CN104135025B - Microgrid connection economic optimization method based on fuzzy particle swarm algorithm - Google Patents

Abstract

The invention provides a microgrid economic operation optimization method based on a fuzzy particle swarm algorithm and an energy saving system. The method comprises the following steps of: (1) determining an economic dispatch strategy of a micro source in the microgrid; (2) building a microgrid grid-connected economic operation mathematical model combined with economic benefit and environment benefit; (3) putting forward a fuzzy control strategy for charging/discharging control of the energy saving system; and (4) optimizing based on the particle swarm algorithm, and determining the output of all micro sources and the generation cost per day. Compared with a method in which an optimization dispatch scheme is not adopted by the energy saving system, the method has the advantages that the generation cost per day of microgrid running and pollution emission are reduced; and compared with a method in which the fuzzy control charging/discharging strategy is not adopted by the energy saving system, the method has the advantages that optimal solution quality is improved, operational convergence rate is increased, and the reliability of microgrid running is further enhanced.

Description

Micro-grid connection economic optimization method based on Fuzzy particle swarm optimization

Technical field

The present invention relates to a kind of operation of power system, emulation, analysis and dispatching technique, in particular to one kind are based on mould Paste particle cluster algorithm and the microgrid economic operation optimization method of energy-storage system.

Background technology

With the microgrid as foundation for the certain standard, various distributed power sources, load, energy-storage units and control device etc. are combined Together, form a single controllable, to user's energy supply simultaneously and heat energy, realize cogeneration of heat and power.Microgrid is with its economy section The features such as energy and environmental friendliness, has become the important composition of intelligent grid development, and the economic benefit that microgrid runs is to attract user, And the key factor promoted in power system, it is also the developing direction of intelligent grid simultaneously.The economical operation of micro-grid connection It is related to the coordination problem and the power trade of major network and each micro- source between, optimal economic scheduling is formulated by Rational Decision Strategy, then can reach raising microgrid efficiency of energy utilization, reduce the economic technology requirements such as operating cost.

The economic load dispatching of microgrid is one of its important research direction with optimization operation.And, it is in short supply with fossil energy, Microgrid increasingly receives publicity, and the economical operation of corresponding microgrid also increasingly obtains the attention of more people.Microgrid economical operation has Have the particularitys different from traditional bulk power grid economical operation, its need to consider the characteristic of different types of distributed power source with mutually Between coordination problem.Microgrid equally exists energy management problem as bulk power grid simultaneously, such as how to controlled in microgrid Power supply (miniature gas turbine, fuel cell), uncontrollable power supply (wind-power electricity generation, photovoltaic generation) and energy storage device (all types of storages Battery, flywheel energy storage, Hydrogen Energy circulation equipment, water-storage) carry out energy management, plan its fuel operational version, energy storage device Discharge and recharge scheme and external electrical network power trade scheme etc., ensure that the safety in actual motion, physical property constrain bar simultaneously Part, with this ensure microgrid continue, economy, safe operation.

This area research is still in theoretical research and demonstration Qualify Phase at present, does not have the solution of maturation.Existing grind Study carefully the heuristic strategies that means are mostly focused on excessively simplification, due to being unable to reach mathematical optimum point and convergence rate limit The shortcoming of system and economy and the exploitativeness run cannot be ensured, therefore cannot effectively meet in microgrid actual motion economical The composite request of property, safety and energy-saving and emission-reduction.

Content of the invention

For the deficiencies in the prior art, the present invention provides a kind of microgrid warp based on Fuzzy particle swarm optimization and energy-storage system Ji running optimizatin method, in order to ensure the environment friendly of micro-grid system, adds wind-power electricity generation and photovoltaic generating system, in order to Make full use of the high benefit of cogeneration of heat and power, add miniature gas turbine；In order that exerting oneself in micro- source, scope is lifted further, Add fuel cell, in order to make full use of the benefit of peak load shifting, add the energy-storage battery system of great application prospect System.The strategy of economic load dispatching has taken into full account each micro- source advantage of its own, maximum limit on the basis of ensureing power supply reliably The economy that the lifting microgrid of degree runs；The comprehensive electric generating cost considering micro-grid system in the object function proposing becomes with environment The income of the thermic load of this and cogeneration of heat and power, considers power-balance constraint, the constraint of thermic load, micro- source go out in constraints The constraint of power, microgrid interact the constraint of power and the constraint of energy-storage system operation conditions with power distribution network；Propose a kind of for The fuzzy control strategy of energy-storage system charge and discharge control；Determine the method based on Fuzzy particle swarm optimization optimizing.It is applied to Dispatching of power netwoks department formulates related economy operation of power grid plan.The comprehensive electric generating cost that the present invention considers each microgrid is special Property, consider the power trade problem and external electrical network between simultaneously, and a kind of fuzzy control strategy is taken to energy storage discharge and recharge. Establish the microgrid economical operation mathematical model that comprehensive electric generating cost and Environmental costs are taken into account on this basis, based on grain Swarm optimization optimizing, has the characteristic reducing day cost of electricity-generating and algorithm Fast Convergent.This micro-capacitance sensor scheduling scheme is applied to The micro- source of polymorphic type and energy-storage system composition, the economical operation Optimized Operation in being incorporated into the power networks of cogeneration micro power network.

