CN103151797B - Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode - Google Patents

Abstract

The invention relates to a multi-objective dispatching model-based microgrid energy control method under the grid-connected operation mode. A microgrid comprises a plurality of microsources and a load. The method includes the following specific steps: (1) the information of each microsource and connecting lines in the microgrid is acquired by a processor, and with minimum power generation cost and environmental cost as objectives, a multi-objective economical dispatching model is created; (2) a membership function is created to fuzzify the multi-objective economical dispatching model, so that a non-linear single-objective microgrid economical dispatching model is obtained; (3) an improved genetic algorithm is adopted to optimize the non-linear single-objective microgrid economical dispatching model, so that the optimal active and reactive power data of each microsourcre and the connecting lines are obtained, and the data are transmitted to a microgrid power controller; (4) according to the received optimal active and reactive power data, the microgrid power controller controls the active and reactive power output of each microsoruce and the connecting lines. Compared with the prior art, the method has the advantages of high control precision and the like, and can guarantee the economical operation of the microgrid.

Description

Based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model

Technical field

The present invention relates to a kind of Economical Operation of Power Systems and regulation of energy technology, especially relate to a kind of based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model.

Background technology

Along with the enhancing gradually of people's energy-conserving and environment-protective consciousness, distributed power generation obtains increasing attention and utilization, and particularly attracts people's attention for the microgrid research be made up of low capacity distributed power source.Various distributed power source, load, energy-storage units and control device etc. combine by microgrid, form a single controlled unit, provide electric energy and heat energy to user simultaneously, realize cogeneration of heat and power (combined heat and power, CHP).Microgrid has become a kind of effective technology means of the managing distributed energy in intelligent grid, and the aspect such as operation control for microgrid has carried out extensive research.

The economical operation of microgrid and Optimized Operation are the focuses studied both at home and abroad always.At present, both at home and abroad to the research of microgrid economical operation mainly multiple target such as cost of electricity-generating is minimum, environmental impact network minimal be simplified to single goal and be optimized.But often there is conflict between multiple target, can cause the optimum results of some target can not be fully up to expectations like this, some scholar proposes corresponding method, solves the conversion of multi-objective problem preferably.Domestic scholars thunder gold is brave, Gande is strong etc. establishes the multiple target energy optimizing model considering economy, the feature of environmental protection and reliability, paried comparison is adopted surely to weigh method to determine weight coefficient, multi-objective optimization question is converted to single-object problem (the energy-optimised and energy-saving and emission-reduction performance analysis of distributed power generation energy supplying system by application linear weighted function summation, Automation of Electric Systems, 2009,33 (23): 29-36); Document " Online Management of MicroGrid with Battery StorageUsing Multiobjective Optimization " (Faisal A.Mohamed, Heikki N.Koivo.POWERENG, Setubal, Portugal, 2007:231-236) establish and consider the minimum microgrid Model for Multi-Objective Optimization minimum with environmental pollution of operation expense simultaneously, multi-objective problem is converted to single-objective problem by application linear weighted function summation, and discusses the impact of different weight coefficient on optimum results; Document " the microgrid energy management multiple-objection optimization containing distributed power generation " (Wang Xingang; Ai Qian; xuwei China etc. protecting electrical power system and control; 2009; 37 (20): 79-83) at labor on the basis of distributed power source characteristic and optimization aim Mathematical Modeling; for the optimum management that distributed power source in microgrid is exerted oneself, propose a kind of multi-target immune algorithm of evolving based on microhabitat and optimize each micro-source and exert oneself.But the rack that these research institutes adopt is comparatively simple, the general active balance only considering system, the impact that less consideration is idle; Also do not consider that the meritorious and idle of the micro-source of Optimized Operation is exerted oneself simultaneously, on the other hand, corresponding constraints too simplifies, and seldom considers the indexs such as microgrid Tie line Power, spinning reserve, energy-storage travelling wave tube discharge and recharge and constraints.

Summary of the invention

Object of the present invention be exactly provide to overcome defect that above-mentioned prior art exists a kind of control precision high, ensure microgrid economical operation based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model.

