Day-ahead optimization scheduling method and system for wind-light-hydrogen storage off-grid operation system

Abstract

The invention provides a day-ahead optimization scheduling method and system for a wind-light-hydrogen storage off-grid operation system. The method comprises the following steps: establishing an off-grid planning outer-layer capacity optimization configuration model by taking the total cost of the wind-light-hydrogen storage off-grid system as a first objective function; in the off-grid operation state of the wind-solar-hydrogen storage coupled system, taking the off-grid operation cost of the wind-solar-hydrogen storage coupled system as a second objective function to establish an off-grid operation inner-layer optimization scheduling model; solving the off-network planning outer-layer capacity optimal configuration model through improved particle swarm optimization iterative optimization to obtain economically optimal hybrid energy storage capacity configuration; and determining the optimal load scheduling amount through the off-grid operation inner-layer optimization scheduling model and the off-grid planning outer-layer capacity optimization configuration model. The unreasonable charging and discharging time sequence of the hybrid energy storage system can be optimized, the cost influence on optimal configuration and operation of the wind-light-hydrogen storage off-grid system can be avoided, and the economical efficiency of the wind-light-hydrogen storage off-grid system is improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of energy storage technology, and in particular relates to a day-ahead optimization scheduling method and system for an off-grid wind, solar, and hydrogen storage operation system. Background Art

[0002] At present, wind and solar systems face difficulties such as low conversion efficiency, long transmission distance, difficulty in local consumption, and difficulty in large-scale development. Wind, solar, hydrogen and off-grid storage systems will become an inevitable choice for the development of wind and solar resources in future energy development. However, the uncontrollable output of wind and solar power and the unreasonable charging and discharging timing of hybrid energy storage such as electricity and hydrogen have an unignorable impact on the optimal configuration and operation of wind, solar, hydrogen and off-grid storage systems. In the prior art, a model of the energy supply end of the energy storage system is established to predict and optimize the cost of the system, such as establishing a model for the electrolyzer and a model for the energy storage battery. However, there is a lack of a method for establishing an internal and external double-layer optimization model for the wind, solar, hydrogen and off-grid storage system for the purpose of shaving peaks and filling valleys and improving the economy of the wind, solar, hydrogen and off-grid storage system. Summary of the invention

[0003] This disclosure proposes a day-ahead optimization scheduling method for off-grid wind, solar, hydrogen and storage systems, which can achieve peak shaving and valley filling and improve the economic efficiency of off-grid wind, solar, hydrogen and storage systems. It can solve the uncontrollable wind and solar output and the unreasonable charging and discharging sequence of hybrid energy storage such as electricity and hydrogen, which affects the optimization configuration and operation cost of off-grid wind, solar, hydrogen and storage systems.

[0004] On the one hand, an embodiment of the present disclosure provides a day-ahead optimization scheduling method for a wind-solar-hydrogen storage off-grid operation system, including:

[0005] An off-grid planning outer capacity optimization configuration model is established with the total cost of the wind-solar-hydrogen storage off-grid system as the first objective function, wherein the first objective function is constructed based on the full life cycle cost of the hybrid energy storage and the penalty cost for wind abandonment;

[0006] In the off-grid operation state of the wind-solar-hydrogen-storage coupling system, an off-grid operation inner-layer optimization scheduling model is established with the off-grid operation cost of the wind-solar-hydrogen-storage coupling system as the second objective function;

[0007] By improving the particle swarm algorithm to iteratively optimize and solve the off-grid planning outer capacity optimization configuration model, the economically optimal hybrid energy storage capacity configuration is obtained;

[0008] The power to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration is substituted into the off-grid operation inner optimization scheduling model, and the optimal load scheduling amount is determined by the off-grid operation inner optimization scheduling model and the off-grid planning outer capacity optimization configuration model.

[0009] Furthermore, the life cycle cost of the hybrid energy storage is determined based on the equipment investment and construction cost and the equipment operation and maintenance cost;

[0010] The equipment investment and construction costs and equipment operation and maintenance cost discount rates are allocated to each scheduling cycle in the planned life cycle to determine the full life cycle cost of the hybrid energy storage.

[0011] Furthermore, the establishment of the off-grid planning outer capacity optimization configuration model also includes:

[0012] Performing capacity constraints on equipment in the electric-hydrogen hybrid energy storage system on the outer capacity configuration model;

[0013] Among them, the capacity cost of electric-hydrogen hybrid energy storage equipment is constrained by the upper and lower limits of the planned capacity of a type of equipment in electric-hydrogen hybrid energy storage.

[0014] Furthermore, an off-grid operation inner optimization scheduling model is established with the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function, including:

[0015] The second objective function is determined based on the water purchase cost of hydrogen production from electricity for the wind-solar-hydrogen storage coupling system, the annual operation and maintenance cost of the wind-solar-hydrogen storage coupling system in off-grid operation, the replacement cost of the wind-solar-hydrogen storage coupling system in off-grid operation, and the load reduction cost of the wind-solar-hydrogen storage coupling system in off-grid operation.

[0016] Further, the water purchase cost of the electric hydrogen production is determined according to the water purchase fee of the wind-solar-hydrogen storage coupling system and the total water volume of the wind-solar-hydrogen storage coupling system during operation;

[0017] Determine the annual operation and maintenance cost of the wind-solar-hydrogen-storage coupling system in an off-grid operation state according to the annual operation and maintenance costs of the wind turbine, photovoltaic, battery, electrolyzer, hydrogen storage tank and fuel cell;

[0018] Obtain the abandoned wind volume and abandoned solar volume according to the rated output of wind power and the rated output of photovoltaic power, and determine the replacement cost of the wind-solar-hydrogen storage coupling system in an off-grid operation state according to the abandoned wind volume and abandoned solar volume;

[0019] The load reduction cost of the wind-solar-hydrogen-storage coupling system in off-grid operation is determined based on the load reduction compensation cost coefficient and the power of the reduced load.

[0020] Furthermore, the method further comprises:

[0021] Performing equipment capacity constraints in the electric-hydrogen hybrid energy storage system on the off-grid operation inner optimization scheduling model;

[0022] The capacity constraints of the equipment in the electric-hydrogen hybrid energy storage system include:

[0023] Power balance constraints on the wind-solar-hydrogen-storage coupling system, power constraints on wind turbines and photovoltaic panels, battery charging and discharging constraints, electrolyzer charging power constraints and fuel cell discharging power constraints.

[0024] Furthermore, the power balance constraint on the wind-solar-hydrogen storage coupling system includes:

[0025] A power balance function is established based on the output power of the wind turbine generator set, the output power of the photovoltaic panel, the charging and discharging power of the battery, the discharging power of the fuel cell, the charging power of the electrolyzer and the power demand of the power load;

[0026] The charging and discharging power of the battery takes a positive value for discharging and a negative value for charging.

[0027] Furthermore, the power constraints of the wind turbine generator set and photovoltaic panels include:

[0028] Set the power constraint of the wind turbine generator set to a positive value, which is less than the maximum output power of the wind turbine generator set;

[0029] Set the power constraint of the photovoltaic panel to a positive value, which is less than the maximum output power of the photovoltaic panel.

[0030] Furthermore, the battery charging and discharging constraints include:

[0031] The current state of charge of the battery is indicated by the ratio of the remaining capacity of the battery to the rated capacity of the battery;

[0032] At a moment during the charging time, the ratio of the remaining capacity of the battery to the rated capacity of the battery is greater than its valley value during the charging time and less than its peak value during the charging time;

[0033] Set the battery charging power to a positive value, which is less than the maximum charging power within the charging time;

[0034] Set the battery discharge power to a positive value, which is less than the maximum discharge power within the discharge time.

[0035] Furthermore, the fuel cell discharge power constraint includes:

[0036] The discharge power of the fuel cell is greater than the lower limit value of the power capacity of the hydrogen storage tank and less than the upper limit value of the power capacity of the hydrogen storage tank.

[0037] Further, the power to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration is substituted into the off-grid operation inner optimization scheduling model, and the optimal load scheduling amount is determined by the off-grid operation inner optimization scheduling model and the off-grid planning outer capacity optimization configuration model, including:

[0038] S21: Initialize basic parameters such as wind speed, light intensity, load, population size, and number of iterations;

[0039] S22: Set the population size of the outer capacity optimization configuration model to M, and randomly generate an initial population consisting of M particles as X = {X i}(i=1, 2, ..., M), where each particle represents a latent solution;

[0040] S23: Set the population size in the inner load optimization scheduling model to N, and randomly generate N particles to form an initial population Y = {Y j}(i=1, 2, ..., N), where each particle represents a latent solution;

[0041] S24: The particle i with the best fitness value in the outer model is set as the individual extreme value pBest, and the optimal pBest is searched among multiple individual extreme values ​​and set as the outer group extreme value gBest; similarly, the particle j with the best fitness value in the inner optimization scheduling model of off-grid operation is set as the individual extreme value ypBest, and the optimal ypBest is searched among multiple individual extreme values ​​and set as the inner group extreme value ygBest.

[0042] Furthermore, the method further comprises:

[0043] S25: Under the constraints of batteries, electrolyzers, hydrogen storage tanks and hydrogen fuel cells, the position and velocity of particle i in the outer population are randomly initialized;

[0044] S26: Calculate the position X of the outer particle i i The wind power output power and the energy storage absorbable or released power under the condition; and calculate the position X of the outer particle i i The dispatch resources under the above are the power that needs to be allocated to the loads to participate in the dispatch;

[0045] S27: Set the position X of particle i i The scheduling resources are brought into the inner model. Under the premise of satisfying the constraints, the speed and position of the inner population particle j are randomly initialized, and the ypBest and ygBest of the inner model are calculated.

