CN117526387B - Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation - Google Patents

Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation Download PDF

Info

Publication number
CN117526387B
CN117526387B CN202311842055.2A CN202311842055A CN117526387B CN 117526387 B CN117526387 B CN 117526387B CN 202311842055 A CN202311842055 A CN 202311842055A CN 117526387 B CN117526387 B CN 117526387B
Authority
CN
China
Prior art keywords
energy storage
particle
energy
storage capacity
cost
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202311842055.2A
Other languages
Chinese (zh)
Other versions
CN117526387A (en
Inventor
孙宏斌
常馨月
薛屹洵
张婧晅
苏珈
李泽宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Taiyuan University of Technology
Original Assignee
Taiyuan University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Taiyuan University of Technology filed Critical Taiyuan University of Technology
Priority to CN202311842055.2A priority Critical patent/CN117526387B/en
Publication of CN117526387A publication Critical patent/CN117526387A/en
Application granted granted Critical
Publication of CN117526387B publication Critical patent/CN117526387B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Artificial Intelligence (AREA)
  • Computer Hardware Design (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Geometry (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Medical Informatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a power distribution network energy storage optimal location and volume-fixing method considering energy storage capacity attenuation, and relates to the field of power distribution network energy storage optimization calculation. Firstly, an energy storage capacity attenuation model considering time and frequent charge and discharge is established, the energy storage capacity attenuation cost is considered in the economic cost of power distribution network planning, and then an improved particle swarm algorithm based on dense distance is provided. According to the invention, the attenuation characteristics of the energy storage capacity along with time and frequent charge and discharge are considered, so that the influence of wind-light output uncertainty on power distribution network planning can be effectively reduced, the renewable energy source grid-connected consumption is promoted, and the system reliability is improved. The improved particle swarm algorithm is used for solving the multi-objective optimization problem of energy storage, location and volume selection of the power distribution network, and the dense distance is used for guiding the optimal particle selection of the population in the process of optimizing and searching; the optimal Pareto solution set has good diversity and distribution, shortens the system investment recovery period, and has high economic and practical values.

