CN117394423A - Wind power storage planning method combining double energy storage scheduling - Google Patents
Wind power storage planning method combining double energy storage scheduling Download PDFInfo
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Abstract
The invention discloses a wind power storage planning method combining double energy storage scheduling, which ensures the reliable realization of planning by wind power configuration double energy storage and daily multi-stage energy management, wherein energy storage A realizes the supply and demand balance of daily planning, energy storage B realizes the daily real-time supply and demand deviation compensation, and the functions of A and B can be alternated. In the day-ahead plan, the running cost of the wind storage system and the total amount of grid-connected exchange power are minimized, the power generation and load prediction are used as references, the energy storage charge state is used as constraint, a multi-target model is established, a golden search algorithm is used for solving, and an optimized wind storage power curve is selected as the day-ahead plan. In the daily scheduling optimization stage, the available capacity of energy storage is considered, after the set ideal capacity double-energy storage different working conditions, the applicability of the actual working conditions is considered, the double-energy storage and grid-connected exchange power under the independent power supply and grid-connected working conditions is optimized, and the nearby power supply requirement is met. The dynamic adaptability of wind power to load can be improved, and the grid connection stability of new energy power generation is ensured.
Description
Technical Field
The invention discloses a wind power storage planning method combining double energy storage scheduling, and relates to the technical field of wind power storage planning.
Background
The demand for energy sources is also increasing with the development of socioeconomic performance. The wind power generation system utilizes renewable energy, namely wind energy, so that the social energy gap is filled, and the pollution to the environment is reduced. Meanwhile, based on the influence of wind energy resource distribution, the nearby power supply of renewable energy sources is realized by using a distributed power generation control strategy, so that the utilization rate of wind energy resources can be effectively improved, and the loss in the electric energy transmission process can be reduced. However, wind energy has the characteristics of uncertainty and intermittence naturally, which easily causes adverse effects on the safe operation of a power system and also easily affects the stability of local power utilization in distributed power generation. In order to reduce the influence of wind power uncertainty and intermittence on the nearby power supply of the load, the prior art introduces an energy storage system and a wind power generation system to complement each other. And planning the energy scheduling behavior of the energy storage battery in the day-ahead scheduling stage based on the wind power generation prediction data to realize peak clipping and valley filling of a wind power generation output curve, so as to ensure that the generated energy of the wind power storage system meets the local load power demand. However, the existing prediction technology still cannot ensure that the wind power generation predicted value obtained based on the historical data is completely consistent with the actual situation, so that the charging and discharging of the energy storage battery needs to be adjusted based on the actual power generation situation to correct the future plan. In the prior art, a heuristic algorithm is adopted to solve the planned output of each distributed power supply in a day-ahead scheduling stage, and rolling correction is further carried out on each individual power supply in the day-ahead scheduling stage based on actual wind power generation and load demand data. The system is limited by the cost and capacity of the energy storage system and the requirement of the energy storage system for tracking the load power in real time, and the other energy storage system is added on the basis of the single energy storage system, so that the two functions of tracking the planned power before the day and correcting the power deviation in the day before the day can be realized. Meanwhile, the characteristics that the double energy storage systems can independently run or can be controlled in a combined mode are utilized, the double energy storage charge and discharge control modes are timely controlled and converted through real-time detection and comparison of the charge states of the double energy storage systems, so that the reasonable application of the energy storage capacity is achieved, and the daily scheduling curve is finally corrected. For this, a proper daily economic scheduling objective function needs to be set, and a daily double energy storage control strategy is reasonably designed.
Disclosure of Invention
The invention aims to provide an energy management method which is controlled by a double energy storage system and ensures stable operation of load, improves the utilization rate of new energy and improves the stable operation capacity of a wind-storage nearby power supply system.
The technical scheme of the invention is as follows:
a wind power planning method combining double energy storage scheduling comprises the following steps:
and step 1, ensuring balance between load demand power and power generation power of the wind storage system in order to improve new energy utilization rate, independent operation capability and economic benefit of the wind storage system. The wind storage system consists of four modules, namely a wind power generation module, a double energy storage system module, a user load side module and a power grid module. And each module coordinates and schedules, and improves the economic benefit and the independent operation capability of the system on the basis of realizing the stable operation of the system.
And 2, stably operating the wind storage system under normal conditions, wherein the output power of the wind storage system can meet the operation of local load. And (3) making a proper day-ahead power plan by planning the energy scheduling behavior of the energy storage battery in the day-ahead scheduling stage.
And 3, in the intra-day scheduling stage, based on the deviation between a day-ahead plan and an intra-day actual condition, the charge and discharge control modes of different energy storage are flexibly switched by utilizing the real-time change of the charge states of the double energy storage, the start of the wind storage system network and/or the off-network is reasonably controlled, and the local load requirement is met.
