CN111146791B - Operation and maintenance economic optimization control method of virtual super capacitor - Google Patents
Operation and maintenance economic optimization control method of virtual super capacitor Download PDFInfo
- Publication number
- CN111146791B CN111146791B CN202010008140.9A CN202010008140A CN111146791B CN 111146791 B CN111146791 B CN 111146791B CN 202010008140 A CN202010008140 A CN 202010008140A CN 111146791 B CN111146791 B CN 111146791B
- Authority
- CN
- China
- Prior art keywords
- virtual
- vir
- energy
- capacitance value
- load
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
Abstract
The invention relates to an operation and maintenance economic optimization control method of a virtual super capacitor, which comprises the steps of establishing a conversion relation between the rotation kinetic energy of a controllable load and the charging and discharging energy of a direct-current capacitor energy storage element, virtualizing an asynchronous motor with the rotating speed regulation capacity into the super capacitor, comprehensively analyzing the load economic loss and the energy storage life depreciation yield in the virtual super capacitor control process, establishing an economic operation and maintenance evaluation model of the virtual super capacitor, solving by utilizing a particle swarm algorithm, providing a calculation basis for a virtual capacitance value, and realizing the economic and stable operation of a system.
Description
Technical Field
The invention relates to the technical field of power grid control, in particular to an operation and maintenance economic optimization control method of a virtual super capacitor.
Background
In the prior art, new energy power generation is more and more emphasized in order to reduce the damage to the environment. However, new energy sources such as wind power and photovoltaic with intermittence and volatility are connected to the power grid, and safe and stable operation of the power grid system can be threatened. In order to improve the operation stability of the power grid, an energy storage device with unbalanced power stabilization has received important attention. However, the investment of energy storage devices such as storage batteries and super capacitors will increase the initial construction cost and the later operation and maintenance cost of the power grid.
In a direct-current micro-grid, a load side actively participates in system power regulation, so that the capacity configuration of an energy storage device and the aging and damage of the service life of the energy storage device in the charging and discharging processes can be obviously reduced theoretically, the operation and maintenance cost is saved, and the direct-current micro-grid becomes one of feasible schemes for improving the system operation economy. At present, management on the load demand side mainly focuses on maintaining the dynamic balance of system power by changing the temperature control load power to be matched with a traditional power supply, and the profit loss after load adjustment is not considered. In addition, the problem of operation cost after the change of the energy storage charging and discharging mode is rarely involved in the existing research.
Disclosure of Invention
In view of this, the present invention provides an operation and maintenance economic optimization control method for a virtual supercapacitor to overcome the defects in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
an operation, maintenance and economic optimization control method for a virtual super capacitor, wherein the power grid system comprises a storage battery, the super capacitor and a controllable load, and the method comprises the following steps:
determining an expression of a virtual capacitance value and an expression of a virtual charge state according to the conversion relation between the rotation kinetic energy of the controllable load and the charge-discharge energy of the super capacitor;
establishing a load economic loss model under the control of a virtual capacitor according to the expression of the virtual capacitance value and the expression of the virtual charge state;
determining a system operation and maintenance economic evaluation model containing a virtual capacitance value according to the load economic loss model;
solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm to obtain a corresponding virtual capacitance value when the profit is the maximum;
and controlling the operation condition of the controllable load according to the virtual capacitance value.
Optionally, the determining the expression of the virtual capacitance value and the expression of the virtual state of charge according to the conversion relationship between the rotational kinetic energy of the controllable load and the charge-discharge energy of the supercapacitor specifically includes:
the energy relation between the mechanical kinetic energy and the stored energy of the capacitor is established
Wherein E is Cvir Virtual energy of the virtual energy storage device; c vir Is a virtual capacitance value of the virtual energy storage device; j. the design is a square s 、ω r 、p n The rotor rotational inertia, the electrical angular velocity and the pole pair number of the motor are respectively; u shape C Is the supercapacitor voltage;
virtual capacitance C of t period vir (t) can be represented by
Defining state of charge (SOC) of virtual energy storage equipment based on energy angle vir Is composed of
Optionally, the expression of the system operation and maintenance economic evaluation model is as follows:
in the formula: f is the total economic benefit of the system; f in (t) system revenue for time period t; f out (t) battery life loss for time t.
Optionally, the battery life loss of the battery at the t period is calculated by the following formula:
wherein, W total For the purchase price of the storage battery, SOH (t) is the health value of the storage battery at the time t, and is defined as the maximum available capacity E of the storage battery in the time t Bmax (t) and rated capacity E Bnom The ratio of (A) to (B); SOH min Is the health value at the end of the life of the storage battery.