Description

Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation
Technical Field
The invention relates to the field of energy storage optimization calculation of a power distribution network, in particular to a power distribution network energy storage optimal location and volume-fixing method considering energy storage capacity attenuation.
Background
With the exhaustion of conventional fossil energy, climate change and environmental protection becoming increasingly prominent, renewable energy sources such as wind power generation and photovoltaic power generation, which emit neither pollutants nor carbon dioxide, have been widely used to replace conventional fossil energy sources in power distribution networks due to their green and clean characteristics. However, renewable energy sources have uncertainty in output and are susceptible to environmental factors, and their high permeability will have a great impact on the stable operation of the distribution network. The energy storage quick response capability can inhibit the adverse effect of renewable energy sources to a certain extent, and improves the running stability of the system. However, the current research on the access position and capacity of the energy storage does not consider the attenuation of the energy storage capacity along with time and charge and discharge times, and how to research the optimal site-specific capacity of the energy storage of the power distribution network considering the attenuation of the energy storage capacity becomes one of the research hot spots in recent years.
First, the most main and critical technical problems of the research are: firstly, when the energy storage in the power distribution network is subjected to site selection and volume metering planning, the energy storage is usually researched as a distributed resource, the attenuation of the energy storage capacity along with time and charge and discharge times is ignored, and the capacity attenuation of the energy storage also affects the installation cost and the operation cost of the energy storage. Therefore, when the energy storage in the power distribution network is optimally addressed and fixed in volume, an energy storage capacity attenuation model is established by taking the capacity attenuation characteristics of the energy storage into consideration. Secondly, the site selection and volume fixation planning of energy storage in the power distribution network is an optimization problem, and the energy storage can be solved through a particle swarm algorithm. The conventional particle swarm algorithm ignores the possibility that particles in the sparse region are globally optimal, and the result may be a locally optimal solution instead of a globally optimal solution. Therefore, when solving the optimization problem, the possibility that the particles in the sparse region are globally optimal should be considered, and the conventional particle swarm algorithm is improved to avoid the algorithm from being trapped in the local optimal.
In summary, the capacity attenuation characteristic and the particle swarm algorithm of the stored energy need to be comprehensively considered, and finally, the optimal site-specific capacity planning is performed on the stored energy in the power distribution network.
Disclosure of Invention
The invention provides a power distribution network energy storage optimal location and volume-fixing method considering energy storage capacity attenuation, aiming at solving the problems that when energy storage in a power distribution network is subjected to location and volume-fixing planning, the energy storage capacity is attenuated along with time and charge and discharge times, and when solving the energy storage optimal location and volume-fixing optimization problem, the traditional particle swarm algorithm is probably a local optimal solution rather than a global optimal solution.
The invention is realized by the following technical scheme: the optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation mainly comprises the following steps of planning optimal power distribution network energy storage locating and sizing considering energy storage capacity attenuation and an improved particle swarm algorithm for solving a power distribution network cost objective function. According to the method, firstly, an energy storage capacity attenuation model considering time and frequent charge and discharge is established, the energy storage capacity attenuation cost is considered in the economic cost of power distribution network planning, then an improved particle swarm algorithm based on dense distance is provided, the economical efficiency of power distribution network investment, environmental benefit and wind and light discarding are comprehensively considered to construct a multi-target power distribution network energy storage planning model, and finally a power distribution network cost objective function is solved according to the improved particle swarm algorithm. The method specifically comprises the following steps:
s1: establishing an energy storage capacity attenuation model comprising time attenuation and cyclic attenuation;
the energy storage can effectively relieve the uncertainty of the power generation side wind light output, and peak clipping and valley filling of the power-assisted load can be realized. However, the energy storage capacity decays over time and with frequent charging and discharging. The specific steps are therefore as follows:
energy storage capacity decay model: energy storage capacity decay generally includes time decay and cyclic decay; the time decay reflects the inherent decay of the energy storage capacity over time, and the cyclic decay reflects the decay of the energy storage capacity due to repeated charging and discharging;
time decayThe model is as follows, affected by temperature T and time T:
（1）
in the method, in the process of the invention,is a function of temperature T and time T,is an exponential factor of the number of times,is the activation energy, R is the gas constant;
defining the energy storage charge and discharge states asJudging according to the following conditions: 1): the stored energy is neither charged nor discharged; 2): storing energy and discharging; 3): storing energy and charging; when (when)When the energy storage device is used, the energy storage device is defined as an energy storage charging and discharging cycle; cyclic attenuationCharge/discharge rate with temperature T, first energy storage charge/discharge cycleAnd energy throughputThe model is as follows:
（2）
（3）
in the method, in the process of the invention,anda pre-exponential factor and an exponential factor that are functions of temperature T; DOD is the charge/discharge energy relative to the stored energy,is the energy storage capacity in the first energy storage charge-discharge cycle:
