considers a ladder carbon transaction mechanism and a demand response, and aims at minimizing the sum of the annual running cost and the annual carbon transaction cost of the system; the objective function is equation (20):
wherein ,F1 Optimizing a target value for the lower layer; f (F) fuel For annual fuel costs, only the fuel consumption of the diesel generator is considered; f (F) dr Response to user participationThe corresponding annual load transfer compensation cost; f (F) grid Annual power interaction cost with large power grids; f (F) waste Punishment costs for annual renewable energy waste; omega shape D Is a typical day set comprising three typical days of summer, winter and spring and autumn;days taken in one year for typical day d; c (C) fuel Is diesel unit price;Power for the diesel generator during a typical time period t of day d; a. b is the slope and intercept coefficient in the fuel consumption-power curve respectively; c (C) dr The transfer compensation cost of the load of the unit electric quantity is calculated; c (C) grid,buy (t) is the electricity purchase price in the t period; c (C) grid,sell (t) electricity selling price for period t;The power of purchasing and selling of the micro-grid at the time period t in the typical day d is respectively; c (C) waste Penalty fees are wasted for the unit renewable energy sources;Wasting power for renewable energy sources of time period t in typical day d;
b. establishing constraint conditions of lower optimization model
Constraint 1: power balance constraint: as shown in formula (21):
wherein ,output power of the photovoltaic array in a t period in a typical day d;The output power of the wind driven generator in the t period of the typical day d is given;Load power for a period t within a typical day d;
constraint 2: distributed power supply output constraint: as shown in formula (22):
constraint 3: hydrogen energy storage system constraints: the upper and lower limit constraint of the power of the electrolytic tank, the fuel cell and the hydrogen storage tank in the hydrogen energy storage are shown in formulas (23) - (25), the upper and lower limit constraint of the charge state of the hydrogen storage tank is shown in formula (26), and in order to ensure that the hydrogen energy storage can continuously and effectively work, the charge state of the hydrogen energy storage is ensured to be equal at the beginning and the end of a dispatching period, and is shown in equation constraint of formula (27);
SOC ht (t 0 )＝SOC ht (t N ) (27)
wherein ,the state of charge of the hydrogen storage tank is the period t in a typical day d; t is t 0 、t N Respectively starting and ending moments of a scheduling period; SOC (State of Charge) ht (t 0) and SOCht (t N ) The charge states of the hydrogen storage tanks are respectively the beginning and the end of the dispatching period;
constraint 4: power exchange constraints: the grid-connected micro-grid is connected with the large power grid, and electricity purchasing and selling can be performed, but because the power limit of a circuit and the excessive electricity selling power can influence the large power grid, the electricity purchasing and selling power of the micro-grid should meet the inequality constraint shown in the formula (28);
wherein , andThe maximum power value of the micro-grid for purchasing and selling electricity to the large power grid is respectively;
constraint 5: discarding capacity constraint of renewable energy sources: in order to ensure the utilization rate of renewable energy sources, the renewable energy source waste capacity of the micro-grid should meet the inequality constraint shown in the formula (29);
wherein ,discarding the maximum renewable energy source electricity quantity in a typical day d;
(3) solving the constructed double-layer optimal configuration model
Adopting a genetic algorithm and a CPLEX solver to carry out joint solving, firstly, reading annual meteorological data, annual load data and related parameters, and clustering wind speed, illumination and load according to summer, winter, spring and autumn by using a k-means algorithm to obtain a typical scene; secondly, the upper model adopts a genetic algorithm to randomly generate a population, generates a capacity configuration scheme and transmits the capacity configuration scheme to the lower layer; then, the lower model calculates and obtains the load after the demand response on the basis of the equipment capacity output by the upper model, carries out micro-grid operation simulation according to the load, takes the minimum sum of annual operation cost and annual carbon transaction cost as a target, adopts a CPLEX solver to obtain an optimal operation scheme, and transmits the optimal operation scheme to the upper layer; and finally, calculating fitness values by the upper model according to the capacity configuration scheme and the optimal operation scheme of the lower model, selecting, crossing and mutating the population, transmitting the generated new population to the lower optimizing model for cyclic iterative calculation, stopping the circulation after the required cyclic times are reached, and outputting the population of the last generation.
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