The present invention seeks to realized using following technical characteristics:

A kind of microgrid economic operation optimization method based on Fuzzy particle swarm optimization and energy-storage system, its improvements exists In methods described includes:

(1) determine the Economic Scheduling Policy in micro- source in micro-capacitance sensor；

(2) set up the micro-grid connection economical operation mathematical model that economic benefit and environmental benefit combine；

(3) fuzzy control strategy to energy-storage system charge and discharge control is proposed；

(4) it is based on particle cluster algorithm optimizing, determine to exert oneself and day cost of electricity-generating in each micro- source.

Preferably, in described step (1), micro- source includes wind-power electricity generation-wt, photovoltaic generation-pv, fuel cell-fc, miniature Gas turbine-mt and energy-storage system-es.

Preferably, described step (1) includes:

(1.1) pv and wt generating tracing control maximum power output；

(1.2) determine that the active of mt is exerted oneself by thermic load；

(1.3) power purchase balance power and the sale of electricity balance power of fc and mt is calculated according to the price of buying and selling electricity of different periods；

(1.4) when wt, pv and mt active exert oneself when cannot meet microgrid electric load peak when, make energy-storage system output have Work(, detects the charging and discharging state of energy-storage system simultaneously；

(1.5) when wt, pv, mt and es always active exert oneself cannot meet microgrid electric load when, fc and mt power purchase balance Continue in power to generate electricity.

Further, described step (1.4) includes:

Energy-storage system meet microgrid safe and reliable operation in the range of exerting oneself it is allowed to increase energy-storage system active power to Outer net sale of electricity, otherwise maintains former exerting oneself.

Preferably, described step (2) includes: consider in the object function of proposition micro-grid system comprehensive electric generating cost, The income of the thermic load of Environmental costs and cogeneration of heat and power, considers the power-balance constraint, constraint of thermic load, micro- in constraints The constraint exerted oneself in source, microgrid interact the constraint of power and the constraint of energy-storage system operation conditions with power distribution network；

Wherein, (2.1) object function:

$\min c=\underset{t=1}{\overset{t}{\mathrm{\σ}}}\[c_{fu}\left(t\right)+c_{om}\left(t\right)+c_{pcc}\left(t\right)+c_{gas}\left(t\right)-c_{sh}\left(t\right)\]---\left(1\right)$

In formula:

$c_{fu}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\σ}}}{f}_i\left(p_i\right(t\left)\right)---\left(2\right)$

$c_{om}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\σ}}}{k}_{om,i}*p_i\left(t\right)---\left(3\right)$

$c_{pcc}\left(t\right)=\{\begin{array}{cc}{p}_{pcc}\left(t\right)*p_b\left(t\right)& p_{pcc}\left(t\right)\&greaterequal;0\\ p_{pcc}\left(t\right)*p_s\left(t\right)& p_{pcc}\left(t\right)<0\end{array}---\left(4\right)$

$c_{gas}\left(t\right)=\underset{j=1}{\overset{m}{\mathrm{\&sigma;}}}{\mathrm{\&alpha;}}_j\left(\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{\mathrm{\&beta;}}_{ij}{p}_i\right(t\left)\right)---\left(5\right)$

$c_{sh}\left(t\right)=q_{he}\left(t\right)*k_{ph}---\left(6\right)$

Wherein, c_fu(t)、c_om(t)、c_pcc(t)、c_gas(t)、c_shT () is respectively the fuel cost in each micro- source of t, operation Maintenance cost and power distribution network interact cost, Environmental costs and co-generation unit heat income；

f_iFuel cost function for i-th micro- source；

p_iT () is the active power output of i-th micro- source t；

N is the number in micro- source；

k_om,iUnit quantity of electricity operation expense coefficient for i-th micro- source；

α_jFor the punishment unit price of jth class emission, discharge type is no_x、so₂、co₂；

β_ijEmission factor for i-th micro- source jth class emission；

M is the species of emission；

p_pcc(t) be t period microgrid and power distribution network interact power；

p_b(t) and p_sT () is respectively t microgrid to the power purchase price of outer net and sale of electricity price；

q_heT () is the thermic load amount of t；

k_phPrice for unit heating capacity；

(2.2) constraints:

(2.2.1) power-balance constraint:

When energy-storage system charges:

$p_l\left(t\right)+p_{loss}\left(t\right)=p_{wt}\left(t\right)+p_{pv}\left(t\right)+p_{mt}\left(t\right)+p_{fc}\left(t\right)+\frac{p_{es}\left(t\right)}{{\mathrm{\&eta;}}_{ch}}+p_{pcc}\left(t\right)---\left(7\right)$

During energy storage system discharges:

p_l(t)+p_loss(t)=p_wt(t)+p_pv(t)+p_mt(t)+p_fc(t)+η_dip_es(t)+p_pcc(t) (8)

Wherein, p_wt(t)、p_pv(t)、p_mt(t)、p_fc(t)、p_pcc(t)、p_lossT () is respectively t period Wind turbines, photovoltaic Battery, miniature gas turbine, the output of fuel cell, microgrid interact power and power attenuation with major network；

p_esT () is the output of t energy-storage system；

η_ch、η_diCharging and discharging efficiency for energy-storage system；

(2.2.2) thermic load constraint:

q_mt(t)≥q_he(t) (9)

Q in formula_mtT () is the heating capacity that miniature gas turbine provides；

(2.2.3) micro- source units limits: each micro- source at any time sent out power will the constraint of oneself bound it Interior；

p_i ^min≤p_i(t)≤p_i ^max(10)

(2.2.4) the through-put power constraint that microgrid is interacted with power distribution network:

p_pcc,min≤p_pcc(t)≤p_pcc,max(11)

P in formula_pcc,min、p_pcc,maxIt is respectively microgrid and power distribution network allows minimum, the peak power of alternating transmission；

(2.2.5) constraint of energy-storage system operation conditions:

A, a cycle for scheduling, the electricity of the energy-storage system whole story keeps identical；

$\underset{t=1}{\overset{t}{\&sigma;}}{p}_{es}\left(t\right)\mathrm{\&delta;}=0---\left(12\right)$

In formula, δ is the duration of t period lasts；

B, the electricity of any moment energy-storage system during dispatching all in allowed band within；

${\mathrm{soc}}^{min}\&le;s_{init}-\underset{i=1}{\overset{t}{\&sigma;}}{p}_{es}\left(t\right)\mathrm{\&delta;}\&le;{\mathrm{soc}}^{max}---\left(13\right)$

Wherein, soc^min、soc^max、s_initIt is respectively the minima that in energy-storage system, storing electricity allows, maximum and storage Energy system is in the electricity of scheduling start time.

Further, the described fuel cost following formula of described formula (2) inclusion calculates:

The fuel cost function of mt:

c_mt=(c_nl/l)σ[p_mt(t)δt/η_e(t)] (14)

${\mathrm{\&eta;}}_e=0.0753\&times;{\left(\frac{p_{mt}}{65}\right)}^3-0.3095{\left(\frac{p_{mt}}{65}\right)}^2+0.4174\left(\frac{p_{mt}}{65}\right)+0.1068---\left(15\right)$

$q_{mt}=\frac{p_{mt}(1-{\mathrm{\&eta;}}_e-{\mathrm{\&eta;}}_1)}{{\mathrm{\&eta;}}_e}---\left(16\right)$

Wherein, q_mtFor thermal power；

η₁For gas turbine radiation loss coefficient；

c_nlFor Gas Prices；

The fuel cost function of fc:

c_fc=(c_nl/l)σ[p_fc(t)δt/η_fc(t)] (17)

η_fc=-0.0023p_fc+0.6735 (18)

Preferably, described step (3) includes:

The schedule periods of one day 24h are divided three classes the type period: peak phase, flat phase, Gu Qi；

The size of the electricity soc that energy-storage system stores in t itself is divided into third gear: high-grade, middle-grade and low-grade；

The watt level of energy-storage system discharge and recharge is divided into five ranks: Large Copacity charging-nb, low capacity charging-ns, great Rong Amount electric discharge-pb, low capacity electric discharge-ps and discharge and recharge-zb in the range of very little.

Preferably, described step (4) includes:

(4.1) each micro- source, load, environment punishment, electric price parameter and particle cluster algorithm parameter are inputted；

(4.2) build fuzzy control model, determine fuzzy control parameter；

(4.3) by system, initially related input quantity is converted into the input quantity of fuzzy control, is calculated based on fuzzy control rule Output, and the initial range of exerting oneself of energy-storage system is determined based on this；

(4.4) data initialization, the flight speed of each particle of random initializtion simultaneously；

(4.5) calculate the fitness of each particle；

(4.6) record extreme value；

(4.7) iterationses add 1:k=k+1, update flight speed and particle in the position of solution space；

(4.8) recalculate each particle fitness function value now, judge whether to update pbesti and gbest；

(4.9) judge whether to restrain；When meeting one of following condition, iteration stopping；If overall desired positions continuous hundred times Unchanged or reach prespecified maximum iteration time；Otherwise go to step (4.7)；

(4.10) export each micro- source to exert oneself and day cost of electricity-generating result.

Compared with prior art, the invention has the benefit that

The present invention decreases the day generating expense of microgrid operation compared with the energy-storage system not situation using Optimized Operation scheme With and discharge amount of pollution, improve the matter of optimal solution compared with the energy-storage system not situation using fuzzy control discharge and recharge strategy Amount, improves the convergence rate of computing, and then improves the reliability of operation of power networks.

The fuzzy control strategy mechanism that proposed is simple, but but the Based Intelligent Control of energy-storage system and electricity price are changed and The dump energy (soc) of energy-storage system effectively combines, and adequately achieves the optimization to energy-storage system and uses, can fully incorporate electricity In the mechanism in power market and combine self-condition thus obtaining economic interests.

Brief description

The schematic diagram of the micro-grid system structure that Fig. 1 provides for the present invention.

Fig. 2 for the present invention provide based on each micro- source of each moment that obscure particle colony optimization algorithm (fpso) draws exert oneself with And microgrid and major network interact power schematic diagram.

Employing particle swarm optimization algorithm (pso) that Fig. 3 provides for the present invention and the optimization convergence curve that fpso obtains are illustrated Figure.

Specific embodiment

Below in conjunction with the accompanying drawings the specific embodiment of the present invention is described in further detail.

As shown in figure 1, example used in the present invention includes 20kw wind-powered electricity generation (wt), 10kw photovoltaic (pv), 40kw fuel electricity Pond (fc), 65kw miniature gas turbine (mt), 100kw battery energy storage system (es)；

A kind of micro-grid connection economic optimization method based on Fuzzy particle swarm optimization that the present invention provides, under the method includes State step:

Step (1): determine the respective Economic Scheduling Policy of each element in micro-capacitance sensor.It is defeated that wt and pv follows the tracks of peak power Go out, the environment friendly being possessed due to itself, preferentially it is exerted oneself；Fc and mt provides when cost of electricity-generating is less than electricity price and exerts oneself, Stop when cost of electricity-generating is higher than electricity price exerting oneself；Es controls it to charge also or electric discharge and discharge and recharge using fuzzy control method Intensity size.Specifically include:

1.1) because pv and wt generates electricity, there is uncontrollability, and directly do not consume fuel as regenerative resource, do not pollute Environment, therefore preferentially exerted oneself using it, tracing control maximum power output；

1.2) it is to make co-generation unit operational efficiency highest, it, by the way of " electricity determining by heat ", is determined by thermic load The active of mt is exerted oneself；

1.3) power purchase balance power and the sale of electricity balance work(of fc and mt is calculated according to the price of buying and selling electricity difference of different periods Rate.When wt, pv and mt active exert oneself exceed microgrid electric load and network loss when, the part exceeding when peak to outer net sell, this When energy-storage system in allowed band to outer net sale of electricity.Charge to energy-storage system with Gu Shixian at ordinary times. as energy-storage system is full of Then to outer net sale of electricity.Now to outer net sale of electricity in fc and mt electric equilibrium on sale power bracket.

1.4) when wt, pv and mt active exert oneself cannot meet microgrid electric load when, peak season energy-storage system output active, Detect the charging and discharging state of energy-storage system simultaneously；If energy-storage system can meet in the range of exerting oneself microgrid safe and reliable operation ( Not on the basis of cutting load, microgrid can run under the Prescribed Properties meeting) it is contemplated that allowing to increase having of energy-storage system Work(power, to outer net sale of electricity, otherwise remains former and exerts oneself, now to outer net sale of electricity in fc and mt electric equilibrium on sale power bracket；Gu Shi Do not consider energy storage system discharges, now fc and mt generates electricity in power purchase balance power, from outer net power purchase if not, now needs To charge to energy-storage system.Energy storage system discharges at ordinary times, fc and mt generates electricity in power purchase balance power, if being still unsatisfactory for load Demand, then need from outer net power purchase in the range of Tie line Power, if still inadequate, according to load significance level according to Secondary excision.

Step 2: include 2.1) object function:

$\min c=\underset{t=1}{\overset{t}{\&sigma;}}\&lsqb;c_{fu}\left(t\right)+c_{om}\left(t\right)+c_{pcc}\left(t\right)+c_{gas}\left(t\right)-c_{sh}\left(t\right)\&rsqb;---\left(1\right)$

In formula:

$c_{fu}\left(t\right)=\underset{i=1}{\overset{n}{\&sigma;}}{f}_i\left(p\right(t\left)\right)---\left(2\right)$

$c_{om}\left(t\right)=\underset{i=1}{\overset{n}{\&sigma;}}{k}_{om,i}*p_i\left(t\right)---\left(3\right)$

$c_{pcc}\left(t\right)=\left\{\begin{array}{cc}{p}_{pcc}\left(t\right)*p_b\left(t\right)& p_{pcc}\left(t\right)\&greaterequal;0\\ p_{pcc}\left(t\right)*p_s\left(t\right)& p_{pcc}\left(t\right)<0\end{array}\right.---\left(4\right)$

$c_{gas}\left(t\right)=\underset{j=1}{\overset{m}{\&sigma;}}{\mathrm{\&alpha;}}_j\left(\underset{i=1}{\overset{n}{\&sigma;}}{\mathrm{\&beta;}}_{ij}{p}_i\right(t\left)\right)---\left(5\right)$

c_sh(t)=q_he(t)*k_ph(6)

Wherein, c_fu(t)、c_om(t)、c_pcc(t)、c_gas(t)、c_shT () is respectively the fuel cost in each micro- source of t, operation Maintenance cost and power distribution network interact cost, Environmental costs and co-generation unit heat income；f_iFor i-th micro- source Fuel cost function；p_iT () is the active power output of i-th micro- source t；N is the number in micro- source；k_om,iMicro- for i-th The unit quantity of electricity operation expense coefficient in source；α_jFor the punishment unit price of jth class emission, discharge type is no_x、so₂、co₂； β_ijEmission factor for i-th micro- source jth class emission；M is the species of emission；p_pccT () is t period microgrid and power distribution network Interactive power (microgrid to during power distribution network power purchase be just, otherwise be negative)；p_b(t) and p_sT () is respectively t microgrid to outer net Power purchase price and sale of electricity price；q_heT () is the thermic load amount of t；k_phFor the price of unit heating capacity, take 0.1 yuan/ kwh.

2.2) constraints:

2.2.1) power-balance constraint:

When energy-storage system charges:

$p_l\left(t\right)+p_{loss}\left(t\right)=p_{wt}\left(t\right)+p_{pv}\left(t\right)+p_{mt}\left(t\right)+p_{fc}\left(t\right)+\frac{p_{es}\left(t\right)}{{\mathrm{\&eta;}}_{ch}}+p_{pcc}\left(t\right)---\left(7\right)$

During energy storage system discharges:

p_l(t)+p_loss(t)=p_wt(t)+p_pv(t)+p_mt(t)+p_fc(t)+η_dip_es(t)+p_pcc(t) (8)

Wherein, p_wt(t)、p_pv(t)、p_mt(t)、p_fc(t)、p_pcc(t)、p_lossT () is respectively t period Wind turbines, photovoltaic Battery, miniature gas turbine, the output of fuel cell, microgrid interact power and power attenuation with major network；p_esT () is The output (be negative during charging, otherwise be just) of t energy-storage system；η_ch、η_diCharging and discharging effect for energy-storage system Rate；

2.2.2) thermic load constraint:

q_mt(t)≥q_he(t) (9)

Q in formula_mtT () is the heating capacity that miniature gas turbine provides；

2.2.3) each micro- source units limits: each micro- source at any time sent out power will the constraint of oneself bound it Interior:

p_i ^min≤p_i(t)≤p_i ^max(10)

2.2.4 the through-put power constraint that) microgrid is interacted with power distribution network:

p_pcc,min≤p_pcc(t)≤p_pcc,max(11)

Wherein, p_pcc,min、p_pcc,maxIt is respectively microgrid and power distribution network allows minimum, the peak power of alternating transmission；

2.2.5) the constraint of energy-storage system operation conditions:

A, a cycle for scheduling, the electricity of the energy-storage system whole story keeps identical；

$\underset{t=1}{\overset{t}{\&sigma;}}{p}_{es}\left(t\right)\mathrm{\&delta;}=0---\left(12\right)$

In formula, δ is the duration of t period lasts；

B, the electricity of any moment energy-storage system during dispatching all in allowed band within:

${\mathrm{soc}}^{min}\&le;s_{init}-\underset{i=1}{\overset{t}{\&sigma;}}{p}_{es}\left(t\right)\mathrm{\&delta;}\&le;{\mathrm{soc}}^{max}---\left(13\right)$

Wherein, soc^min、soc^max、s_initIt is respectively the minima that in energy-storage system, storing electricity allows, maximum and storage Energy system is in the electricity of scheduling start time；

In step 2, the fuel cost described in formula (2) is asked for by following function formula:

The fuel cost function of mt:

c_mt=(c_nl/l)σ[p_mt(t)δt/η_e(t)] (14)

${\mathrm{\&eta;}}_e=0.0753\&times;{\left(\frac{p_{mt}}{65}\right)}^3-0.3095{\left(\frac{p_{mt}}{65}\right)}^2+0.4174\left(\frac{p_{mt}}{65}\right)+0.1068---\left(15\right)$

$q_{mt}=\frac{p_{mt}(1-{\mathrm{\&eta;}}_e-{\mathrm{\&eta;}}_1)}{{\mathrm{\&eta;}}_e}---\left(16\right)$

In above-mentioned several formula, q_mtFor thermal power.η₁For gas turbine radiation loss coefficient, take 0.03.c_nlFor natural gas Price, is 2.05 yuan/m³；

The fuel cost function of fc:

c_fc=(c_nl/l)σ[p_fc(t)δt/η_fc(t)] (17)

η_fc=-0.0023p_fc+0.6735 (18)

The k in operation expense in step 2, described in formula (3)_om,iMiniature gas turbine and fuel cell are divided Other value is 0.047 yuan/kwh and 0.1 yuan/kwh.

In step 2, power purchase price p of the t described in formula (4)_b(t) and sale of electricity price p_sT () utilizes in the following manner Obtain:

Dispatching cycle is set to 24h, is divided into peak, paddy, puts down three periods:

Peak period: 10:00～15:00；18:00～21:00；

Flat period phase: 7:00～10:00；15:00～18:00；21:00～23:00；

Low-valley interval: 23:00～7:00；

Peak, paddy, the power purchase price of flat three periods and sale of electricity price are as shown in the table:

In step 2, the α described in formula (5)_jAnd β_ijAsked for by following table respectively:

In step 2: micro battery the sent out power bound described in formula (10) is determined by following table respectively:

In step 3, the discharge and recharge of energy-storage system uses fuzzy control strategy as follows:

Schedule periods 24h of one day are divided three classes period of type: peak phase, flat phase, Gu Qi；Energy-storage system stores up in t itself The size of the dump energy soc depositing is divided into third gear: high-grade, middle-grade, low-grade.The watt level of energy-storage system discharge and recharge is also classified into Three ranks: Large Copacity charges (nb), Large Copacity electric discharge (pb), discharges (pe) in the range of very little, charges in the range of very little (ne), in the range of very little, the corresponding relation of discharge and recharge (ze) each element is as follows:

In step 4, the optimizing step based on particle cluster algorithm is as follows:

(1) each micro- source, load, environment punishment, electric price parameter, particle cluster algorithm parameter are inputted.

(2) build fuzzy control model, determine fuzzy control parameter.

(3) by system, initially related input quantity is converted into the input quantity of fuzzy control, is calculated based on fuzzy control rule Output, and the initial range of exerting oneself of energy-storage system is determined based on this.

(4) data initialization.Iterationses k=0, initialization d-1 dimension variable (wherein energy storage system first in the range of exerting oneself System to determine initial range according to fuzzy control strategy by the input quantity of fuzzy control), d dimension variable passes through constraints formula (12), (13) are obtained, choosing value again of more prescribing a time limit.Produce n particle, as initialization population, the position of each particle corresponds to The value exerted oneself in one group of micro- source；The flight speed of each particle of random initializtion simultaneously.

(5) calculate the fitness of each particle.

(6) record extreme value.Record particle i first (i=1,2 ..., n) current individual extreme value pbesti and corresponding mesh Offer of tender numerical value f (pbesti), determines overall extreme value gbest from pbesti, and records gbest corresponding object function f (gbest).

(7) iterationses add 1:k=k+1.The position updating flight speed and particle in solution space is (wherein, based on fuzzy Control strategy regularly updates the speed related to energy-storage system and position range according to the input quantity of fuzzy control).

(8) recalculate each particle fitness function value now, judge whether to update pbesti and gbest.

(9) judge whether to restrain.When meeting one of following condition, iteration stopping: if continuous hundred nothings of overall desired positions Change or reach prespecified maximum iteration time；Otherwise go to step (7).

(10) output result.

In step 4, according to predetermined control strategy, shown that each moment drawing based on fpso in Fig. 2 is each micro- Source exert oneself and microgrid and major network interact power.

From the figure 3, it may be seen that from the 01:00 paddy period, es starts to charge up, and the power that mt sends out meets thermic load and a part Electric load, the cost of electricity-generating of fc is more than the cost to major network power purchase, therefore does not exert oneself；Section when 8:00 is flat, fc starts to exert oneself, and es is little In the range of charge and discharge；11:00 enters the peak period, and fc is almost exerting oneself under maximum discharge power, due to major network sale of electricity Income is more than the cost of electricity-generating of mt, therefore mt high power discharge, now es also electric discharge under relatively large power, simultaneously to major network Sale of electricity is to obtain economic benefit；Start to major network power purchase when 16:00 is flat, the electric discharge of fc, es reduces accordingly, mt discharges to meet Fixing thermic load；The 19:00 peak period, now mt and fc high power discharge, es is also allowing larger journey in the range of dump energy The electric discharge of degree, the hardly outwards net purchase electricity also not sale of electricity of this section；Section when 22:00 is flat, mt generates electricity to meet thermic load, fc generating work( Rate accordingly declines, and es small range electric discharge in dump energy allowed band, to electrical network power purchase to fill up power shortage；24:00 paddy Period, es is high-power to charge to initial quantity of electricity, is that the scheduling of second day is prepared, and fc stops generating electricity, and mt sends out power and meets hot bearing The demand of lotus, now needs to electrical network power purchase.

The convergence rate and economy of fpso and pso is contrasted, Fig. 3 is to be received using the optimization that pso and fpso obtains Hold back curve it is evident that as can be seen that fpso algorithm is due to introducing fuzzy control strategy to energy-storage system discharge and recharge, its computing is fast The precision that degree is conciliate is obtained for raising, has good convergence.

Using technique scheme, the present invention has and takes into full account the characteristic in each micro- source, filling energy-storage system in microgrid Discharge power is connected with Spot Price and itself soc value so that each several part is all whole microgrid economic optimization fortune well Row service.

Finally it should be noted that: above example is only not intended to limit in order to technical scheme to be described, to the greatest extent Pipe has been described in detail to the present invention with reference to above-described embodiment, and those of ordinary skill in the art are it is understood that still The specific embodiment of the present invention can be modified or equivalent, and any without departing from spirit and scope of the invention Modification or equivalent, it all should be covered in the middle of scope of the presently claimed invention.

Claims (6)

1. a kind of microgrid economic operation optimization method based on Fuzzy particle swarm optimization and energy-storage system is it is characterised in that described Method includes:

(1) determine the Economic Scheduling Policy in micro- source in micro-capacitance sensor；

(2) set up the micro-grid connection economical operation mathematical model that economic benefit and environmental benefit combine；

(3) fuzzy control strategy to energy-storage system charge and discharge control is proposed；

(4) it is based on particle cluster algorithm optimizing, determine to exert oneself and day cost of electricity-generating in each micro- source；

In described step (1), micro- source includes wind-power electricity generation-wt, photovoltaic generation-pv, fuel cell-fc, miniature gas turbine-mt With energy-storage system-es；

Described step (1) includes:

(1.1) pv and wt generating tracing control maximum power output；

(1.2) determine that the active of mt is exerted oneself by thermic load；

(1.3) power purchase balance power and the sale of electricity balance power of fc and mt is calculated according to the price of buying and selling electricity of different periods；

(1.4) when wt, pv and mt active exert oneself when cannot meet microgrid electric load peak when, make energy-storage system output active, Detect the charging and discharging state of energy-storage system simultaneously；

(1.5) when wt, pv, mt and es always active exert oneself cannot meet microgrid electric load when, fc and mt power purchase balance power Interior continuation generates electricity.

2. a kind of microgrid economical operation optimization side based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1 Method is it is characterised in that described step (1.4) includes:

Energy-storage system meets microgrid safe and reliable operation it is allowed to increase the active power of energy-storage system to outer net in the range of exerting oneself Sale of electricity, otherwise maintains former exerting oneself.

3. a kind of microgrid economical operation optimization side based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1 Method is it is characterised in that described step (2) includes: considers comprehensive electric generating cost, the ring of micro-grid system in the object function of proposition The income of the thermic load of border cost and cogeneration of heat and power, considers power-balance constraint, the constraint of thermic load, micro- source in constraints The constraint exerted oneself, microgrid interact the constraint of power and the constraint of energy-storage system operation conditions with power distribution network；

Wherein, (2.1) object function:

$\min c=\underset{t=1}{\overset{t}{\mathrm{\&sigma;}}}\&lsqb;c_{fu}\left(t\right)+c_{om}\left(t\right)+c_{pcc}\left(t\right)+c_{gas}\left(t\right)-c_{sh}\left(t\right)\&rsqb;---\left(1\right)$

In formula:

$c_{fu}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{f}_i\left(p_i\right(t\left)\right)---\left(2\right)$

$c_{om}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{k}_{om,i}*p_i\left(t\right)---\left(3\right)$

$c_{pcc}\left(t\right)=\left\{\begin{array}{cc}{p}_{pcc}\left(t\right)*p_b\left(t\right)& p_{pcc}\left(t\right)\&greaterequal;0\\ p_{pcc}\left(t\right)*p_s\left(t\right)& p_{pcc}\left(t\right)<0\end{array}\right.---\left(4\right)$

$c_{gas}\left(t\right)=\underset{j=1}{\overset{m}{\mathrm{\&sigma;}}}{\mathrm{\&alpha;}}_j\left(\underset{i=1}{\overset{n}{\mathrm{\&sigma;}}}{\mathrm{\&beta;}}_{ij}{p}_i\right(t\left)\right)---\left(5\right)$

c_sh(t)=q_he(t)*k_ph(6)

Wherein, c_fu(t)、c_om(t)、c_pcc(t)、c_gas(t)、c_shT () is respectively the fuel cost in each micro- source of t, operation maintenance Cost and power distribution network interact cost, Environmental costs and co-generation unit heat income；

f_iFuel cost function for i-th micro- source；

p_iT () is the active power output of i-th micro- source t；

N is the number in micro- source；

k_om,iUnit quantity of electricity operation expense coefficient for i-th micro- source；

α_jFor the punishment unit price of jth class emission, discharge type is no_x、so₂、co₂；

β_ijEmission factor for i-th micro- source jth class emission；

M is the species of emission；

p_pcc(t) be t period microgrid and power distribution network interact power；

p_b(t) and p_sT () is respectively t microgrid to the power purchase price of outer net and sale of electricity price；

q_heT () is the thermic load amount of t；

k_phPrice for unit heating capacity；

(2.2) constraints:

(2.2.1) power-balance constraint:

When energy-storage system charges:

$p_l\left(t\right)+p_{loss}\left(t\right)=p_{wt}\left(t\right)+p_{pv}\left(t\right)+p_{mt}\left(t\right)+p_{fc}\left(t\right)+\frac{p_{es}\left(t\right)}{{\mathrm{\&eta;}}_{ch}}+p_{pcc}\left(t\right)---\left(7\right)$

During energy storage system discharges:

p_l(t)+p_loss(t)=p_wt(t)+p_pv(t)+p_mt(t)+p_fc(t)+η_dip_es(t)+p_pcc(t) (8)

Wherein, p_wt(t)、p_pv(t)、p_mt(t)、p_fc(t)、p_pcc(t)、p_loss(t) be respectively t period Wind turbines, photovoltaic cell, Miniature gas turbine, the output of fuel cell, microgrid interact power and power attenuation with major network；

p_esT () is the output of t energy-storage system；

η_ch、η_diCharging and discharging efficiency for energy-storage system；

(2.2.2) thermic load constraint:

q_mt(t)≥q_he(t) (9)

Q in formula_mtT () is the heating capacity that miniature gas turbine provides；

(2.2.3) micro- source units limits: sent out power will be within the constraint of oneself bound at any time in each micro- source；

p_i ^min≤p_i(t)≤p_i ^max(10)

P in formula_i ^minSent out lower limit by i-th micro- source；

p_i ^maxSent out power upper limit by i-th micro- source；

(2.2.4) the through-put power constraint that microgrid is interacted with power distribution network:

p_pcc,min≤p_pcc(t)≤p_pcc,max(11)

P in formula_pcc,min、p_pcc,maxIt is respectively microgrid and power distribution network allows minimum, the peak power of alternating transmission；

(2.2.5) constraint of energy-storage system operation conditions:

A, a cycle for scheduling, the electricity of the energy-storage system whole story keeps identical；

$\underset{t=1}{\overset{t}{\mathrm{\&sigma;}}}{p}_{es}\left(t\right)\mathrm{\&delta;}=0---\left(12\right)$

In formula, δ is the duration of t period lasts；

B, the electricity of any moment energy-storage system during dispatching all in allowed band within；

${\mathrm{soc}}^{\min }\&le;s_{init}-\underset{i=1}{\overset{t}{\mathrm{\&sigma;}}}{p}_{es}\left(t\right)\mathrm{\&delta;}\&le;{\mathrm{soc}}^{max}---\left(13\right)$

Wherein, soc^min、soc^max、s_initIt is respectively the minima that in energy-storage system, storing electricity allows, maximum and energy-storage system Electricity in scheduling start time.

4. a kind of microgrid economical operation optimization side based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 3 Method is it is characterised in that the described fuel cost following formula of described formula (2) inclusion calculates:

The fuel cost function of mt:

c_mt=(c_nl/l)∑[p_mt(t)δt/η_e(t)] (14)

In formula, l is natural gas low grade fever calorific value；

δ t is gas turbine operation unit interval length；

η_eT () is the generating efficiency of t miniature gas turbine；

${\mathrm{\&eta;}}_e=0.0753\&times;{\left(\frac{p_{mt}}{65}\right)}^3-0.3095{\left(\frac{p_{mt}}{65}\right)}^2+0.4174\left(\frac{p_{mt}}{65}\right)+0.1068---\left(15\right)$

$q_{mt}=\frac{p_{mt}(1-{\mathrm{\&eta;}}_e-{\mathrm{\&eta;}}_1)}{{\mathrm{\&eta;}}_e}---\left(16\right)$

Wherein, q_mtFor thermal power；

η₁For gas turbine radiation loss coefficient；

c_nlFor Gas Prices；

The fuel cost function of fc:

c_fc=(c_nl/l)∑[p_fc(t)δt/η_fc(t)] (17)

Wherein, p_fcT () is the generated output of t fuel cell；

η_fcT () is the generating efficiency of t fuel cell；

η_fc=-0.0023p_fc+0.6735 (18)

5. a kind of microgrid economical operation optimization side based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1 Method is it is characterised in that described step (3) includes:

The schedule periods of one day 24h are divided three classes the type period: peak phase, flat phase, Gu Qi；

The size of the electricity soc that energy-storage system stores in t itself is divided into third gear: high-grade, middle-grade and low-grade；

The watt level of energy-storage system discharge and recharge is divided into five ranks: Large Copacity charging-nb, low capacity charging-ns, Large Copacity are put Electricity-pb, low capacity electric discharge-ps and discharge and recharge-zb in the range of very little.

6. a kind of microgrid economical operation optimization side based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1 Method is it is characterised in that described step (4) includes:

(4.1) each micro- source, load, environment punishment, electric price parameter and particle cluster algorithm parameter are inputted；

(4.2) build fuzzy control model, determine fuzzy control parameter；

(4.3) by system, initially related input quantity is converted into the input quantity of fuzzy control, calculates output based on fuzzy control rule Amount, and the initial range of exerting oneself of energy-storage system is determined based on this；

(4.4) data initialization, the flight speed of each particle of random initializtion simultaneously；

(4.5) calculate the fitness of each particle；

(4.6) record extreme value；

(4.7) iterationses add 1:k=k+1, update flight speed and particle in the position of solution space；

(4.8) recalculate each particle fitness function value now, judge whether to update pbesti and gbest；

(4.9) judge whether to restrain；When meeting one of following condition, iteration stopping；If overall desired positions no become for continuous hundred times Change or reach prespecified maximum iteration time；Otherwise go to step (4.7)；

(4.10) export each micro- source to exert oneself and day cost of electricity-generating result.