CN110676861A - Capacity optimization configuration method for composite energy storage device of power distribution network - Google Patents

Capacity optimization configuration method for composite energy storage device of power distribution network Download PDF

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CN110676861A
CN110676861A CN201910856604.9A CN201910856604A CN110676861A CN 110676861 A CN110676861 A CN 110676861A CN 201910856604 A CN201910856604 A CN 201910856604A CN 110676861 A CN110676861 A CN 110676861A
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power
bat
storage battery
super capacitor
charging
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王昕�
高强
吴瑞春
周洪青
张晶
李建飞
藏玉清
陈迪雨
杨强
杨迷霞
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Taizhou Hongyuan Electric Power Design Institute Co Ltd
Taizhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Taizhou Hongyuan Electric Power Design Institute Co Ltd
Taizhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a capacity optimal configuration method of a composite energy storage device of a power distribution network, which comprises the following steps: the method comprises the following steps of (1) constructing a calculation model of the state of charge (SOC) of the energy storage device; step (2) acquiring a load value of the independent microgrid system; step (3) providing an energy distribution strategy based on a low-pass filtering principle; step (4), establishing a target function; step (5) establishing constraint conditions of the objective function; step (6), constructing a simulated annealing particle swarm algorithm; step (7), acquiring relevant parameters of the storage battery and the super capacitor; and (8) solving to obtain parameters such as the capacity of the storage battery, the capacity of the super capacitor, the annual average cost and the like, and further applying the steps (1) and (3) to obtain the charging and discharging power of the storage battery and the super capacitor. According to the invention, the capacity of the composite energy storage device of the distribution network is optimally configured, so that the distributed renewable energy consumption capability of the distribution network can be obviously enhanced, and the electric energy quality and safety of power supply of the distribution network under the condition of high-proportion distributed power supply access are improved.

Description

Capacity optimization configuration method for composite energy storage device of power distribution network
Technical Field
The invention relates to the technical field of power grids, in particular to a capacity optimization configuration method for a composite energy storage device of a power distribution network under high-proportion distributed photovoltaic access.
Background
Along with the rapid development of the distributed photovoltaic power generation system in the power distribution network, the traditional power distribution network is changed into an active power distribution network, so that the power distribution network has more flexible characteristics such as demand response and load coordination control, and meanwhile, some problems are brought to the stable operation control of the power distribution network:
1) after the distributed power supply is connected to the power distribution network, the power distribution network is converted from a single power supply to a distributed active power distribution network with medium and small power supplies, the stable operation control of the distributed active power distribution network faces higher requirements, particularly the high-power-density distributed photovoltaic power distribution network with intermittence and randomness, the power fluctuation of the power distribution network has randomness, the stabilizing difficulty is greatly increased, and even the power supply requirement of a user is difficult to meet;
2) the large-scale access of the distributed photovoltaic power supply brings huge influence on the electric energy quality of the power distribution network, devices such as reactive compensation and three-phase imbalance management equipped in the distribution network area are lack, and the problems of electric energy quality such as voltage drop and three-phase imbalance and the like after the high-power-density distributed photovoltaic power supply is accessed into the power distribution network are difficult to solve.
The storage battery is widely applied to energy storage equipment, has high energy density and meets the requirement of distributed power generation on the energy density. However, due to the limitation of electrochemical reaction rate, the power density of the storage battery is relatively low, and when the load power suddenly changes, the target power cannot be absorbed or released quickly, so that the dynamic requirements of the system are difficult to meet. The super capacitor is characterized by large power density due to the fact that physical changes occur inside the super capacitor during charging and discharging, can provide large power in a short time and provide buffering for other equipment, but is low in energy density. Therefore, the super capacitor and the storage battery have strong complementarity in performance, the two energy storage elements can be connected in a certain mode to form a composite energy storage system, the advantages of the super capacitor and the storage battery are fully exerted, and the system obtains better performance; on one hand, aiming at long-time power fluctuation of the power distribution network, the storage battery provides power grid energy support; on one hand, when the distribution network has power quality problems or reactive power shortage, the energy storage system can provide reactive compensation and improve the power quality.
Specifically, the composite energy storage is formed by polymerizing single energy storage with complementary advantages of different characteristics, and has the following obvious advantages: 1) the composite energy storage device can realize the advantage complementation of different energy storages and exert the respective characteristics, so that the space for the different energy storage devices to exert the advantages thereof is expanded; 2) the combination and complementation of power and energy characteristics can be realized, so that multiple requirements of a power grid are met, and the power supply reliability is improved; 3) different energy storage devices can operate in the optimized working interval by means of regulation and control, the charge and discharge states of the devices are optimized, and the service cycle and the cycle life are prolonged; 4) under the rational configuration, reduce energy memory's operation cost, optimize utilization ratio, enlarge the industrial market, obtain great profit. In energy scheduling management and microgrid integrated control, energy storage capacity optimal configuration is a key problem, and the rationality of configuration directly influences the utilization rate of a distributed power supply and the economy and stability of a microgrid system. Therefore, capacity allocation of the composite energy storage system is a problem that must be solved at present.
Disclosure of Invention
Aiming at the defects of the capacity configuration method of the energy storage device in the power distribution network system under the existing high-proportion distributed new energy access, the invention aims to provide the capacity optimal configuration method of the composite energy storage device in the power distribution network automation system, and through reasonable configuration, the utilization rate of a distributed power supply and the economy and stability of a micro-grid system are improved.