Alternatively, the battery t is calculated by the following formulaTime interval maximum available capacity E Bmax (t)：
Wherein E is B Is the available capacity of the battery.
Optionally, the load economic loss model has an expression as follows:
wherein, F in (t) as the load profit F in (t)，C vir (t) is a virtual capacitance value,
U C is the supercapacitor voltage.
Optionally, solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm specifically includes:
step 1: generating an initial group, setting a particle position and a updating speed, wherein the particle i position is a virtual capacitance value in a period t: c vir (t) i ；
Step 2: calculating C from the objective function vir (t) i Corresponding system revenue;
and step 3: for each particle, judge C vir (t) i If the system gain is greater than the historical maximum system gain, if so, updating C vir (t) i ；
And 4, step 4: judging C of each particle vir (t) i If the corresponding system gain is larger than that of the global optimal position, updating the global optimal position C vir (t)；
And 5: updating the position C of each particle vir (t) i And speed;
and 6: and if the convergence precision is met or the iteration times are reached, stopping the algorithm, outputting the corresponding virtual capacitance value when the total yield of the system is highest, otherwise, returning to the step 2, and continuing the iteration.
The invention has the following technical effects:
1. according to the invention, by introducing the virtual capacitor and the virtual charge state parameters, the load has the operation parameters similar to those of the capacitor, so that the direct-current power grid can obtain additional energy storage standby from the load side.
2. The invention evaluates the load loss after realizing the virtual capacitor and the cost of breaking the storage battery, provides an economic operation method for enabling the controllable load to be equivalent to the super capacitor energy storage device to participate in system power regulation, not only can improve the reliability of the system, but also can reduce the operation and maintenance cost of the system.
3. The operation and maintenance economic optimization control method for the system comprising the virtual capacitor, provided by the invention, can provide a calculation basis for the optimized value of the virtual capacitor, effectively reduces the investment of an energy storage device, and improves the operation economy of the system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of an operation, maintenance and economic optimization control method for a virtual super capacitor according to the present invention;
FIG. 2 is a grid system simulation topology of the present invention;
FIG. 3 shows the virtual capacitance values obtained in different periods of time according to the present invention;
FIG. 4 is a graph of wind power prediction data;
FIG. 5 is a graph of load power before/after the virtual energy storage is put into operation versus revenue change;
FIG. 6 shows P before/after the virtual energy storage is put into C 、SOC C 、P B 、SOC B And a change curve of the storage battery life and cost;
FIG. 7 is a comparison graph of total system revenue before and after virtual energy storage is invested.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
The controllability of the virtual capacitor can become a feasible scheme for improving the system operation capability on the load side, but the adjustment of the virtual capacitor influences the load benefit. Therefore, the optimization control problem of the virtual capacitor is necessarily existed in the operation process. According to the model of the virtual capacitor derived from the controllable load, the load gain and the service life loss of the storage battery are considered, the economic operation and maintenance evaluation model of the system containing the virtual capacitor is established, and the relation between the virtual capacitor and the total gain of the system is analyzed. The economic loss in the controllable load speed regulation process and the energy storage breaking loss gain in the virtual energy storage control process are comprehensively analyzed, an economic operation and maintenance evaluation model is solved by using a particle swarm algorithm, a calculation basis is provided for a virtual capacitance value, and an operation and maintenance economic optimization control method of a virtual super capacitor is provided.
Alternative embodiments of the present application will now be described in further detail with reference to the accompanying drawings:
referring to fig. 1, a specific process of an operation, maintenance and economic optimization control method for a virtual super capacitor according to an embodiment of the present application is as follows:
step 101: determining an expression of a virtual capacitance value and an expression of a virtual charge state according to the conversion relation between the rotation kinetic energy of the controllable load and the charge-discharge energy of the super capacitor; an asynchronous motor with speed regulation capability can be virtualized as a supercapacitor.
Step 102: establishing a load economic loss model under the control of a virtual capacitor according to the expression of the virtual capacitor value and the expression of the virtual charge state;
step 103: determining a system operation and maintenance economic evaluation model containing a virtual capacitance value according to the load economic loss model;
step 104: solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm to obtain a corresponding virtual capacitance value when the profit is the maximum;
step 105: and controlling the operation condition of the controllable load according to the virtual capacitance value.