Wherein step 2 comprises the following steps:
step 2-1: and obtaining wind power generation prediction data and load demand power prediction data of 24 hours in the future according to the wind power generation history data and the nearby power supply load demand power history data.
Step 2-2: based on the wind power generation and load power demand prediction data from 24 hours, constructing a day-ahead planning and scheduling model of the wind storage system by taking the minimum running cost and the minimum grid-connected exchange total power as optimization targets, and taking the double-energy-storage charge state as constraint conditions; the day-ahead planning scheduling model is specifically as follows:
wherein F is 1 、F 2 The total running cost of the wind storage system and the grid-connected exchange total power of the wind storage system are respectively, N=24 is the total sampling point number of 24 hours before the day, and f 1 、f 2 、f 3 The running cost of the wind driven generator, the application cost of the energy storage system and the deviation penalty in the nth sampling point time period are respectively calculated; wherein:
f 1 ＝k 1 P g
P g 、P load wind power generation power, local load demand power and grid exchange power respectively, Δp, where Δp=p load -(P g +P b )；P b E is the power throughput of the dual energy storage system BA The total throughput of energy within the usage period for energy storage; e (E) b Is the rated capacity of the stored energy. k (k) 1 、k 2 、k 3 、k 4 、k 5 The wind power generation unit power cost, the unit energy storage power operation cost, the unit energy storage capacity cost, the unit electricity lack power penalty and the unit wind abandon power penalty are respectively adopted; cg_sel and Cg_buy are respectively the electricity selling and purchasing prices of the power grid in the unit time period;
the constraints are as follows:
P g +P b +ΔP＝P load
0≤P g ≤P gr
|P g (n)-P g (n-1)|≤αP wm
|P b |≤P e_max
wherein α=15% is the wind power grid-connected power fluctuation coefficient. P (P) wm 、P e_min And P e_max The upper limit and the lower limit of the rated power of the wind power plant and the power of the energy storage system in unit time are respectively set. SOC (State of Charge) min 、SOC max 、SOC 0 And SOC (System on chip) 24 Respectively obtaining a minimum value and a maximum value of the state of charge of the energy storage system and initial and final states of charge of the energy storage system;
step 2-3: solving a day-ahead planning scheduling model through a golden search multi-objective optimization algorithm;
step 2-4: selecting a day-ahead plan with lower operation cost and less grid-connected exchange total power as a wind storage system according to the optimization result;
the step 3 also specifically comprises the following steps:
step 3-1: in the initial stage of daily scheduling, according to the daily updated wind power generation power and load demand power, obtaining the energy storage and energy storage day-ahead planned power delta P 1 Calculating the deviation delta error between the energy storage scheduling plans in the day before; the calculation formula is as follows:
ΔP 1 ＝P g +ΔP-P load
ΔP 2 ＝P w_r -P load_r
Δerror＝ΔP 2 -ΔP 1
wherein P is w_r 、P load_r The power is the actual power generated by the wind power in the day and the actual power required by the local load. ΔP 1 And delta P 2 Respectively storing planned power before the day and actual power shortage of the load in the day;
step 3-2: and in the daily scheduling energy storage operation control stage, the functions of the energy storage system A and the energy storage system B are preset. Wherein the energy storage system A acts on the solar wind power storage plan compensation, namely the deviation power delta P is absorbed 1 . The energy storage system B acts on the daily actual deviation compensation, namely, the deviation power delta error is consumed;
step 3-3: when the power generated by the wind power storage system is larger than the actual load demand, the ideal situation of the capacity limitation of the energy storage system is not considered. In actual daily rolling scheduling, the roles and the charge and discharge working states of the double-energy-storage power supply can be determined simply according to the charge states of the double-energy-storage power supply so as to maintain island operation of nearby power supply.
The different modes of operation for the ideal island operation are shown in the following table:
in the table +, -indicates the polarity of the supply-demand deviation.
Step 3-4: specific considerations in the actual scheduling of wind energy storage systemsLimitation of available capacity of energy storage charge and discharge combined with delta P 1 、ΔP 2 And the positive and negative conditions of delta error, and the normal operation meeting the local load is targeted. And adjusting the charge-discharge operation control mode of the double energy storage to realize balance between the generated energy of the wind storage system and the load electricity consumption demand. The calculation of the energy storage charge state and the charge and discharge allowance is as follows:
wherein ΔSOC a 、ΔSOC 1 、ΔSOC 2 And SOC (System on chip) B The maximum change quantity of the charge/discharge state of the energy storage A, the available discharge allowance and the available charge allowance after the energy storage A absorbs the plan deviation, and the charge states of the current stages of the energy storage system A and the energy storage system B are respectively obtained. Delta ch 、δ dh Respectively, the charge/discharge efficiency of the energy storage battery.
If delta error is larger than 0 in the scheduling process in the day, the fact that the energy storage power planned before the day of the wind storage system is compensated excessively at the moment is indicated, the energy storage system B is required to absorb and store excessive wind power in actual operation, namely the energy storage system B is switched to a charging control mode, and delta P is combined 1 And determining the charge and discharge control mode of the energy storage system A at the moment. Meanwhile, according to the initial charge state of the double energy storage and the calculated charge margin delta SOC a 、ΔSOC i (i=1, 2) determining if energy scheduling between energy storage a and energy storage B and wind storage system on/off network switching periods are required during the intra-day scheduling. If delta error is smaller than 0, the fact that the energy storage power planned before the day of the wind storage system is insufficient in compensation is indicated, and the energy storage system B is required to compensate the residual load required power in actual operation, namelyThe energy storage B is switched to a discharge control mode. Binding ΔP 1 And determining the charge and discharge control mode of the energy storage system A at the moment. Meanwhile, according to the initial charge state of the double energy storage and the calculated charge margin delta SOC a 、ΔSOC i (i=1, 2) determining the stored energy power scheduling margin available to the energy storage system a after the state of charge of the energy storage system B is out of range and the wind storage system on/off network switching period. If Δerror is equal to 0, the wind reservoir nearby power supply system operates according to the day-ahead schedule.
Therefore, the dual energy storage operating conditions after considering the actual energy storage available capacity are shown in the following table:
the power variation calculation formula shown in the table is as follows:
where Δerror_r is the current day-to-day bias power after updating with the energy storage a capacity limit. ΔP' 1 Is the deviation power which needs to be absorbed by the energy storage A to assist the energy storage B. SOC' i Is to correct the energy storage A/B charge state after charging and discharging power.
Step 3-4: in normal operation, the nearby power supply system cannot realize the consumption and the supplement of wind power generation power plans under ideal conditions due to the fact that energy storage is limited by self capacity and charge-discharge SOC, and the energy storage can be damaged due to frequent charge-discharge switching, so that the operation cost of the system is increased. Therefore, frequent power planning deviations from purchasing/selling multiple wind power to the grid are inevitably required. And the optimization model is required to optimize the dual-energy-storage different working modes to jointly schedule the charge and discharge power, so that the parallel/off-grid time and the parallel/off-grid exchange power can be flexibly controlled. Therefore, a daily wind storage scheduling multi-objective function model is established according to the calculated deviation delta error and the residual capacity of the double energy storage system, and power of different scheduling working conditions is corrected based on actual conditions, wherein the specific model is as follows.
minF 3 ＝|P grid |
minF 4 ＝f 4 +f 5 +f 6
Wherein F is 3 、F 4 The daily operation wind storage grid-connected exchange power and the daily scheduling cost are respectively f 4 、f 5 、f 6 The operation cost of the daily scheduling energy storage system B, the actual application cost of the daily scheduling energy storage system A and the electricity purchasing and selling cost of the power grid are respectively. The specific intra-day scheduling function is shown below.
The constraints are as follows:
P grid ＝P load_r -(Δerror+P Br +ΔP 1 +ΔP Ar )
SOC min ≤SOC i ≤SOC max (i＝A/B)
P e_min ≤Δerror+ΔP Br ≤P e_max
P e_min ≤ΔP 1 +ΔP Ar ≤P e_max
wherein P is grid 、ΔP Br And delta P Ar The power is respectively the grid-connected exchange power of the wind storage system, the scheduling power of the energy storage battery B in the day and the scheduling change power of the energy storage battery A in the day.
Step 3-5: and solving the intra-day scheduling model by using a golden search multi-objective optimization algorithm.
Step 3-6: the wind-storage nearby power supply system repeats the above steps.
The invention adopts the technical proposal and has the following beneficial effects:
(1) Based on wind power generation and local load power prediction data, double energy storage combined operation is controlled in different time periods before the day and during the day, the local load power balance is ensured, and meanwhile, the wind energy utilization rate and the independent operation capacity of a wind storage nearby power supply system are improved.
(2) In the daily scheduling stage, the respective charge states of the double energy storage batteries are considered, and the flexible grid connection capacity of the wind storage system is improved through reasonable use of respective charge and discharge allowance of the double energy storage systems.
Drawings
FIG. 1 is a general block diagram of a dual energy storage system;
FIG. 2 is a general flow chart of a dual energy storage system dispatch;
FIG. 3 is a flow chart of a golden search optimization algorithm;
FIG. 4 is a graph of day-ahead plan optimization results for a wind storage system;
FIG. 5 is a dual energy storage flow diagram of the wind storage system during a day;
FIG. 6 is a flow chart of one-machine double-storage-day scheduling control;
FIG. 7 is a graph of intra-day scheduling results in the case of an ideal isolated network;
FIG. 8 is a graph of intra-day scheduling results after grid-tie optimization;
fig. 9 is a graph of dual energy storage SOC variation during the day schedule phase.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in FIG. 1, the wind power system topology structure related by the invention comprises four modules, including a wind power generation operation module, a double energy storage system module, a user load side module and a power grid module. And each module coordinates and schedules, and improves economic benefits of the system and independent operation capability of the system on the basis of realizing nearby power supply of the load system.
As shown in fig. 2, the embodiment of the invention discloses a wind power plan combined with double energy storage scheduling, and the method for reliably supplying power to a load of a wind power storage system nearby comprises the following steps:
step 1, combining solar power prediction data of an air storage system and double-energy-storage SOC state information of a previous scheduling period. And establishing a multi-target day-ahead planning function model with minimum running cost and minimum grid-connected exchange total power, and taking the double-energy-storage charge state as a constraint condition. And solving by a multi-objective optimization algorithm to obtain a future plan of the wind storage system.
And 2, in an initial day scheduling stage, according to the daily updated wind power and load demand power. And calculating the actual power shortage of the energy storage power supply and the load, and according to the working objects set by the double energy storage systems, taking the charge state of the double energy storage and the available charge and discharge allowance into consideration to realize power compensation and role exchange between the double energy storage, and finally realizing dynamic balance between the generated energy of the wind storage system and the power consumption demand of the load to obtain the working states of the double energy storage under different working conditions under the condition of meeting the power supply of the load nearby.
And step 3, in the daily scheduling optimization stage, based on working states under different working conditions in the double-energy storage daily, the daily operation cost of the wind storage system and the total amount of grid-connected exchange power of the wind storage system are the lowest, and a daily scheduling function model of the wind storage system is built. And calculating by utilizing a multi-objective optimization function, optimizing the charge and discharge power under different working modes of double energy storage, and flexibly controlling the parallel/off-grid time and the parallel/off-grid exchange power of the wind storage system.
Referring to fig. 3 and 4, the specific steps of step 1 are as follows:
step 1-1: based on wind power generation and load power demand prediction data of 24 hours in the future, establishing a day-ahead planning and scheduling model of a wind storage system by taking the minimum running cost and the minimum power exchange power of a power grid as optimization targets, and taking a double-energy-storage charge state as a constraint condition; the day-ahead planning scheduling model is specifically as follows.
Wherein F is 1 、F 2 The total power of the running cost of the wind storage system and the grid-connected exchange of the wind storage system is respectively calculated, N=24 is the total sampling point number of 24 hours before the day, and f 1 、f 2 、f 3 And respectively punishing the running cost of the wind driven generator, the application cost of the energy storage system and the deviation in the nth sampling point time period. Wherein:
f 1 ＝k 1 P g (3)
P g 、P load Δp is wind power generation power, local load demand power and grid exchange power, respectively, where Δp=p load -(P g +P b )；P b E is throughput of dual energy storage system power BA The total throughput of energy within the usage period for energy storage; e (E) b Is the rated capacity of the stored energy. k (k) 1 、k 2 、k 3 、k 4 、k 5 The wind power generation unit power cost, the unit energy storage power operation cost and the unit energy storage cost are respectivelyEnergy capacity cost, unit electricity lack power penalty and unit wind abandon power penalty; cg_sel and Cg_buy are respectively the electricity selling and purchasing prices of the power grid in the unit time period.
The constraints are as follows:
P g +P b +ΔP＝P load (6)
0≤P g ≤P gr (7)
|P g (n)-P g (n-1)|≤αP wm (8)
|P b |≤P e_max (9)
wherein α=15% is the wind power grid-connected power fluctuation coefficient. P (P) wm 、P e_min And P e_max The upper limit and the lower limit of the rated power of the wind power plant and the power of the energy storage system in unit time are respectively set; SOC (State of Charge) min 、SOC max 、SOC 0 And SOC (System on chip) 24 The minimum value and the maximum value of the charge state of the energy storage system and the initial charge state and the final charge state of the energy storage system are respectively obtained.
Step 1-2: based on the golden search multi-objective optimization algorithm shown in fig. 3, the day-ahead power planning curve with lower running cost and lower grid-connected total power of the wind power storage system is finally obtained through solving. The calculation formula involved therein is as follows:
X i ＝b imin +rand(0,1)×(b imax -b imin )(i＝1,2,…,N) (11)
V ti (t+1)＝T i V ti (t)+C1cos(r 1 )(Obest i -x i (t))+C 2 sin(r 2 )(Ogbest i -x i (t)) (13)
X i (t+1)＝X i (t)+V ti (t) (14)
wherein N and maxIter are the population number and the maximum iteration number of the optimization algorithm, b imin 、b imax Respectively represent the lower limit and the upper limit of the energy storage power in the wind storage system, and rand (0, 1) represents a random number between 0 and 1 generated randomly, X i 、V ti Represents the energy storage power particle position and the particle speed corresponding to the ith population, T represents the attenuation conversion operator corresponding to the T-th iteration in the iteration process, and C 1 、C 2 Are all acceleration constants, r 1 、r 2 Is two mutually independent random numbers with the value range between 0 and 1. Obest i 、Ogbest i The method comprises the steps of calculating a historical optimal value and a global optimal value respectively, wherein the historical optimal value and the global optimal value respectively comprise two data of wind storage system cost and wind storage system grid-connected total power in a multi-objective optimization problem of the wind storage system.
The specific optimization process is that firstly, a golden search algorithm optimizing population is initialized according to a formula (11), namely, the planned power before the energy storage day of the wind storage system is initialized, and according to the multi-objective optimization function appointed in the step 1-1, wind power generation prediction data and load demand power prediction data are substituted, and corresponding fitness values, namely, the wind storage system cost and the grid-connected total power of the wind storage system are calculated; and then calculating for each iterative transformation operator through a formula (12), and respectively calculating the population particle speed and the position of the next iteration period after each iteration by combining the attenuation transformation operator T with formulas (13) and (14), namely updating the energy storage power once. Then, each optimized iteration is performed, the fitness value is compared with the fitness value of the previous iteration period, and the smaller fitness value is recorded into Obest i . And finally, carrying out maxIter iteration on the N populations to obtain an energy storage planning curve with the minimum fitness value, namely, a day-ahead plan of the wind storage system with the minimum running cost of the wind storage system and the minimum grid-connected exchange total power.
FIG. 4 is a plot of the final optimization result of step 1, i.e., the planned output curves of each system of the wind energy storage system before the day. From fig. 4, it can be seen that the final result of the day-ahead plan considers the influence of grid connection of the wind storage system, and realizes dynamic balance of the output and the load demand power of the wind storage system.
Referring to fig. 5, 6 and 7, step 2 is specifically as follows:
step 2-1: as shown in fig. 5, during the preparation phase of the daily schedule, the charge states of the double energy storage are considered and the planned power delta P of the energy storage date is calculated 1 Actual power shortage delta P with daily load 2 And the deviation delta error between the two. According to the polarity of the three and the energy storage double-energy storage state of charge, the initial states of charge of the energy storage A and the energy storage B and the available charge and discharge allowance in dispatching are determined, and a specific calculation formula is as follows:
ΔP 1 ＝P g +ΔP-P load (15)
ΔP 2 ＝P w_r -P load_r (16)
Δerror＝ΔP 2 -ΔP 1 (17)
step 2-2: FIG. 6 is a daily schedule flow chart of the wind energy storage system after determining the respective states of charge and charge-discharge margins of the dual energy storage systems. Binding ΔP 1 、ΔP 2 And the positive and negative conditions of delta error, and the normal operation meeting the local load is targeted. And adjusting the charge-discharge operation control mode of the double energy storage to realize balance between the generated energy of the wind storage system and the load electricity consumption demand. As shown in the ideal situation, if delta error is larger than 0 in the scheduling process in the day, the fact that the planned stored energy power is excessive before the wind energy storage system is daily is indicated, the energy storage system B is required to absorb and store excessive wind power in actual operation, namely the energy storage system B is switched to a charging control mode, and delta P is combined at the same time 1 And determining the charge and discharge control mode of the energy storage system A at the moment. Meanwhile, according to the initial charge state of the double energy storage and the calculated charge margin delta SOC a 、ΔSOC i (i=1, 2) determining if energy scheduling between energy storage a and energy storage B and wind storage system on/off network switching periods are required during the intra-day scheduling. If delta error is smaller than 0, the fact that the energy storage power planned before the day of the wind storage system is insufficient in compensation is indicated, and the energy storage system B is required to compensate the residual load required power in actual operation, namely the energy storage system B is switched to a discharge control mode. Binding ΔP 1 And determining the charge and discharge control mode of the energy storage system A at the moment. At the same time according to the initial charge of the double energy storageState and calculated charge margin Δsoc a 、ΔSOC i (i=1, 2) determining an energy storage power scheduling margin available to the energy storage system a and a wind storage system on/off-grid switching period after the state of charge of the energy storage system B crosses the boundary during the intra-day scheduling. If Δerror is equal to 0, the wind power system operates according to the day-ahead schedule.
Fig. 7 shows the final optimization result of step 2. In the image, the polarity of the load demand power is set to be negative, the polarity of the wind power generation power is set to be positive, and the polarity of the charging/discharging power of the energy storage system is set to be negative/positive respectively. The method has the advantages that the wind power and the load power are not completely accurately predicted in the future, the operation of the energy storage B is required to be controlled to eliminate the deviation caused by the problem, and because the energy storage has enough margin under ideal conditions, four curves of the actual load demand, the wind power output, the output of the energy storage system A and the output of the energy storage system B are 0, and the method can ensure that the load can be effectively powered in the normal operation process of the wind storage system.
Referring to fig. 8 and 9, step 3 specifically includes:
according to the calculated deviation delta error and the residual capacity of the double energy storage system, a daily wind storage scheduling multi-objective function model is established, a golden search multi-objective algorithm is adopted for solving, power of different scheduling working conditions is corrected based on actual conditions, and flexible and/off-grid control of the wind storage system is realized.
FIG. 8 is a graph showing the wind-stored schedule results after correction of the intra-day schedule function. Because the adopted wind power data are lower, more power exchange is needed with a power grid to ensure the power supply of a load, and meanwhile, the configured energy storage capacity is reduced to about 2.5MW due to the influence of the added power grid, so that compared with the configuration of ideal island operation, the configuration of the energy storage capacity is greatly reduced, and the power generation cost of a wind storage system is reduced. Fig. 9 shows that the change of the double stored SOC value is within a safe range throughout the scheduling period. Figures 8 and 9 prove that the method realizes safe operation of energy storage while guaranteeing reliable power supply of the load, reduces the operation cost of the wind storage system to a certain extent, and maintains the implementation of a load nearby power supply control strategy.
The description and examples of the present invention are intended to be readily understood and appreciated by those skilled in the art, and modifications may be made without departing from the principles of the present invention. Accordingly, modifications or improvements may be made without departing from the spirit of the invention and are also to be considered within the scope of the invention.
Claims (6)
1. The wind power storage planning method of the combined double energy storage scheduling is characterized by comprising the following steps of:
step 1: based on wind power generation prediction data and load demand prediction data, establishing a day-ahead planning scheduling model of the wind storage system, and solving by adopting a golden search multi-objective optimization algorithm to obtain a day-ahead plan of the wind storage system;
step 2: based on a day-ahead plan of the wind storage system, combining the wind power generation power and the load demand power which are updated in a rolling way in a day-ahead stage, and calculating to obtain the deviation between the day-ahead energy storage actual power and the power demand of the power generation plan; establishing a double-energy-storage daily combined optimization control strategy by combining the charge states of the double-energy-storage system at the daily stage; and a double-energy-storage intra-day scheduling function model is built to optimize wind storage systems and/or off-grid control under different operation conditions, so that the reliability of nearby load power supply is guaranteed to the greatest extent.
2. The method for planning wind power generation by combining dual energy storage scheduling according to claim 1, wherein the step 1 is specifically:
1.1: obtaining wind power generation prediction data and load demand power prediction data of 24 hours in the future according to wind power generation history data and nearby power supply load demand power history data;
1.2: based on wind power generation and load power demand prediction data of 24 hours in the future, constructing a day-ahead planning and scheduling model of a wind storage system by taking the minimum running cost and the minimum grid-connected exchange total power as optimization targets, and taking the double-energy-storage charge state as constraint conditions; the day-ahead planning scheduling model is specifically as follows:
wherein F is 1 、F 2 The total running cost of the wind storage system and the grid-connected exchange total power of the wind storage system are respectively, N=24 is the total sampling point number of 24 hours before the day, and f 1 (n)、f 2 (n)、f 3 (n) respectively punishing the running cost of the wind driven generator, the application cost of the energy storage system and the deviation in the nth sampling time period; wherein:
f 1 ＝k 1 P g
P g 、P load Δp is wind power generation power, local load demand power and grid-connected exchange power respectively, wherein Δp=p load -(P g +P b )；P b E is the power throughput of the dual energy storage system BA The total throughput of energy within the usage period for energy storage; e (E) b Is the rated capacity of energy storage; k (k) 1 、k 2 、k 3 、k 4 、k 5 The wind power generation unit power cost, the unit energy storage power operation cost, the unit energy storage capacity cost, the unit electricity lack power penalty and the unit wind abandon power penalty are respectively adopted; cg_sel and Cg_buy are respectively the electricity selling and purchasing prices of the power grid in the unit time period;
the constraints are as follows:
P g +P b +ΔP＝P load
0≤P g ≤P gr
|P g (n)-P g (n-1)|≤αP wm
|P b |≤P e_max
wherein alpha=15% is the fluctuation coefficient of wind power grid-connected power; p (P) wm 、P e_min And P e_max The upper limit and the lower limit of the rated power of the wind power plant and the power of the energy storage system in unit time are respectively set; SOC (State of Charge) min 、SOC max 、SOC 0 And SOC (System on chip) 24 Respectively obtaining a minimum value and a maximum value of the state of charge of the energy storage system and initial and final states of charge of the energy storage system;
1.3: solving a day-ahead planning scheduling model through a golden search multi-objective optimization algorithm;
1.4: and selecting the wind power storage system with lower running cost and less total power of grid-connected exchange as a day-ahead plan of the wind power storage system according to the optimization result.