In order to solve the technical problems, the invention adopts the following technical scheme: a capacity optimization configuration method of a power distribution network composite energy storage device is disclosed, wherein the power distribution network composite energy storage device comprises a super capacitor and a storage battery, and is characterized by comprising the following steps:
step (1), constructing a calculation model of the state of charge (SOC) of an energy storage device;
step (2), acquiring a load value of the independent microgrid system;
and (3) providing an energy distribution strategy based on a low-pass filtering principle to reasonably schedule two kinds of energy stored: the super capacitor has the characteristics of high power density and high response speed and is used for compensating high-frequency components in the shortage power; the energy type storage battery is used for compensating the rest of the low-frequency component;
step (4), establishing an objective function minf ═ gammabatCbatEbat+γucCucEucWherein, CbatRepresents the unit price ($/kwh), C of the batteryucRepresents the unit price ($/kwh), γ, of the supercapacitorbat、γucRepresenting the investment in accumulators and supercapacitorsCost coefficient, consisting of operation coefficient, maintenance coefficient and depreciation coefficient, i.e. gammabat/uc＝γbat/uc,p+γbat/uc,o+γbat/uc,m；
Step (5), establishing constraint conditions of the objective function, including:
(5.1), transient power balance constraint: ppv,t+Pbat,t+Puc,t+λPlack,t＝Pload,t+(1-λ)Pwaste,tThe lambda represents a system state value, only 0 or 1 value is obtained, when the system is in an excess state, the value is 0, excess electric quantity is solved through the unloader, when the system is in an electricity shortage state, the value is 1, non-important loads need to be cut off to ensure operation of the important loads, and the photovoltaic power generation power is Ppv,tLoad power of Pload,tThe power of the storage battery is Pbat,tThe power of the super capacitor is Puc,t，Pwaste,tRepresenting surplus power, P, of the power generation systemlack,tRepresenting the power generation system power shortage;
(5.2), state of charge constraint: SOCbat,min≤SOCbat,t≤SOCbat,max，SOCuc,min≤SOCuc,t≤SOCuc,max；
(5.3), energy spill ratio constraint:wherein, deltasRepresenting the energy spill over ratio, δsmaxIs the maximum value of the energy overflow ratio;
(5.4), load loss rate constraint:
Figure BDA0002198513870000032
wherein, deltalRepresenting the rate of loss of current, deltalmaxRepresenting the maximum power loss rate of the load;
step (6), constructing a simulated annealing particle swarm algorithm;
step (7), obtaining relevant parameters of the storage battery bat and the super capacitor uc, comprising: maximum state of charge SOCbat/uc,maxState of charge minimum SOCbat/uc,minRated power(kw)Pbat/uc,NRunning coefficient gammabat/uc,pMaintenance factor gammabat/uc,oDepreciation coefficient gammauc/bat,mDischarge efficiency etabat/uc,dCharging efficiency etabat/uc,cMonovalent ($/kwh) Cbat/ucMaximum value of energy overflow ratio δsmaxMaximum value delta of load loss ratelmax；
And (8) solving the optimization problem constructed in the steps (4) and (5) by using the simulated annealing particle swarm algorithm in the step (6) to obtain the capacity of the storage battery, the capacity of the super capacitor and the annual average cost, and further obtaining the charging and discharging power of the storage battery and the super capacitor by using the steps (1) and (3).
Further, in the step (1),
when the energy storage system is in a charging state,
Figure BDA0002198513870000041
when the energy storage system is in a discharge state,
Figure BDA0002198513870000042
therein, SOCbat/uc,tRepresenting the state of charge value of the storage battery bat or the super capacitor uc at the end of the t period, wherein omega represents the automatic discharge loss rate of the energy storage device; pbat/uc,tThe charging and discharging power of the energy storage device in the time period t is shown, discharging is shown when the charging and discharging power is a positive value, and charging is shown when the charging and discharging power is a negative value; Δ t represents a sampling period; ebat/ucThe capacity of the storage battery or the super capacitor is expressed in kwh; etabat/uc,dIndicating the discharge efficiency; etabat/uc,cIndicating the charging efficiency.
Further, in step (3), the energy distribution strategy based on the low-pass filtering principle is implemented in the following two steps:
(3.1) calculating an ideal power compensation value of the composite energy storage system: suppose that the photovoltaic power generation power is P in the t sampling periodpv,tThe photovoltaic power generation power is constant in the time interval, and the load power is Pload,tThe power shortage is Pt *Indicating the ideal power compensation value of the composite energy storage system during dischargePositive value, negative value during charging, Pt *＝Pload,t-Ppv,t；
And (3.2) correcting the charge and discharge power of the composite energy storage system.
Further, the charging and discharging power correction process of the composite energy storage system is divided into seven steps, and the specific steps when the energy storage system is discharged are as follows:
(3.2.1) acquiring ideal charging and discharging power of the storage battery, acquiring the ideal charging and discharging power through application of a low-pass filtering principle, and expressing the ideal charging and discharging power by the formula (1):
Figure BDA0002198513870000051
wherein (1) is obtained by performing time domain discretization on equations (2) and (3):
Figure BDA0002198513870000052
wherein, T isLDenotes the sampling period, fLRepresenting the demarcation compensation frequency of the supercapacitor and the accumulator, by P*Selecting the frequency spectrum analysis; pbat,t-1Representing the actual power of charging and discharging the storage battery in the t-1 time period; 0 to fLThe fluctuation component in the range is compensated by the storage battery, and the super capacitor bears more than fLA frequency band component of (a);
(3.2.2), once correcting the power of the storage battery, and once correcting the ideal charging and discharging power of the storage battery by considering the power limit value of the storage battery:
if it is
Figure BDA0002198513870000054
Then orderThe power of the storage battery is corrected for one time,
wherein, Pbat,NRepresents the rated power of the storage battery;
(3.2.3), secondary correction of the power of the storage battery, namely performing secondary correction on the ideal charge and discharge power of the storage battery by judging whether the charge state of the storage battery after the compensation energy of the primary correction of the storage battery is lower than a charge state limit value or not:
first, by the formulaObtaining the state of charge of the storage battery after the compensation energy is corrected for once;
if it is
Figure BDA0002198513870000057
Then order
Figure BDA0002198513870000061
If it is
Figure BDA0002198513870000062
Then order
Figure BDA0002198513870000063
Wherein, Pbat,tThe actual charging and discharging power of the storage battery in the tth time period is obtained;
Figure BDA0002198513870000064
representing a primary corrected value of the ideal charging and discharging power of the storage battery;
(3.2.4) calculating the ideal charging and discharging power of the super capacitor
Figure BDA0002198513870000065
Which is the difference between the ideal charge and discharge power required by the energy storage device during the t-th period and the actual charge and discharge power of the storage battery during the t-th period, i.e., the difference
Figure BDA0002198513870000066
(3.2.5), the super capacitor power is corrected once, and the ideal charging and discharging power of the super capacitor is corrected once by considering the power limit value of the super capacitor:
if it is
Figure BDA0002198513870000067
Then order
Figure BDA0002198513870000068
The power of the super capacitor is corrected for one time,
wherein, Puc,NRepresenting the rated power of the super capacitor;
(3.2.6), performing secondary correction on the ideal charging and discharging power of the super capacitor by judging whether the charge state of the super capacitor after the compensation energy is subjected to primary correction is lower than a charge state limit value or not:
first, by the formula
Figure BDA0002198513870000069
The charge state of the super capacitor after the compensation energy is corrected once is obtained,
if it isThen order
Figure BDA00021985138700000611
If it is
Figure BDA00021985138700000612
Then order
Figure BDA00021985138700000613
Wherein, Puc,tThe actual charging and discharging power of the super capacitor in the t time period is obtained;
Figure BDA00021985138700000614
representing a primary corrected value of the charging and discharging ideal power of the super capacitor;
(3.2.7) calculating the state of charge value of the composite energy storage device, and after the actual charging and discharging power of the electric power storage and the super capacitor is determined, calculating the state of charge value of the composite energy storage device according to a formula
Figure BDA0002198513870000071
And respectively calculating the state of charge values of the two at the end of the time period.
Further, in the step (6), the simulated annealing particle swarm algorithm is constructed and carried out in the following three steps:
(6.1) improving the PSO of the traditional particle swarm algorithm, namely, adopting an iterative formula xij(n+1)＝xij(n)+vij(n +1) is changed to xij(n+1)＝xij(n)+vij(n+1)*t，xij(n) denotes the position of the ith particle in the jth dimension at the nth iteration, vij(n +1) represents the velocity of the ith particle in the jth dimension at the (n +1) th iteration;
(6.2) modifying the simulated annealing algorithm SA to use linear annealing temperature coefficients and inertial weights, T (T) ((0.95-0.8) × T/maximum +0.8) × T (T-1);
(6.3) combining the two to obtain a simulated annealing particle swarm algorithm SAPSO, and then selecting a method for adding random disturbance to the current solution as a new position function of the particle: x (i, j) — (4 + 2) ×, where X (i, j) is the coordinate of the jth dimension of any particle population and rand is a uniform distribution between 0 and 1.
Further, in the step (6), the specific steps of constructing the simulated annealing particle swarm algorithm are as follows:
(6.01) initializing parameters of a simulated annealing algorithm and a particle swarm;
(6.02) determining the power required by the load and the distributed photovoltaic output power;
(6.03), constructing an objective optimization function model and a constraint function model;
(6.04) evaluation of the nascent particles according to the formula
Figure BDA0002198513870000072
Calculating a fitness value of each particle objective function;
(6.05) randomly generating a new position for the particle according to a position formula X (i, j) ═ X (i, j) — (4 + 2) · rand), and calculating an increment Δ, Δ (t) ═ F (t) -F (t-1) (t ═ 1,2,3 ·, N) of the fitness value at that time;
(6.06), if Δ<0, replacing the old position with the new position, performing an annealing temperature operation on the particles, if delta>0, randomly generating rand, wherein the value range of rand is between 0 and 1; if rand < e-Δ/T(t)If yes, the decision variable enters a new position, the particle swarm executes the annealing temperature operation, and if not, the step (6.05) is executed;
(6.07), determining whether the particle is on a constraint set, namely whether the particle is a feasible solution, and updating and recording the local optimal value and the global optimal value of the particle according to the result;
(6.08), calculating and updating the speed and the position of the particle at the moment;
(6.09), judging whether the ending condition is met, namely whether the iteration times are greater than maximum or whether the precision requirement is met. If yes, the program is jumped out and the optimal solution at the moment is output, otherwise, the step (6.05) is carried out.
By adopting the technical scheme, the energy scheduling strategy for reasonably configuring the composite energy storage capacity is provided by establishing the calculation model of the charge state of the energy storage device, the optimization objective function with the minimum annual cost as the target is determined, the constraint conditions such as instantaneous power balance, load state, load power shortage rate, energy overflow ratio and the like are considered, the optimization model of the load energy storage capacity configuration is established, and the optimization problem is solved by adopting the simulated annealing particle swarm optimization algorithm, so that the cost of the composite energy storage capacity configuration can be saved, and a more economic and effective solution is provided for the capacity configuration problem of the energy storage device in the power distribution network automation system. By optimally configuring the capacity of the composite energy storage device of the distribution network, the distributed renewable energy consumption capability of the distribution network can be obviously enhanced, and the electric energy quality and safety of power supply of the distribution network under the condition of high-proportion distributed power supply access are improved.