According to the method shown in the figure 1, an asynchronous motor with the rotating speed adjusting capacity is virtualized into a super capacitor by establishing a conversion relation between the rotating kinetic energy of a controllable load and the charging and discharging energy of a direct current capacitor energy storage element, the economic loss in the controllable load speed adjusting process and the energy storage loss yield in the virtual energy storage control process are comprehensively analyzed, a system economic operation and maintenance evaluation model is established, the particle swarm algorithm is used for solving, a calculation basis is provided for a virtual capacitance value, and the economic and stable operation of the system is realized.
In step 101, the asynchronous motor is used as a load unit, and when the rotation speed of the rotor of the asynchronous motor changes, the electromagnetic power changes accordingly, and the process is analogous to the charging and discharging process of the super capacitor, so that the load can be regarded as a virtual energy storage device to share the unbalanced power of the system borne by the energy storage device. Establishing an energy relation between mechanical kinetic energy and capacitive energy storage as follows:
in the formula: e Cvir Virtual energy provided for virtual energy storage; c vir A capacitance value for the virtual stored energy; j. the design is a square s 、ω r 、p n The rotor rotational inertia, the electrical angular velocity and the pole pair number of the motor are obtained; u shape C Is the supercapacitor voltage.
Virtual capacitance C of t period vir (t) can be expressed as:
the virtual capacitance value of the controllable load at any moment can be obtained by the formula (2), so that the power and load change of the energy storage device are coordinated, and the running economy of the system is improved.
By using the charge state definition of the energy storage element for reference, the charge state SOC of the virtual energy storage equipment can be defined from the energy point of view vir Is composed of
In step 104, solving the virtual capacitance values at different moments by adopting a particle swarm optimization algorithm comprises the following steps:
step 1: generating an initial group, setting a particle position and a updating speed, wherein the particle i position is a virtual capacitance value in a period t: c vir (t) i ；
Step 2: calculating C from the objective function vir (t) i Corresponding system revenue;
and step 3: for each particle, judge C vir (t) i If the system gain is greater than the historical maximum system gain, if so, updating C vir (t) i ；
And 4, step 4: judging C of each particle vir (t) i If the corresponding system gain is larger than the system gain of the global optimal position, updating the global optimal position C vir (t)；
And 5: updating the position C of each particle vir (t) i And speed;
step 6: and if the convergence precision is met or the iteration times are reached, stopping the algorithm, outputting the corresponding virtual capacitance value when the total yield of the system is highest, otherwise, returning to the step 2, and continuing the iteration.
The embodiment of the invention takes the seawater desalination device as a controllable load, and the main economic benefit of the system is derived from fresh water production. The service life loss of the super capacitor can be ignored, and a service life loss system caused by charging and discharging of the storage battery is used as the main operation and maintenance cost of the system. Therefore, the objective function of the operation, maintenance and economic evaluation model of the direct current microgrid with the virtual energy storage function can be expressed as
In the formula: f is the total economic benefit of the system; f in (t) the yield of the system water production in the period t; f out (t) is the battery life penalty for time period t.
The load gain of the seawater desalination depends on the water production flow, and the expression of the water production flow Q (t) is
Q(t)＝2.741-2.408cos[0.1216P vir (t)]+1.324sin[0.1216P vir (t)] (5)
the yield of the seawater desalination plant in the t period can be expressed as
F in (t)＝kQ(t)Δt (6)
In the formula: k is the unit price of each ton of fresh water; q (t) is the water production flow in the period of t; Δ t is the time interval.
From equation (2), the electrical angular velocity ω of the asynchronous motor at time t r (t)
Virtual energy storage charging and discharging power in t time period
According to the formulas (7) and (8), the electrical angular velocity omega of the asynchronous motor is changed by adjusting the charging and discharging power through virtual energy storage r (t) whereby the seawater desalination load exhibits the energy regulation characteristics of a supercapacitor with variable capacitance. Combining the formulas (5) and (6), the load economic loss model under the control of the virtual capacitor can be established
In the formula:
the system operation and maintenance cost mainly depends on the service life loss F of the storage battery out (t)
In the formula: w is a group of total Investments are purchased for storage batteries; SOH (t) is the health condition of the storage battery at t time, and is defined as the maximum available capacity E of the storage battery at t time Bmax (t) and rated capacity E Bnom The ratio of (a) to (b); SOH min The health condition value at the end of the service life of the storage battery can be 0.8.
Maximum available capacity E of accumulator t period Bmax (t), can be represented by
In the formula: and A is the linear aging coefficient of the storage battery.
During charging and discharging, the storage capacity of the storage battery changes, E B (t) can be represented by
In the formula: p B (t) the charging and discharging power of the storage battery in a period of t; eta C And η D Respectively showing the charging and discharging efficiency of the storage battery.
The system power balance should satisfy the following conditions
P B (t)＝P vir (t)-P W (t)-P C (t) (13)
Equation (8) determines the relationship between the virtual stored energy charge and discharge power and the virtual capacitance, and it can be derived from equation (13) that the virtual capacitance will affect the battery power. Further, the power change of the virtual capacitor will affect the life loss cost F of the storage battery out (t)。
In conclusion, after the system is accessed by virtual energy storage at different time intervals, the load gain and the operation cost become two key factors for evaluating the economy of the system. Therefore, the total system yield is maximized, and the optimal solution analysis needs to be performed on the system economic evaluation model after the virtual energy storage is accessed.
According to the invention, a simulation model shown in FIG. 2 is established, and the simulation model comprises a wind power generation module, a storage battery and super capacitor hybrid energy storage module and a controllable load module. The hybrid energy storage module composed of the storage battery and the super capacitor is used for stabilizing system power fluctuation, and the controllable load is used for sharing the charging and discharging pressure of the hybrid energy storage module. The permanent magnet direct-drive fan is used as a wind power generation unit and connected with a direct current bus through a converter (WVSC), a storage battery and a super capacitor are connected to the direct current bus through bidirectional DC/DC converters (BVSC and CVSC), and an asynchronous motor is used as a controllable load module and connected with the direct current bus through a DC/AC converter (LVSC).
Fig. 3 shows the corresponding virtual super-capacitance value when the system gains the most. Fig. 4 shows wind power fluctuation data collected by the present invention, and fig. 5 is a comparison graph of load power before and after the virtual energy storage is performed and the profit, as can be seen by combining fig. 4 and fig. 5. Before the virtual energy storage is put into use, the load power is constant and is 16kW, the water yield is in a uniform ascending trend, and the water yield is about 26.22 yuan after 1 h. After the virtual energy storage is put into, the load power is not constant any more, but is adjusted along with the fluctuation of the wind power, the maximum load is about 18.2kW, the load is close to 9kW at the minimum, and because the load power is not constant any more, the water yield is not a straight line passing through the original point any more, when 40s, the output power of the fan is gradually reduced, the load power is reduced accordingly, and the water yield curve growth speed is reduced. After the simulation is finished, the yield of water production is about 23.22 yuan, and the yield is reduced when virtual energy storage is not input.
FIG. 6 shows the output power P of the supercapacitor bank before and after the virtual energy storage is performed C SOC of super capacitor C And the output power P of the storage battery pack B And the state of charge SOC of the storage battery pack B And dynamic response comparison of battery life depreciation cost. Combining the wind power data of FIG. 4, it can be shown that the system power fluctuation is mainly due to the fact that virtual energy storage is not performedThe super capacitor is stable, and when the self regulation capacity of the super capacitor is not enough to balance power fluctuation, the storage battery is put into operation. For example, when the time is 26min, the charge state of the super capacitor reaches 84.3%, the output power of the wind turbine generator continuously rises for a period of time, and in order to prevent the super capacitor from being overcharged, the capacitor stops running, and the storage battery absorbs the surplus wind power. And (4) 36-60min, the wind power is reduced, the super capacitor is discharged, the discharge power is higher, the charge state of the super capacitor is sharply reduced and is reduced to 11.6% in 40min, and the super capacitor stops running. And then, the output power of the wind driven generator is continuously reduced, the output power of the storage battery is increased for ensuring load power supply, the output power is close to 8kW, the slope of the state of charge curve of the storage battery is increased, and the reduction trend is obvious. In the process, the output power of the storage battery is increased, so that the service life of the storage battery is obviously reduced, the cost of the storage battery is obviously increased, the system operation cost is relatively high, and the reduction cost of the storage battery is about 8.92 yuan after the simulation is finished. On the contrary, after the virtual energy storage is put into use, the load adjusts the power of the storage battery along with the change of the wind power, the output of the storage battery is more stable, and the maximum charging and discharging power in the whole simulation process does not exceed 1kW, so that the aging cost of the storage battery is reduced. In the period of 40-60min, the wind power output power is in an obvious descending trend, but the virtual energy storage adjusts the load power demand, the output power change of the storage battery is greatly reduced, the descending trend of the charge state of the storage battery is obviously slowed down compared with the situation that the virtual energy storage is not put into, the service life aging cost of the storage battery is also obviously reduced, and the cost of the storage battery is about 0.16 yuan after the simulation is finished.
Fig. 7 compares the total profit of the system before and after the operation, maintenance and economic optimization control method of the system with the virtual capacitor is adopted. When the controllable load is assumed to be a super capacitor, although the load gain is reduced by participating in power regulation, the cost of the life of the storage battery is greatly reduced. It can be seen from the figure that after the seawater desalination load is controlled by adopting the virtual energy storage, the total profit of the system is higher than that of the system without the load response. The system obtains more benefits, and simultaneously, the safe operation level of the system is improved under the condition that the source-load-storage jointly participates in power regulation.
The controllable load has virtual energy storage capacity, a virtual capacitance value can be virtualized by establishing a conversion relation between the rotation kinetic energy of the controllable load and the charging and discharging energy of the direct-current capacitor energy storage element, the charging and discharging power of the traditional energy storage element can be shared, and the power fluctuation can be stabilized in time.
The invention researches a system operation and maintenance economic optimization control method containing a virtual capacitor, assembles virtual energy storage equipment for a system by using controllable load, and matches the virtual energy storage equipment with hybrid energy storage through economic analysis, thereby improving the economic and stable operation level of the system, having remarkable effects on relieving the power regulation pressure of the energy storage equipment and reducing the charging and discharging times of a storage battery pack, and having the potential of reducing the operation and maintenance cost of the system. Through economic nature contrastive analysis around virtual energy storage drops into, virtual energy storage can also make the system operation obtain better overall profit when alleviating energy memory regulating pressure.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar contents in other embodiments may be referred to for the contents which are not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (4)
1. An operation, maintenance and economic optimization control method for a virtual super capacitor is characterized in that a power grid system comprises a storage battery, the super capacitor and a controllable load, and the method comprises the following steps:
determining an expression of a virtual capacitance value and an expression of a virtual charge state according to the conversion relation between the rotation kinetic energy of the controllable load and the charge-discharge energy of the super capacitor;
establishing a load economic loss model under the control of a virtual capacitor according to the expression of the virtual capacitor value and the expression of the virtual charge state;
determining a system operation and maintenance economic evaluation model containing a virtual capacitance value according to the load economic loss model;
solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm to obtain a corresponding virtual capacitance value when the profit is the maximum;
controlling the operating condition of the controllable load according to the virtual capacitance value;
the expression of the system operation and maintenance economic evaluation model is as follows:
in the formula:Fthe total economic benefit of the system;F in (t) Is composed oftTime interval system revenue;F out (t) Is composed oftTime-interval storage battery life loss;
calculating the battery by the following formulatTime period battery life loss:
wherein, the first and the second end of the pipe are connected with each other,W total the purchase price of the storage battery is set,SOH(t) Is composed oftDefining the health state value of the storage battery at the moment as the storage batterytTime interval maximum available capacityE Bmax (t) To rated capacityE Bnom The ratio of (A) to (B);SOH min the health condition value at the end of the service life of the storage battery;
wherein the content of the first and second substances,P vir (t-1) is the load power,C vir (t) As a virtual capacitance value, a capacitance value,
，U C is the supercapacitor voltage.
2. The method according to claim 1, wherein the determining the expression of the virtual capacitance value and the expression of the virtual state of charge according to the conversion relationship between the rotational kinetic energy of the controllable load and the charging and discharging energy of the super capacitor comprises:
the energy relationship between the mechanical kinetic energy and the stored energy of the capacitor is established
Wherein the content of the first and second substances,E Cvir virtual energy of the virtual energy storage device;C vir a virtual capacitance value of the virtual energy storage device;J s 、ω r 、p n the rotor rotational inertia, the electrical angular velocity and the pole pair number of the motor are respectively;U C is the supercapacitor voltage;
tvirtual capacitance of time periodC vir (t) Is shown as
Defining a state of charge of a virtual energy storage device based on an energy angleSOC vir Is composed of
3. The method of claim 1, wherein the battery is calculated by the following equationtTime interval maximum available capacityE Bmax (t)：
Wherein the content of the first and second substances,E B is the available capacity of the battery.
