CN110620388A - Capacity configuration method for hybrid energy storage system of power distribution network - Google Patents
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Abstract
A capacity configuration method for a power distribution network hybrid energy storage system belongs to the technical field of power systems. The invention aims to provide a capacity configuration method of a power distribution network hybrid energy storage system and a capacity configuration method of the power distribution network hybrid energy storage system for determining the optimal capacity of a hybrid storage unit to be installed, based on the minimum fluctuation rate of distributed energy, the minimum power loss rate of the system and the minimum investment cost for installing energy storage equipment. The method comprises the following steps: analyzing the characteristics of the hybrid energy storage equipment and providing a hybrid energy storage model; based on the power distribution network under various conditions, various optimization targets are provided, and the capacity of the hybrid energy storage equipment in the power distribution network is selected; another set of capacities is selected by modifying the genetic algorithm and iterative calculations are performed. The invention accelerates the calculation convergence speed through improvement, and further improves the efficiency.
Description
Technical Field
The invention belongs to the technical field of power systems.
Background
Power systems are evolving from traditional systems of concentrated generation coupled to a transmission network to a deregulated configuration that allows small generators to be coupled directly to a distribution network. Such networks have thus become active, commonly referred to as "active power distribution networks", in which new technologies should facilitate adaptation to such active environments and make possible the use of the "smart grid" concept.
Energy storage systems are a promising technology that can support the incorporation of smart grids. The gradual increase of the power generation permeability of the distributed renewable energy sources can generate great influence on the safe operation of the power distribution network, and the peak regulation pressure of the power system is increased by the 'reverse peak regulation' characteristic of the renewable energy sources such as wind and light and the load side peak-valley difference of the urban power grid in summer. Meanwhile, the industrial structure of China is upgraded, and the requirements of high-technology enterprises and novel industrial parks on power supply reliability and power quality are stricter. Theoretical research and energy storage project practice at home and abroad show that the distributed energy storage system is an effective way for solving the problems. The distributed energy storage system participates in demand response by implementing peak clipping and valley filling, so that the peak clipping pressure of a power grid can be reduced, the operation efficiency of the power grid is improved, the construction of a power supply and the power grid is delayed and reduced, and the demand of peak load power supply is relieved. By improving the distributed renewable energy power generation characteristics, the ability of the power grid to accommodate distributed renewable energy power generation may also be facilitated. In addition, the distributed energy storage can also be applied in frequency modulation, so that auxiliary service means of a power grid are enriched, the power supply reliability and the power quality are improved, and the power consumption quality of a user is improved.
Disclosure of Invention
The invention aims to provide a capacity configuration method of a power distribution network hybrid energy storage system and a capacity configuration method of the power distribution network hybrid energy storage system for determining the optimal capacity of a hybrid storage unit to be installed, based on the minimum fluctuation rate of distributed energy, the minimum power loss rate of the system and the minimum investment cost for installing energy storage equipment.
The method comprises the following steps:
step 1: analyzing the characteristics of the hybrid energy storage equipment and providing a hybrid energy storage model;
step 2: based on the power distribution network under various conditions, various optimization targets are provided, and the capacity of the hybrid energy storage equipment in the power distribution network is selected;
and step 3: the method simultaneously considers the minimum fluctuation rate of the distributed energy, the minimum power loss rate of a system and the minimum investment cost for installing energy storage equipment, sets the minimum parameters as an objective function, sets a group of constraint conditions, solves the objective function by adopting an improved genetic algorithm, and selects another group of capacity by the improved genetic algorithm and carries out iterative computation if the solution is not optimal.
Because the existing power distribution network has more distributed power supplies, the invention provides a method for distributing energy storage systems in the power distribution system based on an improved genetic algorithm based on the minimum fluctuation rate of distributed energy, the minimum power loss rate of the system and the minimum investment cost for installing energy storage equipment, and provides a method for realizing the beneficial effects by adopting a hybrid energy storage system. Finally, based on the theory, the calculation convergence speed is accelerated through improvement by the proposed solving method based on the improved genetic algorithm and the linear programming, and the efficiency is further improved.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a diagram of the hybrid energy storage system of the present invention.
Detailed Description
The method comprises the following steps:
step 1: analyzing the characteristics of the hybrid energy storage equipment and providing a hybrid energy storage model;
step 2: based on the power distribution network under various conditions, various optimization targets are provided, and the capacity of the hybrid energy storage equipment in the power distribution network is selected;
and step 3: the method simultaneously considers the minimum fluctuation rate of the distributed energy, the minimum power loss rate of a system and the minimum investment cost for installing energy storage equipment, sets the minimum parameters as an objective function, sets a group of constraint conditions, solves the objective function by adopting an improved genetic algorithm, and selects another group of capacity by the improved genetic algorithm and carries out iterative computation if the solution is not optimal.
The capacity configuration method of the hybrid energy storage system of the power distribution network shown in fig. 1 comprises the following steps:
firstly, analyzing the characteristics of hybrid energy storage equipment and providing a hybrid energy storage model;
then, based on the power distribution network under various conditions, various optimization targets are provided, and the capacity of the hybrid energy storage device in the power distribution network is selected;
finally, the method considers the minimum fluctuation rate of the distributed energy, the minimum power loss rate of the system and the minimum investment cost for installing energy storage equipment, sets the minimum parameters as an objective function, sets a group of constraint conditions, solves the objective function by adopting an improved genetic algorithm, and selects another group of capacity by the improved genetic algorithm and carries out iterative computation if the solution is not optimal. Once the optimal solution is found, the optimal capacity of the hybrid energy storage system can be determined.
The following detailed description of embodiments and steps of the invention is provided in connection with the accompanying drawings.
Step 1, analyzing the characteristics of hybrid energy storage equipment, and providing a hybrid energy storage model:
(1) hybrid energy storage device features
1) Lithium iron phosphate battery characteristics
The lithium iron phosphate battery is a lithium ion battery using lithium iron phosphate as a positive electrode material. The anode material of the lithium ion battery mainly comprises lithium cobaltate, lithium manganate, lithium nickelate, ternary material, lithium iron phosphate and the like. Lithium cobaltate is a positive electrode material used by most lithium ion batteries at present. The lithium iron phosphate power battery has seven advantages: firstly, the lead-acid battery with ultra-long service life has the cycle life of about 300 times, namely 500 times at most, while the lithium iron phosphate power battery produced by Shandong Haiba energy group Limited has the cycle life of more than 2000 times, and can be used for standard charging (5 hour rate) of 2000 times. The lead-acid battery with the same quality is 'new half year, old half year, and half year after maintenance', the maximum time is 1-1.5 years, and the lithium iron phosphate battery can be used under the same condition for 7-8 years. Comprehensively considered, the cost performance ratio is more than 4 times of that of the lead-acid battery. The lithium iron phosphate is safe to use, the potential safety hazard problem of lithium cobaltate and lithium manganate is completely solved by the lithium iron phosphate, the lithium cobaltate and the lithium manganate can explode under strong collision to threaten the life safety of consumers, and the lithium iron phosphate can not explode even in the worst traffic accident through strict safety test. And thirdly, the battery can be charged and discharged rapidly under the condition of large current and 2C, the battery can be fully charged within 40 minutes after being charged under 1.5C by a special charger, the starting current can reach 2C, and the lead-acid battery does not have the performance at present. And fourthly, the lithium iron phosphate is high-temperature resistant, the electric heating peak value of the lithium iron phosphate can reach 350-500 ℃, and the lithium manganate and the lithium cobaltate are only about 200 ℃. Fifthly, large capacity. Sixthly, no memory effect exists. And seventhly, the environment is protected.
Lithium iron phosphate batteries also have their disadvantages: for example, the lithium iron phosphate positive electrode material has a low tap density, and the lithium iron phosphate battery with the same capacity has a larger volume than the lithium ion battery such as lithium cobaltate, so that the lithium iron phosphate positive electrode material has no advantages in the aspect of a micro battery.
2) Super capacitor
A supercapacitor is a novel component that stores energy through an interfacial double layer formed between electrodes and an electrolyte. When the electrode contacts with the electrolyte, the solid-liquid interface generates stable double-layer charges with opposite signs under the action of coulomb force, intermolecular force and interatomic force, and the double-layer charges are called as interface double layers. The electric double layer supercapacitor is considered to be 2 inactive porous plates suspended in an electrolyte, and a voltage is applied to the 2 plates. The potential applied to the positive plate attracts negative ions in the electrolyte and the negative plate attracts positive ions, thereby forming an electric double layer capacitor on the surfaces of the two electrodes. The electric double layer capacitor may be classified into a carbon electrode double layer supercapacitor, a metal oxide electrode supercapacitor, and an organic polymer electrode supercapacitor according to the difference in electrode materials.
Compared with a storage battery and a traditional physical capacitor, the super capacitor is mainly characterized in that:
the power density is high. Can reach 102-104W/kg, which is far higher than the power density level of the storage battery.
The cycle life is long. After 50-100 ten thousand high-speed deep charge-discharge cycles of a few seconds, the characteristic change of the super capacitor is small, and the capacity and the internal resistance are only reduced by 10-20%.
The working temperature limit is wide. Because the adsorption and desorption speed of ions in the super capacitor is not greatly changed in a low-temperature state, the capacity change of the super capacitor is far smaller than that of a storage battery. The working temperature range of the commercial super capacitor can reach minus 40 ℃ to plus 80 ℃. And the maintenance is free. The super capacitor has high charging and discharging efficiency, has certain bearing capacity on overcharge and overdischarge, can be stably and repeatedly charged and discharged, and theoretically does not need maintenance.
Is green and environment-friendly. Heavy metal and other harmful chemical substances are not used in the production process of the super capacitor, and the service life of the super capacitor is long, so that the super capacitor is a novel green and environment-friendly power supply.
(2) Hybrid energy storage model
The hybrid energy storage method proposed herein is applied to an active power distribution network having a looped network structure. In the ring network structure, a plurality of micro network groups, hybrid energy storage and loads are included. The hybrid energy storage system has the functions of coordinating power output among all micro grids, meeting load requirements and realizing coordination optimization control of all distributed energy sources.
The super capacitor is combined with the lithium iron phosphate battery, so that the power quality and the system stability can be effectively improved. When the hybrid energy storage and the distributed power generation are jointly connected to the alternating current bus for coordination control, the specific structure is shown in fig. 2.
Step 2, based on the power distribution network under various conditions, various optimization targets are provided, and the capacity of the hybrid energy storage device in the power distribution network is selected:
along with the gradual improvement of the construction and development of the active power distribution network, the response of the demand side and the energy storage system are mutually coordinated, the time deviation between the demand side and the power generation side is reduced, and the economic operation of the power distribution network is improved. When the system has peak load or fault, the user responds to the signal request of the dispatching department to reduce or interrupt the load consumption. Therefore, the active power distribution network becomes the key point of development at present, and some targets of the power distribution network need to be optimized in order to configure the energy storage capacity of the power distribution network.
(1) Stabilizing the volatility of distributed energy
The power demand of the hybrid energy storage system may be expressed as follows:
PLi(t)+PSC(t)＝PDG(t)+PG(t)-Lf(t)-[Lin(t)-Lout(t)] (1)
in the formula, PLiThe output power of the lithium iron phosphate battery. PSCIs the output power of the supercapacitor, PGIs supplying power to the grid. In the formula, LfIs the amount of load without regard to demand response. Lin and Lout are load transfer-in and load transfer-out, PDGIs a distributed power output.
To minimize the fluctuation ratio of the distributed energy source and maximize the output power of the super capacitor, it can be expressed as follows:
in the formula eta1Efficiency of DC/DC, η2Efficiency of DC/AC, etaSC,1The discharge efficiency of the super capacitor is improved.
The rated output energy of a lithium iron phosphate battery is expressed as follows, taking into full account the converter efficiency, the energy storage system charging efficiency and the maximum load requirement during one duty cycle (24 hours).
In the formula, SOCLi,max，SOCLi,minIs the upper and lower limits, SOC, of the lithium batteryLi,0Is the initial state of charge.
(2) Minimizing system power loss rate
When a 400V bus in an active power distribution network is embedded into an energy storage device, the minimum power loss rate of the system is taken as an optimization target, and an objective function is expressed as follows.
(3) Minimizing installation investment costs for energy storage devices
In the planning process, in order to determine the installation capacity of the hybrid energy storage system, the investment cost for installing the energy storage equipment is considered, the investment cost is a linear function of the rated capacity and the rated power of the energy storage equipment, and the calculation formula is as follows:
in the formula, Pr is the rated power of the energy storage device, Cr is the rated capacity of the energy storage device, CESIs the cost per unit capacity coefficient, P, of the energy storage deviceESThe coefficient is the cost coefficient of unit power of the energy storage device, Y is the service life of the energy storage device, and lambda is the annual rate.
And 3, considering the minimum fluctuation rate of the distributed energy, the minimum power loss rate of the system and the minimum investment cost for installing energy storage equipment, setting the minimum parameters as an objective function, setting a group of constraint conditions, solving the objective function by adopting an improved genetic algorithm, and if the solution is not optimal, selecting another group of capacities by the improved genetic algorithm and carrying out iterative computation. Once the optimal solution is found, the optimal capacity of the hybrid energy storage system can thus be determined:
(1) objective function
The method establishes a total objective function by combining the minimum fluctuation rate of the distributed energy, the minimum power loss rate of the system and the minimum investment cost of installing energy storage equipment so as to realize the optimal scheduling of the charging of the electric automobile. Due to the difference in units, this text is for f1-1,2、f2And f3Normalization processing is carried out to obtain F1-1,2、F2And F3Therefore, the overall objective function can be expressed as follows:
Min F＝λ1(c1F1-1+c2F1-2)+λ2F2+λ3F3 (6)
in the formula, λ1，λ2And lambda3Is a weight coefficient of each object, and satisfies lambda1+λ2+λ3＝1，c1+c21, where λ1，λ2，λ3≥0。
(2) Constraint conditions
1) Power balance constraint
The power distribution network tide balance relationship is as follows:
in the formula, Pkt、QktRespectively the active and reactive output power, U, of node k at time tkt、UmtThe voltage values of the nodes k and m at the time t, thetakmtFor the phase difference of the branch km at time t, Gkm、BkmRespectively the conductance and susceptance of the branch km.
2) Voltage amplitude constraint
The voltage amplitude of each node of the power distribution network should satisfy:
Umin≤Uk≤Umax
in the formula of UkIs the voltage of node k, Umin、UmaxRespectively, a lower voltage amplitude limit and an upper voltage amplitude limit.
(3) Improved genetic algorithm
In order to reduce the number of infeasible solutions generated in the calculation process, reduce the iteration times and improve the calculation efficiency, the genetic algorithm is improved.
The genetic algorithm is a random search algorithm based on natural selection of organisms and genetic mechanisms, and unlike conventional search algorithms, the genetic algorithm starts the search process from a set of randomly generated initial solutions called "populations". Each individual in the population is a solution to the problem, called a "chromosome". A chromosome is a string of symbols, such as a binary string. These chromosomes evolve continuously in subsequent iterations, called inheritance. The "fitness value" is used in each generation to measure the quality of the chromosomes, and the next generation of chromosomes is called offspring. Offspring are formed from the previous generation of chromosomes by crossover or mutation operations. In the process of forming a new generation, partial offspring is selected according to proper size, and partial offspring is eliminated. Thereby keeping the population size constant. The probability that chromosomes with high fitness values are selected is high, so that over several generations the algorithm converges to the best chromosome, which is likely to be the best or sub-best solution to the problem. The flow chart is shown in fig. 1, and the step flow chart of the location determination and capacity determination of the distributed energy storage system based on the improved genetic algorithm and linear programming provided by the patent comprises the following steps:
1) the genetic operation parameters are coded, and binary coding is adopted for individual coding, so that the operation is more convenient, and the control variable is the capacity of mixed energy storage;
2) generating an initial population, setting the length of chromosomes and the maximum iteration number, and randomly selecting the chromosomes to form the initial population (the capacity of mixed energy storage) when the calculation is started;
3) calculating the fluctuation rate of the distributed energy, determining the power loss rate of the system through load flow analysis, and calculating the installation investment cost of the energy storage equipment;
4) and then setting the target minimum as an objective function, and setting a group of constraint conditions. Solving a fitness function when the minimum value is solved, wherein the fitness function is the reciprocal of the target function F;
5) selecting individuals with higher individual fitness to be inherited to the next generation, and rejecting the individuals with lower fitness;
6) the method simulates the gene recombination of a natural organism chromosome, exchanges partial genes of two individuals in a parent to generate new filial generations to provide individual diversity, and adopts an improved genetic algorithm crossing process, which has the following specific details:
in the crossing process of the genetic algorithm, the number of 1 in a certain chromosome segment is the equivalent length of the chromosome segment. The current crossover process of genetic algorithms has a problem: the total actual length of the chromosome may vary, and if the equivalent lengths of the crossed chromosome segments are the same, but the actual lengths are different, such as "1001" and "101", the overall lengths of the two chromosomes vary after crossing.
To solve this problem, the present invention provides a method of randomly deleting a corresponding number of 0 s from chromosomes having a reduced actual chromosome length by supplementing 0 s to the ends of chromosomes and chromosomes having an increased actual chromosome length by deleting 0 s from the front parts of intersections. This step of processing can be considered as simultaneous crossover and mutation. The probability theory knowledge shows that the probability of the two events occurring at the same time is very small, and in a general system, the number of the tie line switches is usually much smaller than that of the branch line switches, which results in that the situations with the same equivalent length but different actual lengths are few, so the probability of the occurrence of the situations is also very small, which accords with the biological evolution theory;
7) and (3) carrying out a genetic algorithm variation process on some individuals in the population. The invention adopts an improved genetic algorithm variation process, and the specific details are as follows:
in the invention, the solution result is more stable when the mutation probability is determined by experiments to be expressed by the following formula:
in the formula: p is a radical ofpIs the mutation probability, nIFor the number of iterations, max nIIs the maximum number of iterations.
The variation probability is converted from static state to dynamic state related to the square of the iteration times, the variation cross probability changes along with the change of the iteration times, and experiments show that the optimization result is more stable than the static fixed probability and the optimization effect is better.
The initial population is subjected to selection, crossing and variation operation to obtain a next generation population;
8) and judging whether the current iteration times reach the maximum iteration times, if so, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and stopping the calculation. Otherwise, turning to step 3.
Claims (1)
1. A capacity configuration method for a hybrid energy storage system of a power distribution network is characterized by comprising the following steps: the method comprises the following steps:
step 1: analyzing the characteristics of the hybrid energy storage equipment and providing a hybrid energy storage model;
step 2: based on the power distribution network under various conditions, various optimization targets are provided, and the capacity of the hybrid energy storage equipment in the power distribution network is selected;
and step 3: the method simultaneously considers the minimum fluctuation rate of the distributed energy, the minimum power loss rate of a system and the minimum investment cost for installing energy storage equipment, sets the minimum parameters as an objective function, sets a group of constraint conditions, solves the objective function by adopting an improved genetic algorithm, and selects another group of capacity by the improved genetic algorithm and carries out iterative computation if the solution is not optimal.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114243688A (en) * | 2021-12-13 | 2022-03-25 | 国网内蒙古东部电力有限公司经济技术研究院 | Power distribution network stability and investment scheme determination method based on unit division |
| CN116742673A (en) * | 2023-06-13 | 2023-09-12 | 华能罗源发电有限责任公司 | Addressing and volume-fixing double-layer optimization method and system for hybrid energy storage system of active power distribution network |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114243688A (en) * | 2021-12-13 | 2022-03-25 | 国网内蒙古东部电力有限公司经济技术研究院 | Power distribution network stability and investment scheme determination method based on unit division |
| CN114243688B (en) * | 2021-12-13 | 2023-09-26 | 国网内蒙古东部电力有限公司经济技术研究院 | Power distribution network stability and investment scheme determination method based on unit division |
| CN116742673A (en) * | 2023-06-13 | 2023-09-12 | 华能罗源发电有限责任公司 | Addressing and volume-fixing double-layer optimization method and system for hybrid energy storage system of active power distribution network |
| CN116742673B (en) * | 2023-06-13 | 2024-02-06 | 华能罗源发电有限责任公司 | Addressing and volume-fixing double-layer optimization method and system for hybrid energy storage system of active power distribution network |
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