The following detailed description of the present invention will be provided in conjunction with the accompanying drawings.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a graph of photovoltaic output;
FIG. 2 is a graph of ideal power compensation values;
FIG. 3 is a graph of the results of the spectral analysis;
fig. 4 is a charge-discharge power diagram.
Detailed Description
The Micro-Grid (Micro-Grid) is also translated into a Micro-Grid, which refers to a small power generation and distribution system composed of a distributed power supply, an energy storage device, an energy conversion device, a load, a monitoring and protecting device and the like.
The invention provides a capacity optimal configuration method of a composite energy storage device in a distribution network automation system, which improves the utilization rate of a distributed power supply and the economy and stability of a micro-grid system through reasonable configuration. The composite energy storage device of the power distribution network comprises a super capacitor and a storage battery.
Step (1), constructing a calculation model of the state of charge (SOC) of the energy storage device: when the energy storage system is in a charging state,
Figure BDA0002198513870000091
when the energy storage system is in a discharge state,
Figure BDA0002198513870000092
wherein the SOCbat/uc,tRepresents the state of charge value of the accumulator (bat) or of the supercapacitor (uc) at the end of the period t, ω representing the rate of automatic discharge loss of the energy storage device; pbat/uc,tThe charging and discharging power of the energy storage device in the t time period is shown, discharging is shown when the charging and discharging power is a positive value, and charging is shown when the charging and discharging power is a negative value; Δ t represents a sampling period; ebat/ucThe capacity of the storage battery or the super capacitor is expressed in kwh;
step (2), acquiring a load value of the independent microgrid system;
and (3) providing an energy distribution strategy based on a low-pass filtering principle to reasonably schedule two kinds of energy stored: the super capacitor has the characteristics of high power density and high response speed and is used for compensating high-frequency components in the shortage power; the energy type storage battery is used for compensating the rest of the low-frequency component. The strategy is implemented in two steps:
(3.1) calculating an ideal power compensation value of the composite energy storage system: suppose that the photovoltaic power generation power is P in the t sampling periodpv,tThe photovoltaic power generation power is constant in the time interval, and the load power is Pload,tThe power shortage is Pt *And the ideal power compensation value of the composite energy storage system is represented, wherein the ideal power compensation value is a positive value during discharging and a negative value during charging, and then P is obtainedt *＝Pload,t-Ppv,t；
(3.2) correcting the charging and discharging power of the composite energy storage system, wherein the correcting process comprises seven steps, and the specific steps are as follows when the energy storage system discharges:
and (3.2.1) acquiring ideal charging and discharging power of the storage battery. By application of the low-pass filtering principle, the acquisition can be expressed by the formula (1):
Figure BDA0002198513870000101
wherein (1) is obtained by performing time domain discretization on equations (2) and (3):
Figure BDA0002198513870000102
Figure BDA0002198513870000103
wherein f isLThe boundary compensation frequency of the super capacitor and the storage battery can be represented by P*Selecting the frequency spectrum analysis; pbat,t-1Representing the actual power of the charging and discharging of the storage battery in the t-1 time period; 0 to fLThe fluctuation component in the range is compensated by the storage battery, and the super capacitor bears more than fLA frequency band component of (a);
and (3.2.2) correcting the power of the storage battery at one time. The ideal charging and discharging power of the storage battery is corrected for once by considering the power limit value of the storage battery:
if it is
Figure BDA0002198513870000104
Then order
Figure BDA0002198513870000105
And performing primary correction on the power of the storage battery.
Wherein, Pbat,NRepresents the rated power of the storage battery;
and (3.2.3) secondarily correcting the power of the storage battery. And performing secondary correction on the ideal charge-discharge power of the storage battery by judging whether the charge state of the storage battery after the compensation energy of the primary correction is lower than a charge state limit value:
first, the formulaAnd obtaining the state of charge of the storage battery after the compensation energy is corrected once.
If it is
Figure BDA0002198513870000107
Then order
Figure BDA0002198513870000108
If it is
Figure BDA0002198513870000111
Then order
Figure BDA0002198513870000112
Wherein, Pbat,tThe actual charging and discharging power of the storage battery in the tth time period is obtained;representing a primary corrected value of the ideal charging and discharging power of the storage battery;
(3.2.4) calculating the ideal charging and discharging power of the super capacitorWhich is the difference between the ideal charge and discharge power required by the energy storage device during the t-th period and the actual charge and discharge power of the storage battery during the t-th period, i.e., the difference
Figure BDA0002198513870000115
(3.2.5), the super capacitor power is corrected once. The ideal charging and discharging power of the super capacitor is corrected for one time by considering the power limit value of the super capacitor:
if it is
Figure BDA0002198513870000116
Then order
Figure BDA0002198513870000117
And performing primary correction on the power of the super capacitor.
Wherein, Puc,NRepresenting the rated power of the super capacitor.
And (3.2.6) secondarily correcting the power of the super capacitor. And performing secondary correction on the ideal charge-discharge power of the super capacitor by judging whether the charge state of the super capacitor after the compensation energy of the primary correction is lower than a charge state limit value:
first, the formula
Figure BDA0002198513870000118
And obtaining the charge state of the super capacitor after the compensation energy is corrected once.
If it isThen order
Figure BDA00021985138700001110
If it is
Figure BDA00021985138700001111
Then order
Figure BDA00021985138700001112
Wherein, Puc,tThe actual charging and discharging power of the super capacitor in the t time period is obtained;
Figure BDA00021985138700001113
and the primary corrected value of the ideal charging and discharging power of the super capacitor is shown.
And (3.2.7) calculating the state of charge value of the composite energy storage device. After the actual charging and discharging power of the storage and the super capacitor is determined, the actual charging and discharging power is passed
Figure BDA0002198513870000121
And respectively calculating the state of charge values of the two at the end of the time period.
Step (4), establishing an objective function minf ═ gammabatCbatEbat+γucCucEucWherein, CbatRepresents the unit price ($/kwh), C of the batteryucRepresents the unit price ($/kwh), γ, of the supercapacitorbat、γucThe coefficient of investment cost of the storage battery and the super capacitor is composed of an operation coefficient, a maintenance coefficient and a depreciation coefficient, namely gammabat/uc＝γbat/uc,p+γbat/uc,o+γbat/uc,m；
Step (5), establishing constraint conditions of the objective function, including:
(5.1), transient power balance constraint: ppv,t+Pbat,t+Puc,t+λPlack,t＝Pload,t+(1-λ)Pwaste,tThe lambda represents a system state value, only 0 or 1 is taken as the value, when the system is in an excess state, the value is 0, excess electric quantity is solved through the unloader, when the system is in an electricity shortage state, the value is 1, and non-important loads need to be cut off to ensure the operation of important loads;
(5.2), state of charge constraint: SOCbat,min≤SOCbat,t≤SOCbat,max，SOCuc,min≤SOCuc,t≤SOCuc,max，；
(5.3), energy spill ratio constraint:wherein, deltasRepresenting the energy spill over ratio, δsmaxIs the maximum value of the energy overflow ratio;
(5.4), load loss rate constraint:
Figure BDA0002198513870000123
wherein, deltalRepresenting the rate of loss of current, deltalmaxRepresenting the maximum power loss rate of the load;
step (6), constructing a simulated annealing particle Swarm Algorithm (SAPSO), and performing the following three steps:
(6.1) improving the traditional Particle Swarm Optimization (PSO), namely, the iterative formula xij(n+1)＝xij(n)+vij(n +1) is changed to xij(n+1)＝xij(n)+vij(n+1)*t，xij(n) denotes the position of the ith particle in the jth dimension at the nth iteration, vij(n +1) represents the velocity of the ith particle in the jth dimension at the (n +1) th iteration;
(6.2) modifying the simulated annealing algorithm (SA) by using linear annealing temperature coefficients and inertial weights, having T (T) ((0.95-0.8) × T/maximum +0.8) × T (T-1);
(6.3) combining the two to obtain a Simulated Annealing Particle Swarm Optimization (SAPSO), and then selecting a method for adding random disturbance to the current solution as a new position function of the particle: x (i, j) — (4 + 2) ×, where X (i, j) is the coordinate of the jth dimension of any particle population and rand is a uniform distribution between 0 and 1.
The specific steps of the algorithm are as follows:
(1) initializing a simulated annealing algorithm and parameters of the particle swarm;
(2) determining the power required by the load and the distributed photovoltaic output power;
(3) constructing a target optimization function model and a constraint function model;
(4) evaluating the nascent particle according to the formula
Figure BDA0002198513870000131
Calculating a fitness value of each particle objective function;
(5) randomly generating a new position for the particle according to a position formula X (i, j) ═ X (i, j) — 4+2 × rand, and calculating the increment delta of the fitness value at the moment, wherein the delta (t) ═ F (t) (-F (t-1) (t ═ 1,2,3 ·, N);
(6) if Δ is<0, replacing the old position with the new position, performing an annealing temperature operation on the particles, if delta>0, randomly generating rand, wherein the value range of rand is between 0 and 1; if rand < e-Δ/T(t)If yes, the decision variable enters a new position, the particle swarm executes annealing temperature operation, and if not, the step (5) is executed;
(7) determining whether the particle is on a constraint set, namely whether the particle is a feasible solution, and updating and recording a local optimal value and a global optimal value of the particle according to results;
(8) calculating and updating the speed and the position of the particle at the moment;
(9) and judging whether the ending condition is met, namely whether the iteration times are greater than maximum or whether the precision requirement is met. If yes, jumping out of the program and outputting the optimal solution at the moment, otherwise, turning to the step (5);
a step (7) of obtaining the relevant parameters of the accumulator (bat) and the super capacitor (uc), comprising: maximum state of charge SOCbat/uc,maxState of charge minimum SOCbat/uc,minRated power (kw) Pbat/uc,NRunning coefficient gammabat/uc,pMaintenance factor gammabat/uc,oDepreciation coefficient gammauc/bat,mDischarge efficiency etabat/uc,dCharging efficiency etabat/uc,cMonovalent ($/kwh) Cbat/ucMaximum value of energy overflow ratio δsmaxMaximum value delta of load loss ratelmax；
And (8) solving the optimization problem constructed in the steps (4) and (5) by using the simulated annealing particle swarm algorithm in the step (6) to obtain parameters such as the capacity of the storage battery, the capacity of the super capacitor, the annual average cost and the like, and further obtaining the charging and discharging power of the storage battery and the super capacitor by using the steps (1) and (3).