4. The method of claim 1, wherein solving the system operation and maintenance economic evaluation model based on a particle swarm optimization algorithm specifically comprises:
step 1: generating an initial population, setting particle positions and update rates, the particlesiIs positioned astTime period virtual capacitance value:C vir (t) i ；
step 2: from the objective functionC vir (t) i Corresponding system revenue;
and 3, step 3: for each particle, judgeC vir (t) i If the system gain is greater than the historical maximum system gain, if so, updatingC vir (t) i ；
And 4, step 4: for each particleC vir (t) i If the corresponding system gain is larger than the system gain of the global optimal position, updating the global optimal positionC vir (t)；
And 5: updating the position of each particleC vir (t) i And speed;
and 6: and if the convergence precision is met or the iteration times are reached, stopping the algorithm, outputting the corresponding virtual capacitance value when the total yield of the system is highest, otherwise, returning to the step 2, and continuing the iteration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010008140.9A CN111146791B (en) | 2020-01-06 | 2020-01-06 | Operation and maintenance economic optimization control method of virtual super capacitor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010008140.9A CN111146791B (en) | 2020-01-06 | 2020-01-06 | Operation and maintenance economic optimization control method of virtual super capacitor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111146791A CN111146791A (en) | 2020-05-12 |
| CN111146791B true CN111146791B (en) | 2022-10-28 |
Family
ID=70523629
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010008140.9A Active CN111146791B (en) | 2020-01-06 | 2020-01-06 | Operation and maintenance economic optimization control method of virtual super capacitor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111146791B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116093998B (en) * | 2022-11-28 | 2024-03-08 | 国网冀北电力有限公司经济技术研究院 | Virtual storage battery control method based on controllable load |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013162623A (en) * | 2012-02-03 | 2013-08-19 | Toshiba Corp | Power supply system |
| CN106026150B (en) * | 2016-05-12 | 2022-04-05 | 中国电力科学研究院 | Commercial park source-storage-load optimal configuration method |
-
2020
- 2020-01-06 CN CN202010008140.9A patent/CN111146791B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111146791A (en) | 2020-05-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021164112A1 (en) | Frequency control method and system during using wind farm as black-start power source by means of optimal configuration of energy storage | |
| CN103986190B (en) | Based on the wind-solar-storage joint electricity generation system smooth control method of generated output curve | |
| CN106651026B (en) | Multi-time scale microgrid energy management optimization scheduling method | |
| CN106099965B (en) | Exchange the control method for coordinating of COMPLEX MIXED energy-storage system under micro-grid connection state | |
| JP3970083B2 (en) | Operation method of redox flow battery system | |
| CN101232192A (en) | Hybrid power generation of wind-power generator and battery energy storage system | |
| CN107465212A (en) | Wind turbines microgrid operation virtual inertia control system and method based on virtual synchronous generator techniques | |
| CN109217343B (en) | Fuzzy control-based wind power hybrid energy storage power smooth control method | |
| CN114629139A (en) | Wind-storage combined system frequency modulation control method considering energy storage capacity optimization | |
| CN107147135A (en) | A kind of method and system for stabilizing microgrid power swing | |
| CN111146791B (en) | Operation and maintenance economic optimization control method of virtual super capacitor | |
| CN115714435A (en) | Photovoltaic hybrid energy storage system power distribution and virtual inertia control method based on virtual synchronous generator | |
| CN109327031A (en) | Directly driven wind-powered multi-computer system power association control method and system based on battery energy storage | |
| CN106953318A (en) | A kind of micro-capacitance sensor optimal control method based on cost | |
| CN108599197A (en) | A kind of method that virtual energy storage participates in system power smoothing fluctuations | |
| CN117081128A (en) | Micro-grid hybrid energy storage capacity optimal configuration method based on variation modal decomposition | |
| CN111049180A (en) | Island microgrid voltage frequency control method and system based on hybrid energy storage | |
| CN103887808A (en) | Wind farm energy storage lithium-ion electricity optimizing control method based on set inertial energy storage | |
| CN113780638B (en) | Comprehensive energy system optimization scheduling method considering energy storage life attenuation | |
| CN115425636A (en) | Flywheel energy storage-containing direct current microgrid virtual inertia self-adaptive control method | |
| CN110943487B (en) | Energy optimization method and device for park energy system | |
| CN115021336A (en) | Optimization method for high-proportion new energy power system inertia deficiency scene | |
| JP7292042B2 (en) | Combined power generation system in isolated operation | |
| CN109713695B (en) | Micro-grid energy management method and system based on lithium battery charging and discharging states | |
| CN110601190A (en) | Regional power grid operation domain division method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |