CN111030149A - Micro-grid power supply method based on composite energy storage device and electronic equipment - Google Patents
Micro-grid power supply method based on composite energy storage device and electronic equipment Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a micro-grid power supply method and electronic equipment based on a composite energy storage device, wherein the method comprises the following steps: establishing a multi-objective optimization model by taking the cost of the composite energy storage device, the matching of supply and demand power and the fluctuation suppression of power output as objective functions and taking the energy storage capacity and the charge and discharge power as optimization variables; calculating the charge and discharge power of the first energy storage device and the second energy storage device at each moment according to the generated power and the load power of the microgrid within a set time period; inputting the charging and discharging power into the multi-objective optimization model to obtain a single-variable optimization model of the energy storage capacity of the first energy storage device and the second energy storage device; solving the optimal solution of the univariate optimization model by adopting an optimization algorithm to obtain the energy storage capacity of the first and second energy storage devices in a set time period; the invention can ensure that the power supply power change in the micro-grid is minimum after the composite energy storage compensation, and the difference between the instantaneous deviation and the average deviation of the supply power and the demand power is minimum, so as to maintain the stability of the voltage and the frequency of the system and provide stable voltage and power for the load.
Description
Technical Field
The invention belongs to the technical field of micro-grid power supply, and particularly relates to a micro-grid power supply method based on a composite energy storage device and oriented to new energy power generation.
Background
At present, the demand of society for electric energy is increasing day by day, so that renewable energy power generation develops rapidly, and becomes an important component of a microgrid. But the renewable energy power generation has great fluctuation and uncertainty, which will cause the imbalance of supply and demand of the system and bring new challenges. In order to reduce the adverse effect of uncertainty of renewable energy output on the operation of a microgrid and promote the large-scale application of intermittent energy such as wind power generation, energy storage equipment is usually arranged to assist the operation of a renewable energy unit. Therefore, the research on the electric energy storage technology for improving the renewable energy consumption capability of the micro-grid system has very important application value.
The energy storage devices are mainly classified into energy storage devices mainly based on storage batteries and lithium batteries and power storage devices mainly based on super-capacitor type and superconducting magnetic energy storage. The energy type energy storage device has the characteristics of high energy density and high capacity, and the power type energy storage device has the advantages of high power density and high response speed. Therefore, at present, an energy type energy storage device and a power type energy storage device are generally combined to realize a composite energy storage mode, the output of the energy storage device is reasonably distributed, the cost of the energy storage device can be reduced, the composite energy storage system can meet the requirements of long-term large capacity and short-term large power, and the voltage, the frequency, the active power and the reactive power of a power grid can be easily adjusted; for a composite energy storage system, the larger the capacity is and the faster the response speed is, the more prominent the stabilizing effect of the system fluctuation is, but the economic problem is inevitably caused; therefore, the capacity configuration of each of the energy storage device and the power storage device is concerned.
The capacity of the traditional composite energy storage device is optimized, or the capacity of the energy storage device and the capacity of the power storage device are fixed, or the energy storage capacity of the energy storage device and the capacity of the power storage device are optimized only by taking the full life cycle cost of the composite energy storage device as an optimization target. Besides the economic problem, the method has a remarkable influence on the operation stability of the microgrid; if the configuration of the energy storage capacity is improper, the supply and demand power of the microgrid is unbalanced, the output voltage and frequency of the system are unstable, and stable voltage cannot be provided for the load.
Therefore, it is necessary to improve the optimal configuration of the existing energy storage system to ensure the safe, efficient and stable operation of the system.
Disclosure of Invention
Aiming at least one defect or improvement requirement in the prior art, the invention provides a micro-grid power supply method based on a composite energy storage device and electronic equipment, wherein the charging and discharging power of a first energy storage device and a second energy storage device at each moment is calculated according to the generating power and the load power of the micro-grid at each moment in a set time period, and the energy storage capacity of the first energy storage device and the energy storage capacity of the second energy storage device in the set time period are calculated in real time according to the charging and discharging power; ensuring that the power supply power change in the micro-grid is minimum after the composite energy storage compensation and the difference between the instantaneous deviation and the average deviation of the supply and demand power is minimum, so that the power of the micro-grid is balanced, the voltage and the frequency of the system are stable, and stable voltage and power are provided for a load; in addition, the requirements of long-term large capacity and short-term large power can be met simultaneously, and the economical efficiency of the energy storage device configuration is improved.
