CN112598232A - Energy storage system optimal configuration method in VPP (virtual private Point) of improved quantum particle swarm - Google Patents
Energy storage system optimal configuration method in VPP (virtual private Point) of improved quantum particle swarm Download PDFInfo
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Abstract
The invention discloses an optimized configuration method of an energy storage system in VPP (virtual private Point) of an improved quantum particle swarm, which comprises the steps of analyzing electric quantity interaction between a virtual power plant and a power grid, an internal control strategy of the virtual power plant and a control strategy of each part of energy storage units in a hybrid energy storage system on the premise of considering time-of-use electricity price; on the premise of allowing light abandonment, the unit power generation cost of a gas turbine and a photovoltaic is calculated and analyzed, the photovoltaic output and the charging and discharging of an energy storage system in a virtual power plant are considered comprehensively, the net income of the virtual power plant can be calculated by subtracting the cost from the income of each part in the electric quantity interaction process with a power grid, an objective function with the maximum net income of VPP is established, the constraint condition of an economic model of the virtual power plant is formulated, the improved quantum-particle swarm algorithm is solved based on the basic principle of the analysis improved quantum-particle swarm algorithm, and finally the capacity configuration of the energy storage system is contrasted and analyzed through the quantum-particle swarm algorithm and the improved quantum-swarm algorithm.
Description
Technical Field
The invention relates to an energy storage system optimal configuration method in VPP (virtual private Point) of an improved quantum particle swarm, and belongs to the technical field of intelligent power distribution networks.
Background
A Virtual Power Plant (VPP) is a Virtual integrated control system, which integrates a distributed Power supply, a controllable load, and an energy storage system in a Power system together to realize the transmission of electric energy and the operation of equipment, and by modulating the logical relationship between the distributed Power supply and a smart grid, the energy in the grid is delivered to users, thereby further realizing a comprehensive Power Plant with economic value.
The method is divided according to the operation control mode of the virtual power plant and mainly divided into a centralized type, a centralized-distributed type and a completely distributed type. Centralized type: the centralized control mode provides a top-down way to manage the distributed power sources. Centralized-decentralized type: in a centralized-decentralized control architecture, virtual power plants generally divide it into low-level control and high-level control. In the low-level control, the distributed generator sets are controlled by local controllers, the distributed power controllers control the output conditions of the distributed power supplies, and the local controllers control the distributed power controllers by using a logic algorithm. And (3) complete dispersion type: in the fully decentralized control, the virtual power plant is divided into a plurality of self-managed and intelligent subsystems, each subsystem is interactively cooperated through communication to sense the running state of other subsystems, and the control center becomes a data exchange and processing center.
Energy storage is configured for a virtual power plant, distributed power sources and power loads are managed comprehensively, and negative effects caused by the distributed power sources on the safety and reliability of the whole power system can be reduced. With the progress of energy storage technology and the reduction of cost, the application prospect in a virtual power plant is wide, and a planning and configuration method of an energy storage system needs to be researched. Development strategies of the energy Internet in the coming decade indicate that by 2020, popularization of intelligent products such as distributed power supplies, energy storage systems, intelligent electric meters and the like is realized, and infrastructure of the energy Internet is constructed; by 2023, the electricity trading market mechanism was basically established; in 2025, intelligent evolution and opening of the power distribution network side are realized, and in 2030, the establishment of the middle-level energy internet is realized through technical means such as big data, artificial intelligence and the like; as an important component of the future energy internet, a virtual power plant is necessary for research. However, the energy storage device is one of the core devices of the virtual power plant, and the application cost is still high at present, so that the optimal configuration of the energy storage capacity is one of the key problems in the planning process of the virtual power plant.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides an energy storage system optimal configuration method in VPP (vacuum pressure pulse) for improving quantum particle swarm.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an energy storage system optimal configuration method in VPP of improved quantum particle swarm comprises the following specific implementation steps:
solving an objective function with the maximum net income of the virtual power plant by using an improved quantum particle swarm algorithm to obtain n-time photovoltaic output power, n-time gas turbine output and n-time energy storage system discharge power;
controlling the photovoltaic to charge the energy storage system on the premise that the photovoltaic output meets the load; when the photovoltaic output cannot meet the load requirement, controlling an energy storage system to supply power to the load;
under the premise of guaranteeing that the distributed photovoltaic power generation amount is preferentially consumed, when the time t is in a peak period, the virtual power plant is controlled to sell power to the power grid on the premise of meeting the load demand; when the time t is in the valley period, controlling the virtual power plant to purchase power from the power grid, supplying load and interacting with the energy storage system; when the time t is in the flat time period, the output cost of the gas turbine in the virtual power plant is considered to be compared with the power price of the power grid at the time, when the power price of the power grid is larger than the output cost of the gas turbine, the virtual power plant is controlled not to interact with the power grid, and when the power price of the power grid is smaller than the output cost of the gas turbine, the virtual power plant is controlled to buy power from the power grid.
As a preferred scheme, the objective function calculation formula for the maximum net profit of the virtual power plant is as follows:
maxIn＝Pn-Cn
wherein n represents a time sequence, and takes 15min as a time period, and n is 1,2, …, 96; i isnRepresents the n-slot VPP net gain; pnRepresents the VPP revenue for the n period; cnRepresents the total cost of the n-period VPP;
representing the photovoltaic output power in n time periods;
representing the gas turbine output for a period of n;
representing the discharge power of the lithium titanate battery in n time period;
representing the discharge power of the all-vanadium redox flow battery in the n period;
representing the discharge power of the super capacitor in the n period;
representing the output power of the power grid to the virtual power plant in the period of n;
representing the charging power of the lithium titanate battery in the n time period;
representing the charging power of the all-vanadium redox flow battery in the n period;
representing the supercapacitor charge power over an n period;
representing the output power of the virtual power plant to the power grid in the period of n;
respectively representing the operation management cost of the virtual power plant at the n period, the energy consumption cost of the virtual power plant at the n period and the punishment cost of the virtual power plant at the n period;
respectively representing a photovoltaic operation management cost coefficient, a gas turbine operation management cost coefficient and an energy storage system operation management cost coefficient; pGTRepresents the fuel cost per unit of power generation of the gas turbine; dn、
And the method respectively represents the declared planned output of the virtual power plant in the n period, the electricity purchasing price in the n period and the electricity selling price in the n period.
As a preferred scheme, the constraint conditions of the objective function with the maximum net profit of the virtual power plant are as follows:
and power balance constraint:
where Δ represents the deviation of the predicted force from the actual force the next day of VPP over a period of n.
Gas turbine power constraint:
wherein,
respectively the upper and lower power limits of the gas turbine in the normal working state.
And (3) restricting the climbing rate of the gas turbine:
in the formula,
the ramp rates up and down, respectively, of the gas turbine unit.
And (3) energy storage battery electric quantity constraint:
in the formula,
is the electric quantity of the lithium titanate battery within n time period etac1、ηd1Respectively the charging and discharging efficiency of the lithium titanate battery;
is the electric quantity, eta, of the full vanadium flow battery in the n periodc2、ηd2Respectively representing the charging efficiency and the discharging efficiency of the all-vanadium redox flow battery;
is the electric quantity of the super capacitor in the n period of time, etac3、ηd3Respectively the charging and discharging efficiency of the super capacitor.
And (3) charge and discharge restraint of the energy storage system:
in the formula:
respectively the upper and lower limits of the capacity of the lithium titanate battery;
respectively representing the upper limit and the lower limit of the capacity of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the capacity of the super capacitor;
respectively is the upper limit and the lower limit of the charging power of the lithium titanate battery;
respectively setting the upper limit and the lower limit of the charging power of the all-vanadium redox flow battery;
respectively charging the upper limit and the lower limit of the power of the super capacitor;
respectively is the upper limit and the lower limit of the discharge power of the lithium titanate battery;
respectively is the upper limit and the lower limit of the discharge power of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the discharge power of the super capacitor;
respectively representing the charge and discharge state variables of the lithium titanate battery, the all-vanadium redox flow battery and the super capacitor, and taking the value of 0 or 1.
As a preferred scheme, the specific steps of charging the energy storage system by the photovoltaic system are as follows:
carrying out fast Fourier transform and inverse transform on the photovoltaic output signal, and dividing the output signal into high-frequency, medium-frequency and low-frequency signals;
a high-frequency part is absorbed by a super capacitor, a medium-frequency part is absorbed by a lithium titanate battery, and a low-frequency part is absorbed by an all-vanadium redox flow battery.
Preferably, the fuel cost per unit power generation of the gas turbine is as follows:
PGT＝PNG/(ηe*LNG)
in the formula, PNGIs the natural gas price; etaeGenerating efficiency for the gas turbine; l isNGIs the low calorific value of natural gas.
As a preferred scheme, the calculation flow of the improved quantum particle swarm optimization algorithm is as follows:
(1) in the search space D, a population is initialized.
(2) Let t be 0, initialize all the particles in D-dimensional space, where the position of each particle is Xi,kAnd let the individual history optimal position be Pi,k。
(3) And calculating the fitness of the particles and the average optimal position of the population.
(4) Calculating a diversity metric value of the population, judging, and if 0, performing the step (4); if so, performing the step (5);
(5) performing a mutation operation according to formula (37);
(6) updating the particle position, judging whether the ending condition is met, and if not, returning to the step (2); and if so, ending and outputting the optimal solution.
Has the advantages that: the optimization configuration method of the energy storage system in the VPP for improving the quantum particle swarm, provided by the invention, is beneficial to improving the economic benefit of power grid operation, reducing the construction investment of the power grid and reducing the occupation of land resources.
Drawings
FIG. 1 is a schematic diagram of a virtual plant control strategy.
FIG. 2 is a flow chart of a virtual plant export strategy.
Fig. 3 is a typical load for four seasons.
Fig. 4 is four typical photovoltaic power curves.
FIG. 5 is an interactive power based on quantum-behaved particle swarm optimization.
Fig. 6 is an interactive power based on an improved quantum-behaved particle swarm algorithm.
Fig. 7 is a lithium titanate battery output based on a quantum-behaved particle swarm algorithm.
Fig. 8 is a lithium titanate battery output based on an improved quantum-behaved particle swarm algorithm.
FIG. 9 is the output of the all-vanadium redox flow battery based on the quantum particle swarm optimization.
FIG. 10 is the output of an all vanadium redox flow battery based on quantum particle swarm optimization.
FIG. 11 is a virtual plant contribution based on quantum-behaved particle swarm optimization.
FIG. 12 is a virtual plant contribution based on an improved quantum-behaved particle swarm algorithm.
FIG. 13 is a graph of the convergence of the net profit for a virtual power plant based on quantum-behaved particle swarm optimization.
FIG. 14 is a graph of the convergence of the net profit for a virtual power plant based on quantum-behaved particle swarm optimization.
Detailed Description
The present invention will be further described with reference to the following examples.
An energy storage system optimal configuration method in VPP of improved quantum particle swarm comprises the following specific implementation steps:
The virtual power plant economic model under the condition of allowing light to be abandoned comprises the steps of establishing a VPP economic optimization objective function and power balance constraint, a gas turbine power constraint, a gas turbine climbing rate constraint, an energy storage battery electric quantity constraint and an energy storage system charge-discharge constraint which comprehensively consider photovoltaic output, energy storage system charge-discharge and a gas turbine in the virtual power plant and are in the real-time market.
Comprehensively considering photovoltaic output, energy storage system charge and discharge and a gas turbine in a virtual power plant, the VPP economic optimization objective function of the real-time market is as follows:
maxIn＝Pn-Cn (12)
wherein n represents a time sequence, and takes 15min as a time period, and n is 1,2, …, 96; i isnRepresents the n-slot VPP net gain; pnRepresents the VPP revenue for the n period; cnRepresents the total cost of the n-period VPP;
representing the photovoltaic output power in n time periods;
representing the gas turbine output for a period of n;
representing the discharge power of the lithium titanate battery in n time period;
representing the discharge power of the all-vanadium redox flow battery in the n period;
representing the discharge power of the super capacitor in the n period;
representing the output power of the power grid to the virtual power plant in the period of n;
representing the charging power of the lithium titanate battery in the n time period;
representing the charging power of the all-vanadium redox flow battery in the n period;
representing the supercapacitor charge power over an n period;
representing the output power of the virtual power plant to the power grid in the period of n;
respectively representing the operation management cost of the virtual power plant at the n period, the energy consumption cost of the virtual power plant at the n period and the punishment cost of the virtual power plant at the n period;
respectively representing a photovoltaic operation management cost coefficient, a gas turbine operation management cost coefficient and an energy storage system operation management cost coefficient; pGTRepresents the fuel cost per unit of power generation of the gas turbine; dn、
And the method respectively represents the declared planned output of the virtual power plant in the n period, the electricity purchasing price in the n period and the electricity selling price in the n period.
And power balance constraint:
where Δ represents the deviation of the predicted force from the actual force the next day of VPP over a period of n.
Gas turbine power constraint:
wherein,
respectively the upper and lower power limits of the gas turbine in the normal working state.
And (3) restricting the climbing rate of the gas turbine:
in the formula,
the ramp-up and ramp-down rates of the gas turbine set are respectively, and delta t is the time difference between the n time period and the n-1 time period.
And (3) energy storage battery electric quantity constraint:
in the formula,
is the electric quantity of the lithium titanate battery within n time period etac1、ηd1Respectively the charging and discharging efficiency of the lithium titanate battery;
is the electric quantity, eta, of the full vanadium flow battery in the n periodc2、ηd2Respectively representing the charging efficiency and the discharging efficiency of the all-vanadium redox flow battery;
is the electric quantity of the super capacitor in the n period of time, etac3、ηd3Respectively the charging and discharging efficiency of the super capacitor.
And (3) charge and discharge restraint of the energy storage system:
in the formula:
respectively the upper and lower limits of the capacity of the lithium titanate battery;
respectively representing the upper limit and the lower limit of the capacity of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the capacity of the super capacitor;
respectively is the upper limit and the lower limit of the charging power of the lithium titanate battery;
respectively setting the upper limit and the lower limit of the charging power of the all-vanadium redox flow battery;
respectively charging the upper limit and the lower limit of the power of the super capacitor;
respectively is the upper limit and the lower limit of the discharge power of the lithium titanate battery;
respectively is the upper limit and the lower limit of the discharge power of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the discharge power of the super capacitor;
respectively representing the charge and discharge state variables of the lithium titanate battery, the all-vanadium redox flow battery and the super capacitor, and taking the value of 0 or 1.
Step 3, the energy storage capacity configuration method for improving the quantum particle swarm algorithm comprises the following steps: analyzing the basic principle of the improved quantum particle swarm algorithm, and solving the improved quantum particle swarm algorithm.
Example 1:
1. analyzing a control strategy of the virtual power plant:
in the process of participating in power supply to a load, firstly, the electric quantity generated by the distributed photovoltaic is guaranteed to be preferentially consumed, when the photovoltaic output is larger than the load demand, the judgment is carried out according to the price of the electricity sold at that time and the charge state of the energy storage system, the energy storage system is charged or the electricity sold to the power grid, when the photovoltaic output is smaller than the load demand, the energy storage system supplies power to the load in the VPP, and if the energy storage system is not enough to participate in regulation and control at that time, the power generation of the gas turbine or the electricity purchase from the power grid are judged according to the price of the electricity sold and. Meanwhile, earning benefits in a mode of ensuring stable operation of the system and carrying out low-buying and high-selling with a power grid company in the whole stage. FIG. 1 is a schematic diagram of a virtual plant control strategy.
As shown in fig. 1, the virtual power plant control center implements first-level regulation by centralized control of distributed photovoltaic, gas turbines, multiple types of energy storage systems, and loads. The line segment a represents distributed photovoltaic output, gas turbine output and residual electric quantity of the energy storage system, and under the condition of considering the time-of-use electricity price, the electricity purchasing price and the output cost of the power grid are analyzed and compared to determine whether electricity is sold to the power grid or not; the line segment b represents whether electricity is bought from the power grid on the premise of meeting the load requirement by considering the electricity selling price of the power grid in the period and the output cost analysis of the virtual power plant; and the line segment c represents the electric quantity interaction between the energy storage system and the power grid. The second layer of regulation and control is to divide the multi-type energy storage system, divide the output of the energy storage system into high-frequency, medium-frequency and low-frequency signals in the time interval through fast Fourier transform and inverse transform, and respectively consume the signals by a super capacitor, a lithium titanate battery and an all-vanadium redox flow battery.
Fig. 2 is a flow chart of a virtual power plant output strategy, which considers the power interaction with the power grid and satisfies the load power demand. According to the method, a day is divided into 96 time intervals, each time interval is 15min, according to the graph shown in fig. 2, firstly, an energy storage system is initialized, namely, the throughput electric quantity of the energy storage system is guaranteed to be balanced every day, secondly, the power price bought and sold by a power grid at the moment t is read according to the time-of-use power price, and whether the power price at the moment is in the flat time interval, the peak time interval or the valley time interval is judged. Meanwhile, the photovoltaic output condition at the time t is read, and the following judgment is made:
1) according to the time-of-use electricity price operation mode and the analysis of the interaction control strategy with the power grid, according to the output strategy of the virtual power plant, electricity can be stored in the electricity large-generation period of the virtual power plant through the time-of-use electricity price, electricity is sold to the power grid in the electricity price peak period, income is earned, meanwhile, the electricity generation cost is reduced, and meanwhile, the electricity is bought from the power grid for load supply and storage in the electricity price valley period, so that the effects of peak clipping and valley filling are achieved.
Under the premise of considering the time-of-use electricity price, the virtual power plant and the power grid carry out electricity quantity interaction, and when the time t is in the peak time period, the virtual power plant sells electricity to the power grid on the premise of meeting the load requirement so as to earn the maximum benefit; when the time t is in a valley period, the virtual power plant purchases power from the power grid, supplies load and interacts with the hybrid energy storage system; when the time t is in the flat time period, the output cost of the gas turbine in the virtual power plant is considered to be compared with the power price of the power grid at the time, when the power price of the power grid is larger than the output cost of the gas turbine, the virtual power plant does not perform power interaction with the power grid, and when the power price of the power grid is smaller than the output cost of the gas turbine, the virtual power plant buys power to the power grid.
2) Virtual power plant internal control strategy analysis
Judging the magnitude of photovoltaic output and load demand at the moment t: on the premise of ensuring that all the photovoltaic is consumed, when the photovoltaic output is greater than the load demand, the photovoltaic output directly supplies power to the load. Charging the energy storage system or selling electricity to the power grid by the residual photovoltaic output; and when the photovoltaic output is less than the load requirement, the photovoltaic output is fully used for supplying the load. Analyzing a charge and discharge strategy of the energy storage system: when the photovoltaic output meets the load, the energy storage system is charged preferentially, and when the energy storage system does not need to be charged, electricity is sold to the power grid; when the photovoltaic output can not meet the load requirement, the energy storage system preferentially supplies power to the load. Analysis of gas turbine output strategy: the gas turbine mainly has the functions of generating power to supply load, storing energy and a power grid; and comparing the power generation cost with the electricity price of the power grid at the moment, and outputting power to supply to a load and an energy storage system. When the output cost of the gas turbine is more than the electricity selling price of the power grid, the gas turbine does not output power, and when the output cost of the gas turbine is less than the electricity selling price of the power grid, the gas turbine outputs power.
3) The control strategy analysis hybrid energy storage system of each part of energy storage units in the hybrid energy storage system comprises a lithium titanate battery, an all-vanadium redox flow battery and a super capacitor, output signals are divided into high-frequency signals, intermediate-frequency signals and low-frequency signals due to instability of photovoltaic output, and according to different functions of the energy storage units, when photovoltaic power generation electric quantity is remained, the super capacitor absorbs the high-frequency part, the lithium titanate battery absorbs the intermediate-frequency part, and the all-vanadium redox flow battery absorbs the low-frequency part.
2. Establishment of virtual power plant economic model
The net income of the virtual power plant is the maximum of an objective function, and the net income of the virtual power plant can be calculated by subtracting the cost from the income of each part in the electric quantity interaction process of the virtual power plant and the power grid. The profit comprises the profit of the photovoltaic power generation supply load, the power generation profit of the gas turbine, the net profit of the power selling profit minus the power purchasing cost, and the net profit of the energy storage system supply load profit minus the charging cost of the energy storage system; the cost includes an operation management cost, an energy consumption cost and an operation punishment cost of the virtual power plant. The operation management cost comprises photovoltaic, gas turbine and energy storage system; the energy consumption cost is the energy consumption cost of the gas turbine; the operation punishment cost is the punishment cost caused by the difference between the actual output and the predicted output of the virtual power plant.
Comprehensively considering photovoltaic output, energy storage system charge and discharge and a gas turbine in a virtual power plant, the VPP economic optimization objective function of the real-time market is as follows:
maxIn＝Pn-Cn (23)
wherein n represents a time sequence, and takes 15min as a time period, and n is 1,2, …, 96; i isnRepresents the n-slot VPP net gain; pnRepresents the VPP revenue for the n period; cnRepresents the total cost of the n-period VPP;
representing the photovoltaic output power in n time periods;
representing the gas turbine output for a period of n;
representing the discharge power of the lithium titanate battery in n time period;
representing the discharge power of the all-vanadium redox flow battery in the n period;
representing the discharge power of the super capacitor in the n period;
representing the output power of the power grid to the virtual power plant in the period of n;
representing the charging power of the lithium titanate battery in the n time period;
representing the charging power of the all-vanadium redox flow battery in the n period;
representing the supercapacitor charge power over an n period;
representing the output power of the virtual power plant to the power grid in the period of n;
respectively representing the operation management cost of the virtual power plant at the n period, the energy consumption cost of the virtual power plant at the n period and the punishment cost of the virtual power plant at the n period;
respectively representing a photovoltaic operation management cost coefficient, a gas turbine operation management cost coefficient and an energy storage system operation management cost coefficient; pGTRepresents the fuel cost per unit of power generation of the gas turbine; dn、
And the method respectively represents the declared planned output of the virtual power plant in the n period, the electricity purchasing price in the n period and the electricity selling price in the n period.
The unit power generation fuel cost of the gas turbine is as follows:
PGT＝PNG/(ηe*LNG) (29)
in the formula, PNGIs the natural gas price; etaeGenerating efficiency for the gas turbine; l isNGIs the low calorific value of natural gas. The virtual power plant declares planned output to the power distribution network:
in the formula, DnFor n-time-interval distributed photovoltaic planned output, eta is an n-time-interval gas turbine output coefficient;
the maximum output of the gas turbine.
The constraint conditions for establishing the economic optimal virtual power plant model are as follows:
and power balance constraint:
where Δ represents the deviation of the predicted force from the actual force the next day of VPP over a period of n.
Gas turbine power constraint:
wherein,
respectively the upper and lower power limits of the gas turbine in the normal working state.
And (3) restricting the climbing rate of the gas turbine:
in the formula,
the ramp rates up and down, respectively, of the gas turbine unit.
And (3) energy storage battery electric quantity constraint:
in the formula,
is the electric quantity of the lithium titanate battery within n time period etac1、ηd1Respectively the charging and discharging efficiency of the lithium titanate battery;
is the electric quantity, eta, of the full vanadium flow battery in the n periodc2、ηd2Respectively representing the charging efficiency and the discharging efficiency of the all-vanadium redox flow battery;
is the electric quantity of the super capacitor in the n period of time, etac3、ηd3Respectively the charging and discharging efficiency of the super capacitor.
And (3) charge and discharge restraint of the energy storage system:
in the formula:
respectively, the capacity of the lithium titanate batteryA lower limit;
respectively representing the upper limit and the lower limit of the capacity of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the capacity of the super capacitor;
respectively is the upper limit and the lower limit of the charging power of the lithium titanate battery;
respectively setting the upper limit and the lower limit of the charging power of the all-vanadium redox flow battery;
respectively charging the upper limit and the lower limit of the power of the super capacitor;
respectively is the upper limit and the lower limit of the discharge power of the lithium titanate battery;
respectively is the upper limit and the lower limit of the discharge power of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the discharge power of the super capacitor;
respectively representing the charge and discharge state variables of the lithium titanate battery, the all-vanadium redox flow battery and the super capacitor, and taking the value of 0 or 1.
3. Energy storage capacity configuration based on quantum particle swarm algorithm
The quantum particle swarm algorithm is a method for calculating based on the population probability, and the reason is firstly proposed because the traditional particle swarm algorithm is not a global convergence algorithm, the speed of particles and the searching position of the particle swarm algorithm in the searching process are limited, however, the problem cannot be fundamentally solved through the improvement of the particle swarm algorithm, and the quantum particle swarm algorithm describes the speed of the particles and the searching position by using a wave function in quantum mechanics, so that the problem is solved.
(1) Fundamental principle of improved quantum particle swarm optimization
Attractors play an important role in the convergence process of the algorithm, and therefore, once a guiding error occurs to an attractor, the algorithm is likely to be trapped in a locally optimal condition. The reason for this is that when the attractor approaches the local optimal solution, the particles in the population aggregate to the attractor, and the diversity of the population is reduced, so the improved quantum-behaved particle swarm algorithm is to improve the attractor, so that the diversity of the population can still be ensured in the process of approaching the local optimal solution, and the situation of falling into the local optimal solution is avoided.
When the diversity of the population is low, the population tends to gather to the same position, and the diversity of the population needs to be improved in order to avoid falling into the local optimal solution. A diversity metric is defined herein:
when the diversity value of the population is lower than a set value of 0, performing variation operation on the diversity of the population, wherein the operation objects are the first 10% of particles with optimal positions in the population, and the variation operation is performed according to the following formula.
p＝p+ε (37)
In which ε is a variation factor which is a normal distribution
The random number of (2).
Through the operation, when the population is about to fall into the local optimum, particles can jump out of the local optimum range, and the whole population is guided to be gathered to the global optimum.
(2) Improved quantum particle swarm algorithm solving process
In the D-dimensional space, the target is searched by utilizing a quantum particle swarm algorithm, wherein N sets { X ] composed of potential solutions of the target function exist in the quantum particle swarm algorithm1,k,…,XN,kWhere k is the number of iterations. The ith particle can be represented as: xi,k＝(xi,k-1,…,xi,k-D) Since the particle has no velocity vector, the individual historical optimal position is denoted as Pi,k＝(pi,k-1,…,pi,k-D) The optimal position of the particles of the whole population is denoted as Pg,k＝(pg,k-1,…,pg,k-D)。
The calculation flow of the improved quantum particle swarm optimization is as follows:
(1) in the search space D, a population is initialized.
(2) Let t be 0, initialize all the particles in D-dimensional space, where the position of each particle is Xi,kAnd let the individual history optimal position be Pi,k。
(3) And calculating the fitness of the particles and the average optimal position of the population.
(4) Calculating a diversity metric value of the population, judging, and if 0, performing the step (4); if so, performing the step (5);
(5) performing a mutation operation according to formula (37);
(6) updating the particle position, judging whether the ending condition is met, and if not, returning to the step (2); and if so, ending and outputting the optimal solution.
4. Example simulation and result analysis
(1) Parameter setting
As shown in fig. 3, the selected load and photovoltaic data of scene two is the same as scene one, and 150MW of distributed photovoltaic and 55MW of maximum load are used. The data in this chapter are shown in fig. 4 and 5, the vertical axis in the graph takes the form of per unit values, the load takes the typical load of four seasons, and the photovoltaic also takes four typical curves. The maximum charging and discharging time of the lithium titanate is 0.5h, and the maximum charging and discharging time of the flow battery is 2 h; the SOC interval of the lithium titanate battery is 0.05-0.95, and the SOC interval of the all-vanadium redox flow battery is 0-1; and the photovoltaic operation management cost coefficient is 9.6 CNY/MWh.
This patent is directed against the virtual power plant that hybrid energy storage system and load that distributed photovoltaic, lithium titanate battery and all vanadium redox flow battery constitute, carries out contrastive analysis through quantum particle swarm and improvement quantum group algorithm to energy storage system capacity configuration, and the model that two kinds of algorithms adopted and virtual power plant operation strategy keep unanimous, and the parameter value in the model that two kinds of algorithms used is promptly is above, and have the uniformity. And (3) carrying out mutation operation on the attractors, and comparing and analyzing the advantages and disadvantages of the improved quantum particle swarm algorithm.
TABLE 1 Quantum particle swarm algorithm (improved Quantum particle swarm algorithm) parameter configuration
| Parameter(s) | Numerical |
| Population size | |
| 50 | |
| Number of |
200 |
| Inertia factor | 0.5 |
| Self factor | 2.0 |
| Global factor | 2.0 |
According to the table 1, all parameters of the two algorithms are consistent, the calculation speed of the algorithm is determined by the size of the population scale and the number of the iteration times, and the working efficiency of the two algorithms can be analyzed by comparing the calculation time of the two algorithms; the iteration times are kept consistent, and the quality of the calculation results of the two algorithms can be obtained by analyzing the convergence of the target.
(2) Analysis of simulation results
Fig. 6 is a simulation performed by a quantum-behaved particle swarm algorithm, and belongs to a power interaction curve of a virtual power plant and a power grid within one day in the interaction process of the virtual power plant and the power grid, and fig. 7 is a simulation performed by improving the quantum-behaved particle swarm algorithm. According to fig. 6 and 7, it can be seen that power interaction between the power grid and the virtual power plant is approximately the same in one day, the virtual power plant purchases power from the power grid when the electricity price is in the valley period, the virtual power plant sells power to the power grid when the electricity price is in the peak period, and the virtual power plant sells power first and then purchases power when the electricity price is in the average period.
According to fig. 6 and 7, it can be seen that power interaction between the power grid and the virtual power plant is approximately the same in one day, the virtual power plant purchases power from the power grid when the electricity price is in the valley period, the virtual power plant sells power to the power grid when the electricity price is in the peak period, and the virtual power plant sells power first and then purchases power when the electricity price is in the average period. The difference of the data obtained by the two methods is that the quantum improved quantum particle swarm algorithm is used, the electric quantity interaction between a power grid and a virtual power plant is more frequent in the peak period, and the electric quantity interaction is smoother in the ordinary period relative to the quantum particle swarm algorithm. The fact that electric quantity interaction is carried out between the power grid and the virtual power plant more frequently in the peak period in the simulation process of the improved quantum particle swarm optimization algorithm, namely the frequency of electric quantity interaction between the photovoltaic output and the energy storage system is more obvious according to the load demand and the ordinary period, and the energy storage system basically does not output power. Fig. 8 and 9 are a one-day output curve of a lithium titanate battery based on quantum particle swarm algorithm simulation and a one-day output curve of a lithium titanate battery based on improved quantum particle swarm algorithm simulation, respectively.
