CN103699946A - Method for stabilizing charge and exchange station loads and ensuring electricity economy by using energy storage system - Google Patents
Method for stabilizing charge and exchange station loads and ensuring electricity economy by using energy storage system Download PDFInfo
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- CN103699946A CN103699946A CN201410013608.8A CN201410013608A CN103699946A CN 103699946 A CN103699946 A CN 103699946A CN 201410013608 A CN201410013608 A CN 201410013608A CN 103699946 A CN103699946 A CN 103699946A
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Abstract
The invention relates to a method for stabilizing charge and exchange station loads and ensuring electricity economy by using an energy storage system. The method is optimization on double power fuzzy targets of an energy storage system based on a particle swarm algorithm. According to the technical scheme, the method comprises the following steps: (1) determining optimization targets including an economic target and a power grid target, combining the target 1 with the target 2, so as to obtain win-win dual targets of a power grid and a user; (2) determining a constraint condition; (3) fuzzifying a target function; and (4) solving the problem of double fussy targets based on the particle swarm algorithm. By adopting the intelligent particle swarm algorithm, input/output power of the energy storage system is calculated, so that dual effects of stabilizing charge and exchange station loads and economical electricity are achieved. Different optimization trends can be selected according to different optimization requirements of the charge and exchange station every day, so that the target of flexibly responding to interests between the user and the power grid is achieved, and balance is found out in an interest game of the power grid and the user.
Description
Technical field
The present invention relates to a kind of method of utilizing accumulator system to stabilize to fill electrical changing station load and guaranteeing electricity consumption economy, be based on particle cluster algorithm, the accumulator system fuzzy Bi-objective of exerting oneself to be optimized, belong to accumulator system and under electricity consumption economy double goal, determine the technical field of not exerting oneself in the same time stabilizing to fill electrical changing station load and guarantee.
Background technology
At present, intelligent grid comprehensive construction is obtained considerable progress, to improve Regional Investment Climate from energy strategy angle, implement national energy-saving environmental protection policy angle and realize energy-saving and emission-reduction, the angle that changes life style promotes people's livelihood construction, to meeting the harmonious development of electric power and national economy society, physical environment, play crucial impetus.In order to improve the electrical network quality of power supply, when electric network fault, to power for important load, started the special project construction that echelon utilizes battery energy storage system to demonstrate, be mainly used in load valley energy storage, load peak phase to electric automobile electric energy supplement.Because charging station power consumption exists obvious undulatory property, at charging station, setting up accumulator system is the important solution route of stabilizing charging station load variations.In background technology, not utilizing accumulator system to stabilize and fill the content that electrical changing station is loaded and guaranteed electricity consumption economy, complete to stabilize simultaneously and fill electrical changing station load and guarantee electricity consumption economy Bi-objective, is this area technical matters urgently to be resolved hurrily.
Summary of the invention
The object of the invention is to propose a kind of method of utilizing accumulator system to stabilize to fill electrical changing station load and guaranteeing electricity consumption economy, adopt intelligent granule group algorithm, calculate the I/O power of accumulator system, thereby reach, stabilize the double effects that fills electrical changing station load and economic electricity consumption, solve the problems referred to above that background technology exists.
Utilize accumulator system to stabilize and fill the method that electrical changing station is loaded and guaranteed electricity consumption economy, the Bi-objective of accumulator system being exerted oneself based on particle cluster algorithm is optimized, and comprises the steps:
1. determine optimization aim:
(1) economic goal;
Based on tou power price policy, first formulate and there is the economy operational objective that minimizes the effect of power purchase expense, as target one; Tou power price is according to user power utilization demand and load curve characteristics, the different periods will be divided into for one day, and day part is formulated respectively to different electricity price levels, the production and consumption of guiding electric energy is to encourage a kind of effective measures of user and electricity power enterprise's peak load shifting on electricity supply and demand;
(2) electrical network target;
Then set up the load variance of consideration electrical network interests as target two; For electrical network, in order to reduce the startup-shutdown number of times of conventional power generation usage unit and the capacity of spinning reserve, the target of electrical network is that load curve is as far as possible smooth; On mathematics, variance is used for measuring the departure degree between stochastic variable and its mathematical expectation, and the variance of load can reflect the smooth degree of load curve;
Combining target one and target two, just obtained the Bi-objective to electrical network and user's doulbe-sides' victory; So the present invention is the comprehensive optimisation strategy of having considered electrical network and user's common interest;
2. determine constraint condition:
(1) battery charge power constraint: due to the restriction of electronic power convertor and battery body, the output power in each moment of battery can not super overpowering bound, i.e. P
low≤ p (i)≤P
high;
(2) energy content of battery constraint: E
low≤ e (i)≤E
high, i=0,1,2 ..., N;
(3) battery charge state SOC constraint: SOC
low≤ soc (i)≤SOC
high;
(4) uninterrupted power source UPS power constraint: P
ulow≤ pu (i)≤P
uhigh;
(5) regularly determine SOC constraint: SOC (k)=C%;
Wherein the implication of a symbol is:
P
low: energy storage device charge power lower limit
P (i): energy storage device real time charging power
P
high: the energy storage device charge power upper limit
E
low: energy content of battery lower limit
E (i): the real-time energy content of battery
E
high: the energy content of battery upper limit
I: superincumbent (2) are inner to illustrate to be i=0,1,2 ..., N, i is one 0 integer that arrives N
N: data amount check
SOC: battery charge state
SOC
low: battery charge state lower limit
Soc (i): battery charge state instantaneous value
SOC
high: the battery charge state upper limit
P
ulow: UPS power lower limit
Pu (i): UPS power instantaneous value
P
uhigh: the UPS power upper limit
SOC (k): k is state-of-charge constantly
C%: number percent constant
3. objective function obfuscation:
Because above-mentioned economic goal and electrical network target have different dimensions, can not be simply by both stacks, so the present invention adopts fuzzy method that objective function is carried out to obfuscation; Solving thinking is: first obtain the optimum solution of each sub-goal under institute's Prescribed Properties, recycle these optimum solutions by each sub-goal Function Modules gelatinization, determine subordinate function, then according to maximum membership grade principle, fuzzy multi-objective optimization question is converted into nonlinear single-object problem, solves the optimum solution of this single goal problem;
4. based on the fuzzy Bi-objective problem of PSO Algorithm:
In particle cluster algorithm, in population, each member is called particle, represent a potential feasible solution, and target location is called globally optimal solution; Globally optimal solution is searched on D dimension space by colony, and each particle has a fitness function value and speed to adjust self-position to move to target location guaranteeing, in moving process, in colony, all particles all have memory capability, particle position (gbest) study to best in the optimum position (pbest) of self-position and self process and population, finally approaches target location.
Beneficial effect of the present invention: the present invention adopts intelligent granule group algorithm, calculates the I/O power of accumulator system, thereby reach, stabilizes the double effects that fills electrical changing station load and economic electricity consumption; Can select different optimization tendencies according to filling electrical changing station different optimization requirement every day, to reach the object of interests demand between flexible response user and electrical network, in the interest game between electrical network and user, find balance.
Accompanying drawing explanation
Fig. 1 is embodiment of the present invention process flow diagram;
Fig. 2 is daily load curve before the embodiment of the present invention is optimized;
Fig. 3 is daily load curve after embodiment of the present invention optimization;
Fig. 4 is that embodiment of the present invention echelon is utilized accumulator system output power histogram.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described further:
The present embodiment is that Cao Fei pasture, Tangshan bus fills electrical changing station, and accumulator system adopts echelon to utilize battery energy storage system, and daily load is the daily load of filling electrical changing station.
1, objective function
(1), for economy operational objective, it is objective function one that electrical changing station power purchase expense is filled in setting;
Wherein c is that L fills the active power that electrical changing station is asked for from electrical network according to tou power price policy i power purchase expense constantly;
(2), for electrical network target, assumed load variance is objective function two;
Wherein: N is the number at a number of days strong point, gets 24 here; L (i) is the charging station load of i constantly; C (i) is that i arrives the output power of energy storage device (being just energy storage system discharges) between i+1 constantly constantly.
2, constraint condition
According to Cao Feidian, fill concrete facility and the requirement of electrical changing station, determine following constraint condition;
(1) battery charge power constraint :-25kW≤P≤25kW;
(2) battery capacity constraint: E (i)≤104kWh, i=0,1,2 ..., 24;
(3) battery charge state SOC constraint: 40%≤SOC (i)≤100%;
(4) uninterrupted power supply (ups) Unity power constraint: be that two 10kW change electric machine people and powering under net state 2 hours;
(5) regularly determining SOC retrains: morning every day 5:00, SOC (5)=95%.
3, objective function obfuscation
To solving of Model for Multi-Objective Optimization, the present invention has adopted maximum membership degree function method that multi-objective optimization question is changed into non-linear single-object problem, has also solved the economic goal problem different from electrical network target dimension simultaneously.Fuzzy method is as follows:
(1) take power purchase network minimal as objective function is optimized calculating, obtain minimum power purchase expense, be designated as
f 1m, and by solution substitution objective function now
f 2 try to achieve load variance now, be designated as
f 2M ;
(2) take load variance minimum as objective function is optimized calculating, obtain minimum load variance, be designated as
f 2m , and by solution substitution objective function now
f 1 try to achieve power purchase expense now, be designated as
f 1M ;
(3) objective function is carried out to Fuzzy processing, the present invention chooses and falls half trapezoidal profile as subordinate function,
Wherein:
f imin for single-goal function
f i minimum value under constraint condition, i=1,2.
Comprehensive two objective functions, by fuzzy membership
μ (f 1 )with
μ (f 2 )be weighted, the different attention degrees of weighting representative to target one and target two, above-mentioned fuzzy double-goal optimal model is
max{ α μ (f 1 )+
β μ (f 2 )or
min{-α μ (f 1 )-
β μ (f 2 ).
4, utilize particle cluster algorithm to solve
According to the algorithm flow of Fig. 1, programming solves example.Upgrade particle rapidity and position respectively according to carrying out with minor function:
x
i
(t+1)=x
i
(t)+V
i
(t+1)
V
i
(t+1)=ωV
i
(t)+C
1
rand( )(P
i
(t)-x
i
(t))+C
2
rand( )(P
g
(t)-x
i
(t))
ω=0.9-(0.9-0.4)*i/MaxNum
Vid=Vmax, if Vid>Vmax
Vid=-Vmax, if Vid<-Vmax
Wherein
: v i for the previous speed of particle,
p i for the optimal location (pbest) of particle i process, be " knowing part " study of particle,
p g for particle position best in population (gbest), be " social part " study of particle,
rand ()be 0 to 1 random number,
ωfor inertia weight
, MaxNumfor maximum iteration time, choose here
c 1 ,
c 2 equal 2;
Wherein, the processing of constraint condition is adopted to penalty function method, when certain particle crosses the border, by the penalty factor of this particle optimal-adaptive degree is regulated to the position of particle, and think that this particle position is poor position, should give rejecting.
5, optimum results
(1) do not distinguish the fuzzy Bi-objective optimization of importance
Before optimization, fill electrical changing station daily load curve as shown in Figure 2, original loads variance is 1262.9, and power purchase expense is 3779.8 yuan/day.After optimization, fill electrical changing station daily load curve as shown in Figure 3, load variance is 1008.9, and power purchase expense is 3720.9 yuan/day, and load variance has reduced 20.11%, reduces 58.9 yuan of power purchase spendings every day.With regard to economy, utilize this optimization method will save more than 20,000 yuan every year, and being the echelon with secondary value, energy storage device utilizes battery, no matter be that storage energy operation cost or power purchase expense have higher economy;
(2) distinguish the fuzzy Bi-objective optimization of importance
This fuzzy Bi-objective optimization also can be according to user weight coefficient to the differentiation of different target importance degree and in corresponding adjustment membership function, adjust
μ (f)=α μ (f 1 )+
β μ (f 2 )in
αwith
β, suitably adjust
αwith
βcan obtain the optimum results to target one and target two different attention degrees.The optimum results being obtained by different degree of membership weight coefficients is as shown in table 2.When α > β, economy target is subject to attention degree to stabilize target and be subject to attention degree high than load, and the minimum power purchase expense that fuzzy Bi-objective optimum results obtains can quite approach single goal optimum results.Same, when α < β, load is stabilized target and is subject to attention degree to be subject to attention degree high than economy target, and fuzzy Bi-objective optimum results more can be stabilized load.
Claims (1)
1. utilize accumulator system to stabilize and fill the method that electrical changing station is loaded and guaranteed electricity consumption economy, it is characterized in that, the Bi-objective of accumulator system being exerted oneself based on particle cluster algorithm is optimized, and comprises the steps:
1. determine optimization aim:
(1) economic goal;
Based on tou power price policy, first formulate and there is the economy operational objective that minimizes the effect of power purchase expense, as target one;
(2) electrical network target;
Then set up the load variance of consideration electrical network interests as target two;
Combining target one and target two, obtain the Bi-objective to electrical network and user's doulbe-sides' victory;
2. determine constraint condition:
(1) battery charge power constraint: due to the restriction of electronic power convertor and battery body, the output power in each moment of battery can not super overpowering bound, i.e. P
low≤ p (i)≤P
high;
(2) energy content of battery constraint: E
low≤ e (i)≤E
high, i=0,1,2 ..., N;
(3) battery charge state SOC constraint: SOC
low≤ soc (i)≤SOC
high;
(4) uninterrupted power source UPS power constraint: P
ulow≤ pu (i)≤P
uhigh;
(5) regularly determine SOC constraint: SOC (k)=C%;
Wherein the implication of a symbol is:
P
low: energy storage device charge power lower limit
P (i): energy storage device real time charging power
P
high: the energy storage device charge power upper limit
E
low: energy content of battery lower limit
E (i): the real-time energy content of battery
E
high: the energy content of battery upper limit
I: superincumbent (2) are inner to illustrate to be i=0,1,2 ..., N, i is one 0 integer that arrives N
N: data amount check
SOC: battery charge state
SOC
low: battery charge state lower limit
Soc (i): battery charge state instantaneous value
SOC
high: the battery charge state upper limit
P
ulow: UPS power lower limit
Pu (i): UPS power instantaneous value
P
uhigh: the UPS power upper limit
SOC (k): k is state-of-charge constantly
C%: number percent constant
3. objective function obfuscation:
Adopt fuzzy method that objective function is carried out to obfuscation; First obtain the optimum solution of each sub-goal under institute's Prescribed Properties, recycle these optimum solutions by each sub-goal Function Modules gelatinization, determine subordinate function, then according to maximum membership grade principle, fuzzy multi-objective optimization question is converted into nonlinear single-object problem, solves the optimum solution of this single goal problem;
4. based on the fuzzy Bi-objective problem of PSO Algorithm:
In particle cluster algorithm, in population, each member is called particle, represent a potential feasible solution, and target location is called globally optimal solution; Globally optimal solution is searched on D dimension space by colony, and each particle has a fitness function value and speed to adjust self-position to move to target location guaranteeing, in moving process, in colony, all particles all have memory capability, particle position study to best in the optimum position of self-position and self process and population, finally approaches target location.
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| CN104156789A (en) * | 2014-08-21 | 2014-11-19 | 重庆大学 | Isolated micro-grid optimum economic operation method taking energy storage life loss into consideration |
| CN104239967A (en) * | 2014-08-29 | 2014-12-24 | 华北电力大学 | Multi-target economic dispatch method for power system with wind farm |
| CN104598999A (en) * | 2015-02-04 | 2015-05-06 | 河海大学 | Power distribution network time-of-use power price optimizing method taking reliability into consideration |
| CN105160597A (en) * | 2015-08-27 | 2015-12-16 | 国家电网公司 | Power system-based greenhouse gas emission reduction and control method |
| CN105321038A (en) * | 2014-07-30 | 2016-02-10 | 三星电子株式会社 | Method and apparatus for device management based on device power information and pricing schemes |
| CN106299511A (en) * | 2016-08-02 | 2017-01-04 | 万马联合新能源投资有限公司 | Electric automobile charging station energy storage capacity optimization method |
| CN106651002A (en) * | 2016-11-17 | 2017-05-10 | 云南电网有限责任公司玉溪供电局 | Large-scale electric automobile charge and discharge multi-target optimization method based on sine cosine algorithm |
| CN108334985A (en) * | 2018-02-05 | 2018-07-27 | 国网江西省电力有限公司电力科学研究院 | Resident's intelligent power Optimized model based on PSO Algorithm |
| CN110929961A (en) * | 2019-12-12 | 2020-03-27 | 上海积成能源科技有限公司 | System and method for energy storage optimization analysis management based on multivariable control |
| CN111900726A (en) * | 2020-07-15 | 2020-11-06 | 国网上海市电力公司 | Charging and discharging power control method and device for energy storage system of charging and replacing power station |
| CN113872192A (en) * | 2021-09-26 | 2021-12-31 | 国网电力科学研究院武汉能效测评有限公司 | Hospital power grid load optimization control system and control method |
| CN114204551A (en) * | 2021-11-25 | 2022-03-18 | 哈尔滨工业大学 | Diversified optimization operation method and device for multi-energy complementary micro-grid comprising photovoltaic heat pump |
-
2014
- 2014-01-13 CN CN201410013608.8A patent/CN103699946A/en active Pending
Non-Patent Citations (4)
| Title |
|---|
| 张学清，梁军: "计及风光电源的一种地区电网电动汽车充电调度方法", 《电工技术学报》 * |
| 李人厚，王拓: "《智能控制理论和方法 第2版》", 28 February 2013, 西安电子科技大学出版社 * |
| 陈满，陆志刚: "电池储能系统恒功率削峰填谷优化策略研究", 《电网技术》 * |
| 鲍冠南，陆超: "基于动态规划的电池储能系统削峰填谷实时优化", 《电力系统自动化》 * |
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| CN105321038A (en) * | 2014-07-30 | 2016-02-10 | 三星电子株式会社 | Method and apparatus for device management based on device power information and pricing schemes |
| CN104156789A (en) * | 2014-08-21 | 2014-11-19 | 重庆大学 | Isolated micro-grid optimum economic operation method taking energy storage life loss into consideration |
| CN104156789B (en) * | 2014-08-21 | 2017-04-12 | 重庆大学 | Isolated micro-grid optimum economic operation method taking energy storage life loss into consideration |
| CN104239967A (en) * | 2014-08-29 | 2014-12-24 | 华北电力大学 | Multi-target economic dispatch method for power system with wind farm |
| CN104598999B (en) * | 2015-02-04 | 2017-09-29 | 河海大学 | A kind of power distribution network tou power price optimization method of meter and reliability |
| CN104598999A (en) * | 2015-02-04 | 2015-05-06 | 河海大学 | Power distribution network time-of-use power price optimizing method taking reliability into consideration |
| CN105160597A (en) * | 2015-08-27 | 2015-12-16 | 国家电网公司 | Power system-based greenhouse gas emission reduction and control method |
| CN106299511A (en) * | 2016-08-02 | 2017-01-04 | 万马联合新能源投资有限公司 | Electric automobile charging station energy storage capacity optimization method |
| CN106299511B (en) * | 2016-08-02 | 2019-02-26 | 万马联合新能源投资有限公司 | Electric automobile charging station energy storage capacity optimization method |
| CN106651002A (en) * | 2016-11-17 | 2017-05-10 | 云南电网有限责任公司玉溪供电局 | Large-scale electric automobile charge and discharge multi-target optimization method based on sine cosine algorithm |
| CN106651002B (en) * | 2016-11-17 | 2020-08-04 | 云南电网有限责任公司玉溪供电局 | Large-scale electric vehicle charging and discharging multi-objective optimization method based on sine and cosine algorithm |
| CN108334985A (en) * | 2018-02-05 | 2018-07-27 | 国网江西省电力有限公司电力科学研究院 | Resident's intelligent power Optimized model based on PSO Algorithm |
| CN110929961A (en) * | 2019-12-12 | 2020-03-27 | 上海积成能源科技有限公司 | System and method for energy storage optimization analysis management based on multivariable control |
| CN111900726A (en) * | 2020-07-15 | 2020-11-06 | 国网上海市电力公司 | Charging and discharging power control method and device for energy storage system of charging and replacing power station |
| CN113872192A (en) * | 2021-09-26 | 2021-12-31 | 国网电力科学研究院武汉能效测评有限公司 | Hospital power grid load optimization control system and control method |
| CN113872192B (en) * | 2021-09-26 | 2024-03-12 | 国网电力科学研究院武汉能效测评有限公司 | Hospital power grid load optimization control system and control method |
| CN114204551A (en) * | 2021-11-25 | 2022-03-18 | 哈尔滨工业大学 | Diversified optimization operation method and device for multi-energy complementary micro-grid comprising photovoltaic heat pump |
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Application publication date: 20140402 |