The invention is described in more detail below with reference to examples:
step (1), constructing a calculation model of the state of charge (SOC) of the energy storage device: when the energy storage system is in a charging state,
Figure BDA0002198513870000141
when the energy storage system is in a discharge state,
Figure BDA0002198513870000142
wherein the SOCbat/uc,tRepresents the state of charge value of the accumulator (bat) or the super capacitor (uc) at the end of the t period, ω represents the automatic discharge loss rate of the energy storage device, and can directly take the value of 0.83%/h for the accumulator, and can take the value of 0 for the super capacitor when the scheduling cycle is short; pbat/uc,tThe charging and discharging power of the energy storage device in the t time period is shown, discharging is shown when the charging and discharging power is a positive value, and charging is shown when the charging and discharging power is a negative value; Δ t represents a sampling period; ebat/ucThe capacity of the storage battery or the super capacitor is expressed in kwh;
and (2) acquiring a load value of the independent microgrid system, wherein the selected object is an independent microgrid system of southeast China. The south china honesty steel structure company owns a photovoltaic module with 302.1kw, a load curve of No. 5/month and No. 16 in 2018 is shown in fig. 1 (five minutes is taken as a time period, and 288 spaced points are used), and in addition, an average value of the load power which is 0.95 times of the photovoltaic power is selected;
and (3) providing an energy distribution strategy based on a low-pass filtering principle to reasonably schedule two kinds of energy stored: the super capacitor has the characteristics of high power density and high response speed and is used for compensating high-frequency components in the shortage power; the energy type storage battery is used for compensating the rest of the low-frequency component.
In this example, the ideal power compensation value shown in fig. 2 is first fourier-transformed to obtain a frequency spectrum as shown in fig. 3, and it can be seen from the figure that the amplitude of the power of the high frequency part is small, the amplitude of the power of the low frequency part is large, and a peak appears at the frequency 0.0000018, so that 0.0000018 is taken as a boundary frequency, the fluctuation component in the range of 0 to 0.0000018 is compensated by the storage battery, and the super capacitor bears the frequency band component higher than 0.0000018.
Obtaining a decomposition frequency fsThen, in the process of low-pass filtering the ideal charging and discharging power, a Chebyshev filter is selected, and parameters of the Chebyshev filter are selected as follows:
Wp＝2*pai*0.000001/(fs)
Ws＝2*pai*0.0000032/(fs)
wherein the pass band is cut to the frequency f1Taken as 0.000001, is the first minimum value from the frequency response, the stop band cut-off frequency f2Taken as 0.0000032, was taken from:
f1f2＝fs 2
attenuation in pass band does not exceed rp 0.2dB
Attenuation in the stop band does not exceed rp to 30dB
Step (4), establishing an objective function minf ═ gammabatCbatEbat+γucCucEucWherein, CbatRepresents the unit price ($/kwh), C of the batteryucRepresents the unit price ($/kwh), γ, of the supercapacitorbat、γucThe coefficient of investment cost of the storage battery and the super capacitor is composed of an operation coefficient, a maintenance coefficient and a depreciation coefficient, namely gammabat/uc＝γbat/uc,p+γbat/uc,o+γbat/uc,m；
Step (5), establishing constraint conditions of the objective function
Step (6), constructing a simulated annealing particle Swarm Algorithm (SAPSO)
A step (7) of obtaining the relevant parameters of the accumulator (bat) and the super capacitor (uc), comprising: maximum state of charge SOCbat/uc,maxState of charge minimum SOCbat/uc,minRated power (kw) Pbat/uc,NRunning coefficient gammabat/uc,pMaintenance factor gammabat/uc,oDepreciation coefficient gammauc/bat,mDischarge efficiency etabat/uc,dCharging efficiency etabat/uc,cMonovalent ($/kwh) Cbat/ucMaximum value of energy overflow ratio δsmaxMaximum value delta of load loss ratelmaxAs shown in the following table:
TABLE 1 relevant parameters of storage batteries and supercapacitors
Figure BDA0002198513870000161
And (8) solving the optimization problem constructed in the steps (4) and (5) by using the simulated annealing particle swarm optimization algorithm in the step (6), so as to obtain the capacity of the storage battery of 365.73kwh, the capacity of the super capacitor of 78.51kwh, the annual average cost of 9.56 x 10^4, and further obtain the charge-discharge power of the storage battery and the super capacitor by using the steps (1) and (3) as shown in the figure 4.
Fig. 4 is a charging and discharging power diagram of the storage battery and the super capacitor, and we can see from the diagram that:
(1) the fluctuation of the storage battery charging and discharging is smooth, the super capacitor is charged and discharged frequently all the time in the whole process, the storage battery can process low-frequency components, and the super capacitor can process high-frequency wave bands and is consistent with a scheduling strategy.
(2) The charging and discharging power amplitude of the storage battery is large, and the charging and discharging power amplitude of the super capacitor is small and is consistent with the frequency spectrum analysis result.
(3) Generally, when the whole energy storage system is in a discharging or charging state, the charging and discharging states of the storage battery and the super capacitor are consistent. However, as can be seen from the vicinity of point 200 on the figure, the super capacitor and the storage battery may be in different states, because the number of times of operation of the storage battery is reduced due to frequent charging and discharging of the super capacitor.
(4) In the sampling point from 115 to 198, the storage battery is always in a constant charging state, and the charging and discharging amplitude of the super capacitor in the period has a peak, and the amplitude fluctuation is large. This is because the storage battery is limited by the rated capacity and cannot bear more, and the super capacitor is responsible for the compensation task at the moment by virtue of the advantage of higher power density
(5) And in the sampling points of 15-60 and 250-300, the change trends of the charge and discharge power of the super capacitor and the storage battery are basically consistent, namely, the super capacitor is in a gentle state, and in addition, the super capacitor basically does not or only bears a small part of compensation tasks. This is because the load power is averaged in the two time periods during data processing, and the time period is at night, and the photovoltaic power generation is zero due to the influence of weather, so the charge and discharge power of the storage battery and the super capacitor also maintains a stable state.
And between the sampling points of 0 and 15, the charge and discharge power of the storage battery is in an ascending state, the charge and discharge power of the super capacitor is in a descending state, the charge and discharge power of the storage battery is basically zero at the beginning, and the super capacitor almost bears all compensation tasks. This is because the super capacitor has a fast response speed and high flexibility.
The method takes an independent microgrid system as an example, gives detailed algorithm description, and proves the effectiveness of the method.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.

Claims (6)

1. A capacity optimization configuration method of a power distribution network composite energy storage device is disclosed, wherein the power distribution network composite energy storage device comprises a super capacitor and a storage battery, and is characterized by comprising the following steps:
step (1), constructing a calculation model of the state of charge (SOC) of an energy storage device;
step (2), acquiring a load value of the independent microgrid system;
and (3) providing an energy distribution strategy based on a low-pass filtering principle to reasonably schedule two kinds of energy stored: the super capacitor has the characteristics of high power density and high response speed and is used for compensating high-frequency components in the shortage power; the energy type storage battery is used for compensating the rest of the low-frequency component;
step (4), establishing an objective function minf ═ gammabatCbatEbat+γucCucEucWherein, CbatRepresents the unit price ($/kwh), C of the batteryucRepresents the unit price ($/kwh), γ, of the supercapacitorbat、γucThe coefficient of investment cost of the storage battery and the super capacitor is composed of an operation coefficient, a maintenance coefficient and a depreciation coefficient, namely gammabat/uc＝γbat/uc,p+γbat/uc,o+γbat/uc,m；
Step (5), establishing constraint conditions of the objective function, including:
(5.1), transient power balance constraint: ppv,t+Pbat,t+Puc,t+λPlack,t＝Pload,t+(1-λ)Pwaste,tThe lambda represents a system state value, only 0 or 1 value is obtained, when the system is in an excess state, the value is 0, excess electric quantity is solved through the unloader, when the system is in an electricity shortage state, the value is 1, non-important loads need to be cut off to ensure operation of the important loads, and the photovoltaic power generation power is Ppv,tLoad power of Pload,tThe power of the storage battery is Pbat,tThe power of the super capacitor is Puc,t，Pwaste,tRepresenting surplus power, P, of the power generation systemlack,tRepresenting the power generation system power shortage;
(5.2), state of charge constraint: SOCbat,min≤SOCbat,t≤SOCbat,max，SOCuc,min≤SOCuc,t≤SOCuc,max；
(5.3), energy spill ratio constraint:
Figure FDA0002198513860000011
wherein, deltasRepresenting the energy spill over ratio, δsmaxIs the maximum value of the energy overflow ratio;
(5.4), load loss rate constraint:
Figure FDA0002198513860000021
wherein, deltalRepresenting the rate of loss of current, deltalmaxRepresenting the maximum power loss rate of the load;
step (6), constructing a simulated annealing particle swarm algorithm;
step (7), obtaining relevant parameters of the storage battery bat and the super capacitor uc, comprising: maximum state of charge SOCbat/uc,maxState of charge minimum SOCbat/uc,minRated power (kw) Pbat/uc,NRunning coefficient gammabat/uc,pMaintenance factor gammabat/uc,oDepreciation coefficient gammauc/bat,mDischarge efficiency etabat/uc,dCharging efficiency etabat/uc,cMonovalent ($/kwh) Cbat/ucMaximum value of energy overflow ratio δsmaxMaximum value delta of load loss ratelmax；
And (8) solving the optimization problem constructed in the steps (4) and (5) by using the simulated annealing particle swarm algorithm in the step (6) to obtain the capacity of the storage battery, the capacity of the super capacitor and the annual average cost, and further obtaining the charging and discharging power of the storage battery and the super capacitor by using the steps (1) and (3).
2. The capacity optimization configuration method of the composite energy storage device of the power distribution network according to claim 1, characterized in that: in the step (1), the step (c),
when the energy storage system is in a charging state,
Figure FDA0002198513860000022
when the energy storage system is in a discharge state,
Figure FDA0002198513860000023
therein, SOCbat/uc,tRepresenting the state of charge value of the storage battery bat or the super capacitor uc at the end of the t period, wherein omega represents the automatic discharge loss rate of the energy storage device; pbat/uc,tThe charging and discharging power of the energy storage device in the time period t is shown, discharging is shown when the charging and discharging power is a positive value, and charging is shown when the charging and discharging power is a negative value; Δ t represents a sampling period; ebat/ucThe capacity of the storage battery or the super capacitor is expressed in kwh; etabat/uc,dIndicating the discharge efficiency; etabat/ucAnd c represents charging efficiency.
3. The capacity optimization configuration method of the composite energy storage device of the power distribution network according to claim 2, characterized in that: in the step (3), the energy distribution strategy based on the low-pass filtering principle is implemented by the following two steps:
(3.1) calculating an ideal power compensation value of the composite energy storage system: suppose that the photovoltaic power generation power is P in the t sampling periodpv,tThe photovoltaic power generation power is constant in the time interval, and the load power is Pload,tThe power shortage is Pt *And the ideal power compensation value of the composite energy storage system is represented, wherein the ideal power compensation value is a positive value during discharging and a negative value during charging, and then P is obtainedt *＝Pload,t-Ppv,t；
And (3.2) correcting the charge and discharge power of the composite energy storage system.
4. The capacity optimization configuration method of the composite energy storage device of the power distribution network according to claim 3, characterized in that: the charging and discharging power correction process of the composite energy storage system is divided into seven steps, and the specific steps when the energy storage system discharges are as follows:
(3.2.1) acquiring ideal charging and discharging power of the storage battery, acquiring the ideal charging and discharging power through application of a low-pass filtering principle, and expressing the ideal charging and discharging power by the formula (1):
wherein (1) is obtained by performing time domain discretization on equations (2) and (3):
Figure FDA0002198513860000032
Figure FDA0002198513860000033
wherein, T isLDenotes the sampling period, fLRepresenting the demarcation compensation frequency of the supercapacitor and the accumulator, by P*Frequency ofSpectrum analysis and selection; pbat,t-1Representing the actual power of charging and discharging the storage battery in the t-1 time period; 0 to fLThe fluctuation component in the range is compensated by the storage battery, and the super capacitor bears more than fLA frequency band component of (a);
(3.2.2), once correcting the power of the storage battery, and once correcting the ideal charging and discharging power of the storage battery by considering the power limit value of the storage battery:
if it isThen orderThe power of the storage battery is corrected for one time,
wherein, Pbat,NRepresents the rated power of the storage battery;
(3.2.3), secondary correction of the power of the storage battery, namely performing secondary correction on the ideal charge and discharge power of the storage battery by judging whether the charge state of the storage battery after the compensation energy of the primary correction of the storage battery is lower than a charge state limit value or not:
first, by the formula
Figure FDA0002198513860000041
Obtaining the state of charge of the storage battery after the compensation energy is corrected for once;
if it is
Figure FDA0002198513860000042
Then order
If it is
Figure FDA0002198513860000044
Then order
Figure FDA0002198513860000045
Wherein, Pbat,tThe actual charging and discharging power of the storage battery in the tth time period is obtained;representing a primary corrected value of the ideal charging and discharging power of the storage battery;
(3.2.4) calculating the ideal charging and discharging power of the super capacitorWhich is the difference between the ideal charge and discharge power required by the energy storage device during the t-th period and the actual charge and discharge power of the storage battery during the t-th period, i.e., the difference
Figure FDA0002198513860000048
(3.2.5), the super capacitor power is corrected once, and the ideal charging and discharging power of the super capacitor is corrected once by considering the power limit value of the super capacitor:
if it is
Figure FDA0002198513860000049
Then orderThe power of the super capacitor is corrected for one time,
wherein, Puc,NRepresenting the rated power of the super capacitor;
(3.2.6), performing secondary correction on the ideal charging and discharging power of the super capacitor by judging whether the charge state of the super capacitor after the compensation energy is subjected to primary correction is lower than a charge state limit value or not:
first, by the formula
Figure FDA00021985138600000411
The charge state of the super capacitor after the compensation energy is corrected once is obtained,
if it is
Figure FDA00021985138600000412
Then order
Figure FDA0002198513860000051
If it is
Figure FDA0002198513860000052
Then order
Figure FDA0002198513860000053
Wherein, Puc,tThe actual charging and discharging power of the super capacitor in the t time period is obtained;
Figure FDA0002198513860000054
representing a primary corrected value of the charging and discharging ideal power of the super capacitor;
(3.2.7) calculating the state of charge value of the composite energy storage device, and after the actual charging and discharging power of the electric power storage and the super capacitor is determined, calculating the state of charge value of the composite energy storage device according to a formula
Figure FDA0002198513860000055
And respectively calculating the state of charge values of the two at the end of the time period.
5. The capacity optimization configuration method of the composite energy storage device of the power distribution network according to claim 1, characterized in that: in the step (6), the simulated annealing particle swarm algorithm is constructed and carried out in the following three steps:
(6.1) improving the PSO of the traditional particle swarm algorithm, namely, adopting an iterative formula xij(n+1)＝xij(n)+vij(n +1) is changed to xij(n+1)＝xij(n)+vij(n+1)*t，xij(n) denotes the position of the ith particle in the jth dimension at the nth iteration, vij(n +1) represents the velocity of the ith particle in the jth dimension at the (n +1) th iteration;
(6.2) modifying the simulated annealing algorithm SA to use linear annealing temperature coefficients and inertial weights, T (T) ((0.95-0.8) × T/maximum +0.8) × T (T-1);
(6.3) combining the two to obtain a simulated annealing particle swarm algorithm SAPSO, and then selecting a method for adding random disturbance to the current solution as a new position function of the particle: x (i, j) — (4 + 2) ×, where X (i, j) is the coordinate of the jth dimension of any particle population and rand is a uniform distribution between 0 and 1.
6. The capacity optimization configuration method of the composite energy storage device of the power distribution network according to claim 1, characterized in that: in the step (6), the specific steps of constructing the simulated annealing particle swarm algorithm are as follows:
(6.01) initializing parameters of a simulated annealing algorithm and a particle swarm;
(6.02) determining the power required by the load and the distributed photovoltaic output power;
(6.03), constructing an objective optimization function model and a constraint function model;
(6.04) evaluation of the nascent particles according to the formula
Figure FDA0002198513860000061
Calculating a fitness value of each particle objective function;
(6.05) randomly generating a new position for the particle according to a position formula X (i, j) ═ X (i, j) — (4 + 2) · rand), and calculating an increment Δ, Δ (t) ═ F (t) -F (t-1) (t ═ 1,2,3 ·, N) of the fitness value at that time;
(6.06), if Δ<0, replacing the old position with the new position, performing an annealing temperature operation on the particles, if delta>0, randomly generating rand, wherein the value range of rand is between 0 and 1; if rand < e-Δ/T(t)If yes, the decision variable enters a new position, the particle swarm executes the annealing temperature operation, and if not, the step (6.05) is executed;
(6.07), determining whether the particle is on a constraint set, namely whether the particle is a feasible solution, and updating and recording the local optimal value and the global optimal value of the particle according to the result;
(6.08), calculating and updating the speed and the position of the particle at the moment;
(6.09), judging whether an ending condition is met, namely whether the iteration times are greater than maximum or whether the precision requirement is met, if so, jumping out of the program and outputting the optimal solution, otherwise, turning to the step (6.05).
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Application publication date: 20200110