As can be seen from fig. 8 and 9, the maximum output power of the lithium titanate battery obtained based on the quantum-behaved particle swarm optimization is 2.1437 × 104k W; the maximum output power of the lithium titanate battery obtained based on the improved quantum particle swarm algorithm is 2.2353 x 104k W. For fig. 8, the lithium titanate battery needs to be charged at first, when in the valley period, the lithium titanate battery is only discharged, and when in the peak period and the flat period, the lithium titanate battery basically keeps the charged or discharged state, but the charging and discharging frequency of the peak period is obviously faster than that of the flat period; for fig. 9, the flat and valley states are substantially the same as fig. 9, but the number of charging and discharging times is significantly less than that of fig. 8.
Fig. 10 and fig. 11 are a one-day output curve of a lithium titanate battery based on quantum particle swarm optimization simulation and a one-day output curve of an all-vanadium redox flow battery based on improved quantum particle swarm optimization simulation, respectively.
As can be seen from fig. 10 and 11, the maximum output power of the all-vanadium redox flow battery obtained based on the quantum particle swarm optimization is 5.5507 × 104k W; the maximum output power of the all-vanadium redox flow battery obtained based on the improved quantum particle swarm optimization is 5.9986 x 104k W.
Fig. 12 and fig. 13 are virtual power plant output curves based on the quantum-behaved particle swarm optimization and the improved quantum-behaved particle swarm optimization, respectively.
As can be seen from fig. 12 and 13, since both the lithium titanate battery and the all-vanadium redox flow battery belong to the capacity type, the state of simultaneous charging or simultaneous discharging is basically maintained; the time-of-use electricity price curve graph is added in the simulation graph of the output of the virtual power plant, so that the interaction frequency of the curve for improving the simulation of the quantum particle swarm algorithm between the power grid and the energy storage system in the electricity price peak period is more obvious than that of the simulation graph of the quantum particle swarm algorithm; the energy storage system is basically not charged or discharged in the simulation curve obtained by improving the quantum particle swarm algorithm in the valley period and the ordinary period, and the simulation curve of the quantum particle swarm algorithm shows that the energy storage system almost keeps a charging or discharging state at any time in one day.
Fig. 13 and 14 are graphs of convergence of net yield of a virtual power plant based on two algorithms.
The analysis shows that the simulation results of the quantum particle swarm optimization and the improved quantum particle swarm optimization are the same and different, but the advantages and the disadvantages of the two algorithms cannot be explained. FIGS. 13 and 14 illustrate the convergence of net gains of a virtual power plant based on quantum-behaved particle swarm optimization and improved quantum-behaved particle swarm optimization. Analyzing the fig. 13 and 14 in two dimensions, from the vertical axis dimension, the gains of the fig. 13 and 14 tend to a stable value at the end, the optimal value and the average value of the quantum-behaved particle swarm algorithm do not coincide, which indicates that a certain optimization space still exists, however, the optimal value of 8.403 × 105 is smaller than the optimal value of 8.73 × 105 of the improved algorithm, which indicates that the former falls into the situation of local optimization, and the global optimal solution cannot be found; from the horizontal axis dimension, the optimized value of 200 times in fig. 13 is reached by 30 times in fig. 14, the optimal value of 180 times in fig. 13 tends to be unchanged, and the optimal value of 140 times in fig. 14 tends to be unchanged, so that the convergence rate of the improved algorithm is obviously fast and the improved algorithm is applied to the quantum-behaved particle swarm optimization. The quantum particle group time 22.241845s and the modified quantum particle group algorithm time 16.102983s are the time that the algorithm before the modification is preceded after the modification in view of the calculation speed.
The following table shows the energy storage capacity required to be configured by the hybrid energy storage system in the VPP based on quantum particle swarm optimization and improved quantum particle swarm optimization simulation.
TABLE 2 simulation results of two algorithms
| Simulation result of quantum particle swarm algorithm | Numerical value | Improved quantum particle swarm algorithm simulation result | Numerical value |
| Lithium titanate battery configuration capacity/MWh | 8.8 | Lithium titanate batteryPool configured capacity/MWh | 6.2 |
| All-vanadium redox flow battery configuration capacity/MWh | 23.9 | All-vanadium redox flow battery configuration capacity/MWh | 21.6 |
| Calculating time/s | 22.24 | Calculating time/s | 16.10 |
| VPP Net profit/CNY | 8.403×105 | VPP Net profit/CNY | 8.73×105 |
The capacity of the energy storage system is set according to the maximum capacity (i.e. the integral of the charge and discharge power over time) of the energy storage system for continuous charging and discharging.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (6)
1. An energy storage system optimization configuration method in VPP of improved quantum particle swarm is characterized in that: the method comprises the following steps:
solving an objective function with the maximum net income of the virtual power plant by using an improved quantum particle swarm algorithm to obtain n-time photovoltaic output power, n-time gas turbine output and n-time energy storage system discharge power;
controlling the photovoltaic to charge the energy storage system on the premise that the photovoltaic output meets the load; when the photovoltaic output cannot meet the load requirement, controlling an energy storage system to supply power to the load;
under the premise of guaranteeing that the distributed photovoltaic power generation amount is preferentially consumed, when the time t is in a peak period, the virtual power plant is controlled to sell power to the power grid on the premise of meeting the load demand; when the time t is in the valley period, controlling the virtual power plant to purchase power from the power grid, supplying load and interacting with the energy storage system; when the time t is in the flat time period, the output cost of the gas turbine in the virtual power plant is considered to be compared with the power price of the power grid at the time, when the power price of the power grid is larger than the output cost of the gas turbine, the virtual power plant is controlled not to interact with the power grid, and when the power price of the power grid is smaller than the output cost of the gas turbine, the virtual power plant is controlled to buy power from the power grid.
2. The method for optimizing and configuring the energy storage system in the VPP of the improved quantum particle swarm of claim 1, wherein the method comprises the following steps: the objective function calculation formula of the virtual power plant with the maximum net income is as follows:
max In＝Pn-Cn
wherein n represents a time sequence, and takes 15min as a time period, and n is 1,2, …, 96; i isnRepresents the n-slot VPP net gain; pnRepresents the VPP revenue for the n period; cnRepresents the total cost of the n-period VPP;
representing the photovoltaic output power in n time periods;
representing the gas turbine output for a period of n;
representing the discharge power of the lithium titanate battery in n time period;
representing the discharge power of the all-vanadium redox flow battery in the n period;
representing the discharge power of the super capacitor in the n period;
representing the output power of the power grid to the virtual power plant in the period of n;
representing the charging power of the lithium titanate battery in the n time period;
representing the charging power of the all-vanadium redox flow battery in the n period;
representing the supercapacitor charge power over an n period;
representing the output power of the virtual power plant to the power grid in the period of n;
respectively representing the operation management cost of the virtual power plant at the n period, the energy consumption cost of the virtual power plant at the n period and the punishment cost of the virtual power plant at the n period;
respectively representing a photovoltaic operation management cost coefficient, a gas turbine operation management cost coefficient and an energy storage system operation management cost coefficient; pGTRepresents the fuel cost per unit of power generation of the gas turbine; dn、
And the method respectively represents the declared planned output of the virtual power plant in the n period, the electricity purchasing price in the n period and the electricity selling price in the n period.
3. The method for optimizing and configuring the energy storage system in the VPP of the improved quantum particle swarm of claim 1, wherein the method comprises the following steps: the constraint conditions of the objective function with the maximum net income of the virtual power plant are as follows:
and power balance constraint:
wherein, delta represents the deviation of the predicted output force and the actual output force of the VPP next day in the n time period;
gas turbine power constraint:
wherein,
respectively representing the upper and lower power limits of the gas turbine in the normal working state;
and (3) restricting the climbing rate of the gas turbine:
in the formula,
the ramp rates of the gas turbine unit are upward and downward respectively;
and (3) energy storage battery electric quantity constraint:
in the formula,
is the electric quantity of the lithium titanate battery within n time period etac1、ηd1Respectively the charging and discharging efficiency of the lithium titanate battery;
for the electricity of the n-period all-vanadium flow batteryAmount ηc2、ηd2Respectively representing the charging efficiency and the discharging efficiency of the all-vanadium redox flow battery;
is the electric quantity of the super capacitor in the n period of time, etac3、ηd3Respectively the charging and discharging efficiency of the super capacitor;
and (3) charge and discharge restraint of the energy storage system:
in the formula:
respectively the upper and lower limits of the capacity of the lithium titanate battery;
respectively representing the upper limit and the lower limit of the capacity of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the capacity of the super capacitor;
respectively is the upper limit and the lower limit of the charging power of the lithium titanate battery;
respectively setting the upper limit and the lower limit of the charging power of the all-vanadium redox flow battery;
respectively charging the upper limit and the lower limit of the power of the super capacitor;
respectively is the upper limit and the lower limit of the discharge power of the lithium titanate battery;
respectively is the upper limit and the lower limit of the discharge power of the all-vanadium redox flow battery;
respectively the upper limit and the lower limit of the discharge power of the super capacitor;
respectively representing the charge and discharge state variables of the lithium titanate battery, the all-vanadium redox flow battery and the super capacitor, and taking the value of 0 or 1.
4. The method for optimizing and configuring the energy storage system in the VPP of the improved quantum particle swarm of claim 1, wherein the method comprises the following steps: the photovoltaic energy storage system charging method comprises the following specific steps:
carrying out fast Fourier transform and inverse transform on the photovoltaic output signal, and dividing the output signal into high-frequency, medium-frequency and low-frequency signals;
a high-frequency part is absorbed by a super capacitor, a medium-frequency part is absorbed by a lithium titanate battery, and a low-frequency part is absorbed by an all-vanadium redox flow battery.
5. The method for optimizing and configuring the energy storage system in the VPP of the improved quantum particle swarm of claim 1, wherein the method comprises the following steps: the unit power generation fuel cost of the gas turbine is as follows:
PGT＝PNG/(ηe*LNG)
in the formula, PNGIs the natural gas price; etaeGenerating efficiency for the gas turbine; l isNGIs the low calorific value of natural gas.
6. The method for optimizing and configuring the energy storage system in the VPP of the improved quantum particle swarm of claim 1, wherein the method comprises the following steps: the calculation flow of the improved quantum particle swarm optimization is as follows:
(1) in the search space D, a population is initialized.
(2) Let t be 0, all lie in D-dimensional spaceParticles are initialized, each particle being located at Xi,kAnd let the individual history optimal position be Pi,k。
(3) And calculating the fitness of the particles and the average optimal position of the population.
(4) Calculating a diversity metric value of the population, judging, and if 0, performing the step (4); if so, performing the step (5);
(5) performing a mutation operation according to formula (37);
(6) updating the particle position, judging whether the ending condition is met, and if not, returning to the step (2); and if so, ending and outputting the optimal solution.
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