CN116805803A - Energy scheduling method of wind-solar energy storage off-grid hydrogen production system based on self-adaptive MPC - Google Patents
Energy scheduling method of wind-solar energy storage off-grid hydrogen production system based on self-adaptive MPC Download PDFInfo
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Abstract
The invention discloses an energy scheduling method of a wind-solar energy storage off-grid hydrogen production system based on self-adaptive MPC, which comprises the steps of establishing a state space prediction model, a day-ahead optimization scheduling model and a day-ahead real-time scheduling model of the system, solving according to ultra-short-term prediction power increment of a power generation unit and a load unit and the day-ahead optimization scheduling model to obtain a day-ahead plan value, updating an optimized prediction time domain and a control time domain into the day-ahead optimization scheduling model, solving the wind-solar energy storage state space prediction model by taking the day-ahead plan value as a reference track to obtain a control sequence in a control time domain, and issuing a control sequence of a first scheduling period backwards at the current moment at the current scheduling moment. The invention solves the problems of wind and light abandoning generated in the process of the digestion of renewable energy sources, and the self-adaptive time domain optimization strategy designed based on the fuzzy control algorithm can improve the uncertainty capacity of the power generation unit of the system, can control the power distribution of the system in real time, and can improve the economic benefit of the system while ensuring the stable operation of the system.
Description
Technical Field
The invention relates to the technical fields of power grid technology, distributed power generation technology and water electrolysis hydrogen production, in particular to an energy scheduling method of a wind-solar energy storage off-grid hydrogen production system.
Background
In order to solve the problems of resource exhaustion, ecological environment deterioration and the like caused by the large-scale use of fossil energy, renewable energy sources such as wind power, photovoltaic and the like are greatly developed, and the promotion of the power generation proportion of the renewable energy sources has become widely known. However, wind driven generators and photovoltaic power generation have strong randomness and uncertainty, and stability and safety of a power grid can be affected when the wind driven generators and the photovoltaic power generation are connected to the power grid. In some areas with abundant wind and light resources, because of remote distance, the construction cost of a power grid is high, an independent micro-grid is generally configured, renewable energy sources such as wind power, photovoltaic and the like are utilized for generating electricity to meet local electricity consumption requirements, but the electric energy storage of wind and light conversion is difficult, and the problem of wind abandoning and light abandoning can be caused by insufficient absorption capacity of the micro-grid. The hydrogen energy has a series of advantages of high energy density, high conversion efficiency, green, clean, low carbon and the like, and the on-site water hydrogen production by utilizing renewable energy sources such as wind and light and the like and the cooperation of the hydrogen storage device are one of key ways for solving the problems of wind abandoning and light abandoning.
The running state of the micro-grid system consisting of renewable energy sources is complicated because renewable energy sources and loads can change randomly. The micro-grid optimized energy scheduling is an effective means for ensuring the stable and reliable operation of the system and improving the economic benefit of the system, and a reasonable energy scheduling strategy is a foundation for ensuring the coordinated interaction of electric energy and hydrogen energy in the system and ensuring the stable operation of the system for the off-grid wind-light hydrogen storage comprehensive energy micro-grid.
The MPC (model predictive control) is a closed-loop optimization control strategy based on a model in a limited domain, a model is built to predict the dynamic behavior of the system for a period of time in the future, the optimal control sequence is solved and the current control is implemented according to the preset objective function and constraint conditions by continuous rolling optimization, and the prediction of the future dynamic process is realized by continuous correction of real-time information in each rolling optimization step. The control structure block diagram is shown in figure 1, and the control system predicts the output quantity y in the future limited time according to the past and present input and output information and the change of the control quantity m (k+i) for the error between the predicted value and the actual output value, adding the error to the predicted output by feedback correction to calibrate the output value, thereby obtaining the predicted output value y p (k+i), predicted value and reference trajectory y r (k+i) performing continuous rolling optimization on target performance in a specified rolling optimization function, continuously tracking and correcting, finally enabling a predicted value of the system to be more approximate to an actual output value, and continuously repeating the rolling optimization process, namely solving an optimal control sequence by online rolling optimization by combining the current state of the system, a controlled system model and constraint conditions of optimization control.
Model Predictive Control (MPC) can process control problems of a constraint system in real time, has low model accuracy requirements on a control system, and is increasingly applied to control and optimization scheduling of a micro-grid system. The MPC mainly comprises three basic parts of a prediction model, rolling optimization and feedback correction. The prediction model predicts the output quantity of the system in a period of time in the future according to the history information and the control quantity change of the controlled object, wherein the control quantity of the controlled object is a variable which can be optimized, and the predicted output can be continuously and recently expected output by modifying the value of the control quantity; the rolling optimization is a key part of MPC, and by repeated rolling optimization of a target, actual output and deviation value of predicted output caused by uncertainty factors such as external interference, model mismatch and the like are optimized, so that an optimal dynamic control scheme in a future period is obtained, wind, light and load in a wind, light and hydrogen coupling system have uncertainty, the result of each step of optimization of rolling optimization is established on the basis of the latest actual state of the system, the influence of errors on the system is reduced to the greatest extent, and a better control effect is obtained under the interference of the uncertainty factors; the feedback correction and the rolling optimization are combined, the rolling optimization provides a control strategy of advance, so that errors generated by prediction cannot be corrected, the feedback correction corrects the model according to the prediction errors, closed-loop control is formed, and the control precision and the robustness of the system are improved.
At present, a large amount of related documents at home and abroad apply MPC to the optimal scheduling of a micro-grid system, and an effective energy scheduling strategy is provided.