3. The method for planning wind power generation by combining dual energy storage scheduling according to claim 2, wherein the step 2 is specifically:
2.1: in the initial stage of daily scheduling, a wind power generation power plan of the wind storage system obtained by combining and optimizing daily updated wind power generation power and load demand power is obtained, and the daily planned power delta P of the energy storage system is obtained 1 Calculating the power deviation delta error between the actual power of the stored energy and the power generation plan power in the day before; the calculation formula is as follows:
ΔP 1 ＝P g +ΔP-P load
ΔP 2 ＝P w_r -P load_r
Δerror＝ΔP 2 -ΔP 1
wherein P is w_r 、P load_r Respectively, the daily actual wind power generation power and the local load actual demand power; ΔP 1 And delta P 2 Respectively storing planned power before the day and actual power shortage of the load in the day;
2.2: the energy storage operation control stage is scheduled in the day, and functions of the energy storage system A and the energy storage system B are preset; wherein the energy storage system A acts on the solar wind power storage plan compensation, namely the deviation power delta P is absorbed 1 The method comprises the steps of carrying out a first treatment on the surface of the The energy storage system B acts on the daily actual deviation compensation, namely the power deviation delta error is absorbed;
2.3: and (3) establishing a daily wind storage scheduling multi-objective function model according to the calculated deviation delta error and the residual capacity of the double energy storage system, and correcting the power under different scheduling working conditions based on actual conditions, wherein the specific model is as follows.
min F 3 ＝|P grid |
min F 4 ＝f 4 +f 5 +f 6
Wherein F is 3 、F 4 The total power of the wind storage grid-connected exchange and the daily scheduling cost of the wind storage system are respectively calculated in the daily operation mode, f 4 、f 5 、f 6 The operation cost of the daily scheduling energy storage system B and the actual application cost of the daily scheduling energy storage system A are respectively the electricity purchasing and selling cost of the power grid; the specific intra-day scheduling function is as follows:
the constraints are as follows:
P grid ＝P load_r -(Δerror+P Br +ΔP 1 +ΔP Ar )
SOC min ≤SOC i ≤SOC max (i＝A/B)
P e_min ≤Δerror+ΔP Br ≤P e_max
P e_min ≤ΔP 1 +ΔP Ar ≤P e_max
wherein P is grid 、ΔP Br And delta P Ar The grid-connected power of the wind storage system, the scheduling power of the energy storage battery B in the day and the scheduling change power of the energy storage battery A in the day are respectively;
2.4: solving a daily plan scheduling model by using a golden search multi-objective optimization algorithm;
2.5: the wind-storage nearby power supply system repeats the above steps.
4. A wind power planning method in combination with dual energy storage scheduling according to claim 3, wherein: when the power generation power of the wind storage system is larger than the actual load demand and the ideal condition of capacity limitation of the energy storage system is not considered, in the actual daily rolling scheduling, determining roles of the wind storage system in the scheduling process according to the respective charge states of the double energy storage, and charging and discharging working states so as to maintain reliable power supply of the nearby island;
the different modes of operation for the ideal island operation are shown in the following table:
in the table +, -indicates the polarity of the supply-demand deviation.
5. A wind power planning method in combination with dual energy storage scheduling according to claim 3, wherein: in the actual scheduling of the wind storage system, the limitation of the available capacity of specific energy storage charge and discharge is combined, and delta P 1 、ΔP 2 And (3) the positive and negative conditions of delta error, aiming at the normal operation of meeting local load, adjusting the charge-discharge operation control mode of double energy storage to realize the balance between the generated energy of the wind storage system and the electricity consumption demand of the load, wherein the calculation of the energy storage charge state and the charge-discharge allowance is as follows:
wherein ΔSOC a 、ΔSOC 1 、ΔSOC 2 And SOC (System on chip) B The maximum change quantity of the charge/discharge state of the energy storage A, the available discharge allowance and the available charge allowance after the energy storage A absorbs the scheduling power plan, and the charge states of the current stages of the energy storage system A and the energy storage system B are respectively; delta ch 、δ dh Respectively, the charge/discharge efficiency of the energy storage battery;
if delta error is larger than 0 in the scheduling process in the day, the fact that the energy storage power planned before the day of the wind storage system is compensated excessively at the moment is indicated, the energy storage system B is required to absorb and store excessive wind power in actual operation, namely the energy storage system B is switched to a charging control mode, and delta P is combined at the same time 1 Determining the charge and discharge control mode of the energy storage system A at the moment; meanwhile, according to the initial charge state of the double energy storage and the calculated charge margin delta SOC a 、ΔSOC i Determining whether energy scheduling between the energy storage A and the energy storage B and a wind storage system and/or off-grid switching period are needed in the daily scheduling process, wherein i=1 and 2; if delta error is smaller than 0, the fact that the energy storage power planned before the day of the wind storage system is insufficient in compensation is indicated, and the energy storage system B is required to compensate the residual load required power in actual operation, namely the energy storage system B is switched to a discharge control mode; binding ΔP 1 And determining the charge and discharge control mode of the energy storage system A at the moment. Meanwhile, according to the initial charge state of the double energy storage and the calculated charge margin delta SOC a 、ΔSOC i Determining available power scheduling allowance of the energy storage system A after the state of charge of the energy storage system B is out of range as a switching period of the energy storage system and/or off-grid; if delta error is equal to 0, the wind power storage nearby power supply system operates according to a day-ahead plan;
the double energy storage operation conditions after the actual energy storage available capacity is considered are shown in the following table:
the power variation calculation formula shown in the table is as follows:
wherein delta error_r is the day-to-day deviation power after being updated by the limit of the capacity of the energy storage A; ΔP' 1 The deviation power which is consumed by the energy storage A to assist the energy storage B is needed; SOC' i Is to correct the energy storage A/B charge state after charging and discharging power.
6. A wind power planning method in combination with dual energy storage scheduling according to any one of claims 1-5, wherein: the method for solving the day-ahead planning scheduling model through the golden search multi-objective optimization algorithm comprises the following specific steps of:
3.1: setting the population number N of the optimization algorithm and the maximum iteration number maxIter, and determining the range of the energy storage power, namely determining b imin And b imax Initializing a population; the initialization calculation formula is as follows:
X i ＝b i min +rand(0,1)×(b i max -b i min )i＝1,2,…,N
3.2: initializing a particle position as energy storage power of the wind storage system, substituting the energy storage power, wind power prediction data and load demand prediction data into a fitness function together, and calculating to obtain a fitness value, wherein the fitness value comprises two data of running cost of the wind storage system and total power of wind storage grid connection;
3.3: calculating a transfer operator T, wherein the calculation formula is as follows:
3.4: combining the transfer operator T with the position and the speed of the particles, and calculating the updated position and speed of the particles; comparing the fitness value with the matrix Obest, and storing the smaller fitness value into the matrix Obest i The method comprises the steps of carrying out a first treatment on the surface of the The calculation formula is as follows:
X i (t+1)＝X i (t)+V ti (t)
V ti (t+1)＝T i V ti (t)+C 1 cos(r 1 )(Obest i -x i (t))+C 2 sin(r 2 )(Ogbest i -x i (t))
wherein X is i 、V ti Represents the position and the speed of the energy storage power particles corresponding to the ith population, C 1 、C 2 Are all acceleration constants, r 1 、r 2 Is two mutually independent random numbers with the value range between 0 and 1; obest i 、Ogbest i Respectively calculating a historical optimal value and a global optimal value;
3.5: and (3) obtaining an energy storage power curve with the minimum fitness value after carrying out maxIter iteration on the N populations, namely obtaining wind storage system power data with the minimum wind storage system cost and minimum grid-connected exchange power.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013160427A (en) * | 2012-02-03 | 2013-08-19 | Mitsubishi Electric Corp | Dual refrigeration system |
| JP2014128184A (en) * | 2012-12-27 | 2014-07-07 | Mitsubishi Heavy Ind Ltd | Control device and control method for wind generator system, and wind generator system |
| CN113919620A (en) * | 2021-12-06 | 2022-01-11 | 南通大学 | Day-ahead energy scheduling algorithm for wind-solar storage micro-grid |
| CN114611957A (en) * | 2022-03-17 | 2022-06-10 | 南通大学 | Secondary correction energy storage energy management algorithm for supply and demand prediction deviation |
-
2023
- 2023-10-12 CN CN202311324413.0A patent/CN117394423A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013160427A (en) * | 2012-02-03 | 2013-08-19 | Mitsubishi Electric Corp | Dual refrigeration system |
| JP2014128184A (en) * | 2012-12-27 | 2014-07-07 | Mitsubishi Heavy Ind Ltd | Control device and control method for wind generator system, and wind generator system |
| CN113919620A (en) * | 2021-12-06 | 2022-01-11 | 南通大学 | Day-ahead energy scheduling algorithm for wind-solar storage micro-grid |
| CN114611957A (en) * | 2022-03-17 | 2022-06-10 | 南通大学 | Secondary correction energy storage energy management algorithm for supply and demand prediction deviation |
Non-Patent Citations (3)
| Title |
|---|
| MOHAMMAD NOROOZI: "Golden Search Optimization Algorithm", IEEE ACCESS, 28 March 2022 (2022-03-28), pages 37518 - 37519 * |
| 禹威威: "考虑电动汽车充电和需求侧响应的光伏微电网多目标优化调度", 电力系统及其自动化学报, 15 January 2018 (2018-01-15), pages 90 * |
| 陈科彬: "基于双蓄电池组的微电网两阶段调度优化模型及控制策略", 现代电力, 25 December 2017 (2017-12-25), pages 47 - 49 * |
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