[0046] S28: Update the velocity and position of particle j in the inner model;

[0047] S29: If the fitness value of particle j is better than the original fitness value ypBest, the inner model updates the fitness value ypBest to the current fitness value, corresponding to the best position Y j Update to current location;

[0048] S30: If the fitness value of particle j is better than the original fitness value ygBest at this time, the inner model updates the fitness value ygBest to the current fitness value, and the corresponding global best position is updated to the current position;

[0049] S31: Determine whether the inner model has reached the maximum number of iterations, if yes, go to step S29, if no, go to step S25;

[0050] S32: Bring the optimal solution of the output of the inner model into the outer model, perform optimization calculation, and calculate the fitness values ​​pBest and gBest of particle i;

[0051] S33: Update the velocity and position of particle i in the outer model population, and repeat steps S23 to S29;

[0052] S34: If the fitness value of particle i is better than the original fitness value pBest, the outer model updates the fitness value pBest to the current fitness value, corresponding to the best position X of the outer particle. i Update to the current particle position;

[0053] S35: If the fitness value of particle i is better than the original fitness value gBest, the outer model updates the fitness value gBest to the current fitness value, and the corresponding outer particle best position X i Update to the current particle position;

[0054] S36: Determine whether the outer model has reached the maximum number of iterations, if yes, go to step S34, if not, go to step S30;

[0055] S37: Output particles (X i , Y j ) and the optimal fitness and particle position X i , Y j .

[0056] Based on the same inventive concept, the embodiment of the present application also provides a day-ahead optimization scheduling system for a wind-solar-hydrogen storage off-grid operation system, including:

[0057] A first model building unit is used to establish an off-grid planning outer capacity optimization configuration model with the total cost of the wind-solar-hydrogen storage off-grid system as a first objective function, wherein the first objective function is constructed based on the full life cycle cost of the hybrid energy storage and the penalty cost of wind abandonment;

[0058] The second model building unit is used to establish an off-grid operation inner layer optimization scheduling model with the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function when the wind-solar-hydrogen storage coupling system is in off-grid operation state;

[0059] A solving unit is used to solve the off-grid planning outer capacity optimization configuration model through iterative optimization through an improved particle swarm algorithm to obtain the economically optimal hybrid energy storage capacity configuration;

[0060] The optimization unit is used to substitute the power to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration into the off-grid operation inner layer optimization scheduling model, and determine the optimal load scheduling amount through the off-grid operation inner layer optimization scheduling model and the off-grid planning outer layer capacity optimization configuration model.

[0061] Beneficial effects of the present disclosure:

[0062] The present invention establishes an off-grid planning outer capacity optimization configuration model by taking the total cost of the wind-solar-hydrogen storage off-grid system as the first objective function; in the off-grid operation state of the wind-solar-hydrogen storage coupling system, an off-grid operation inner layer optimization scheduling model is established by taking the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function; the off-grid planning outer layer capacity optimization configuration model is solved by iterative optimization through an improved particle swarm algorithm to obtain the economically optimal hybrid energy storage capacity configuration; the power to be allocated to the load to participate in the scheduling calculated in the hybrid energy storage capacity configuration is substituted into the off-grid operation inner layer optimization scheduling model, and the optimal load scheduling amount is determined by the off-grid operation inner layer optimization scheduling model and the off-grid planning outer layer capacity optimization configuration model. It can be seen from this that the unreasonable charging and discharging timing of the hybrid energy storage system can be optimized, which will have a cost impact on the optimization configuration and operation of the wind-solar-hydrogen storage off-grid system, and improve the economy of the wind-solar-hydrogen storage off-grid system.

[0063] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures indicated in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 This is a flow chart of a day-ahead optimization scheduling method for a wind-solar-hydrogen storage off-grid operation system in an embodiment of the present disclosure;

[0066] Figure 2 A schematic diagram of a day-ahead optimization scheduling system for a wind-solar-hydrogen storage off-grid operation system in an embodiment of the present disclosure;

[0067] Figure 3 This is a flow chart of the improved particle swarm algorithm for solving the wind-solar-hydrogen-storage and off-grid double-layer optimization model in the embodiment of the present disclosure. DETAILED DESCRIPTION

[0068] In order to achieve peak shaving and valley filling and improve the economy of wind-solar-hydrogen storage off-grid systems, this paper comprehensively considers the priority on-site consumption of wind-solar-hydrogen storage off-grid systems and the green production and efficient utilization of hydrogen energy, applies hydrogen production technology to wind-solar-hydrogen storage off-grid systems, establishes a wind-solar-hydrogen storage off-grid double-layer optimization model, and solves it based on the improved particle swarm algorithm.

[0069] For the above-mentioned wind-solar-hydrogen storage off-grid system, the present invention proposes a two-layer optimization model of wind-solar-hydrogen storage off-grid system with economic optimization as the goal and taking into account hydrogen production from electricity. The improved particle swarm optimization algorithm is used to solve the inner and outer models to generate the cost-optimal electric-hydrogen hybrid energy storage system capacity configuration plan and system operation scheduling strategy.

[0070] The present disclosure provides a method for optimizing the day-ahead scheduling of a wind-solar-hydrogen storage off-grid operation system. Figure 1 ,include:

[0071] S101: Establishing an off-grid planning outer capacity optimization configuration model with the total cost of the wind-solar-hydrogen storage off-grid system as the first objective function, wherein the first objective function is constructed based on the full life cycle cost of the hybrid energy storage and the penalty cost for wind abandonment;

[0072] S102: in the off-grid operation state of the wind-solar-hydrogen-storage coupling system, an off-grid operation inner layer optimization scheduling model is established with the off-grid operation cost of the wind-solar-hydrogen-storage coupling system as the second objective function;

[0073] S103: By improving the particle swarm algorithm, an optimal configuration model of the outer capacity of off-grid planning is solved iteratively to obtain the economically optimal hybrid energy storage capacity configuration;

[0074] S104: Substitute the power required to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration into the off-grid operation inner optimization scheduling model, and determine the optimal load scheduling amount through the off-grid operation inner optimization scheduling model and the off-grid planning outer capacity optimization configuration model.

[0075] Specifically, the life cycle cost of the hybrid energy storage is determined based on the equipment investment and construction cost and the equipment operation and maintenance cost;

[0076] The equipment investment and construction costs and equipment operation and maintenance cost discount rates are allocated to each scheduling cycle in the planned life cycle to determine the full life cycle cost of the hybrid energy storage.

[0077] Specifically, the wind-solar-hydrogen-storage off-grid system is planned from the perspective of achieving optimal economic performance, including the full life cycle cost of hybrid energy storage C. cm and wind curtailment penalty cost C cur The objective function is to minimize the total cost.

[0078] minCout =C cm +C cur

[0079] The full life cycle cost of hybrid energy storage includes equipment investment and construction costs and operation and maintenance costs, and is allocated to each scheduling cycle in the planned life cycle through a discount rate. This application takes 1 day as 1 scheduling cycle.

[0080]

[0081] Among them, C cm is the life cycle cost of hybrid energy storage, For electric hydrogen hybrid energy storage The planned capacity construction cost of such equipment; For electric hydrogen hybrid energy storage Unit capacity construction costs of equipment, including batteries, electrolyzers, hydrogen storage tanks and fuel cells; For the The proportion of equipment operation and maintenance costs to construction costs; For the The planned life cycle of the equipment; λ is the discount rate.

[0082] In step S101, the establishment of an off-grid planning outer capacity optimization configuration model further includes:

[0083] Performing capacity constraints on equipment in the electric-hydrogen hybrid energy storage system on the outer capacity configuration model;

[0084] Among them, the upper and lower limits of the planned capacity of a type of equipment in the electric-hydrogen hybrid energy storage are used to constrain the capacity cost of the corresponding electric-hydrogen hybrid energy storage equipment.

[0085] Specifically, the outer capacity optimization configuration model considers the equipment capacity constraints within the electric-hydrogen hybrid energy storage system.

[0086]

[0087] in, They are respectively The upper and lower limits of the planned capacity of this type of equipment.

[0088] In step S102, an off-grid operation inner layer optimization scheduling model is established with the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function, including:

[0089] The second objective function is determined based on the water purchase cost of hydrogen production from electricity for the wind-solar-hydrogen storage coupling system, the annual operation and maintenance cost of the wind-solar-hydrogen storage coupling system in off-grid operation, the replacement cost of the wind-solar-hydrogen storage coupling system in off-grid operation, and the load reduction cost of the wind-solar-hydrogen storage coupling system in off-grid operation.

[0090] Specifically, in order to promote the optimal off-grid operation cost of the wind-solar-hydrogen storage coupling system, its inner optimization scheduling model takes the minimization of the sum of the water purchase cost for hydrogen production, operation and maintenance cost, wind and solar power abandonment cost, and load shedding compensation cost as the objective function.

[0091] f l =C water +C om +C q +C loadcut

[0092] Among them, f l represents the optimization objective function of the wind-solar-hydrogen storage coupling system in off-grid operation; C water represents the water purchase cost of hydrogen production from electricity for the wind-solar-hydrogen storage coupling system; C om represents the annual operation and maintenance cost of the wind-solar-hydrogen storage coupling system in off-grid operation; C q represents the replacement cost of the wind-solar-hydrogen storage coupling system in off-grid operation; C loadcut It represents the load reduction cost of the wind-solar-hydrogen storage coupling system in off-grid operation state.

[0093] Specifically, the water purchase cost of the electric hydrogen production is determined according to the water purchase fee of the wind-solar-hydrogen storage coupling system and the total water volume of the wind-solar-hydrogen storage coupling system during operation;

[0094] Determine the annual operation and maintenance cost of the wind-solar-hydrogen-storage coupling system in an off-grid operation state according to the annual operation and maintenance costs of the wind turbine, photovoltaic, battery, electrolyzer, hydrogen storage tank and fuel cell;

[0095] Obtain the abandoned wind volume and abandoned solar volume according to the rated output of wind power and the rated output of photovoltaic power, and determine the replacement cost of the wind-solar-hydrogen storage coupling system in an off-grid operation state according to the abandoned wind volume and abandoned solar volume;

[0096] The load reduction cost of the wind-solar-hydrogen-storage coupling system in off-grid operation is determined based on the load reduction compensation cost coefficient and the power of the reduced load.

[0097] Specifically, the water purchase cost of the hydrogen production unit of the wind-solar-hydrogen storage coupling system during operation is as follows.

[0098] C Water =k Water ×Q Water

[0099] In the formula, k Water is the water purchase fee of the system, unit: yuan / ton; Q Water It is the total water consumption of the system during operation.

[0100] Specifically, the annual operation and maintenance cost of the wind-solar-hydrogen-storage coupled system in off-grid operation mainly consists of the annual operation and maintenance costs of wind turbines, photovoltaics, batteries, electrolyzers, hydrogen storage tanks and fuel cells.