1. Cai Shanzhong et al in the patent application with the application number 202010650379.6 and named as a micro-grid energy scheduling method based on MPC strategy, propose a micro-grid energy scheduling method, which determines the specific actions of the micro-grid at each moment according to the energy scheduling method of the storage battery SOC design by predicting and monitoring the data of the power generation unit and the power utilization unit in real time, thereby achieving the purpose of reducing the running cost of the system. The method designs an energy scheduling method according to the SOC of the storage battery, but does not consider the influences of the charge and discharge depth and the charge and discharge times of the storage battery on the service life of the storage battery. In addition, the method is designed aiming at a system with energy storage of the grid-connected micro-grid, when the photovoltaic power generation capacity of the fan meets the load, the rest electricity is firstly on the grid and then stored in the storage battery, and the method is not suitable for a wind-solar energy storage off-grid hydrogen production system.
2. In the article entitled "Source load store Multi-timescale Rolling optimization scheduling study", author Qin Dian proposes a Multi-timescale optimized scheduling method for solving the disturbance of source load prediction errors and uncertainties to a system. According to the method, a particle swarm algorithm is adopted to solve a day-ahead model to obtain an economic optimization scheduling plan, MPC is adopted in the day to track the day-ahead economic optimization scheduling plan as a target, and the scheduling plan is corrected in real time in a rolling mode according to the actual running state. However, the scheduling plan formulated according to the method cannot meet the expected running economy of the micro-grid due to the fact that the actual running state cannot be adapted to the large-range fluctuation, and in order to obtain better economy when MPC tracking control is adopted in the day, the day optimization scheduling needs to be improved.
3. In the article entitled "Multiple time-scale energy management strategy for ahydrogen-based Multiple-energy micro grid", the author Xiaolun Fang et al has proposed a multi-time scale energy scheduling method in consideration of renewable energy generation and demand uncertainty to minimize the operating cost of a hydrogen-based multi-energy micro grid. The method comprises the steps of day-ahead energy scheduling and MPC-based real-time energy scheduling in the presence of an electric power market, and electric power and hydrogen can be scheduled and utilized among a plurality of interconnected subsystems, so that the energy utilization efficiency of the whole system is improved. However, the method aims at the coordinated scheduling of energy among multiple parks, and is not applicable to a single off-grid system.
Aiming at the problems of wind abandoning and light abandoning caused by insufficient digestion capability of the micro-grid, the electric power generated by wind abandoning and light abandoning is used for producing hydrogen by water electrolysis, which is an important technical choice for solving the problems of electric abandoning and digestion. The coupling of the power generation source directly formed by the fan photovoltaics in the wind-solar energy storage off-grid hydrogen production system and the water electrolysis hydrogen production equipment can effectively solve the problems of wind abandoning and light abandoning, and is expected to realize low-cost, large-scale and industrialized hydrogen production. But the off-grid system does not interact with the power grid, the power unbalance and power fluctuation between the source and the load can only be balanced by a storage battery and a hydrogen production system, the characteristics of electric energy storage and hydrogen energy storage are different, and an energy scheduling method is required to be designed to optimally process the power distribution of the electric energy storage and the hydrogen energy storage, and the participation of the hydrogen energy storage in the whole system is improved so as to improve the hydrogen yield.
Disclosure of Invention
In view of the above, the invention aims to provide an energy scheduling method of a wind-solar off-grid hydrogen production system based on self-adaptive MPC, which aims to solve the technical problems of controlling power distribution of the system in real time according to output change of a power generation unit and storage battery energy storage and hydrogen energy storage characteristics, enabling components such as a storage battery and an electrolytic tank in the system to work in a proper state, improving hydrogen yield of the system and simultaneously enabling economic operation of the system to be optimal.
The wind-solar energy storage off-grid hydrogen production system comprises a power generation unit, a storage battery unit, a hydrogen energy storage unit and a load unit, wherein each unit is connected with a direct current bus through a converter, the power generation unit comprises a wind power generation unit or/and a photovoltaic power generation unit, the hydrogen energy storage unit comprises an electrolytic tank for producing hydrogen by electrolyzing water and a hydrogen storage tank, the load unit comprises a controllable load and an unresectable key load, and the energy scheduling method of the wind-solar energy storage off-grid hydrogen production system based on the self-adaptive MPC comprises the following steps:
step 1: the method comprises the steps of establishing a state space prediction model of a wind-solar off-grid hydrogen production system, wherein the state space prediction model comprises the following steps of:
1) Establishing a system power balance equation:
P WT +P PV +P Bat ＝+P EL +P Load_total (1)
wherein P is WT And is the output power of the wind power generation unit, P PV For the output power of the photovoltaic power generation unit, P EL For the input power of the electrolytic cell, P BAT For charging and discharging power of accumulator unit, P Load_total Is the total load power;
2) Establishing a system net power and a controllable power equation of the controllable device:
wherein P is Net To net power, P Con For controllable power, P Load The controllable equipment comprises an electrolytic tank, a storage battery and a controllable load;
3) Selecting a vector x (k) = [ P ] formed by the net power of the system, the controllable load power, the input power of the electrolytic cell, the charge and discharge power of the storage battery and the state of charge (SOC) of the storage battery Net P Load P EL P Bat SOC] T Is a state variable; selecting a vector delta u (k) = [ delta P ] formed by the controllable load output increment, the electrolyzer output increment and the accumulator output increment Load ΔP EL ΔP Bat ] T Is a control variable; is composed of controllable load power, input power of electrolyzer, charge-discharge power of accumulator and SOCVector y (k) = [ P ] Load P EL P Bat SOC] T Is an output variable; vector r (k) = [ Δp ] composed of ultrashort-term predicted power increments of power generation unit and load unit f_Net ΔP f_Load ] T As disturbance variables, a state space prediction model of the wind-solar off-grid hydrogen production system is established as follows:
step 2: establishing a day-ahead optimal scheduling model for a wind-solar off-grid hydrogen production system, which comprises the following steps:
1) The objective function of the day-ahead optimal scheduling model is established as follows:
max F＝f1-f2-f3-f4 (5)
wherein F is the total daily gain of the system, F1 is the hydrogen selling gain of the system, F2 is the operation and maintenance cost of each unit, F3 is the aging cost of the storage battery, and F4 is the controllable load compensation cost; the objective function of the day-ahead optimal scheduling model aims at maximizing the daily net total income of the system;
the system hydrogen sales yield expression is as follows:
in the method, in the process of the invention,is unit hydrogen selling unit price, < >>Is the hydrogen selling amount;
the hydrogen selling amount is the hydrogen producing amount of the electrolytic tank, and the expression of the hydrogen producing amount is as follows:
wherein T is d Is a scheduling period; p (P) EL (t) is the input power of the electrolytic cell at the moment t; η (eta) EL (t) is the hydrogen production efficiency of the electrolytic cell at the time t; the HHV is 1 unit of hydrogen and oxygen completely react to generate energy released by liquid water;
the expression of the operation and maintenance cost is as follows:
wherein P is WT (t) is the output power of the wind power generation unit at the time t, P PV (t) is the output power of the photovoltaic power generation unit at the moment t, P EL (t) is the input power of the electrolytic tank at the moment t, P BAT (t) is the charge/discharge power of the storage battery unit at the time t, when P BAT At > 0, the battery cell releases electrical energy when P BAT When the energy is less than 0, the storage battery unit absorbs the electric energy; k (K) WT 、K PV 、K EL And K BAT The unit operation and maintenance costs of the wind power generation unit, the photovoltaic power generation unit, the electrolytic tank and the storage battery unit are respectively;
the expression of the battery cell aging cost is as follows:
wherein ρ is BAT Ageing cost is the unit time of the storage battery;respectively charge and discharge conversion marking positions of the storage battery; />1 indicates that the battery is converted from a discharging state to a charging state, and 0 indicates that the battery is kept in the discharging state; />1 indicates that the battery is converted from a charged state to a discharged state, and 0 indicates that the battery is kept in the charged state;
the expression of the controllable load compensation cost is as follows:
wherein C is load Reference unit price for default; p (P) Load (t) is a controllable load value cut off at time t;
2) Establishing constraint conditions of a day-ahead optimal scheduling model, which comprises the following steps:
a. establishing power constraint conditions of a wind power generation unit, a photovoltaic power generation unit and an electrolytic cell:
wherein P is WT,min 、P PV,min 、P EL,min Respectively the minimum values of the power of the wind power generation unit, the photovoltaic power generation unit and the electrolytic tank; p (P) WT,max 、P PV,max And P EL,max The maximum power of the wind power generation unit, the photovoltaic power generation unit and the electrolytic tank is respectively;
b. establishing a power balance constraint condition:
P WT (t)+P PV (t)+P BAT (t)＝P EL (t)+P Load (t) (12)
c. establishing storage battery cell constraint conditions:
wherein P is BAT,max The maximum value of the charge and discharge power of the storage battery; SOC (State of Charge) max For limiting value of charge state of storage battery, SOC min The limiting value is the limit value of the charge state of the storage battery;
d. establishing constraint conditions of a hydrogen storage tank:
Pre min ≤Pre(t)≤Pre max (14)
pre (t) is the pressure of the hydrogen storage tank at the time t; pre (Pre) min Pre is the minimum pressure allowed by the hydrogen storage tank max Respectively the maximum pressure allowed by the hydrogen storage tank;
step 3: establishing a real-time scheduling model in a day for a wind-solar storage off-grid hydrogen production system, comprising the following steps:
1) The objective function for establishing the intra-day scroll optimization is as follows:
wherein Y is ref (k+t) is a reference track obtained by day-ahead optimal scheduling solution, N p To predict the time domain, N c To control the time domain; q and R are weight matrices. Solving the target track Y according to the above c (k+t|t) and controllable load output increment, electrolyzer output increment and accumulator output increment DeltaU (k+t|k) to control output tracking daily planned value Y ref (k+t) while ensuring that the control variable is as small as possible as the daily rolling optimization objective.
2) Establishing a daily constraint condition:
a. establishing a controllable equipment output constraint condition:
Δu min ≤Δu(k+t|k)≤Δu max (16)
wherein the controllable equipment is an electrolytic tank, a storage battery and a controllable load; deltau min As the lower limit value of the controlled variable of the controllable device, deltau max An upper limit value for the controllable device control quantity; p (P) min Minimum output power for the controllable device; p (P) max Maximum output power for the controllable device;
b. establishing a storage battery SOC constraint condition:
SOC min ≤SOC(k+t|k)≤SOC max (18)
c. establishing constraint conditions of a hydrogen storage tank:
Pre min ≤Pre(t)≤Pre max (19)
step 4: solving a wind-solar energy storage state space prediction model according to ultra-short-term prediction power increment of the power generation unit and the load unit and a day-ahead optimization scheduling model to obtain a day-ahead planning value Y ref (k+t)；
Step 5: selecting an optimal prediction time domain by adopting a fuzzy control algorithm in each daily scheduling period, properly adjusting a control time domain according to the prediction time domain, and finally updating the optimized prediction time domain and the control time domain into a daily optimal scheduling model;
step 6: planned value Y before date ref And (k+t) serving as a reference track, solving the wind-solar storage state space prediction model according to ultra-short-term prediction power increment of the power generation unit and the load unit and the daily optimization scheduling model to obtain a control sequence consisting of controllable load output increment, electrolyzer output increment and storage battery output increment in a control time domain, issuing a control sequence of a first scheduling period backwards at the current scheduling moment only, and repeating the rolling optimization process when the next scheduling period arrives.
Further, in the step 5, selecting the optimal prediction time domain by using a fuzzy control algorithm in each intra-day scheduling period includes: prediction error e of uncontrollable input quantity (fan output power, photovoltaic output power and uncontrollable load power) p Target track power tracking error e y The two input quantities are subjected to fuzzy reasoning to obtain fuzzy output quantities, and a fuzzy operation is performed through a gravity center method shown in the following formula to obtain an accurate value of an output quantity prediction time domain;
wherein A (x i ) As a membership function of the output quantity, x represents a clear value of the output quantity;
rounding the precise value x output by the fuzzy algorithm to obtain the current optimal prediction time domain N p_cur The method comprises the steps of carrying out a first treatment on the surface of the Predicting time domain N according to the current optimal p_cur Prediction error e of uncontrollable input quantity p Power tracking error e y Properly adjusting to obtain the current optimal control time domain N c_cur ；
N p_cur ＝Round(x) (21)
N c_cur ＝Round(α 1 N p_cur +α 2 e p +α 3 e y ) (22)
Wherein Round is a rounding function; alpha 1 For predicting time domain weight coefficients; alpha 2 A prediction error weight coefficient for uncontrollable input quantity; alpha 3 Is a power tracking error weight coefficient.
Further, the day-ahead optimal scheduling model takes 1h as a scheduling period, and the day-ahead optimal scheduling model takes 5min as a scheduling period.
The invention has the beneficial effects that:
1. the invention discloses an energy scheduling method of a wind-solar energy storage off-grid hydrogen production system based on self-adaptive MPC, which aims to solve the problem of wind discarding and light discarding generated in the process of the absorption of renewable energy sources, a hydrogen energy storage unit is added in the off-grid micro-grid system, and the designed energy scheduling method aims at optimizing one-day economic operation of the system, balances power distribution of electric energy storage and hydrogen energy storage, and improves the participation degree of the hydrogen energy storage in the whole system and the hydrogen yield.
2. Compared with the MPC adopting a fixed prediction time domain and a control time domain, the self-adaptive time domain optimization strategy designed based on the fuzzy control algorithm can improve the uncertainty capacity of a power generation unit of the system, can better control the power distribution of the system in real time according to the output change of the power generation unit and the energy storage and hydrogen storage characteristics of the storage battery, so that the storage battery, the electrolytic tank and other parts in the system work in proper states, and the economic benefit of the system is improved while the system stably operates.
Drawings
FIG. 1 is a block diagram of an MPC;
FIG. 2 is a schematic diagram of a wind-solar off-grid hydrogen production system;
FIG. 3 is a dispatch frame diagram of a wind and solar off-grid hydrogen production system;
FIG. 4 is a system dispatch solution flow diagram.
Detailed Description
The invention is further described below with reference to the drawings and examples.
As shown in fig. 1, the wind-solar off-grid hydrogen production system in this embodiment includes a power generation unit, a storage battery unit, a hydrogen storage unit and a load unit, where each unit is connected to a dc bus through a converter, the power generation unit includes a wind power generation unit and/or a photovoltaic power generation unit, the load unit includes a controllable load and an unresectable critical load, the hydrogen storage unit includes an electrolytic tank and a hydrogen storage tank for producing hydrogen by electrolyzing water, and the load unit includes a controllable load and an unresectable critical load. The energy scheduling method of the wind-solar energy storage off-grid hydrogen production system based on the self-adaptive MPC in the embodiment comprises the following steps of:
step 1: the method comprises the steps of establishing a state space prediction model of a wind-solar off-grid hydrogen production system, wherein the state space prediction model comprises the following steps of:
1) Establishing a system power balance equation:
P WT +P PV +P Bat ＝+P EL +P Load_total (1)
wherein P is WT And is the output power of the wind power generation unit, P PV For the output power of the photovoltaic power generation unit, P EL For the input power of the electrolytic cell, P BAT For charging and discharging power of accumulator unit, P Load_total Is the total load power.
2) Establishing a system net power and a controllable power equation of the controllable device:
wherein P is Net To net power, P Con For controllable power, P Load Is the controllable load power. The controllable equipment comprises an electrolytic tank, a storage battery and a controllable load.
3) Selecting a vector x (k) = [ P ] formed by the net power of the system, the controllable load power, the input power of the electrolytic cell, the charge and discharge power of the storage battery and the state of charge (SOC) of the storage battery Net P Load P EL P Bat SOC] T Is a state variable; selecting a vector delta u (k) = [ delta P ] formed by the controllable load output increment, the electrolyzer output increment and the accumulator output increment Load ΔP EL ΔP Bat ] T Is a control variable; vector y (k) = [ P ] composed of controllable load power, electrolyzer power, battery power and SOC Load P EL P Bat SOC] T Is an output variable; vector r (k) = [ Δp ] composed of ultrashort-term predicted power increments of power generation unit and load unit f_Net ΔP f_Load ] T As disturbance variables, a state space prediction model of the wind-solar off-grid hydrogen production system is established as follows:
from the equations (3) and (4), based on the ultra-short-term predicted power increment of the power generation unit and the load unit, by repeatedly iterating the state space prediction model until the forward prediction is performed by p steps, a vector Y consisting of estimated output values of the controllable load power, the electrolyzer power, the battery power and the SOC within the predicted period can be obtained c The expression is as follows:
Y c ＝[P Load (k+1),P EL (k+1),P Bat (k+1),SOC(k+1),…,P Load (k+p),P EL (k+p),P Bat (k+p),SOC(k+p)] T
step 2: establishing a day-ahead optimal scheduling model for a wind-solar off-grid hydrogen production system, which comprises the following steps:
1) The objective function of the day-ahead optimal scheduling model is established as follows:
max F＝f1-f2-f3-f4 (5)
wherein F is the total daily gain of the system, F1 is the hydrogen selling gain of the system, F2 is the operation and maintenance cost of each unit, F3 is the aging cost of the storage battery, and F4 is the controllable load compensation cost. The objective function of the day-ahead optimal scheduling model aims at maximizing the daily net total income of the system, and makes a power plan of each unit of the system on the next day.
The system hydrogen sales yield expression is as follows:
in the method, in the process of the invention,unit hydrogen selling unit price (yuan/kg),>the amount of hydrogen sold (kg).
The hydrogen selling amount is the hydrogen producing amount of the electrolytic tank, and the expression of the hydrogen producing amount is as follows:
wherein T is d Is a scheduling period; p (P) EL (t) is the input power of the electrolytic cell at the moment t; η (eta) EL (t) is the hydrogen production efficiency of the electrolytic cell at the time t; HHV is the energy (kWh/kg) given off by the complete reaction of 1 unit (1 kg in this example) of hydrogen with oxygen to produce liquid water.
In the operation process of the wind-solar energy storage off-grid hydrogen production system, the wind-solar energy storage off-grid hydrogen production system is required to be maintained continuously, and maintenance cost can be generated. The expression of the operation and maintenance cost is as follows:
wherein P is WT (t) is the output power of the wind power generation unit at the time t, P PV (t) is the output power of the photovoltaic power generation unit at the moment t, P EL (t) is the input power of the electrolytic tank at the moment t, P BAT (t) is the charge/discharge power of the storage battery unit at the time t, when P BAT At > 0, the battery cell releases electrical energy when P BAT When the energy is less than 0, the storage battery unit absorbs the electric energy; k (K) WT 、K PV 、K EL And K BAT The unit operation and maintenance costs of the wind power generation unit, the photovoltaic power generation unit, the electrolytic tank and the storage battery unit are respectively;
the aging speed of the storage battery can be increased along with the continuous increase of the charge and discharge times. The expression of the battery cell aging cost is as follows:
wherein ρ is BAT Ageing cost is the unit time of the storage battery;respectively charge and discharge conversion marking positions of the storage battery; />1 indicates that the battery is converted from a discharging state to a charging state, and 0 indicates that the battery is kept in the discharging state; />A value of 1 indicates that the battery is changed from a charged state to a discharged state, and a value of 0 indicates that the battery is kept in a charged state.
When the energy supply of the system is insufficient, a part of controllable load can be cut off to enable the system to reach new energy balance, and penalty cost is generated by the cut-off load. The expression of the controllable load compensation cost is as follows:
wherein C is load Reference unit price for default; p (P) Load And (t) is a controllable load value cut off at the moment t.
2) The constraint condition is to ensure the safety and stability of each device and the whole system in the wind-solar off-grid hydrogen production system. Establishing constraint conditions of a day-ahead optimal scheduling model, which comprises the following steps:
a. establishing power constraint conditions of a wind power generation unit, a photovoltaic power generation unit and an electrolytic cell:
wherein P is WT,min 、P PV,min 、P EL,min Respectively the minimum values of the power of the wind power generation unit, the photovoltaic power generation unit and the electrolytic tank; p (P) WT,max 、P PV,max And P EL,max The maximum power of the wind power generation unit, the photovoltaic power generation unit and the electrolytic tank is respectively.
b. Establishing a power balance constraint condition:
P WT (t)+P PV (t)+P BAT (t)＝P EL (t)+P Load (t) (12)。
c. establishing storage battery cell constraint conditions:
wherein P is BAT,max The maximum value of the charge and discharge power of the storage battery; SOC (State of Charge) max For limiting value of charge state of storage battery, SOC min Is the limit value of the charge state of the storage battery.
d. Establishing constraint conditions of a hydrogen storage tank:
Pre min ≤Pre(t)≤Pre max (14)
pre (t) is the pressure of the hydrogen storage tank at the time t; pre (Pre) min Pre is the minimum pressure allowed by the hydrogen storage tank max Respectively the maximum pressure allowed by the hydrogen storage tanks.
Step 3: establishing a real-time scheduling model in a day for a wind-solar storage off-grid hydrogen production system, comprising the following steps:
1) The objective function for establishing the intra-day scroll optimization is as follows:
wherein Y is ref (k+t) is a reference track obtained by day-ahead optimal scheduling solution, N p To predict the time domain, N c To control the time domain; q and R are weight matrices. Solving the target track Y according to the above c (k+t|t) and controllable load output increment, electrolyzer output increment and accumulator output increment DeltaU (k+t|k) to control output tracking daily planned value Y ref (k+t) while ensuring that the control variable is as small as possible as the daily rolling optimization objective.
2) Establishing a daily constraint condition:
a. establishing a controllable equipment output constraint condition:
Δu min ≤Δu(k+t|k)≤Δu max (16)
wherein the controllable equipment is an electrolytic tank, a storage battery and a controllable load; deltau min As the lower limit value of the controlled variable of the controllable device, deltau max An upper limit value for the controllable device control quantity; p (P) min Minimum output power for the controllable device; p (P) max Maximum output power for the controllable device; b. and establishing a storage battery SOC constraint condition.
SOC min ≤SOC(k+t|k)≤SOC max (18)。
c. Establishing constraint conditions of a hydrogen storage tank:
Pre min ≤Pre(t)≤Pre max (19)。
step 4: solving a wind-solar energy storage state space prediction model according to ultra-short-term prediction power increment of the power generation unit and the load unit and a day-ahead optimization scheduling model to obtain a day-ahead planning value Y ref (k+t)。
Step 5: and selecting an optimal prediction time domain by adopting a fuzzy control algorithm in each daily scheduling period, properly adjusting a control time domain according to the prediction time domain, and finally updating the optimized prediction time domain and the control time domain into a daily optimal scheduling model.
Step 6: planned value Y before date ref And (k+t) serving as a reference track, solving the wind-solar storage state space prediction model according to ultra-short-term prediction power increment of the power generation unit and the load unit and the daily optimization scheduling model to obtain a control sequence consisting of controllable load output increment, electrolyzer output increment and storage battery output increment in a control time domain, issuing a control sequence of a first scheduling period backwards at the current scheduling moment only, and repeating the rolling optimization process when the next scheduling period arrives.
When the conventional MPC performs optimization solution on each sampling time in the whole scheduling period, a fixed prediction time domain and a control time domain are mostly adopted. However, the wind-solar energy storage off-grid hydrogen production system contains uncontrollable distributed power sources such as fans, and for sudden disturbance caused by the sudden increase or the sudden decrease of the generated energy, the traditional MPC controller is difficult to process, and a fixed prediction time domain and a fixed control time domain can cause larger errors in a scheduling result so as to influence the stability of the system. Aiming at the problem of insufficient sudden disturbance processing of the traditional MPC, the MPC is improved, and the prediction time domain and the control time domain of the MPC are adaptively adjusted based on a fuzzy control algorithm.
The fuzzy control algorithm does not need to establish an accurate mathematical model for the control system, uses a fuzzy rule to describe the relation among system variables, has high response speed, strong anti-interference capability, and better fault tolerance and stronger robustness. In the embodiment, the prediction time domain and the control time domain of the MPC are updated in each daily scheduling period, and because the control time domain in the traditional MPC is generally equal to the prediction time domain or one to two steps more than the prediction time domain, the control effect of the MPC is not ideal when the difference between the prediction time domain and the control time domain is larger, wind power generation, photovoltaic power generation and load prediction data are fully utilized, an optimal prediction time domain is selected by adopting a fuzzy control algorithm, the control time domain is properly adjusted according to the prediction time domain, and finally the optimized prediction time domain and the control time domain are updated into the daily scheduling model.
The prediction time domain and the control time domain of the MPC controller have great influence on the accuracy, the real-time performance and the stability of the power tracking. For the prediction time domain: the larger the prediction time domain is, the wider the time span predicted by the MPC controller is, and the more state information of the future moment of the system is obtained, but the error weight at a position far from the current moment of the system is increased, so that the current power tracking precision is reduced; when the prediction time domain is smaller, less state information is obtained at the future moment of the system, the system cannot adjust the scheduling strategy in time under the constraint condition of the control quantity of the system, and the stability is not ensured while the power tracking precision is reduced. For the control time domain: the larger the control time domain is, the more the number of actions can be executed by the MPC controller in the control time domain is, the higher the accuracy and the stability of power tracking are, but the calculation amount of the MPC controller is increased, so that the real-time performance of the system is reduced; when the control time domain is smaller, the MPC controller solves out the control action to ensure that the system state error in the prediction time domain is minimum, and the power tracking precision of the current moment of the system can be sacrificed. When the prediction error of uncontrollable input quantity (photovoltaic output of fan and uncontrollable load power) is large, in order to reduce unnecessary calculation and improve the performance of control system, the prediction time domain and control time domain must be adaptively shortened.
Based on the above analysis, the present embodiment adaptively calculates the prediction time domain in step 5 by:
dividing the prediction error of the uncontrollable input quantity, the prediction error of the input quantity and the prediction time domain into 7 fuzzy subsets: NB (small), NM (small), NS (small), ZO (moderate), PS (large), PM (large), PB (large). Membership function selection: the Gaussian membership functions are used at the two ends of the discourse domain, so that the control is more stable; triangle membership functions are used in the middle area of the discourse domain, so that higher accuracy and sensitivity are ensured.
From the foregoing, it can be seen that the sizes of the prediction time domain and the control time domain can directly affect the final scheduling result, and when the prediction error of the uncontrollable input is large, that is, the system operation is unstable, the prediction time domain needs to be increased appropriately; when the system is in a stable running state but the power tracking error is larger, the prediction time domain needs to be properly reduced; when the system is in an extreme operation scene (sudden change of photovoltaic output of a fan), a larger power tracking error occurs in the system, but the system should be ensured to be stable as a first requirement, namely a larger prediction time domain should be selected. The fuzzy rules are formulated based on the above principles as shown in table 1.
TABLE 1 fuzzy rule TABLE
Selecting an optimal prediction time domain by adopting a fuzzy control algorithm in each daily scheduling period comprises the following steps: vector r (k) = [ delta P ] consisting of uncontrolled input quantities, i.e. ultra-short-term predicted power increments of the power generation unit and the uncontrolled load unit f_Net ΔP f_Load ] T Prediction error e of (2) p Power tracking error, i.e. the system actual trajectory Y c Y between (k+t|t) and reference track ref The two input quantities of the error (k+t) are subjected to fuzzy reasoning to obtain fuzzy output quantity, and a de-fuzzy operation is carried out through a gravity center method shown in the following formula to obtain an accurate value of an output quantity prediction time domain;
wherein A (x i ) As a membership function of the output quantity, x represents a clear value of the output quantity;
rounding the precise value x output by the fuzzy algorithm to obtain the current optimal prediction time domain N p_cur The method comprises the steps of carrying out a first treatment on the surface of the Predicting time domain N according to the current optimal p_cur UncontrollablePrediction error e of input quantity p Power tracking error e y Properly adjusting to obtain the current optimal control time domain N c_cur ；
N p_cur ＝Round(x) (21)
N c_cur ＝Round(α 1 N p_cur +α 2 e p +α 3 e y ) (22)
Wherein Round is a rounding function; alpha 1 For predicting time domain weight coefficients; alpha 2 A prediction error weight coefficient for uncontrollable input quantity; alpha 3 Is a power tracking error weight coefficient.
In the above embodiment, the day-ahead optimal scheduling model takes 1h as a scheduling period, and the day-ahead optimal scheduling model takes 5min as a scheduling period. Of course, in different embodiments, the duration of the scheduling period may also be adjusted as needed.
In the energy scheduling method of the wind-solar energy storage off-grid hydrogen production system based on the self-adaptive MPC, in order to solve the problem of wind and light abandoning generated in the process of the absorption of renewable energy sources, a hydrogen energy storage unit is added into the off-grid micro-grid system, the designed energy scheduling method aims at optimizing one-day economic operation of the system, balances power distribution of electric energy storage and hydrogen energy storage, and improves the participation degree of the hydrogen energy storage in the whole system and the hydrogen yield.
Compared with the MPC adopting a fixed prediction time domain and a control time domain, the self-adaptive time domain optimization strategy designed based on the fuzzy control algorithm can improve the uncertainty capacity of a power generation unit of the system, can better control the power distribution of the system in real time according to the output change of the power generation unit and the energy storage and hydrogen storage characteristics of the storage battery, so that the storage battery, the electrolytic tank and other parts in the system work in proper states, and the economic benefit of the system is improved while the system stably operates.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention.
Claims (3)
1. The wind-solar energy storage off-grid hydrogen production system comprises a power generation unit, a storage battery unit, a hydrogen energy storage unit and a load unit, wherein each unit is connected with a direct current bus through a converter, the power generation unit comprises a wind power generation unit or/and a photovoltaic power generation unit, the hydrogen energy storage unit comprises an electrolytic tank and a hydrogen storage tank for producing hydrogen by electrolyzing water, the hydrogen energy storage unit comprises the electrolytic tank and the hydrogen storage tank for producing hydrogen by electrolyzing water, and the load unit comprises a controllable load and an unresectable key load, and is characterized in that: the method comprises the following steps:
step 1: the method comprises the steps of establishing a state space prediction model of a wind-solar off-grid hydrogen production system, wherein the state space prediction model comprises the following steps of:
1) Establishing a system power balance equation:
P WT +P PV +P Bat ＝+P EL +P Load_total (1)
wherein P is WT And is the output power of the wind power generation unit, P PV For the output power of the photovoltaic power generation unit, P EL For the input power of the electrolytic cell, P BAT For charging and discharging power of accumulator unit, P Load_total Is the total load power;
2) Establishing a system net power and a controllable power equation of the controllable device:
wherein P is Net To net power, P Con For controllable power, P Load The controllable equipment comprises an electrolytic tank, a storage battery and a controllable load;
3) Selecting net power of system and controllingVector x (k) = [ P ] formed by load power, cell input power, battery charge/discharge power, and battery state of charge SOC Net P Load P EL P Bat SOC] T Is a state variable; selecting a vector delta u (k) = [ delta P ] formed by the controllable load output increment, the electrolyzer output increment and the accumulator output increment Load ΔP EL ΔP Bat ] T Is a control variable; vector y (k) = [ P ] composed of controllable load power, electrolyzer power, battery power and SOC Load P EL P Bat SOC] T Is an output variable; vector r (k) = [ Δp ] composed of ultrashort-term predicted power increments of power generation unit and load unit f_Net ΔP f_Load ] T As disturbance variables, a state space prediction model of the wind-solar off-grid hydrogen production system is established as follows:
step 2: establishing a day-ahead optimal scheduling model for a wind-solar off-grid hydrogen production system, which comprises the following steps:
1) The objective function of the day-ahead optimal scheduling model is established as follows:
maxF＝f1-f2-f3-f4 (5)
wherein F is the total daily gain of the system, F1 is the hydrogen selling gain of the system, F2 is the operation and maintenance cost of each unit, F3 is the aging cost of the storage battery, and F4 is the controllable load compensation cost; the objective function of the day-ahead optimal scheduling model aims at maximizing the daily net total income of the system;
the system hydrogen sales yield expression is as follows:
in the method, in the process of the invention,is unit hydrogen selling unit price, < >>Is the hydrogen selling amount;
the hydrogen selling amount is the hydrogen producing amount of the electrolytic tank, and the expression of the hydrogen producing amount is as follows:
wherein T is d Is a scheduling period; p (P) EL (t) is the input power of the electrolytic cell at the moment t; η (eta) EL (t) is the hydrogen production efficiency of the electrolytic cell at the time t; the HHV is 1 unit of hydrogen and oxygen completely react to generate energy released by liquid water;
the expression of the operation and maintenance cost is as follows:
wherein P is WT (t) is the output power of the wind power generation unit at the time t, P PV (t) is the output power of the photovoltaic power generation unit at the moment t, P EL (t) is the input power of the electrolytic tank at the moment t, P BAT (t) is the charge/discharge power of the storage battery unit at the time t, when P BAT At > 0, the battery cell releases electrical energy when P BAT When the energy is less than 0, the storage battery unit absorbs the electric energy; k (K) WT 、K PV 、K EL And K BAT The unit operation and maintenance costs of the wind power generation unit, the photovoltaic power generation unit, the electrolytic tank and the storage battery unit are respectively;
the expression of the battery cell aging cost is as follows:
wherein ρ is BAT Ageing cost is the unit time of the storage battery;respectively charge and discharge conversion marking positions of the storage battery; />1 indicates that the battery is converted from a discharging state to a charging state, and 0 indicates that the battery is kept in the discharging state;1 indicates that the battery is converted from a charged state to a discharged state, and 0 indicates that the battery is kept in the charged state;
the expression of the controllable load compensation cost is as follows:
wherein C is load Reference unit price for default; p (P) load (t) is a controllable load value cut off at time t;
2) Establishing constraint conditions of a day-ahead optimal scheduling model, which comprises the following steps:
a. establishing power constraint conditions of a wind power generation unit, a photovoltaic power generation unit and an electrolytic cell:
wherein P is WT,min 、P PV,min 、P EL,min Respectively the minimum values of the power of the wind power generation unit, the photovoltaic power generation unit and the electrolytic tank; p (P) WT,max 、P PV,max And P EL,max The maximum power of the wind power generation unit, the photovoltaic power generation unit and the electrolytic tank is respectively;
b. establishing a power balance constraint condition:
P WT (t)+P PV (t)+P BAT (t)＝P EL (t)+P Load (t) (12)
c. establishing storage battery cell constraint conditions:
wherein P is BAT,max The maximum value of the charge and discharge power of the storage battery; SOC (State of Charge) max For limiting value of charge state of storage battery, SOC min The limiting value is the limit value of the charge state of the storage battery;
d. establishing constraint conditions of a hydrogen storage tank:
Pre min ≤Pre(t)≤Pre max (14)
pre (t) is the pressure of the hydrogen storage tank at the time t; pre (Pre) min Pre is the minimum pressure allowed by the hydrogen storage tank max Respectively the maximum pressure allowed by the hydrogen storage tank;
step 3: establishing a real-time scheduling model in a day for a wind-solar storage off-grid hydrogen production system, comprising the following steps:
1) The objective function for establishing the intra-day scroll optimization is as follows:
wherein Y is ref (k+t) is a reference track obtained by day-ahead optimal scheduling solution, N p To predict the time domain, N c To control the time domain; q and R are weight matrices; solving the target track Y according to the above c (k+t|t) and controllable load output increment, electrolyzer output increment and accumulator output increment DeltaU (k+t|k) to control output tracking daily planned value Y ref (k+t) while ensuring that the control variable is as small as possible as the daily rolling optimization objective.
2) Establishing a daily constraint condition:
a. establishing a controllable equipment output constraint condition:
Δu min ≤Δu(k+t|k)≤Δu max (16)
wherein the controllable equipment is an electrolytic tank, a storage battery and a controllable load; deltau min As the lower limit value of the controlled variable of the controllable device, deltau max An upper limit value for the controllable device control quantity; p (P) min Minimum output power for the controllable device; p (P) max Maximum output power for the controllable device;
b. establishing a storage battery SOC constraint condition:
SOC min ≤SOC(k+t|k)≤SOC max (18)
c. establishing constraint conditions of a hydrogen storage tank:
Pre min ≤Pre(t)≤Pre max (19)
step 4: solving a wind-solar energy storage state space prediction model according to ultra-short-term prediction power increment of the power generation unit and the load unit and a day-ahead optimization scheduling model to obtain a day-ahead planning value Y ref (k+t)；
Step 5: selecting an optimal prediction time domain by adopting a fuzzy control algorithm in each daily scheduling period, properly adjusting a control time domain according to the prediction time domain, and finally updating the optimized prediction time domain and the control time domain into a daily optimal scheduling model;
step 6: planned value Y before date ref And (k+t) serving as a reference track, solving the wind-solar storage state space prediction model according to ultra-short-term prediction power increment of the power generation unit and the load unit and the daily optimization scheduling model to obtain a control sequence consisting of controllable load output increment, electrolyzer output increment and storage battery output increment in a control time domain, issuing a control sequence of a first scheduling period backwards at the current scheduling moment only, and repeating the rolling optimization process when the next scheduling period arrives.
2. The adaptive MPC-based wind-solar off-grid hydrogen production system energy scheduling method of claim 1, wherein the method is characterized by: in the step 5, selecting the optimal prediction time domain by adopting a fuzzy control algorithm in each daily scheduling period includes: vector r (k) = [ delta P ] consisting of uncontrolled input quantities, i.e. ultra-short-term predicted power increments of the power generation unit and the uncontrolled load unit f_Net ΔP f_Load ] T Prediction error e of (2) p Power tracking error, i.e. the system actual trajectory Y c Y between (k+t|t) and reference track ref The two input quantities of the error (k+t) are subjected to fuzzy reasoning to obtain fuzzy output quantity, and a de-fuzzy operation is carried out through a gravity center method shown in the following formula to obtain an accurate value of an output quantity prediction time domain;
wherein A (x i ) As a membership function of the output quantity, x represents a clear value of the output quantity;
rounding the precise value x output by the fuzzy algorithm to obtain the current optimal prediction time domain N p_cur The method comprises the steps of carrying out a first treatment on the surface of the Predicting time domain N according to the current optimal p_cur Prediction error e of uncontrollable input quantity p Power tracking error e y Properly adjusting to obtain the current optimal control time domain N c_cur ；
N p_cur ＝Round(x) (21)
N c_cur ＝Round(α 1 N p_cur +α 2 e p +α 3 e y ) (22)
Wherein Round is a rounding function; alpha 1 For predicting time domain weight coefficients; alpha 2 A prediction error weight coefficient for uncontrollable input quantity; alpha 3 Is a power tracking error weight coefficient.
3. The adaptive MPC-based wind-solar off-grid hydrogen production system energy scheduling method of claim 1, wherein the method is characterized by: the day-ahead optimal scheduling model takes 1h as a scheduling period, and the day-ahead optimal scheduling model takes 5min as a scheduling period.
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| CN118052419A (en) * | 2024-04-16 | 2024-05-17 | 北京清能互联科技有限公司 | Wind-light-hydrogen-ammonia system production scheduling method and device considering new energy prediction deviation |
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| CN117411087A (en) * | 2023-12-13 | 2024-01-16 | 国网山东省电力公司电力科学研究院 | Collaborative optimization control method and system for wind-solar hydrogen storage combined power generation system |
| CN117411087B (en) * | 2023-12-13 | 2024-04-12 | 国网山东省电力公司电力科学研究院 | Collaborative optimization control method and system for wind-solar hydrogen storage combined power generation system |
| CN118052419A (en) * | 2024-04-16 | 2024-05-17 | 北京清能互联科技有限公司 | Wind-light-hydrogen-ammonia system production scheduling method and device considering new energy prediction deviation |
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