（4）
（5）
（6）
wherein a, b, c, d and e are function coefficients which are constants and are related to specific energy storage models;is the energy storage charging and discharging power in the first energy storage charging and discharging cycle;
in summary, the energy storage capacity decay model is:
（7）
s2: obtaining energy storage capacity attenuation cost according to the energy storage capacity attenuation model;
the energy storage attenuation cost is as follows:
（8）
in the method, in the process of the invention,is the cost of replacement of the stored energy,is the energy storage cycle life:the method comprises the steps of carrying out a first treatment on the surface of the A. B and C are function coefficients, which are constants and related to specific energy storage models;
after considering the attenuation of the energy storage capacity, the equation (8) is converted into, in combination with the equation (7):
（9）。
s3: constructing a planning cost objective function of the power distribution network according to the energy storage capacity attenuation cost;
s3-1: objective function: the objective function is to minimize the total cost C during the investment period:
（10）
in the method, in the process of the invention,is a weight coefficient, satisfiesThe specific numerical value can be set manually;is an economical cost of the product,is the cost of wind and light discarding,is the system stability cost;
（11）
in the method, in the process of the invention,is the total economic cost of the whole life cycle, i is the discount rate, and N is the life expectancy of the system;
（12）
in the method, in the process of the invention,is the installation cost of the energy storage unit capacity,for the purpose of energy storage and installation capacity,、the operating and maintenance costs of renewable energy and stored energy respectively,、the output power of renewable energy and stored energy respectively;、is the electricity purchase price of a large power grid,、is the purchase and sale quantity of electricity to a large power grid,is the residual value of the system equipment and accounts for 5% of the initial installation cost of the system;
（13）
in the method, in the process of the invention,penalty coefficients for wind and light rejection for renewable energy sources;the wind and light amount is:
（14）
in the method, in the process of the invention,the load after the demand response is considered;
（15）
in the method, in the process of the invention,is a punishment coefficient of source-load matching;is the load-following coefficient of the load,is the maximum load tracking coefficient;
（16）
s3-2: constraint conditions:
s3-2-1: renewable energy and energy storage constraint:
to avoid the cost greatly increased by blindly increasing the renewable power source and the energy storage capacity aiming at ensuring the power supply reliability, capacity constraint is set for renewable energy sources and energy storage:
（17）
wherein:、the upper limit of the installation capacity of the renewable energy source and the energy storage respectively;
s3-2-2: power balance constraint:
in order to ensure the stability of the system operation, namely, ensure the source load power balance of the system, the power of the system needs to be constrained:
（18）
s3-2-3: renewable energy sources, energy storage and large power grid output constraint:
the output of renewable energy and stored energy is constrained by charge and discharge power, and the output needs to be kept in a reasonable range, and meanwhile, the invention sets the upper limit and the lower limit for the electric quantity interacted with a large power grid, so that the electricity is prevented from being purchased from the large power grid to meet the load demand;
（19）
wherein:、respectively the upper and lower limits of the output of the renewable energy source;、respectively the upper limit and the lower limit of the energy storage output;、the upper limit and the lower limit of the interaction power with the large power grid are respectively set;
s3-2-4: demand response constraints:
because not all loads can respond to the demand, and uncertainty exists in the demand response to the load due to user wish, a section is set for the change of the demand response electric quantity:
（20）
in the method, in the process of the invention,、the upper limit and the lower limit of the change of the demand response electric quantity are respectively;
s3-2-5: state of charge of stored energyConstraint:
（21）
wherein:、the upper limit and the lower limit of the charge state of the storage battery are respectively;
s4: improving a traditional particle swarm algorithm based on the dense distance;
s4-1: the particle swarm algorithm is an optimization algorithm, and simulates the natural shoal predation and the shoal predation process. Finding a global optimal solution of the problem through cooperation in the group; firstly, randomly initializing a particle swarm in a given solution space, and determining the dimension of the solution space according to the variable number of the problem to be optimized; each particle has an initial position and an initial speed, and then is optimized through iteration; the optimizing method is as follows:
（22）
in the method, in the process of the invention,is the weight of the inertia, which is the weight of the inertia,,is the acceleration factor of the velocity of the vehicle,,is a random number between 0 and 1,is the individual optimal position vector of the ith particle at the kth moment,is the overall optimal position vector at the kth moment;
s4-2: in the process of optimizing by using the particle swarm algorithm, the population can generate a plurality of mutually independent optimal solutions, namely a plurality of non-inferior solutions, in each generation. The result may be a locally optimal solution, rather than a globally optimal solution. The invention provides an optimal solution selection method based on dense distance, which comprises the following steps:
s4-2-1: the dense distance is a density estimation index; given m objective functions, the particleIs a dense distance of (2)The method comprises the following steps:
（23）
wherein,andis closest toIs a particle of (2);finger particlesThe value of the mth objective function of (2);is the maximum of the mth objective function of all particles;
s4-2-2: assume that the optimization problem contains three objective functions:、anddense distance of particlesThe method comprises the following steps:
（24）
then introducing the cross mutation operation in the genetic algorithm into a particle swarm algorithm, and performing the cross mutation operation on the position vector of the particles; taking a weight value vector randomly obtained from a particle position vector as a basis of cross mutation, the optimal solution selection process based on the dense distance is as follows:
(1) the method comprises the following steps Determining weight values of randomly acquired particlesCross rateSum of variance ratio；
(2) The method comprises the following steps Determining the size of the ith particle ifRandomly selecting whether to perform a particle intersection operation process;
(3) the method comprises the following steps For each dimensional component of the position vector of the ith particle, select a value of 0,1]Random number betweenIf (3)Then a cross operation is prepared for the dimensional component of its position vector and the dimensional component of the last selected particle of the cross operation as follows:
（25）
(4) the method comprises the following steps For each dimension of the position vector of the ith particleQuantity selection [0,1 ]]Random number of (a)If (3)The following mutation operation is performed on the corresponding dimensional component of the position vector of the particle:
（26）
(5) the method comprises the following steps Obtaining new population particles after the cross mutation operation, and calculating the dense distance of each Pareto solution;
(6) the method comprises the following steps Each particle is allocated an fitness value, and the corresponding fitness value is equal to the dense distance of the corresponding particle in the Pareto solution;
(7) the method comprises the following steps Selecting roulette according to the fitness value, and randomly selecting a position to be the global optimal position of the particle;
(8) the method comprises the following steps The above steps are repeated until each particle in the population is assigned a global optimal position.
S5: solving a cost objective function of the power distribution network according to an improved particle swarm algorithm to obtain the capacity of the energy storage optimal position:
solving the energy storage, addressing and volume-fixing optimization problem of the power distribution network: when an improved particle swarm algorithm is adopted to solve the energy storage, location, volume and optimization problem of the power distribution network, the position and the power of an energy storage system need to be optimized. Since the access position of the stored energy is an integer, the particle position is subjected to rounding operation when being updated. The step finally obtains the optimal position and capacity of energy storage through repeated iteration of particles.
Compared with the prior art, the invention has the following beneficial effects: according to the optimal location and volume-fixing method for the power distribution network energy storage, which is provided by the invention, the attenuation characteristics of the energy storage capacity along with time and frequent charge and discharge are considered, so that the influence of wind-light output uncertainty on power distribution network planning can be effectively reduced, the grid-connected consumption of renewable energy sources is promoted, and the reliability of the system is improved. The improved particle swarm algorithm is used for solving the multi-objective optimization problem of energy storage, location and volume selection of the power distribution network, and the dense distance is used for guiding the optimal particle selection of the population in the process of optimizing and searching; the optimal Pareto solution set has good diversity and distribution, shortens the system investment recovery period, and has high economic and practical values.
Detailed Description
The invention is further illustrated below with reference to specific examples.
A power distribution network energy storage optimal location and volume-fixing method considering energy storage capacity attenuation comprises the following steps:
s1: establishing an energy storage capacity attenuation model comprising time attenuation and cyclic attenuation;
energy storage capacity decay model: energy storage capacity decay includes time decay and cyclic decay; the time decay reflects the inherent decay of the energy storage capacity over time, and the cyclic decay reflects the decay of the energy storage capacity due to repeated charging and discharging;
time decayThe model is as follows, affected by temperature T and time T:
（1）
in the method, in the process of the invention,is a function of temperature T and time T,is an exponential factor of the number of times,is the activation energy, R is the gas constant;
defining the energy storage charge and discharge states asAccording to the followingAnd (3) condition judgment: 1): the stored energy is neither charged nor discharged; 2): storing energy and discharging; 3): storing energy and charging; when (when)When the energy storage device is used, the energy storage device is defined as an energy storage charging and discharging cycle; cyclic attenuationCharge/discharge rate with temperature T, first energy storage charge/discharge cycleAnd energy throughputThe model is as follows:
（2）
（3）
in the method, in the process of the invention,anda pre-exponential factor and an exponential factor that are functions of temperature T; DOD is the charge/discharge energy relative to the stored energy,is the energy storage capacity in the first energy storage charge-discharge cycle:
（4）
（5）
（6）
wherein a, b, c, d and e are function coefficients and are constants, and are determined by specific energy storage models;is the energy storage charging and discharging power in the first energy storage charging and discharging cycle;
in summary, the energy storage capacity decay model is:
（7）。
s2: obtaining energy storage capacity attenuation cost according to the energy storage capacity attenuation model;
the energy storage attenuation cost is as follows:
（8）
in the method, in the process of the invention,is the cost of replacement of the stored energy,is the energy storage cycle life:the method comprises the steps of carrying out a first treatment on the surface of the A. B and C are function coefficients, which are constants and are determined by specific energy storage models;
after considering the attenuation of the energy storage capacity, the equation (8) is converted into, in combination with the equation (7):
（9）。
s3: constructing a planning cost objective function of the power distribution network according to the energy storage capacity attenuation cost;
s3-1: objective function: the objective function is to minimize the total cost C during the investment period:
（10）
in the method, in the process of the invention,is a weight coefficient, satisfies，Is an economical cost of the product,is the cost of wind and light discarding,is the system stability cost;
（11）
in the method, in the process of the invention,is the total economic cost of the whole life cycle, i is the discount rate, and N is the life expectancy of the system;
（12）
in the method, in the process of the invention,is the installation cost of the energy storage unit capacity,for the purpose of energy storage and installation capacity,、the operating and maintenance costs of renewable energy and stored energy respectively,、the output power of renewable energy and stored energy respectively;、is the electricity purchase price of a large power grid,、is the purchase and sale quantity of electricity to a large power grid,is the residual value of the system equipment and accounts for 5% of the initial installation cost of the system;
（13）
in the method, in the process of the invention,penalty coefficients for wind and light rejection for renewable energy sources;the wind and light amount is:
（14）
in the method, in the process of the invention,the load after the demand response is considered;
（15）
in the method, in the process of the invention,is a punishment coefficient of source-load matching;is the load-following coefficient of the load,is the maximum load tracking coefficient;
（16）
s3-2: constraint conditions:
s3-2-1: renewable energy and energy storage constraint:
（17）
wherein:、the upper limit of the installation capacity of the renewable energy source and the energy storage respectively;
s3-2-2: power balance constraint:
（18）
s3-2-3: renewable energy sources, energy storage and large power grid output constraint:
（19）
wherein:、respectively the upper and lower limits of the output of the renewable energy source;、respectively the upper limit and the lower limit of the energy storage output;、the upper limit and the lower limit of the interaction power with the large power grid are respectively set;
s3-2-4: demand response constraints:
（20）
in the method, in the process of the invention,、the upper limit and the lower limit of the change of the demand response electric quantity are respectively;
s3-2-5: state of charge of stored energyConstraint:
（21）
wherein:、the upper and lower limits of the state of charge of the accumulator cell, respectively.
S4: improving a traditional particle swarm algorithm based on the dense distance;
s4-1: finding a global optimal solution of the problem through cooperation in the group; firstly, randomly initializing a particle swarm in a given solution space, and determining the dimension of the solution space according to the variable number of the problem to be optimized; each particle has an initial position and an initial speed, and then is optimized through iteration; the optimizing method is as follows:
（22）
in the method, in the process of the invention,is the weight of the inertia, which is the weight of the inertia,,is the acceleration factor of the velocity of the vehicle,,is a random number between 0 and 1,is the individual optimal position vector of the ith particle at the kth moment,is the overall optimal position vector at the kth moment;
s4-2: an optimal solution selection method based on dense distance is provided:
s4-2-1: the dense distance is a density estimation index; given m objective functions, the particleIs a dense distance of (2)The method comprises the following steps:
（23）
wherein,andis closest toIs a particle of (2);finger particlesThe value of the mth objective function of (2);is the maximum of the mth objective function of all particles;
s4-2-2: assume that the optimization problem contains three objective functions:、anddense distance of particlesThe method comprises the following steps:
（24）
then introducing the cross mutation operation in the genetic algorithm into a particle swarm algorithm, and performing the cross mutation operation on the position vector of the particles; taking a weight value vector randomly obtained from a particle position vector as a basis of cross mutation, the optimal solution selection process based on the dense distance is as follows:
(1) the method comprises the following steps Determining weight values of randomly acquired particlesCross rateSum of variance ratio；
(2) The method comprises the following steps Determining the size of the ith particle ifRandomly selecting whether to perform a particle intersection operation process;
(3) the method comprises the following steps For each dimensional component of the position vector of the ith particle, select a value of 0,1]Random number betweenIf (3)Then a cross operation is prepared for the dimensional component of its position vector and the dimensional component of the last selected particle of the cross operation as follows:
（25）
(4) the method comprises the following steps Selecting 0,1 for each dimension component of the position vector of the ith particle]Random number of (a)If (3)The following mutation operation is performed on the corresponding dimensional component of the position vector of the particle:
（26）
(5) the method comprises the following steps Obtaining new population particles after the cross mutation operation, and calculating the dense distance of each Pareto solution;
(6) the method comprises the following steps Each particle is allocated an fitness value, and the corresponding fitness value is equal to the dense distance of the corresponding particle in the Pareto solution;
(7) the method comprises the following steps Selecting roulette according to the fitness value, and randomly selecting a position to be the global optimal position of the particle;
(8) the method comprises the following steps The above steps are repeated until each particle in the population is assigned a global optimal position.
S5: solving a cost objective function of the power distribution network according to an improved particle swarm algorithm to obtain the capacity of the energy storage optimal position:
solving the energy storage, addressing and volume-fixing optimization problem of the power distribution network: when an improved particle swarm algorithm is adopted to solve the energy storage, location and volume selection optimization problem of the power distribution network, the position and the power of an energy storage system are optimized; according to the principle that the access position of the stored energy is an integer, the particle position is rounded when being updated; and finally obtaining the optimal position and capacity of energy storage through repeated iteration of the particles.
The scope of the present invention is not limited to the above embodiments, and various modifications and alterations of the present invention will become apparent to those skilled in the art, and any modifications, improvements and equivalents within the spirit and principle of the present invention are intended to be included in the scope of the present invention.

Claims (1)

1. The utility model provides a distribution network energy storage optimal selection site-specific capacity method that considers energy storage capacity decay which characterized in that: the method comprises the following steps:
s1: an energy storage capacity attenuation model comprising time attenuation and cyclic attenuation is established, and the energy storage capacity attenuation model is concretely as follows:
energy storage capacity decay model: energy storage capacity decay includes time decay and cyclic decay; the time decay reflects the inherent decay of the energy storage capacity over time, and the cyclic decay reflects the decay of the energy storage capacity due to repeated charging and discharging;
time decayThe model is as follows, affected by temperature T and time T:
（1）
in the method, in the process of the invention,is a function of the temperature T and the time T, < >>Is an exponential factor,/->Is the activation energy, R is the gas constant;
defining the energy storage charge and discharge states asJudging according to the following conditions: 1)/>: the stored energy is neither charged nor discharged; 2)/>: storing energy and discharging; 3)/>: storing energy and charging; when->When the energy storage device is used, the energy storage device is defined as an energy storage charging and discharging cycle; circulation attenuation->Charge/discharge rate with temperature T, first energy storage charge/discharge cycle +.>And energy throughput->The model is as follows:
（2）
（3）
in the method, in the process of the invention,and->A pre-exponential factor and an exponential factor that are functions of temperature T; DOD is charge/discharge energy relative to stored energy, < >>Is the first time storedEnergy storage capacity at charge-discharge cycle:
（4）
（5）
（6）
wherein a, b, c, d and e are function coefficients and are constants, and are determined by specific energy storage models;is the energy storage charging and discharging power in the first energy storage charging and discharging cycle;
in summary, the energy storage capacity decay model is:
（7）
s2: according to the energy storage capacity attenuation model, energy storage capacity attenuation cost is obtained, and the energy storage capacity attenuation cost is specifically as follows:
the energy storage capacity attenuation cost is as follows:
（8）
in the method, in the process of the invention,is energy storage replacement cost->Is the energy storage cycle life: />The method comprises the steps of carrying out a first treatment on the surface of the A. B and C are function coefficients, which are constants and are determined by specific energy storage models;
after considering the attenuation of the energy storage capacity, the equation (8) is converted into, in combination with the equation (7):
（9）
s3: constructing a planning cost objective function of the power distribution network according to the energy storage capacity attenuation cost; the method comprises the following steps:
s3-1: objective function: the objective function is to minimize the total cost C during the investment period:
（10）
in the method, in the process of the invention,is a weight coefficient, satisfy->，/>Is economical cost, < >>Is the cost of wind and light abandoning, and is->Is the system stability cost;
（11）
in the method, in the process of the invention,is the total economic cost of the whole life cycle, i is the discount rate, and N is the life expectancy of the system;
（12）
in the method, in the process of the invention,installation costs for the energy storage unit capacity +.>Installing capacity for energy storage->、/>Operating and maintenance costs for renewable energy and energy storage, respectively, +.>、/>The output power of renewable energy and stored energy respectively;、/>is the purchase price of electricity of a large power grid, +.>、/>The electricity purchasing quantity of a large power grid is +.>Is the residual value of the system equipment and accounts for 5% of the initial installation cost of the system;
（13）
in the method, in the process of the invention,penalty coefficients for wind and light rejection for renewable energy sources; />The wind and light amount is:
（14）
in the method, in the process of the invention,the load after the demand response is considered;
（15）
in the method, in the process of the invention,is a punishment coefficient of source-load matching; />Is a load tracking factor, +.>Is the maximum load tracking coefficient;
（16）
s3-2: constraint conditions:
s3-2-1: renewable energy and energy storage constraint:
（17）
wherein:、/>the upper limit of the installation capacity of the renewable energy source and the energy storage respectively;
s3-2-2: power balance constraint:
（18）
s3-2-3: renewable energy sources, energy storage and large power grid output constraint:
（19）
wherein:、/>respectively the upper and lower limits of the output of the renewable energy source; />、/>Respectively the upper limit and the lower limit of the energy storage output; />、/>The upper limit and the lower limit of the interaction power with the large power grid are respectively set;
s3-2-4: demand response constraints:
（20）
in the method, in the process of the invention,、/>the upper limit and the lower limit of the change of the demand response electric quantity are respectively;
s3-2-5: state of charge of stored energyConstraint:
（21）
wherein:、/>the upper limit and the lower limit of the charge state of the storage battery are respectively;
s4: the traditional particle swarm algorithm is improved based on the dense distance, and the method is specifically as follows:
s4-1: finding a global optimal solution of the problem through cooperation in the group; firstly, randomly initializing a particle swarm in a given solution space, and determining the dimension of the solution space according to the variable number of the problem to be optimized; each particle has an initial position and an initial speed, and then is optimized through iteration; the optimizing method is as follows:
（22）
in the method, in the process of the invention,is inertial weight, ++>,/>Is an acceleration factor, ++>,/>Is a random number between 0 and 1, ">Is the individual optimal position vector of the ith particle at time k,/for the particle at time k>Is the overall optimal position vector at the kth moment;
s4-2: an optimal solution selection method based on dense distance is provided:
s4-2-1: the dense distance is a density estimation index; given m objective functions, the particleIs>The method comprises the following steps:
（23）
wherein,and->Is closest +.>Is a particle of (2); />Finger particle->The value of the mth objective function of (2); />Is the maximum of the mth objective function of all particles;
s4-2-2: assume that the optimization problem contains three objective functions:、/>and->Dense distance of particles->The method comprises the following steps:
（24）
then introducing the cross mutation operation in the genetic algorithm into a particle swarm algorithm, and performing the cross mutation operation on the position vector of the particles; taking a weight value vector randomly obtained from a particle position vector as a basis of cross mutation, the optimal solution selection process based on the dense distance is as follows:
(1) the method comprises the following steps Determining weight values of randomly acquired particlesCrossing rate->Sum of variance ratio->；
(2) The method comprises the following steps Determining the size of the ith particle ifRandomly selecting whether to perform a particle intersection operation process;
(3) the method comprises the following steps For each dimensional component of the position vector of the ith particle, select a value of 0,1]Random number betweenIf (3)Then a cross operation is prepared for the dimensional component of its position vector and the dimensional component of the last selected particle of the cross operation as follows:
（25）
(4) the method comprises the following steps Selecting 0,1 for each dimension component of the position vector of the ith particle]Random number of (a)If->The position vector of the particleThe corresponding dimensional components of the quantities perform the following mutation operations:
（26）
(5) the method comprises the following steps Obtaining new population particles after the cross mutation operation, and calculating the dense distance of each Pareto solution;
(6) the method comprises the following steps Each particle is allocated an fitness value, and the corresponding fitness value is equal to the dense distance of the corresponding particle in the Pareto solution;
(7) the method comprises the following steps Selecting roulette according to the fitness value, and randomly selecting a position to be the global optimal position of the particle;
(8) the method comprises the following steps Repeating the steps until each particle in the population is assigned a global optimal position;
s5: solving a cost objective function of the power distribution network according to an improved particle swarm algorithm to obtain the capacity of the energy storage optimal position, wherein the method comprises the following steps:
solving the energy storage, addressing and volume-fixing optimization problem of the power distribution network: when an improved particle swarm algorithm is adopted to solve the energy storage, location and volume selection optimization problem of the power distribution network, the position and the power of an energy storage system are optimized; according to the principle that the access position of the stored energy is an integer, the particle position is rounded when being updated; and finally obtaining the optimal position and capacity of energy storage through repeated iteration of the particles.
CN202311842055.2A 2023-12-29 2023-12-29 Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation Active CN117526387B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311842055.2A CN117526387B (en) 2023-12-29 2023-12-29 Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311842055.2A CN117526387B (en) 2023-12-29 2023-12-29 Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation

Publications (2)

Publication Number Publication Date
CN117526387A CN117526387A (en) 2024-02-06
CN117526387B true CN117526387B (en) 2024-03-22

Family

ID=89762951

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311842055.2A Active CN117526387B (en) 2023-12-29 2023-12-29 Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation

Country Status (1)

Country Link
CN (1) CN117526387B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106410861A (en) * 2016-11-04 2017-02-15 浙江工业大学 Microgrid optimizing operation real-time control method based on schedulable ability
CN110633854A (en) * 2019-09-16 2019-12-31 长沙理工大学 Full life cycle optimization planning method considering energy storage battery multiple segmented services
CN111009914A (en) * 2019-11-20 2020-04-14 广西电网有限责任公司 Active power distribution network-oriented energy storage device location and volume determination method
CN112165112A (en) * 2020-09-23 2021-01-01 广东电网有限责任公司肇庆供电局 Distributed energy storage system planning method for solving low voltage of distribution network
CN115600858A (en) * 2022-09-30 2023-01-13 中国电建集团西北勘测设计研究院有限公司（Cn） Wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment
CN115912420A (en) * 2022-11-18 2023-04-04 新疆大学 Wind power collection area energy storage optimization configuration method considering cycle life and operation strategy
CN116169698A (en) * 2022-12-15 2023-05-26 国网江苏省电力有限公司无锡供电分公司 Distributed energy storage optimal configuration method and system for stable new energy consumption
CN116187165A (en) * 2022-12-24 2023-05-30 三峡大学 Power grid elasticity improving method based on improved particle swarm optimization

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9960637B2 (en) * 2015-07-04 2018-05-01 Sunverge Energy, Inc. Renewable energy integrated storage and generation systems, apparatus, and methods with cloud distributed energy management services

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106410861A (en) * 2016-11-04 2017-02-15 浙江工业大学 Microgrid optimizing operation real-time control method based on schedulable ability
CN110633854A (en) * 2019-09-16 2019-12-31 长沙理工大学 Full life cycle optimization planning method considering energy storage battery multiple segmented services
CN111009914A (en) * 2019-11-20 2020-04-14 广西电网有限责任公司 Active power distribution network-oriented energy storage device location and volume determination method
CN112165112A (en) * 2020-09-23 2021-01-01 广东电网有限责任公司肇庆供电局 Distributed energy storage system planning method for solving low voltage of distribution network
CN115600858A (en) * 2022-09-30 2023-01-13 中国电建集团西北勘测设计研究院有限公司（Cn） Wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment
CN115912420A (en) * 2022-11-18 2023-04-04 新疆大学 Wind power collection area energy storage optimization configuration method considering cycle life and operation strategy
CN116169698A (en) * 2022-12-15 2023-05-26 国网江苏省电力有限公司无锡供电分公司 Distributed energy storage optimal configuration method and system for stable new energy consumption
CN116187165A (en) * 2022-12-24 2023-05-30 三峡大学 Power grid elasticity improving method based on improved particle swarm optimization

Also Published As

Publication number Publication date
CN117526387A (en) 2024-02-06

Similar Documents

Publication Publication Date Title
CN105811409B (en) A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile
CN112131733B (en) Distributed power supply planning method considering influence of charging load of electric automobile
CN108944531A (en) A kind of orderly charge control method of electric car
CN106602557B (en) A kind of multi-period optimal reconfiguration method of active distribution network containing electric car
CN112713618B (en) Active power distribution network source network load storage cooperative optimization operation method based on multi-scene technology
CN111178619A (en) Multi-objective optimization method considering distributed power supply and charging station joint planning
CN107730048B (en) Wind power-electric vehicle combined system random robust optimization scheduling method
CN111900727A (en) Light storage, charging and conversion integrated charging station collaborative optimization scheduling method and device based on PSO
CN110138006A (en) Consider more micro electric network coordination Optimization Schedulings containing New-energy electric vehicle
CN107609693A (en) Micro-capacitance sensor Multipurpose Optimal Method based on Pareto archives particle cluster algorithms
CN113326467B (en) Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties
CN108964102A (en) The position of distributed energy storage and capacity configuration optimizing method in power distribution network
CN116667325B (en) Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm
CN114519449A (en) Operation optimization method for park energy system
CN107273968A (en) A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization
CN111619391B (en) Electric vehicle ordered charging and discharging method based on cooperative game and dynamic time-of-use electricity price
CN114243791A (en) Multi-objective optimization configuration method, system and storage medium for wind-solar-hydrogen storage system
CN115471044A (en) Power distribution station electric automobile scheduling method and system with light storage and storage medium
CN114722728A (en) Wind-solar-energy-storage hybrid energy storage capacity optimal configuration method and system
CN115173455A (en) Online charging algorithm for electric vehicle cluster
CN105896580B (en) A kind of micro-capacitance sensor multiobjective optimization control method and device
CN113224747B (en) Electric power resource coordination scheduling method and system considering electric vehicle demand response
CN114462854A (en) Hierarchical scheduling method and system containing new energy and electric vehicle grid connection
CN117526387B (en) Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation
CN115940284B (en) Operation control strategy of new energy hydrogen production system considering time-of-use electricity price

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant