CN104135025A - Microgrid economic operation optimization method based on fuzzy particle swarm algorithm and energy saving system - 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

Microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system

Technical field

The present invention relates to a kind of operation, emulation, analysis and dispatching technique of electric power system, specifically relate to a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system.

Background technology

Microgrid taking certain standard as foundation, combines various distributed power sources, load, energy-storage units and control device etc., forms a single controllable, to user's energy supply simultaneously and heat energy, realizes cogeneration of heat and power.Microgrid has become the important composition of intelligent grid development with features such as its economical and energy saving and environmental friendliness, the economic benefit of microgrid operation is to attract user, and the key factor of being promoted in electric power system, is also the developing direction of intelligent grid simultaneously.The grid-connected economical operation of microgrid relates to the coordination problem between power trade and each micro-source of major network, formulates optimal economic scheduling strategy by Rational Decision, can reach to improve microgrid efficiency of energy utilization, reduce the economic technology requirements such as operating cost.

The economic dispatch of microgrid is one of its important research direction with optimizing operation.And, in short supply along with fossil energy, microgrid more and more receives publicity, and the economical operation of corresponding microgrid also more and more obtains more people's attention.Microgrid economical operation has the particularity different from the large economy operation of power grid of tradition, and it need to consider the characteristic of dissimilar distributed power source and mutual coordination problem.Microgrid and the large the same energy management problem that exists equally of electrical network simultaneously, for example, how to the controllable electric power (miniature gas turbine in microgrid, fuel cell), uncontrollable power supply (wind power generation, photovoltaic generation) and energy storage device (all types of storage batterys, flywheel energy storage, Hydrogen Energy circulation equipment, pumped storage) carry out energy management, plan its fuel operational version, energy storage device discharges and recharges scheme, with external electrical network power trade scheme etc., ensure the fail safe in actual motion simultaneously, physical property constraints, ensure continuing of microgrid with this, economical, safe operation.

This area research still in theoretical research and demonstration Qualify Phase, does not have ripe solution at present.Existing research means concentrates on the heuristic strategies of too simplifying mostly, cannot ensure economy and the exploitativeness of operation owing to cannot reaching the shortcoming of mathematical optimum point and convergence rate restriction, therefore cannot effectively meet the composite request of economy, fail safe and energy-saving and emission-reduction in microgrid actual motion.

Summary of the invention

For the deficiencies in the prior art, the invention provides a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system, in order to ensure the environment friendly of micro-grid system, wind power generation and photovoltaic generating system are added, in order to make full use of the high benefit of cogeneration of heat and power, add miniature gas turbine; For micro-source scope of exerting oneself is further promoted, add fuel cell, in order to make full use of the benefit of peak load shifting, add the energy-storage battery system that has application prospect.The strategy of economic dispatch has taken into full account himself advantage of each micro-source, in the economy that ensures to promote to greatest extent on the reliable basis of power supply microgrid operation; In the target function proposing, consider the income of the comprehensive electric generating cost of micro-grid system and the heat load of Environmental costs and cogeneration of heat and power, in constraints, considered the constraint of constraint, microgrid and the mutual power of power distribution network and the constraint of energy-storage system operation conditions exerted oneself in power-balance constraint, the constraint of heat load, micro-source; A kind of fuzzy control strategy that discharges and recharges control for energy-storage system has been proposed; Determine the method based on Fuzzy particle swarm optimization optimizing.It is applicable to dispatching of power netwoks department and formulates relevant economy operation of power grid plan.The present invention considers the comprehensive electric generating cost behavior of each microgrid, considers the power trade problem between external electrical network simultaneously, and energy storage is discharged and recharged and takes a kind of fuzzy control strategy.Set up on this basis the microgrid economical operation Mathematical Modeling that comprehensive electric generating cost and Environmental costs are taken into account, based on particle cluster algorithm optimizing, there is the characteristic that reduces day cost of electricity-generating and algorithm Fast Convergent.This micro-dispatching of power netwoks scheme is applicable to the economical operation Optimized Operation micro-source of polymorphic type and energy-storage system composition, in being incorporated into the power networks of cogeneration micro power network.

The present invention seeks to adopt following technical characterictic to realize:

A microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system, its improvements are, described method comprises:

(1) determine the Economic Scheduling Policy in micro-source in micro-electrical network;

(2) set up the grid-connected economical operation Mathematical Modeling of microgrid that economic benefit and environmental benefit combine;

(3) proposition discharges and recharges the fuzzy control strategy of control to energy-storage system;

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

Preferably, in described step (1), micro-source comprises wind power generation-WT, photovoltaic generation-PV, fuel cell-FC, miniature gas turbine-MT and energy-storage system-ES.

Preferably, described step (1) comprising:

(1.1) PV and WT generating are followed the tracks of and are controlled maximum power output;

(1.2) determine that by heat load the meritorious of MT exert oneself;

(1.3) price of buying and selling electricity of different periods of foundation calculates power purchase balanced power and the sale of electricity balanced power of FC and MT;

(1.4) in the time that WT, PV and MT meritorious exerted oneself peak cannot meet microgrid electric loading time, make energy-storage system output meritorious, detect the charging and discharging state of energy-storage system simultaneously;

(1.5) when WT, PV, MT and ES total meritorious exerts oneself cannot meet microgrid electric loading time, FC and MT continue to generate electricity in power purchase balanced power.

Further, described step (1.4) comprising:

Energy-storage system meets microgrid safe and reliable operation in the scope of exerting oneself, and allows the active power that increases energy-storage system to outer net sale of electricity, exerts oneself otherwise remain former.

Preferably, described step (2) comprising: in the target function of proposition, considered the income of the heat load of comprehensive electric generating cost, Environmental costs and the cogeneration of heat and power of micro-grid system, considered the constraint of constraint, microgrid and the mutual power of power distribution network and the constraint of energy-storage system operation conditions exerted oneself in power-balance constraint, the constraint of heat load, micro-source in constraints;

Wherein, (2.1) target function:

$\min C=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}[C_{\mathrm{fu}}\left(t\right)+C_{\mathrm{OM}}\left(t\right)+C_{\mathrm{PCC}}\left(t\right)+C_{\mathrm{gas}}\left(t\right)-C_{\mathrm{sh}}\left(t\right)]---\left(1\right)$

In formula:

$C_{\mathrm{fu}}\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{F}_i\left(P_i\left(t\right)\right)---\left(2\right)$

$C_{\mathrm{OM}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{K}_{\mathrm{OM},i}*P_i\left(t\right)---\left(3\right)$

$C_{\mathrm{PCC}}\left(t\right)=\left\{\begin{array}{cc}{P}_{\mathrm{PCC}}\left(t\right)*P_b\left(t\right)& P_{\mathrm{PCC}}\left(t\right)\&GreaterEqual;0\\ P_{\mathrm{PCC}}\left(t\right)*P_s\left(t\right)& P_{\mathrm{PCC}}\left(t\right)<0\end{array}\right.---\left(4\right)$

$C_{\mathrm{gas}}\left(t\right)=\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_j\left(\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&beta;}}_{\mathrm{ij}}{P}_i\left(t\right)\right)---\left(5\right)$

C _sh(t)＝Q _he(t)*K _ph (6)

Wherein, C _fu(t), C _oM(t), C _pCC(t), C _gas(t), C _sh(t) be respectively the fuel cost, operation expense in t each micro-source of moment, the income that heats with mutual cost, Environmental costs and the co-generation unit of power distribution network;

F _iit is the fuel cost function in i micro-source;

P _i(t) be the active power output in i micro-source t moment;

N is the number in micro-source;

K _{oM, i}it is the unit quantity of electricity operation expense coefficient in i micro-source;

α _jbe the punishment unit price of j class emission, discharge type is NO _x, SO ₂, CO ₂;

β _ijit is the emission factor of i micro-source j class emission;

M is the kind of emission;

P _pCC(t) be the mutual power of t period microgrid and power distribution network;

P _band P (t) _s(t) be respectively power purchase price and the sale of electricity price of t moment microgrid to outer net;

Q _he(t) be the heat load amount in t moment;

K _phfor the price of system of units heat;

(2.2) constraints:

(2.2.1) power-balance constraint:

When energy-storage system charging:

$P_L\left(t\right)+P_{\mathrm{Loss}}\left(t\right)=P_{\mathrm{WT}}\left(t\right)+P_{\mathrm{PV}}\left(t\right)+P_{\mathrm{MT}}\left(t\right)+P_{\mathrm{FC}}\left(t\right)+\frac{P_{\mathrm{ES}}\left(t\right)}{{\mathrm{\&eta;}}_{\mathrm{ch}}}+P_{\mathrm{PCC}}\left(t\right)---\left(7\right)$

When 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 mutual power and the power loss of power output, microgrid and the major network of t period wind-powered electricity generation unit, photovoltaic cell, miniature gas turbine, fuel cell;

P _eS(t) be the power output of t moment energy-storage system;

η _ch, η _difor the charging and discharging efficiency of energy-storage system;

(2.2.2) heat load constraint:

Q _MT(t)≥Q _he(t) (9)

Q in formula _mT(t) be the heating capacity that miniature gas turbine provides;

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

$P_i^{\min }\&le;P_i\left(t\right)\&le;P_i^{\max }---\left(10\right)$

(2.2.4) the mutual through-put power constraint of microgrid and power distribution network:

P _PCC,min≤P _PCC(t)≤P _PCC,max(11)

P in formula _{pCC, min}, P _{pCC, max}be respectively microgrid and power distribution network and allow mutual minimum, the maximum power of transmitting;

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

A, for the one-period of scheduling, it is identical that the electric weight at energy-storage system whole story keeps;

$\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ES}}\left(t\right)\mathrm{\&delta;}=0---\left(12\right)$

In formula, δ is lasting duration of t period;

B, at the electric weight of any moment energy-storage system between schedule periods all within allowed band;

${\mathrm{SOC}}^{\min }\&le;S_{\mathrm{init}}-\underset{i=1}{\overset{t}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ES}}\left(t\right)\mathrm{\&delta;}\&le;{\mathrm{SOC}}^{\max }---\left(13\right)$

Wherein, SOC ^min, SOC ^max, S _initbe respectively minimum value, maximum and the energy-storage system of storing electricity permission in energy-storage system at the scheduling electric weight of the zero hour.

Further, described formula (2) comprises that described fuel cost following formula calculates:

The fuel cost function of MT:

C _MT＝(C _nl/L)Σ[P _MT(t)Δt/η _e(t)] (14)

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

$Q_{\mathrm{MT}}=\frac{P_{\mathrm{MT}}(1-{\mathrm{\&eta;}}_e-{\mathrm{\&eta;}}_1)}{{\mathrm{\&eta;}}_e}---\left(16\right)$

Wherein, Q _mTfor thermal power;

η ₁for gas turbine heat loss due to radiation coefficient;

C _nlfor natural gas low heat value;

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) comprising:

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

Energy-storage system is divided into third gear in the size of the electric weight SOC that the t moment self stores: high-grade, middle-grade and low-grade;

The watt level that energy-storage system discharges and recharges is divided into five ranks: large capacity charging-NB, low capacity charging-NS, large capacity electric discharge-PB, low capacity electric discharge-PS and discharge and recharge in very among a small circle-ZB.

Preferably, described step (4) comprising:

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

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

(4.3) by system initially relevant input variable be converted into the input variable of fuzzy control, calculate output variable based on fuzzy control rule, and determine the initial range of exerting oneself of energy-storage system based on this;

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

(4.5) calculate the fitness of each particle;

(4.6) record extreme value;

(4.7) iterations adds 1:k=k+1, upgrades flying speed and the particle position in solution space;

(4.8) recalculate each particle fitness function value now, judge whether to upgrade Pbesti and Gbest;

(4.9) judge whether convergence; When meeting one of following condition, iteration stopping; If overall situation desired positions continuous hundred times is unchanged or reach the maximum iteration time of predetermining; Otherwise go to step (4.7);

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

Compared with prior art, beneficial effect of the present invention is:

Day generating expense and the discharge amount of pollution of microgrid operation compared with the situation that the present invention does not adopt Optimized Operation scheme with energy-storage system, are reduced, do not adopt fuzzy control to discharge and recharge the quality that has improved optimal solution compared with tactful situation with energy-storage system, improve the convergence rate of computing, and then improved the reliability of operation of power networks.

The fuzzy control strategy mechanism proposing is simple, but but by the effectively combination of dump energy (SOC) of the Based Intelligent Control of energy-storage system and electricity price variation and energy-storage system, fully realize the optimization of energy-storage system has been used, thereby can fully incorporate in the mechanism of electricity market and obtain economic interests in conjunction with self-condition.

Brief description of the drawings

Fig. 1 is the schematic diagram of micro-grid system structure provided by the invention.

Fig. 2 is to exert oneself and the mutual power schematic diagram of microgrid and major network in each micro-source of the each moment drawing based on obscure particle colony optimization algorithm (FPSO) provided by the invention.

Fig. 3 is the optimization convergence curve schematic diagram that employing particle swarm optimization algorithm provided by the invention (PSO) and FPSO obtain.

Embodiment

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

As shown in Figure 1, in the present invention, example used comprises 20KW wind-powered electricity generation (WT), 10KW photovoltaic (PV), 40KW fuel cell (FC), 65KW miniature gas turbine (MT), 100KW battery energy storage system (ES);

A kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system provided by the invention, the method comprises the steps:

Step (1): determine each element Economic Scheduling Policy separately in micro-electrical network.WT and PV follow the tracks of maximum power output, the environment friendly possessing due to itself, and preferentially it is exerted oneself; FC and MT provide during lower than electricity price and exert oneself in cost of electricity-generating, stop exerting oneself during higher than electricity price in cost of electricity-generating; ES also utilizes its charging of fuzzy control method control or the intensity size of discharging and discharging and recharging.Specifically comprise:

1.1) because PV and WT generating has uncontrollability, and as direct consume fuel of regenerative resource, free from environmental pollution, therefore preferentially utilize it to exert oneself, follow the tracks of and control maximum power output;

1.2) for making co-generation unit operational efficiency the highest, it adopts the mode of " electricity determining by heat ", determines that by heat load the meritorious of MT exert oneself;

1.3) the price of buying and selling electricity difference of different periods of foundation calculates power purchase balanced power and the sale of electricity balanced power of FC and MT.Exert oneself while exceeding microgrid electric loading and network loss when WT, PV and MT meritorious, the part exceeding is sold to outer net in the time of peak, now energy-storage system in allowed band to outer net sale of electricity.Charging to energy-storage system with Gu Shixian at ordinary times. as energy-storage system is full of to outer net sale of electricity.Now FC and MT electric equilibrium power bracket on sale is interior to outer net sale of electricity.

1.4) exert oneself cannot meet microgrid electric loading time when WT, PV and MT meritorious, peak energy-storage system in season output is meritorious, detects the charging and discharging state of energy-storage system simultaneously; If energy-storage system can meet microgrid safe and reliable operation (on the basis of cutting load not in the scope of exerting oneself, microgrid can move under Prescribed Properties meeting), can consider allowing the active power that increases energy-storage system to outer net sale of electricity, exert oneself otherwise remain former, now FC and MT electric equilibrium power bracket on sale is interior to outer net sale of electricity; When paddy, do not consider energy storage system discharges, now FC and MT generate electricity in power purchase balanced power, if not just from outer net power purchase, now need to charge to energy-storage system.Energy storage system discharges at ordinary times, FC and MT generate electricity in power purchase balanced power, if still do not meet workload demand, need to, from outer net power purchase within the scope of Tie line Power, if still inadequate, excise successively according to the significance level of load.

Step 2: comprise 2.1) target function:

$\min C=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}[C_{\mathrm{fu}}\left(t\right)+C_{\mathrm{OM}}\left(t\right)+C_{\mathrm{PCC}}\left(t\right)+C_{\mathrm{gas}}\left(t\right)-C_{\mathrm{sh}}\left(t\right)]---\left(1\right)$

In formula:

$C_{\mathrm{fu}}\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{F}_i\left(P_i\left(t\right)\right)---\left(2\right)$

$C_{\mathrm{OM}}=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{K}_{\mathrm{OM},i}*P_i\left(t\right)---\left(3\right)$

$C_{\mathrm{PCC}}\left(t\right)=\left\{\begin{array}{cc}{P}_{\mathrm{PCC}}\left(t\right)*P_b\left(t\right)& P_{\mathrm{PCC}}\left(t\right)\&GreaterEqual;0\\ P_{\mathrm{PCC}}\left(t\right)*P_s\left(t\right)& P_{\mathrm{PCC}}\left(t\right)<0\end{array}\right.---\left(4\right)$

$C_{\mathrm{gas}}\left(t\right)=\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_j\left(\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&beta;}}_{\mathrm{ij}}{P}_i\left(t\right)\right)---\left(5\right)$

C _sh(t)＝Q _he(t)*K _ph ( ₆)

Wherein, C _fu(t), C _oM(t), C _pCC(t), C _gas(t), C _sh(t) be respectively the fuel cost, operation expense in t each micro-source of moment, the income that heats with mutual cost, Environmental costs and the co-generation unit of power distribution network; F _iit is the fuel cost function in i micro-source; P _i(t) be the active power output in i micro-source t moment; N is the number in micro-source; K _{oM, i}it is the unit quantity of electricity operation expense coefficient in i micro-source; α _jbe the punishment unit price of j class emission, discharge type is NO _x, SO ₂, CO ₂; β _ijit is the emission factor of i micro-source j class emission; M is the kind of emission; P _pCC(t) be t period microgrid and power distribution network mutual power (microgrid during to power distribution network power purchase for just, otherwise for negative); P _band P (t) _s(t) be respectively power purchase price and the sale of electricity price of t moment microgrid to outer net; Q _he(t) be the heat load amount in t moment; K _phfor the price of system of units heat, get 0.1 yuan/kWh.

2.2) constraints:

2.2.1) power-balance constraint:

When energy-storage system charging:

$P_L\left(t\right)+P_{\mathrm{Loss}}\left(t\right)=P_{\mathrm{WT}}\left(t\right)+P_{\mathrm{PV}}\left(t\right)+P_{\mathrm{MT}}\left(t\right)+P_{\mathrm{FC}}\left(t\right)+\frac{P_{\mathrm{ES}}\left(t\right)}{{\mathrm{\&eta;}}_{\mathrm{ch}}}+P_{\mathrm{PCC}}\left(t\right)---\left(7\right)$

When 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 mutual power and the power loss of power output, microgrid and the major network of t period wind-powered electricity generation unit, photovoltaic cell, miniature gas turbine, fuel cell; P _eS(t) be the power output (when charging for negative, otherwise for just) of t moment energy-storage system; η _ch, η _difor the charging and discharging efficiency of energy-storage system;

2.2.2) heat load constraint:

Q _MT(t)≥Q _he(t) (9)

Q in formula _mT(t) be the heating capacity that miniature gas turbine provides;

2.2.3) each micro-source units limits: each micro-source at any time the power of sending out will be within the constraint of own bound:

$P_i^{\min }\&le;P_i\left(t\right)\&le;P_i^{\max }---\left(10\right)$

2.2.4) the mutual through-put power constraint of microgrid and power distribution network:

P _PCC,min≤P _PCC(t)≤P _PCC,max(11)

Wherein, P _{pCC, min}, P _{pCC, max}be respectively microgrid and power distribution network and allow mutual minimum, the maximum power of transmitting;

2.2.5) constraint of energy-storage system operation conditions:

A, for the one-period of scheduling, it is identical that the electric weight at energy-storage system whole story keeps;

$\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ES}}\left(t\right)\mathrm{\&delta;}=0---\left(12\right)$

In formula, δ is lasting duration of t period;

B, at the electric weight of any moment energy-storage system between schedule periods all within allowed band:

${\mathrm{SOC}}^{\min }\&le;S_{\mathrm{init}}-\underset{i=1}{\overset{t}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ES}}\left(t\right)\mathrm{\&delta;}\&le;{\mathrm{SOC}}^{\max }---\left(13\right)$

Wherein, SOC ^min, SOC ^max, S _initbe respectively minimum value, maximum and the energy-storage system of storing electricity permission in energy-storage system at the scheduling electric weight of the zero hour;

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

The fuel cost function of MT:

C _MT＝(C _nl/L)Σ[P _MT(t)Δt/η _e(t)](14)

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

$Q_{\mathrm{MT}}=\frac{P_{\mathrm{MT}}(1-{\mathrm{\&eta;}}_e-{\mathrm{\&eta;}}_1)}{{\mathrm{\&eta;}}_e}---\left(16\right)$

In above-mentioned several formula, Q _mTfor thermal power.η ₁for gas turbine heat loss due to radiation coefficient, get 0.03.C _nlfor Gas Prices, be 2.05 yuan/m ³; C _nlfor natural gas low heat value, get 9.7kWh/m ³;

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)

In step 2, the K in the described operation expense of formula (3) _{oM, i}for miniature gas turbine and fuel cell respectively value be 0.047 yuan/kWh and 0.1 yuan/kWh.

In step 2, the power purchase price P in the t moment described in formula (4) _band sale of electricity price P (t) _s(t) profit is obtained with the following methods:

Be made as 24h dispatching cycle, be divided into peak, paddy, flat 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;

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

In step 2, the α described in formula (5) _jand β _ijask for by following table respectively:

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

In step 3, discharging and recharging of energy-storage system uses fuzzy control strategy as follows:

The schedule periods 24h of one day is divided three classes period of type: peak phase, flat phase, Gu Qi; Energy-storage system is divided into third gear in the size of the remaining capacity SOC that the t moment self stores: high-grade, middle-grade, low-grade.The watt level that energy-storage system discharges and recharges is also divided into three ranks: large capacity charging (NB), large capacity electric discharge (PB), electric discharge (PE) in very among a small circle, charging (NE) in very among a small circle, the corresponding relation that discharges and recharges (ZE) each element in very is among a small circle as following table:

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

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

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

(3) by system initially relevant input variable be converted into the input variable of fuzzy control, calculate output variable based on fuzzy control rule, and determine the initial range of exerting oneself of energy-storage system based on this.

(4) data initialization.Iterations k=0, first initialization D-1 dimension variable (wherein energy-storage system is determined initial range according to fuzzy control strategy by the input variable of fuzzy control) in the scope of exerting oneself, D dimension variable is obtained by constraints formula (12), (13), choosing value again when out-of-limit.Produce N particle, as initialization population, the corresponding one group of value exerted oneself in micro-source in the position of each particle; The flying speed of the each particle of random initializtion simultaneously.

(5) calculate the fitness of each particle.

(6) record extreme value.First record particle i (i=1,2 ..., N) and current individual extreme value Pbesti and corresponding target function value F (Pbesti), from Pbesti, determine overall extreme value Gbest, and record the objective function F that Gbest is corresponding (Gbest).

(7) iterations adds 1:k=k+1.Upgrade flying speed and particle in the position of solution space (wherein, based on fuzzy control strategy according to the input variable regular update of the fuzzy control speed relevant to energy-storage system and position range).

(8) recalculate each particle fitness function value now, judge whether to upgrade Pbesti and Gbest.

(9) judge whether convergence.When meeting one of following condition, iteration stopping: if overall desired positions continuously hundred times unchanged or reach the maximum iteration time of predetermining; Otherwise go to step (7).

(10) Output rusults.

In step 4, according to predetermined control strategy, draw exerting oneself and the mutual power of microgrid and major network of the each each micro-source of moment drawing based on FPSO in Fig. 2.

As shown in Figure 3, from the 01:00 paddy period, ES starts charging, and the power that MT sends out meets heat load and a part of electric loading, and the cost of electricity-generating of FC is greater than to the cost of major network power purchase, therefore do not exert oneself; Section when 8:00 is flat, FC starts to exert oneself, and ES is charge and discharge among a small circle; 11:00 enters the peak period, FC exerts oneself almost approaching under maximum discharge power, owing to being greater than the cost of electricity-generating of MT to the income of major network sale of electricity, therefore MT high power discharge, now ES also discharges under relatively large power, simultaneously to major network sale of electricity to obtain economic benefit; When 16:00 is flat, start to major network power purchase, the electric discharge of FC, ES reduces accordingly, and MT discharges to meet fixing heat load; The 19:00 peak period, now MT and FC high power discharge, ES is also allowing electric discharge largely within the scope of dump energy, the outwards also not sale of electricity of net purchase electricity hardly of this section; Section when 22:00 is flat, MT generates electricity to meet heat load, the corresponding decline of FC generated output, ES in dump energy allowed band among a small circle electric discharge, to electrical network power purchase to fill up power shortage; The 24:00 paddy period, the high-power initial quantity of electricity that charges to of ES, is that the scheduling of second day is prepared, and FC stops generating, and MT sends out power and meet the demand of heat load, now needs to electrical network power purchase.

Convergence rate and economy to FPSO and PSO contrast, Fig. 3 is the optimization convergence curve that adopts PSO and FPSO to obtain, clearly can find out, FPSO algorithm is due to energy-storage system is discharged and recharged and introduced fuzzy control strategy, the precision that its arithmetic speed is conciliate is all improved, and has good convergence.

Adopt technique scheme, the present invention has and takes into full account the characteristic in each micro-source in microgrid, the discharge and recharge power and Spot Price and self SOC value of energy-storage system are connected, and making each several part is well all the operation service of whole microgrid economic optimization.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.

Claims (8)

1. the microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system, is characterized in that, described method comprises:

(1) determine the Economic Scheduling Policy in micro-source in micro-electrical network;

(2) set up the grid-connected economical operation Mathematical Modeling of microgrid that economic benefit and environmental benefit combine;

(3) proposition discharges and recharges the fuzzy control strategy of control to energy-storage system;

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

2. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1, it is characterized in that, in described step (1), micro-source comprises wind power generation-WT, photovoltaic generation-PV, fuel cell-FC, miniature gas turbine-MT and energy-storage system-ES.

3. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1, is characterized in that, described step (1) comprising:

(1.1) PV and WT generating are followed the tracks of and are controlled maximum power output;

(1.2) determine that by heat load the meritorious of MT exert oneself;

(1.3) price of buying and selling electricity of different periods of foundation calculates power purchase balanced power and the sale of electricity balanced power of FC and MT;

(1.4) in the time that WT, PV and MT meritorious exerted oneself peak cannot meet microgrid electric loading time, make energy-storage system output meritorious, detect the charging and discharging state of energy-storage system simultaneously;

(1.5) when WT, PV, MT and ES total meritorious exerts oneself cannot meet microgrid electric loading time, FC and MT continue to generate electricity in power purchase balanced power.

4. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 3, is characterized in that, described step (1.4) comprising:

Energy-storage system meets microgrid safe and reliable operation in the scope of exerting oneself, and allows the active power that increases energy-storage system to outer net sale of electricity, exerts oneself otherwise remain former.

5. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1, it is characterized in that, described step (2) comprising: in the target function of proposition, considered the income of the heat load of comprehensive electric generating cost, Environmental costs and the cogeneration of heat and power of micro-grid system, considered the constraint of constraint, microgrid and the mutual power of power distribution network and the constraint of energy-storage system operation conditions exerted oneself in power-balance constraint, the constraint of heat load, micro-source in constraints;

Wherein, (2.1) target function:

In formula:

C _sh(t)＝Q _he(t)*K _ph (6)

Wherein, C _fu(t), C _oM(t), C _pCC(t), C _gas(t), C _sh(t) be respectively the fuel cost, operation expense in t each micro-source of moment, the income that heats with mutual cost, Environmental costs and the co-generation unit of power distribution network;

F _iit is the fuel cost function in i micro-source;

P _i(t) be the active power output in i micro-source t moment;

N is the number in micro-source;

K _{oM, i}it is the unit quantity of electricity operation expense coefficient in i micro-source;

α _jbe the punishment unit price of j class emission, discharge type is NO _x, SO ₂, CO ₂;

β _ijit is the emission factor of i micro-source j class emission;

M is the kind of emission;

P _pCC(t) be the mutual power of t period microgrid and power distribution network;

P _band P (t) _s(t) be respectively power purchase price and the sale of electricity price of t moment microgrid to outer net;

Q _he(t) be the heat load amount in t moment;

K _phfor the price of system of units heat;

(2.2) constraints:

(2.2.1) power-balance constraint:

When energy-storage system charging:

When 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 mutual power and the power loss of power output, microgrid and the major network of t period wind-powered electricity generation unit, photovoltaic cell, miniature gas turbine, fuel cell;

P _eS(t) be the power output of t moment energy-storage system;

η _ch, η _difor the charging and discharging efficiency of energy-storage system;

(2.2.2) heat load constraint:

Q _MT(t)≥Q _he(t) (9)

Q in formula _mT(t) be the heating capacity that miniature gas turbine provides;

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

(2.2.4) the mutual through-put power constraint of microgrid and power distribution network:

P _PCC,min≤P _PCC(t)≤P _PCC,max (11)

P in formula _{pCC, min}, P _{pCC, max}be respectively microgrid and power distribution network and allow mutual minimum, the maximum power of transmitting;

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

A, for the one-period of scheduling, it is identical that the electric weight at energy-storage system whole story keeps;

In formula, δ is lasting duration of t period;

B, at the electric weight of any moment energy-storage system between schedule periods all within allowed band;

Wherein, SOC ^min, SOC ^max, S _initbe respectively minimum value, maximum and the energy-storage system of storing electricity permission in energy-storage system at the scheduling electric weight of the zero hour.

6. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 5, is characterized in that, described formula (2) comprises that described fuel cost following formula calculates:

The fuel cost function of MT:

C _MT＝(C _nl/L)Σ[P _MT(t)Δt/η _e(t)] (14)

Wherein, Q _mTfor thermal power;

η ₁for gas turbine heat loss due to radiation coefficient;

C _nlfor natural gas low heat value;

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) 。

7. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1, is characterized in that, described step (3) comprising:

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

Energy-storage system is divided into third gear in the size of the electric weight SOC that the t moment self stores: high-grade, middle-grade and low-grade;

The watt level that energy-storage system discharges and recharges is divided into five ranks: large capacity charging-NB, low capacity charging-NS, large capacity electric discharge-PB, low capacity electric discharge-PS and discharge and recharge in very among a small circle-ZB.

8. a kind of microgrid economical operation optimization method based on Fuzzy particle swarm optimization and energy-storage system as claimed in claim 1, is characterized in that, described step (4) comprising:

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

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

(4.3) by system initially relevant input variable be converted into the input variable of fuzzy control, calculate output variable based on fuzzy control rule, and determine the initial range of exerting oneself of energy-storage system based on this;

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

(4.5) calculate the fitness of each particle;

(4.6) record extreme value;

(4.7) iterations adds 1:k=k+1, upgrades flying speed and the particle position in solution space;

(4.8) recalculate each particle fitness function value now, judge whether to upgrade Pbesti and Gbest;

(4.9) judge whether convergence; When meeting one of following condition, iteration stopping; If overall situation desired positions continuous hundred times is unchanged or reach the maximum iteration time of predetermining; Otherwise go to step (4.7);

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