[0101] C om =C wt,om +C pv,om +C bat,om +C el,om +C tank,om +C fc,om

[0102] Among them, the annual operation and maintenance costs of wind power, photovoltaic power, electrolyzer and fuel cell in off-grid operation are as follows:

[0103] C wt,om =c wt,om P wt,t

[0104] C pv,om =c pv,om P pv,t

[0105] C el,om =c el,om P el,t

[0106] C fc,om =c fc,om P fc,t

[0107] Among them, c wt,om 、c pv,om 、c el,om 、c fc,om They respectively represent the unit output operation and maintenance cost coefficients of wind turbines, photovoltaics, electrolyzers, fuel cells and other sub-units or equipment in off-grid operation, and the unit is: Yuan / kW.

[0108] The annual operation and maintenance costs of batteries and hydrogen storage tanks in off-grid operation are as follows:

[0109] C bat,om =c bat,om E bat,r

[0110] C tank,om =c tank,om E tank,r

[0111] Among them, c bat,om 、c tank,om They are the operation and maintenance cost coefficients of off-grid operation of batteries and hydrogen storage tanks, respectively, in units of Yuan / (kW·h).

[0112] Specifically, the replacement cost is the cost of replacing old equipment with new equipment when the equipment performance declines after a certain period of operation. The annual replacement cost of the wind-solar-hydrogen storage coupling system in off-grid operation is expressed as follows:

[0113]

[0114] The lifespan of wind turbines, photovoltaics, and hydrogen storage tanks in off-grid operation is consistent with the lifespan of the wind-solar-hydrogen-storage coupling system, which is calculated as 20 years and does not need to be replaced. However, the lifespan of batteries, electrolyzers, and fuel cells is shorter and needs to be replaced within the life cycle of the wind-solar-hydrogen-storage coupling system in off-grid operation.

[0115] P wtq (t) = P wtr (t)-P wt (t)

[0116] P pvq (t) = P pvr (t)-P pv (t)

[0117] Among them, μ wt , μ pv Respectively represent the penalty coefficients for wind and solar power abandonment, RMB / kW; P wtq (t), P pvq (t) represents the abandoned wind power and abandoned solar power, kW; P wtr (t), P pvr (t) represents the rated output of wind power and photovoltaic power, kW; P wt (t), P pv (t) represents the actual output of wind power and photovoltaic power, kW.

[0118] Specifically, when the wind-solar-hydrogen-storage coupling system is in off-grid operation, load reduction is an important management measure to ensure power balance, but it will also generate certain compensation costs. The non-compensable costs are expressed as:

[0119] C loadcut =c loadut ×P loadcut

[0120] In the formula, C loadut represents the compensation cost, c loadut represents the load reduction compensation cost coefficient, RMB / kW; P loadcut Indicates the power of load reduction, unit: kW.

[0121] The equipment capacity constraints of the inner optimization scheduling model in this disclosure are as follows:

[0122] Performing equipment capacity constraints in the electric-hydrogen hybrid energy storage system on the off-grid operation inner optimization scheduling model;

[0123] The capacity constraints of the equipment in the electric-hydrogen hybrid energy storage system include:

[0124] Power balance constraints on the wind-solar-hydrogen-storage coupling system, power constraints on wind turbines and photovoltaic panels, battery charging and discharging constraints, electrolyzer charging power constraints and fuel cell discharging power constraints.

[0125] Specifically, the power balance constraints on the wind-solar-hydrogen storage coupling system include:

[0126] A power balance function is established based on the output power of the wind turbine generator set, the output power of the photovoltaic panel, the charging and discharging power of the battery, the discharging power of the fuel cell, the charging power of the electrolyzer and the power demand of the power load;

[0127] The charging and discharging power of the battery takes a positive value for discharging and a negative value for charging.

[0128] Specifically, the wind-solar-hydrogen-storage coupling system must meet the power balance constraints to ensure normal operation:

[0129] P wt +P pv +P bat +P fc =P el +P load -P loadcut

[0130] Among them, P wt Indicates the output power of the wind turbine generator set; P pv Represents the output power of photovoltaic panels; P bat Indicates the charging and discharging power of the battery, with positive values ​​for discharging and negative values ​​for charging; P fc Represents the discharge power of the fuel cell; P el Indicates the charging power of the electrolyzer; P load Indicates the power demand of the electrical load.

[0131] Specifically, the power constraints of the wind turbine generator set and photovoltaic panels include:

[0132] Set the power constraint of the wind turbine generator set to a positive value, which is less than the maximum output power of the wind turbine generator set;

[0133] The power constraint of the photovoltaic panel is set to a positive value, which is less than the maximum output power of the photovoltaic panel. More specifically, the power constraints of the wind turbine and the photovoltaic panel are expressed as:

[0134] 0≤P wt (t)≤Pwt-max

[0135] 0≤P pv (t)≤P pv-max

[0136] Among them, P wt-max and P pv-max Represent the maximum output power of wind turbines and photovoltaic panels respectively.

[0137] Specifically, the battery charging and discharging constraints include:

[0138] The current state of charge of the battery is indicated by the ratio of the remaining capacity of the battery to the rated capacity of the battery;

[0139] At a moment during the charging time, the ratio of the remaining capacity of the battery to the rated capacity of the battery is greater than its valley value during the charging time and less than its peak value during the charging time;

[0140] Set the battery charging power to a positive value, which is less than the maximum charging power within the charging time;

[0141] Set the battery discharge power to a positive value, which is less than the maximum discharge power within the discharge time.

[0142] More specifically, SOC is the ratio of the remaining capacity to the rated capacity of the battery, indicating the current state of charge of the battery.

[0143] S OC-min ≤S OC (t)≤S OC-max

[0144] 0≤P c (t)≤P ch-max

[0145] 0≤P d (t)≤P dch-max

[0146] Among them, S OC (t) represents the SOC value of the battery at time t; S OC-min , S OC-max Indicates the minimum and maximum values ​​of SOC; P ch-max , P dch-max Respectively represent the maximum charging and discharging power of the battery.

[0147] The electrolyzer charging power constraint is

[0148] 0≤P el (t)≤P el-max

[0149] Among them, P el-maxIndicates the maximum charging power of the electrolyzer.

[0150] Specifically, the fuel cell discharge power constraint includes:

[0151] The discharge power of the fuel cell is greater than the lower limit value of the power capacity of the hydrogen storage tank and less than the upper limit value of the power capacity of the hydrogen storage tank.

[0152] More specifically, the fuel cell discharge power constraint is

[0153] E tank,min ≤E tank (t)≤E tank,max

[0154] Among them, E tank,max and E tank,min They respectively represent the upper and lower limits of the capacity of the hydrogen storage tank.

[0155] The following is a detailed description of the solution of the off-grid double-layer optimization model based on the improved ion swarm algorithm in the present disclosure.

[0156] Specifically, see Figure 3 , the power to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration is substituted into the off-grid operation inner layer optimization scheduling model, and the optimal load scheduling amount is determined by the off-grid operation inner layer optimization scheduling model and the off-grid planning outer layer capacity optimization configuration model, including:

[0157] S21: Initialize basic parameters such as wind speed, load, population size, and number of iterations;

[0158] S22: Set the population size of the outer capacity optimization configuration model to M, and randomly generate an initial population consisting of M particles as X = {X i}(i=1, 2, ..., M), where each particle represents a latent solution;

[0159] S23: Set the population size in the inner load optimization scheduling model to N, and randomly generate N particles to form an initial population Y = {Y j}(i=1, 2, ..., N), where each particle represents a latent solution;

[0160] S24: The particle i with the best fitness value in the outer model is set as the individual extreme value pBest, and the optimal pBest is searched among multiple individual extreme values ​​and set as the outer group extreme value gBest; similarly, the particle j with the best fitness value in the inner optimization scheduling model of off-grid operation is set as the individual extreme value ypBest, and the optimal ypBest is searched among multiple individual extreme values ​​and set as the inner group extreme value ygBest.

[0161] Furthermore, the method further comprises:

[0162] S25: Under the constraints of batteries, electrolyzers, hydrogen storage tanks and hydrogen fuel cells, the position and velocity of particle i in the outer population are randomly initialized;

[0163] S26: Calculate the position X of the outer particle i i The wind power output power and the energy storage absorbable or released power under the condition; and calculate the position X of the outer particle i i The dispatch resources under the above are the power that needs to be allocated to the loads to participate in the dispatch;

[0164] S27: Bring the scheduling resources of particle i's position Xi into the inner model, randomly initialize the speed and position of particle j in the inner population under the premise of satisfying the constraints, and calculate ypBest and ygBest of the inner model;

[0165] S28: Update the velocity and position of particle j in the inner model;

[0166] S29: If the fitness value of particle j is better than the original fitness value ypBest, the inner model updates the fitness value ypBest to the current fitness value, corresponding to the best position Y j Update to current location;

[0167] S30: If the fitness value of particle j is better than the original fitness value ygBest at this time, the inner model updates the fitness value ygBest to the current fitness value, and the corresponding global best position is updated to the current position;

[0168] S31: Determine whether the inner model has reached the maximum number of iterations, if yes, go to step S29, if no, go to step S25;

[0169] S32: Bring the optimal solution of the output of the inner model into the outer model, perform optimization calculation, and calculate the fitness values ​​pBest and gBest of particle i;

[0170] S33: Update the velocity and position of particle i in the outer model population, and repeat steps S23 to S29;

[0171] S34: If the fitness value of particle i is better than the original fitness value pBest at this time, the outer model updates the fitness value pBest to the current fitness value, and the corresponding outer particle best position Xi is updated to the position of the current particle;

[0172] S35: If the fitness value of particle i is better than the original fitness value gBest, the outer model updates the fitness value gBest to the current fitness value, and the corresponding outer particle best position X i Update to the current particle position;

[0173] S36: Determine whether the outer model has reached the maximum number of iterations, if yes, go to step S34, if not, go to step S30;

[0174] S37: Output particles (X i , Y j ) and the optimal fitness and particle position X i , Y j .

[0175] Based on the same inventive concept, the present application also provides a day-ahead optimization scheduling system for a wind-solar-hydrogen storage off-grid operation system, see Figure 2 ,include:

[0176] The first model building unit 201 is used to establish an off-grid planning outer capacity optimization configuration model with the total cost of the wind-solar-hydrogen storage off-grid system as the first objective function, wherein the first objective function is constructed based on the full life cycle cost of the hybrid energy storage and the penalty cost of wind abandonment;

[0177] The second model building unit 202 is used to establish an off-grid operation inner layer optimization scheduling model with the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function when the wind-solar-hydrogen storage coupling system is in an off-grid operation state;

[0178] A solving unit 203 is used to solve the off-grid planning outer capacity optimization configuration model by iterative optimization through an improved particle swarm algorithm to obtain an economically optimal hybrid energy storage capacity configuration;

[0179] The optimization unit 204 is used to substitute the power required to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration into the off-grid operation inner layer optimization scheduling model, and determine the optimal load scheduling amount through the off-grid operation inner layer optimization scheduling model and the off-grid planning outer layer capacity optimization configuration model.

[0180] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A day-ahead optimization scheduling method for a wind-solar-hydrogen storage off-grid operation system, characterized in that: include: An off-grid planning outer capacity optimization configuration model is established with the total cost of the wind-solar-hydrogen storage off-grid system as the first objective function, wherein the first objective function is constructed based on the full life cycle cost of the hybrid energy storage and the penalty cost for wind abandonment; In the off-grid operation state of the wind-solar-hydrogen-storage coupling system, an off-grid operation inner-layer optimization scheduling model is established with the off-grid operation cost of the wind-solar-hydrogen-storage coupling system as the second objective function; By improving the particle swarm algorithm to iteratively optimize and solve the off-grid planning outer capacity optimization configuration model, the economically optimal hybrid energy storage capacity configuration is obtained; The power to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration is substituted into the off-grid operation inner optimization scheduling model, and the optimal load scheduling amount is determined by the off-grid operation inner optimization scheduling model and the off-grid planning outer capacity optimization configuration model.

2. The method according to claim 1, characterized in that The full life cycle cost of the hybrid energy storage is determined based on the equipment investment and construction cost and the equipment operation and maintenance cost; The equipment investment and construction costs and equipment operation and maintenance cost discount rates are allocated to each scheduling cycle in the planned life cycle to determine the full life cycle cost of the hybrid energy storage.

3. The method according to claim 1 or 2, characterized in that: The establishing of the off-grid planning outer capacity optimization configuration model also includes: Performing capacity constraints on equipment in the electric-hydrogen hybrid energy storage system on the outer capacity configuration model; Among them, the capacity cost of the electric-hydrogen hybrid energy storage equipment is corresponding to the upper and lower limits of the planned capacity of a type of equipment in the electric-hydrogen hybrid energy storage.

4. The method according to claim 1, characterized in that Taking the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function, an off-grid operation inner optimization scheduling model is established, including: The second objective function is determined based on the water purchase cost of hydrogen production from electricity for the wind-solar-hydrogen storage coupling system, the annual operation and maintenance cost of the wind-solar-hydrogen storage coupling system in off-grid operation, the replacement cost of the wind-solar-hydrogen storage coupling system in off-grid operation, and the load reduction cost of the wind-solar-hydrogen storage coupling system in off-grid operation.

5. The method according to claim 4, characterized in that Determine the water purchase cost for hydrogen production by electricity according to the water purchase fee of the wind-solar-hydrogen-storage coupling system and the total water volume of the wind-solar-hydrogen-storage coupling system during operation; Determine the annual operation and maintenance cost of the wind-solar-hydrogen-storage coupling system in an off-grid operation state according to the annual operation and maintenance costs of the wind turbine, photovoltaic, battery, electrolyzer, hydrogen storage tank and fuel cell; Obtain the abandoned wind volume and abandoned solar volume according to the rated output of wind power and the rated output of photovoltaic power, and determine the replacement cost of the wind-solar-hydrogen storage coupling system in an off-grid operation state according to the abandoned wind volume and abandoned solar volume; The load reduction cost of the wind-solar-hydrogen-storage coupling system in off-grid operation is determined based on the load reduction compensation cost coefficient and the power of the reduced load.

6. The method according to claim 5, characterized in that The method further comprises: Performing equipment capacity constraints in the electric-hydrogen hybrid energy storage system on the off-grid operation inner optimization scheduling model; The capacity constraints of the equipment in the electric-hydrogen hybrid energy storage system include: Power balance constraints on the wind-solar-hydrogen-storage coupling system, power constraints on wind turbines and photovoltaic panels, battery charging and discharging constraints, electrolyzer charging power constraints and fuel cell discharging power constraints.

7. The method according to claim 6, characterized in that The power balance constraints on the wind-solar-hydrogen storage coupling system include: A power balance function is established based on the output power of the wind turbine generator set, the output power of the photovoltaic panel, the charging and discharging power of the battery, the discharging power of the fuel cell, the charging power of the electrolyzer and the required power of the power load; The charging and discharging power of the battery takes a positive value for discharging and a negative value for charging.

8. The method according to claim 6, characterized in that The power constraints of the wind turbine generator set and photovoltaic panels include: Set the power constraint of the wind turbine generator set to a positive value, which is less than the maximum output power of the wind turbine generator set; Set the power constraint of the photovoltaic panel to a positive value, which is less than the maximum output power of the photovoltaic panel.

9. The method according to claim 6, characterized in that The battery charge and discharge constraints include: The current state of charge of the battery is indicated by the ratio of the remaining capacity of the battery to the rated capacity of the battery; At a moment during the charging time, the ratio of the remaining capacity of the battery to the rated capacity of the battery is greater than its valley value during the charging time and less than its peak value during the charging time; Set the battery charging power to a positive value, which is less than the maximum charging power within the charging time; Set the battery discharge power to a positive value, which is less than the maximum discharge power within the discharge time.

10. The method according to claim 6, characterized in that The fuel cell discharge power constraint includes: The discharge power of the fuel cell is greater than the lower limit value of the power capacity of the hydrogen storage tank and less than the upper limit value of the power capacity of the hydrogen storage tank.

11. The method according to claim 1, characterized in that: Substituting the power to be allocated to the load for participating in the scheduling calculated in the hybrid energy storage capacity configuration into the off-grid operation inner layer optimization scheduling model, and determining the optimal load scheduling amount through the off-grid operation inner layer optimization scheduling model and the off-grid planning outer layer capacity optimization configuration model, including: S21: Initialize basic parameters such as wind speed, light intensity, load, population size, and number of iterations; S22: Set the population size of the outer capacity optimization configuration model to M, and randomly generate an initial population consisting of M particles as X = {X i }(i=1, 2, ..., M), where each particle represents a latent solution; S23: Set the population size in the inner load optimization scheduling model to N, and randomly generate N particles to form an initial population Y = {Y j }(i=1, 2, ..., N), where each particle represents a latent solution; S24: The particle i with the best fitness value in the outer model is set as the individual extreme value pBest, and the optimal pBest is searched among multiple individual extreme values ​​and set as the outer group extreme value gBest; similarly, the particle j with the best fitness value in the inner optimization scheduling model of off-grid operation is set as the individual extreme value ypBest, and the optimal ypBest is searched among multiple individual extreme values ​​and set as the inner group extreme value ygBest.

12. The method according to claim 11, characterized in that The method further comprises: S25: Under the constraints of batteries, electrolyzers, hydrogen storage tanks and hydrogen fuel cells, the position and velocity of particle i in the outer population are randomly initialized; S26: Calculate the position X of the outer particle i i The wind power output power and the energy storage absorbable or released power under the condition; and calculate the position X of the outer particle i i The dispatch resources under the above are the power that needs to be allocated to the loads to participate in the dispatch; S27: Set the position X of particle i i The scheduling resources are brought into the inner model. Under the premise of satisfying the constraints, the speed and position of the inner population particle j are randomly initialized, and the ypBest and ygBest of the inner model are calculated. S28: Update the velocity and position of particle j in the inner model; S29: If the fitness value of particle j is better than the original fitness value ypBest, the inner model updates the fitness value ypBest to the current fitness value, corresponding to the best position Y j Update to current location; S30: If the fitness value of particle j is better than the original fitness value ygBest at this time, the inner model updates the fitness value ygBest to the current fitness value, and the corresponding global best position is updated to the current position; S31: Determine whether the inner model has reached the maximum number of iterations, if yes, go to step S29, if no, go to step S25; S32: Bring the optimal solution of the output of the inner model into the outer model, perform optimization calculation, and calculate the fitness values ​​pBest and gBest of particle i; S33: Update the velocity and position of particle i in the outer model population, and repeat steps S23 to S29; S34: If the fitness value of particle i is better than the original fitness value pBest, the outer model updates the fitness value pBest to the current fitness value, corresponding to the best position X of the outer particle. i Update to the current particle position; S35: If the fitness value of particle i is better than the original fitness value gBest, the outer model updates the fitness value gBest to the current fitness value, and the corresponding outer particle best position X i Update to the current particle position; S36: Determine whether the outer model has reached the maximum number of iterations, if yes, go to step S34, if not, go to step S30; S37: Output particles (X i , Y j ) and the optimal fitness and particle position X i , Y j .

13. A day-ahead optimization dispatching system for off-grid wind-solar-hydrogen storage operation system, characterized in that: include: A first model building unit is used to establish an off-grid planning outer capacity optimization configuration model with the total cost of the wind-solar-hydrogen storage off-grid system as a first objective function, wherein the first objective function is constructed based on the full life cycle cost of the hybrid energy storage and the penalty cost of wind abandonment; The second model building unit is used to establish an off-grid operation inner layer optimization scheduling model with the off-grid operation cost of the wind-solar-hydrogen storage coupling system as the second objective function when the wind-solar-hydrogen storage coupling system is in off-grid operation state; A solving unit is used to solve the off-grid planning outer capacity optimization configuration model through iterative optimization through an improved particle swarm algorithm to obtain the economically optimal hybrid energy storage capacity configuration; The optimization unit is used to substitute the power to be allocated to the load for scheduling calculated in the hybrid energy storage capacity configuration into the off-grid operation inner layer optimization scheduling model, and determine the optimal load scheduling amount through the off-grid operation inner layer optimization scheduling model and the off-grid planning outer layer capacity optimization configuration model.

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