To achieve the above object, according to one aspect of the present invention, there is provided a method for supplying power to a microgrid based on a composite energy storage device, the composite energy storage device including a first energy storage device of a power type and a second energy storage device of an energy type, the method including the steps of:
s1: creating a multi-objective optimization model of the composite energy storage device, wherein the multi-objective optimization model comprises a target function which is the minimization of the cost of the composite energy storage device, the variation of power supply power in the micro-grid after composite energy storage compensation and the difference between the instantaneous deviation and the average deviation of the supply and demand power of the micro-grid, and takes the energy storage capacity and the charging and discharging power of the first and second energy storage devices as optimization variables;
s2: collecting the generated power and the load power of the microgrid at each moment in a set time period, and sequentially calculating the charge and discharge power of the first energy storage device and the second energy storage device at each moment according to a preset output priority, the generated power and the load power;
s3: inputting the charging and discharging power into the multi-objective optimization model to obtain a single-variable optimization model of the energy storage capacity of the first energy storage device and the energy storage capacity of the second energy storage device;
s4: respectively taking an upper limit threshold and a lower limit threshold of the energy storage capacity of the first energy storage device and the second energy storage device as constraint conditions, and solving the optimal solution of the univariate optimization model by adopting an optimization algorithm to respectively obtain the energy storage capacity of the first energy storage device and the energy storage capacity of the second energy storage device in a set time period;
s5: and controlling the first energy storage device and the second energy storage device to supply power to the load in the microgrid according to the energy storage capacity of each energy storage device in a set time period.
Preferably, in the microgrid power supply method, the univariate optimization model is a weighted sum of each objective function.
Preferably, in the microgrid power supply method, the objective function corresponding to the cost of the composite energy storage device is specifically as follows:
in the formula: f. of1Represents the cost of the composite energy storage device; n represents the number of the composite energy storage devices; w1nAnd W2nη capacity of the first and second energy storage devices to be configured in the microgrid1nAnd η2nThe efficiency of energy conversion of the first energy storage device and the second energy storage device is respectively; c. Ce1A price per unit capacity for the first energy storage device; c. Ce2Is the price per unit capacity of the second energy storage device; c. Cm1And cm2Maintenance costs for stored energy; p1nAnd P2nIs the power of the first and second energy storage devices, which is the accumulated power, which is the arithmetic sum of the energy storage devices exchanging power over a long period of time.
Preferably, in the microgrid power supply method, the objective function corresponding to the power supply power variation in the microgrid after the composite energy storage compensation is specifically:
in the formula: f. of2Represents a set time period t11,t12]The sum of the power supply power variation in the micro-grid after the composite energy storage compensation at any moment; i represents each time sequence number in a set time period; p1,iAnd P2,iThe charging and discharging power of the first energy storage device and the second energy storage device in the period i are respectively; pg,iThe actual generated power of the power supply in the system without composite energy storage compensation is obtained; pG,iThe actual generated power of the power supply in the system after the composite energy storage compensation.
Preferably, in the microgrid power supply method, the objective function corresponding to the difference between the instantaneous deviation and the average deviation of the microgrid power supply and demand is specifically:
in the formula: f. of3Represents a set time period t11,t12]The sum of the difference values between the instantaneous deviation and the average deviation of the supply and demand power of the microgrid at any time in the microgrid; pL,iIs the load power; pavgAnd the average value of the supply and demand difference after energy storage compensation.
Preferably, in the microgrid power supply method, when the priority of output of the first energy storage device is higher than that of the second energy storage device, the process of sequentially calculating the charge and discharge power of the first and second energy storage devices at each time specifically includes:
calculating the system shortage power according to the generated power and the load power of the microgrid at each moment in a set time period;
the method comprises the steps of obtaining the charge state of a first energy storage device, and calculating the charge and discharge power of the first energy storage device according to the charge state when the charge state meets a preset first constraint range;
when the charging and discharging power meets a preset second constraint range, setting the charging and discharging power as the charging and discharging power of the first energy storage device;
when the charging and discharging power of the first energy storage device is smaller than the system shortage power, the charging and discharging power of the second energy storage device is obtained, and when the charging and discharging power of the first energy storage device meets a preset third constraint range, the charging and discharging power of the second energy storage device is calculated according to the charging and discharging state;
and when the charge and discharge power meets a preset fourth constraint range, setting the charge and discharge power as the charge and discharge power of the second energy storage device.
Preferably, in the microgrid power supply method, when the state of charge of the first energy storage device exceeds a preset first constraint range, the charging and discharging power of the first energy storage device is calculated according to an upper threshold or a lower threshold of the first constraint range;
and when the charging and discharging power of the first energy storage device overflows a preset second constraint range, setting the upper limit threshold or the lower limit threshold of the second constraint range as the charging and discharging power of the first energy storage device.
Preferably, in the microgrid power supply method, when the state of charge of the second energy storage device exceeds a preset third constraint range, the charging and discharging power of the second energy storage device is calculated according to an upper threshold or a lower threshold of the third constraint range;
and when the charging and discharging power of the second energy storage device overflows a preset fourth constraint range, setting the upper limit threshold or the lower limit threshold of the fourth constraint range as the charging and discharging power of the second energy storage device.
Preferably, in the microgrid power supply method, the optimization algorithm is a genetic algorithm.
According to another aspect of the present invention, there is also provided an electronic device comprising at least one processing unit, and at least one memory unit, wherein the memory unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the methods described above.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the invention provides a micro-grid power supply method and electronic equipment based on a composite energy storage device.A multi-objective optimization model of the composite energy storage device is firstly established, the multi-objective optimization model takes the cost of the composite energy storage device, the matching of supply and demand power and the fluctuation suppression of power output as optimization targets, and takes the energy storage capacity and the charging and discharging power of a first energy storage device and a second energy storage device as optimization variables; calculating the charging and discharging power of the first energy storage device and the second energy storage device at each moment according to the generated power and the load power of the microgrid at each moment in a set time period, and calculating the energy storage capacity of each of the first energy storage device and the second energy storage device in the set time period in real time according to the charging and discharging power; the power compensation is carried out on the load in the micro-grid, the power supply power change in the micro-grid after the composite energy storage compensation is ensured to be minimum, and the difference between the instantaneous deviation and the average deviation of the supply and demand power is minimum, so that the power of the micro-grid is balanced, the stability of the voltage and the frequency of the system is maintained, and the stable voltage is provided for the load; in addition, the requirements of long-term large capacity and short-term large power can be met simultaneously, and the economical efficiency of the energy storage device configuration is improved.
Drawings
Fig. 1 is a flowchart of a microgrid power supply method based on a composite energy storage device according to an embodiment of the present invention;
fig. 2 is a topology structure diagram of a three-machine nine-bus system provided in the embodiment of the present invention; in the figure, 1, 4, 6-bus; 2. 3, 5-load; 7-a wind power generator; 8. 9-a fuel generator set;
FIG. 3 is a diagram of a solution idea of a genetic algorithm provided by an embodiment of the present invention;
FIG. 4 is a basic flow diagram of a genetic algorithm provided by an embodiment of the present invention;
FIG. 5 is a diagram illustrating an optimization result based on a genetic algorithm according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment provides a micro-grid power supply method based on a composite energy storage device, and as shown in fig. 1, the method mainly comprises the following steps:
s1: creating a multi-objective optimization model of the composite energy storage device, wherein the multi-objective optimization model comprises the steps of taking the cost of the composite energy storage device, the matching of supply and demand power and the fluctuation suppression of power output as optimization targets, and taking the energy storage capacity and the charging and discharging power of the first and second energy storage devices as optimization variables;
the present embodiment is described by taking a micro-grid system including wind power generation as an example, where the micro-grid system includes a fuel generator, a wind power generator, a load, and a composite energy storage device. In the embodiment, the energy type storage battery and the power type SMES are respectively selected as the composite energy storage device equipped in the microgrid, and the advantages of the two are combined to reasonably configure the system to improve the capability of receiving renewable energy.
In the embodiment, a three-machine nine-bus system is selected to perform optimization example analysis on the composite energy storage capacity and is used as a structure of a typical micro-grid, the three-machine nine-bus system is simple in topology, and power flow calculation of power generation containing renewable energy sources is easy to perform. The topology of a three machine nine bus system is shown in figure 2. Wherein, the No. 8 and No. 9 nodes are provided with fuel generator sets, and the No. 7 node is provided with a wind driven generator; 2. the No. 3 and No. 5 nodes are load nodes; the line parameters adopt the typical parameters of a three-machine nine-bus system. In this example, one battery and one SMES are provided, respectively.
The electrical energy stored by the energy storage device at a certain moment can be represented by equation (1):
in the formula: c (t) is the total electric quantity of the energy storage equipment at the moment t; c (t-1) is the total electric energy of the energy storage equipment at the time of t-1; Δ t is the stored time interval; pch(t) and Pdis(t) charging and discharging power of the energy storage device at time t, respectively ηchAnd ηdisRespectively the charge and discharge efficiency of the energy storage device.
As can be seen from the above formula, the electric energy stored in the energy storage device at a certain time needs to be represented by the charge-discharge power at that time, so in this embodiment, the generated power and the load power of the system are set as known information, and the capacity of the composite energy storage device is optimally configured, that is, the energy storage capacity is used as an optimization variable. Based on the uncertainty of wind power generation in the microgrid, in order to improve the power supply stability of the microgrid, the embodiment takes the supply and demand power matching and the fluctuation suppression of the power output as technical targets, and simultaneously takes the running cost of the composite energy storage device as an economic target, performs multi-objective optimization on the capacity of the composite energy storage device, and establishes a target optimization model for the three optimization targets respectively.
Step S11: establishing a target optimization model of composite energy storage configuration operation cost
The configuration of the storage battery and the SMES ensures that the cost is minimum under the condition of meeting the power compensation requirement of a power grid; accordingly, an objective function of the cost can be established, which includes the initial configuration cost and the operation cost, as shown in equation (2):
in the formula: f. of1Represents the cost of the composite energy storage device; n represents the number of energy storage devices; wSMESnAnd Wbatnη capacities of SMES and accumulator, respectively, in the network1nAnd η2nEfficiency of SMES and battery energy conversion, respectively; c. Ce1Is SMES price per unit capacity; c. Ce2Is the unit capacity price of the storage battery; c. Cm1And cm2Maintenance costs for stored energy; pSMESnAnd PbatnThe power of the SMES and the storage battery is accumulated power, and is the arithmetic sum of the long-term exchange power of the energy storage device.
In this embodiment, n is 1, ce1Is 100 yuan, ce2Is 10-membered, cm1Is 5-membered, cm2Is 10 yuan.
Step S12: establishing a target optimization model corresponding to fluctuation suppression of power output
The scheduling period in the micro-grid can be one day, one month, one quarter, one year and the like; the total scheduling period is divided into a plurality of time intervals, and the power distribution of the power grid is ensured to be basically unchanged in each time interval. In the invention, the SMES and the storage battery are considered to be in a stable state, and transient processes are not counted.
Setting composite energy storage device at11,t12]Participating in system power output fluctuation suppression in a time period, the embodiment establishes an optimization objective function with the minimum power supply power change in the microgrid after the composite energy storage compensation, specifically as shown in formula (3):
in the formula: f. of2Represents a set time period t11,t12]The sum of the power supply power variation in the micro-grid after the composite energy storage compensation at any moment; i represents [ t ]11,t12]The ith time within the time period; pbat,iAnd PSMES,iThe charging and discharging power of the storage battery and the SMES in the period i are respectively; pg,iThe actual power generation power of the power supply in the system without composite energy storage compensation is the sum of the generated energy of the fan and the diesel generator; pG,iThe actual generated power of the power supply in the system after the composite energy storage compensation.
Step S13: establishing a target optimization model for matching supply and demand power of a microgrid
The power of the grid must be guaranteed at any timeBalancing to maintain the stability of system voltage and frequency; setting the regulation time as [ t21，t22]Establishing an optimization objective function by using the principle that the difference between the instantaneous deviation and the average deviation of the supply and demand power is minimum, wherein the optimization objective function is specifically shown as a formula (4):
in the formula: f. of3Represents a set time period t11,t12]The sum of the difference values between the instantaneous deviation and the average deviation of the supply and demand power of the microgrid at any time in the microgrid; pL,iIs the load power; pavgAnd the average value of the supply and demand difference after energy storage compensation.
In consideration of the above economic and technical goals, the multi-objective optimization model of the capacity of the composite energy storage device of the microgrid is shown as formula (5):
the model takes the cost of the composite energy storage device, the matching of supply and demand power and the fluctuation suppression of power output as optimization targets, and takes the energy storage capacity and the charge and discharge power of the storage battery and the SMES as dual-target optimization variables.
S2: collecting the generated power and the load power of the microgrid at each moment in a set time period, and sequentially calculating the charge and discharge power of the first energy storage device and the second energy storage device at each moment according to a preset output priority, the generated power and the load power;
based on the advantages of large power density and high response speed of a power type energy storage device, SMES (state of charge) should respond to power imbalance in a microgrid, and the power required by an operation strategy of the SMES, namely real exchange power, is determined through the constraints of an upper limit and a lower limit of a state of charge (SOC) and the limitation of energy storage exchange power; and if the SMES operation strategy requires that the power cannot supplement the system supply and demand power shortage in the period through calculation, responding the rest power shortage by the storage battery, otherwise, not participating in power exchange by the storage battery. Therefore, the determination of SMES in the present embodiment takes priority over the battery.
The simulation time length is set to be 10h, the interval is set to be 1h, all power in each period is unchanged, and load demand power data and power generation data in the microgrid system are shown in a table 1.
TABLE 1 load demand power data and Power Generation data
Generating power P at a time in known systemsdgAnd load power PLIn the case of (2), assuming that the system power shortage is completely supplemented by SMES, the state of charge of the SMES at that time is first calculated, as shown in equation (6):
SOC(t)＝SOC(t-1)-P1(t)/w1(6)
where SOC (t) is the state of charge of SMES at time t, P1(t) is the charging and discharging power (positive for discharging and negative for charging) of SMES at time t, w1Is the capacity of SMES. In this example, the initial state of charge of SMES is 0.85.
And (3) judging whether the state of charge of the SMES is within the constraint range at the moment, wherein the formula (7) is as follows:
SOCmin≤SOC(t)≤SOCmax(7)
in this example, the maximum state of charge SOC of SMESmax0.9, minimum state of charge SOCminIs 0.2.
If the state of charge of the SMES at the moment meets the constraint condition, directly judging whether the charging and discharging power of the SMES at the current moment meets the constraint of the energy storage exchange power; if the state of charge of the SMES overflows the constraint of the SMES, the upper limit or the lower limit of the state of charge of the SMES is taken to recalculate the charging and discharging power of the SMES, and whether the constraint of the energy storage exchange power is met is judged, as shown in a formula (8):
PSMESmin≤PSMES(t)≤PSMESmax(8)
in the formula: pSMESAnd (t) is the exchange power between the SMES and the system at the time t. In this example, PSMESmaxIs 40 kW.
If the constraint is met, the real exchange power of the SMES at the moment is directly obtained, otherwise, the real exchange power of the SMES at the moment is taken as the upper limit or the lower limit, namely the charge and discharge power.
When the charging and discharging power of the SMES at the moment can supplement the shortage of the supply and demand power of the system, the storage battery does not participate in the power exchange at the moment, otherwise, the storage battery needs to bear the rest charging and discharging work. Assuming that the rest of the power shortage is completely supplemented by the storage battery, calculating the state of charge of the storage battery at the moment, as shown in the formula (9):
SOC(t)＝SOC(t-1)-P2(t)/w2(9)
where SOC (t) is the state of charge of the battery at time t, P2(t) represents the charging and discharging power (positive during discharging and negative during charging) of the storage battery at time t, and w represents2Is the capacity of the battery. In this example, the initial state of charge of the battery is 0.6.
Similarly, it is determined whether the state of charge of the battery at this time is within its constraint, as in equation (7). In this example, the maximum state of charge SOC of SMESmax0.8, minimum state of charge SOCminIs 0.2. If the charge state at the moment meets the constraint of the charge state, directly judging whether the charge and discharge power at the moment meets the constraint of the energy storage exchange power; if the state of charge overflows the constraint, the upper limit or the lower limit is taken to recalculate the charging and discharging power of the storage battery, and whether the constraint of the energy storage exchange power is met is judged, as shown in the formula (10):
Pbatmin≤Pbat(t)≤Pbatmax(10)
in the formula: pbatAnd (t) is the discharge power of the storage battery at time t. In this example, PbatmaxIs 20 kW.
As with SMES, the actual exchange power, i.e., the charge/discharge power of the battery at that time can be obtained from this.
S3: inputting the charging and discharging power of the first energy storage device and the second energy storage device into the multi-objective optimization model to obtain a single-variable optimization model of the energy storage capacity of the first energy storage device and the energy storage capacity of the second energy storage device;
in step S1, a multi-objective optimization model of an economic objective and a technical objective of the capacity of the composite energy storage device is respectively established, and variables in the objective include the capacity and the charge-discharge power of the composite energy storage device; and S2, calculating the charging and discharging power of the SMES and the storage battery at each moment according to the generated power and the load power of the microgrid within a set time period, and inputting the charging and discharging power into the multi-objective optimization model to obtain the single-variable optimization model of the energy storage capacity.
In order to jointly optimize the three objective functions, the embodiment determines the weights of the objective functions by a specialist method and then aggregates the weights into a single objective for optimization; the process of determining the weight by the expert method is as follows:
three experts are selected to score, each expert scores the expert according to the familiarity degree of the target, the scores are 9, 7, 5, 3 and 1 in sequence from familiarity to unfamiliarity, and the experts score according to the importance degree of the sub-targets considered by the experts, and the scores are 0-1. The total score of the expert on the cognition level of the main question can be obtained by multiplying and adding the two scores of each expert on each sub-target, and the expert weight of the expert can be obtained by dividing the total score of the expert by the sum of the total scores of all the experts. Combining the expert weight and the score of the expert on a certain target function, the weight of each index can be obtained.
The specific evaluation results are shown in table 2:
TABLE 2 expert method for determining weights for objective functions
From this, the objective function f1Weight of lambda10.6792, objective function f2Weight of lambda20.1336, objective function f3Weight of lambda3Is 0.1872.
The univariate optimization model after polymerization is shown as the formula (11):
min f＝λ1f1+λ2f2+λ3f3(11)
s4: respectively taking an upper limit threshold and a lower limit threshold of the energy storage capacity of the first energy storage device and the second energy storage device as constraint conditions, and solving the optimal solution of the univariate optimization model by adopting an optimization algorithm to respectively obtain the energy storage capacity of the first energy storage device and the energy storage capacity of the second energy storage device in a set time period;
in this embodiment, upper and lower limits of the energy storage capacity of the SMES and the storage battery are set, respectively, as shown in equation (12):
in the formula: w is a1Is the energy storage capacity of SMES, w2The energy storage capacity of the storage battery.
In this example, w1minIs 140 kW.h.w2minIs 100 kW.h.
In the embodiment, the capacity configuration of the composite energy storage device is optimized by adopting a genetic algorithm; the genetic algorithm is a highly parallel, random search and self-adaptive optimization method for simulating natural selection and natural genetic mechanism in the biology world; the solution thought of the genetic algorithm is shown in fig. 3, namely, a target function and constraint conditions are determined firstly, a mathematical model of the target function and the constraint conditions is established to compile a main program, then the main program is quoted by utilizing a genetic algorithm toolbox, and variable constraints are set, so that an optimization result can be obtained, the robustness is good, the global optimal solution is easy to find, and the nonlinear problem can be solved; the basic flow of the genetic algorithm is shown in fig. 4.
In this embodiment, the optimal solution of the univariate optimization model is obtained by using a genetic algorithm, and the optimization result is shown in fig. 5 and table 3.
TABLE 3 energy storage capacity optimization results
As can be seen from fig. 5, as the number of sub-generations increases, the objective function value tends to be smooth and gradually converges. When the generation was inherited to 109, the fluctuation of the objective function value converged to the required range 10-6The capacity value at the moment is in accordance with the requirement, the final optimization result can be obtained, namely the energy storage capacity of the SMES is 152.914kW & h, and the electric power storageThe energy storage capacity of the pool is 144.16 kW.h, and the final optimal target function value is 24817.3.
S5: and controlling the first energy storage device and the second energy storage device to supply power to the load in the microgrid according to the energy storage capacity of each energy storage device in a set time period.
Supplying power to a load in the microgrid according to the SMES obtained in the step S4 and the energy storage capacity of the storage battery, and performing power compensation; ensuring that the power supply power change in the micro-grid is minimum after the composite energy storage compensation and the difference between the instantaneous deviation and the average deviation of the supply and demand power is minimum, so that the power of the micro-grid is balanced, the stability of the system voltage and frequency is maintained, and stable voltage is provided for a load; in addition, the requirements of long-term large capacity and short-term large power can be met simultaneously, and the economical efficiency of the energy storage device configuration is improved.
The present embodiment further provides an electronic device, which includes at least one processor and at least one memory, where the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the microgrid power supply method, where the specific steps refer to the above descriptions, and are not described herein again; in this embodiment, the types of the processor and the memory are not particularly limited, for example: the processor may be a microprocessor, digital information processor, on-chip programmable logic system, or the like; the memory may be volatile memory, non-volatile memory, a combination thereof, or the like.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing terminal, display, etc.), with one or more terminals that enable a user to interact with the electronic device, and/or with any terminals (e.g., network card, modem, etc.) that enable the electronic device to communicate with one or more other computing terminals. Such communication may be through an input/output (I/O) interface. Also, the electronic device may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network, such as the internet) via the Network adapter.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A micro-grid power supply method based on a composite energy storage device is characterized in that the composite energy storage device comprises a first energy storage device in a power type and a second energy storage device in an energy type, and the method comprises the following steps:
collecting the generated power and the load power of the microgrid at each moment in a set time period, and sequentially calculating the charge and discharge power of the first energy storage device and the second energy storage device at each moment according to a preset output priority, the generated power and the load power;
inputting the charge and discharge power into a pre-established multi-objective optimization model, wherein the multi-objective optimization model takes the cost of the composite energy storage device, the variation of the power supply power in the micro-grid after composite energy storage compensation and the minimum difference between the instantaneous deviation and the average deviation of the supply and demand power of the micro-grid as a target function, and takes the energy storage capacity and the charge and discharge power of the first and second energy storage devices as optimization variables to obtain a single-variable optimization model of the energy storage capacity of the first and second energy storage devices;
calculating the optimal solution of the univariate optimization model under the preset constraint condition by adopting an optimization algorithm to respectively obtain the energy storage capacity of the first energy storage device and the second energy storage device in the set time period; the constraint conditions are an upper threshold and a lower threshold of the energy storage capacity of the first energy storage device and the second energy storage device;
and controlling the first energy storage device and the second energy storage device to supply power to the load in the microgrid according to the energy storage capacity of each energy storage device in a set time period.
2. A method of supplying power to a microgrid according to claim 1, characterized in that said univariate optimization model is a weighted sum of objective functions.
3. The microgrid power supply method according to claim 1 or 2, characterized in that the objective function corresponding to the cost of the composite energy storage device is specifically:
in the formula: f. of1Represents the cost of the composite energy storage device; n represents the number of the composite energy storage devices; w1nAnd W2nη capacity of the first and second energy storage devices to be configured in the microgrid1nAnd η2nThe efficiency of energy conversion of the first energy storage device and the second energy storage device is respectively; c. Ce1A price per unit capacity for the first energy storage device; c. Ce2Is the price per unit capacity of the second energy storage device; c. Cm1And cm2Maintenance costs for stored energy; p1nAnd P2nIs the power of the first and second energy storage devices, which is the accumulated power, which is the arithmetic sum of the energy storage devices exchanging power over a long period of time.
4. The microgrid power supply method according to claim 1 or 2, characterized in that the objective function corresponding to the power supply power variation in the microgrid after the composite energy storage compensation is specifically:
in the formula: f. of2Represents a set time period t11,t12]The sum of the power supply power variation in the micro-grid after the composite energy storage compensation at any moment; i represents each time sequence number in a set time period; p1,iAnd P2,iThe charging and discharging power of the first energy storage device and the second energy storage device in the period i are respectively; pg,iThe actual generated power of the power supply in the system without composite energy storage compensation is obtained; pG,iThe actual generated power of the power supply in the system after the composite energy storage compensation.
5. A microgrid power supply method according to claim 1 or 2, characterized in that the objective function corresponding to the difference between the instantaneous deviation and the average deviation of the microgrid power supply and demand is specifically:
in the formula: f. of3Represents a set time period t11,t12]The sum of the difference values between the instantaneous deviation and the average deviation of the supply and demand power of the microgrid at any time in the microgrid; pL,iIs the load power; pavgAnd the average value of the supply and demand difference after energy storage compensation.
6. The microgrid power supply method according to claim 1, wherein when the output priority of the first energy storage device is higher than that of the second energy storage device, the process of sequentially calculating the charge and discharge power of the first and second energy storage devices at each time specifically comprises:
calculating the system shortage power according to the generated power and the load power of the microgrid at each moment in a set time period;
the method comprises the steps of obtaining the charge state of a first energy storage device, and calculating the charge and discharge power of the first energy storage device according to the charge state when the charge state meets a preset first constraint range;
when the charging and discharging power meets a preset second constraint range, setting the charging and discharging power as the charging and discharging power of the first energy storage device;
when the charging and discharging power of the first energy storage device is smaller than the system shortage power, the charging and discharging power of the second energy storage device is obtained, and when the charging and discharging power of the first energy storage device meets a preset third constraint range, the charging and discharging power of the second energy storage device is calculated according to the charging and discharging state;
and when the charge and discharge power meets a preset fourth constraint range, setting the charge and discharge power as the charge and discharge power of the second energy storage device.
7. The microgrid power supply method according to claim 6, characterized in that when the state of charge of the first energy storage device exceeds a preset first constraint range, the charging and discharging power of the first energy storage device is calculated according to an upper threshold or a lower threshold of the first constraint range;
and when the charging and discharging power of the first energy storage device overflows a preset second constraint range, setting the upper limit threshold or the lower limit threshold of the second constraint range as the charging and discharging power of the first energy storage device.
8. The microgrid power supply method according to claim 6, characterized in that when the state of charge of the second energy storage device exceeds a preset third constraint range, the charging and discharging power of the second energy storage device is calculated according to an upper threshold or a lower threshold of the third constraint range;
and when the charging and discharging power of the second energy storage device overflows a preset fourth constraint range, setting the upper limit threshold or the lower limit threshold of the fourth constraint range as the charging and discharging power of the second energy storage device.
9. The microgrid power supply method of claim 1, wherein the optimization algorithm is a genetic algorithm.
10. An electronic device, comprising at least one processing unit and at least one memory unit, wherein the memory unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method of any one of claims 1 to 9.
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