Object of the present invention can be achieved through the following technical solutions:

A kind of based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model, described microgrid comprises multiple micro-source and load, each micro-source is connected by interconnection with load, and described load comprises electric loading and heat load, and described method specifically comprises the following steps:

1) processor obtains the information of each micro-source and interconnection in microgrid, with cost of electricity-generating with Environmental costs are minimum sets up multiple target economic dispatch model for target;

2) set up membership function, obfuscation is carried out to multiple target economic dispatch model, obtain non-linear single goal microgrid economic dispatch model;

3) adopt Improving Genetic Algorithm to be optimized non-linear single goal microgrid economic dispatch model to solve, the optimum obtaining each micro-source and interconnection is gained merit and reactive power data, and these data are transferred to microgrid power controller;

4) microgrid power controller is gained merit according to the optimum received and the meritorious and reactive power of each micro-source of reactive power Data Control and interconnection exports.

Described micro-source comprises photovoltaic, blower fan, miniature gas turbine, fuel cell and storage battery.

Described multiple target economic dispatch model comprises target function and constraints, and described objective function F (t) is intraday cost of electricity-generating F ₁t () is minimum, pollutant is to the Environmental costs F of environmental impact simultaneously ₂t () is minimum, be specially:

minF(t)＝{F ₁(t)，F ₂(t)}t＝1，2...24 (1)

F ₁(t)＝C _f(t)+C _DP(t)+C _OM(t)+C _grid(t)-C _sh(t) (2)

F ₂(t)＝C _e(t) (3)

In formula:

$C_f\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{f}_i\left(P_i\left(t\right)\right)---\left(4\right)$

$C_{\mathrm{DP}}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[\frac{C_{\mathrm{az},i}}{{8706k}_i}\·\frac{r{(1+r)}^{n_i}}{{(1+r)}^{n_i}-1}\·P_i\left(t\right)]---\left(5\right)$

$C_{\mathrm{OM}}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{K}_{\mathrm{OM},i}{\·P}_i\left(t\right)---\left(6\right)$

C _grid(t)＝CP(t)×PR _grid(t) (7)

C _sh(t)＝Q _he(t)×K _ph(8)

$C_e\left(t\right)=\underset{i=1}{\overset{n+1}{\mathrm{\Σ}}}\left(\underset{j=1}{\overset{m}{\mathrm{\Σ}}}((V_{\mathrm{ej}}+V_j){\·Q}_{\mathrm{ij}}\·P_i\left(t\right))\right)---\left(9\right)$

C _f(t), C _dP(t), C _oMt () is respectively fuel cost, the investment depreciable cost and operation expense in each micro-source of t; C _gridt () is for t microgrid is to the purchases strategies of outer net; C _sht () heats income for t co-generation unit; C _et Environmental costs that () is each micro-source of t and the Environmental costs summation of the outside net purchase electricity of microgrid; f _ibe the fuel cost function in i-th micro-source: P _it () is that the active power of i-th micro-source t exports; N is the number in micro-source, and (n+1)th is dominant eigenvalues; C _{az, i}it is the installation cost as per machine capacity in i-th micro-source; k _ibe the capacity factor in i-th micro-source, k _iannual energy output/(rated power in 8760 × this micro-source) in=the i-th micro-source; R is Annual Percentage Rate; n _iit is the pay back period of investment in i-th micro-source; K _{oM, i}it is the unit quantity of electricity operation expense coefficient in i-th micro-source; CP (t) is for t microgrid is to the purchase electricity price of outer net; PR _gridt () is for t microgrid is to the purchase of electricity of outer net; Q _het () is the heating capacity of t microgrid; K _phfor the price of unit heating capacity; V _ejfor the environmental value of jth item pollutant; V _jimpose a fine suffered by jth item pollutant; Q _ijit is the jth item pollutant discharge amount of i-th micro-source unit quantity of electricity; M is the kind of pollutant;

Described constraints comprises:

A, trend retrain:

${\mathrm{\ΔP}}_f=P_{\mathrm{fs}}-\underset{g\∈f}{\overset{h}{\mathrm{\Σ}}}{V}_f{V}_g(G_{\mathrm{fg}}\cos {\mathrm{\θ}}_{\mathrm{fg}}+B_{\mathrm{fg}}\sin {\mathrm{\θ}}_{\mathrm{fg}})=0---\left(10\right)$

${\mathrm{\ΔQ}}_f=Q_{\mathrm{fs}}-\underset{g\∈f}{\overset{h}{\mathrm{\Σ}}}{V}_f{V}_g(G_{\mathrm{fg}}\sin {\mathrm{\θ}}_{\mathrm{fg}}+B_{\mathrm{fg}}\cos {\mathrm{\θ}}_{\mathrm{fg}})=0---\left(11\right)$

In formula: h is the nodes of microgrid, f=1,2 ..., h; G _fg, B _fg, θ _fgbe respectively the admittance between node f and node g and phase angle difference, g ∈ f represents and the node that node f is connected:

B, working voltage retrain:

V _f，min≤V _f≤V _f，max(12)

In formula: V _{f, min}, V _{f, max}be respectively minimum, the maximum of working voltage on node f;

The active power constraint of c, photovoltaic, blower fan and miniature gas turbine:

P _i，min≤P _i≤P _i，max(13)

In formula: P _{i, min}, P _{i, max}be respectively minimum, the maximum of i-th micro-source active power;

D, miniature gas turbine climbing rate retrain:

P during increasing load _mT(t)-P _mT(t-1)≤R _{up, MT}(14)

P during load shedding _mT(t-1)-P _mT(t)≤R _{down, MT}(15)

In formula: P _mTt active power that () is t miniature gas turbine exports, R _{up, MT}, R _{down, MT}be respectively the limit value that miniature gas turbine increases and reduces active power;

E, microgrid and outer net allow mutual through-put power to retrain:

0≤P _Grid(t)≤P _Grid，max(16)

0≤Q _Grid(t)≤Q _grid，max(17)

In formula: P _grid(t), Q _gridt () is respectively t microgrid and outer net allows mutual active power and reactive power; P _{grid, max}, Q _{grid, max}be respectively and allow mutual maximum active power and reactive power; for the power factor of tie-line power transmission; C is the power factor limit value of tie-line power transmission;

F, storage battery run constraint:

P _SB，min≤P _SB(t)≤P _SB，max(19)

-S _inv，SB≤P _SB(t)≤S _inv，SB(20)

$-\sqrt{S_{\mathrm{inv},\mathrm{SB}}^2-P_{\mathrm{SB}}^2\left(t\right)}\≤Q_{\mathrm{SB}}\left(t\right)\≤\sqrt{S_{\mathrm{inv},\mathrm{SB}}^2-P_{\mathrm{SB}}^2\left(t\right)}---\left(21\right)$

SOC _min≤SOC(t)≤SOC _max(22)

In formula: P _{sB, max}, P _{sB, min}be respectively the minimum and maximum active power of storage battery; S _{inv, SB}for the capacity of storage battery inverter; P _sB(t), Q _sBt (), SOC (t) are respectively the discharge and recharge active power of t battery AC side, reactive power and remaining battery capacity; SOC _min, SOC _maxbe respectively the minimum and maximum residual capacity of storage battery;

G, fuel cell operation retrain:

P _FC，min≤P _FC(t)≤P _FC，max(23)

P _FC(t)≤S _inv，FC(24)

$0\≤Q_{\mathrm{FC}}\left(t\right)\≤\sqrt{S_{\mathrm{mv},\mathrm{FC}}^2-P_{\mathrm{FC}}^2\left(t\right)}---\left(25\right)$

In formula: P _{fC, min}, P _{fC, max}be respectively minimum, the maximum of fuel cell active power: P _fC(t), Q _fCt () is respectively active power and the reactive power of t fuel cell AC; S _{inv, FC}for the capacity of fuel cell inverter;

H, spinning reserve retrain:

$P_{\mathrm{PV}}\left(t\right)+P_{\mathrm{WT}}\left(t\right)+P_{\mathrm{MT}}\left(t\right)+\min \{P_{\mathrm{SB},\max },\sqrt{{\left(S_{\mathrm{inv},\mathrm{SB}}\right)}^2-{\left(Q_{\mathrm{SB}}\left(t\right)\right)}^2},(\mathrm{SOC}(t-1)-\mathrm{SO}{C}_{\min }-$

(26)

$D_B{Q}_B^S){\mathrm{\η}}_D\}+\min \{P_{\mathrm{FC},\max },\sqrt{{\left(S_{\mathrm{inv},\mathrm{FC}}\right)}^2-{\left(Q_{\mathrm{FC}}\left(t\right)\right)}^2}\}+P_{\mathrm{Grid},\max }>=P_D\left(t\right)+P_{\mathrm{loss}}\left(t\right)+R\left(t\right)$

In formula: P _pV(t), P _wTt () is respectively the active power of t photovoltaic, blower fan; η _dfor discharging efficiency; D _bfor storage battery self discharge ratio hourly; for the total capacity of storage battery; P _d(t), P _loss(t), R _dt () is respectively the total load of t micro-grid system, total network loss and required reserve capacity.

Described membership function adopts the membership function falling half Γ shape distribution, and for described target function, membership function is:

$u_k\left(t\right)=\left\{\begin{array}{cc}1& F_k\left(t\right){\≤F}_{k\min \left(t\right)}\\ \exp \left(\frac{F_{k\min }\left(t\right)-F_k\left(t\right)}{F_{k\min }\left(t\right)}\right)& F_k\left(t\right){>F}_{k\min \left(t\right)}\end{array}\right.---\left(27\right)$

In formula: F _kmint () is single-goal function F _k(t) minimum value under described constraints, k=1,2; T=1,2 ..., 24.

Non-linear single goal microgrid economic dispatch model after described obfuscation is:

In formula: u (t), u ₁(t), u ₂t () is respectively the satisfaction of the satisfaction of fuzzy optimization, the satisfaction of cost of electricity-generating and Environmental costs, wherein $u\left(t\right)=\underset{k=1}{\overset{2}{\mathrm{\∩}}}{u}_k\left(t\right).$

Described Improving Genetic Algorithm refers to: the crossover probability P in genetic algorithm _cwith mutation probability P _maccording to following formulae discovery:

$P_c=\left\{\begin{array}{c}{P}_{c2}-\frac{(P_{c2}-P_{c3})(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}}{f}^{\&prime;}{\&GreaterEqual;f}_{\mathrm{avg}}\\ P_{c1}-\frac{(P_{c1}-P_{c2})(f^{\&prime;}-f_{\min })}{f_{\mathrm{avg}}-f_{\min }}{f}^{\&prime;}{<f}_{\mathrm{avg}}\end{array}\right.---\left(29\right)$

$P_m=\left\{\begin{array}{c}{P}_{m2}-\frac{(P_{m2}-P_{m3})(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}}{f}^{\&prime;}{\&GreaterEqual;f}_{\mathrm{avg}}\\ P_{m1}-\frac{(P_{m1}-P_{m2})(f^{\&prime;}-f_{\min })}{f_{\mathrm{avg}}-f_{\min }}{f}^{\&prime;}{<f}_{\mathrm{avg}}\end{array}\right.---\left(30\right)$

In formula: f _max, f _minand f _avgbe respectively maximum, the minimum and average fitness in population; F ' is fitness larger in two individualities that will intersect, P _c1, P _c2, P _c3, P _m1, P _m2, P _m3be the constant of setting, and P _c1, P _c2, P _c3∈ (0,1), P _m1, P _m2, P _m3∈ (0,0.1).

Compared with prior art, the present invention has the following advantages:

1, the present invention has considered on the economy of microgrid and the basis of green technology index, multiple target economic dispatch model is set up to microgrid, more definitely reflect the actual motion state of microgrid, control precision is high, can realize good environmental benefit with little operating cost of trying one's best;

2, the present invention adopts blur method and genetic algorithm to solve multi-objective Model, and computational speed is fast, and solution efficiency is high;

3, the present invention is by controlling the meritorious and idle output in each micro-source in microgrid, idle idle the exerting oneself coordinating outer net to provide to microgrid of exerting oneself itself is provided to meet System Reactive Power demand by micro-source, fall less and the additional investment of reactive power compensator has been installed, guaranteed that outer net is without the need to providing excessive reactive power support to microgrid and ensureing that the power transmitted between outer net and microgrid has higher power factor.

Accompanying drawing explanation

Fig. 1 is the schematic flow sheet of the inventive method;

Fig. 2 is the structural representation of micro-grid system;

Fig. 3 is exert oneself and thermoelectricity load schematic diagram in known each micro-source;

Fig. 4 is microgrid active power optimum results schematic diagram under the minimum target of cost of electricity-generating;

Fig. 5 is microgrid reactive power power optimization result schematic diagram under the minimum target of cost of electricity-generating;

Fig. 6 is microgrid active power optimum results schematic diagram under the target that Environmental costs are minimum;

Fig. 7 is microgrid reactive power power optimization result schematic diagram under the target that Environmental costs are minimum;

Fig. 8 is microgrid active power optimum results schematic diagram under multiple target;

Fig. 9 is microgrid reactive power power optimization result schematic diagram under multiple target;

Figure 10 is the Cost comparisons's schematic diagram that generates electricity under single goal and multiple target;

Figure 11 is that under single goal and multiple target, Environmental costs contrast schematic diagram;

Satisfaction schematic diagram under Figure 12 micro-grid connection operational mode.

Embodiment

Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.The present embodiment is implemented premised on technical solution of the present invention, give detailed execution mode and concrete operating process, but protection scope of the present invention is not limited to following embodiment.

Embodiment

As shown in Figure 1, a kind of based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model, described microgrid comprises multiple micro-source and load, each micro-source is connected by interconnection with load, described load comprises electric loading and heat load, the microgrid of this method research is cogeneration of heat and power type micro-grid system, and the method specifically comprises the following steps:

In step 101, processor obtains the information of each micro-source and interconnection in microgrid, with cost of electricity-generating with Environmental costs are minimum sets up multiple target economic dispatch model for target;

In step 102, set up membership function, obfuscation is carried out to multiple target economic dispatch model, obtain non-linear single goal microgrid economic dispatch model;

In step 103, adopt Improving Genetic Algorithm to be optimized non-linear single goal microgrid economic dispatch model and solve, the optimum obtaining each micro-source and interconnection is gained merit and reactive power data;

In step 104, the data of acquisition are transferred to microgrid power controller by processor;

In step 105, microgrid power controller is gained merit according to the optimum received and the meritorious and reactive power of each micro-source of reactive power Data Control and interconnection exports.

Described micro-source comprises (photovoltaic, PV), blower fan (wind turbine, WT), miniature gas turbine (micro turbine, MT), fuel cell (fuel cell, and storage battery (storage battery, SB) FC).In the Δ t of unit interval, this method makes the following assumptions: micro-source meritorious and idle is exerted oneself constant; Optimized distributionl demand is constant; Mutual power invariability between microgrid and major network and mutual electricity price remains constant.Choose different values for Δ t, Optimized model set up below is all applicable, and it is that 1h analyzes that the present embodiment gets Δ t.

The Mathematical Modeling in each micro-source is as described below:

1, miniature gas turbine model

$\left\{\begin{array}{c}{Q}_{\mathrm{MT}}\left(t\right)=P_e\left(t\right)(1-{\mathrm{\&eta;}}_e\left(t\right)-{\mathrm{\&eta;}}_1)/{\mathrm{\&eta;}}_e\left(t\right)\\ Q_{\mathrm{he}}\left(t\right)=Q_{\mathrm{MT}}\left(t\right)K_{\mathrm{he}}\\ V_{\mathrm{MT}}=\mathrm{\&Sigma;}(P_e\left(t\right)\mathrm{\&Delta;t}/\left({\mathrm{\&eta;}}_e\left(t\right)L\right))\end{array}\right.$

In formula: Q _mTt () is t combustion turbine exhaustion waste heat amount; η _et () is the generating efficiency of t gas turbine; η ₁for gas turbine heat loss due to radiation coefficient; P _et electrical power that () exports for t gas turbine; Q _het heating capacity that () provides for t gas turbine flue gas waste heat; K _hefor the heating efficiency of bromine cooling machine; V _mTfor the amount of natural gas that gas turbine consumes; Δ t is the running time of gas turbine; L is natural gas low-heat calorific value, gets 9.7kWh/m ³.

The fuel cost calculating formula of miniature gas turbine is:

C _MT＝(C _n1/L)∑(P _e(t)Δt/η _e(t))

In formula: C _n1for Gas Prices, this method is taken as 2.5 yuan/m ³.

The example of the present embodiment based on the C65 type miniature gas turbine of Capstone company, its η _e(t) and P _ethe function of (t) see document " Study on economical operation of microgrid " (Li Le. Beijing: North China Electric Power University, 2011,3).

2, fuel cell mode

Depletion charge computing formula in fuel cell power generation process is as follows:

C _FC＝(C _n1/L)∑(P _FC(t)Δt/η _FC(t))

In formula: P _fC(t), η _fCt () is respectively power output and the gross efficiency of the fuel cell of its t.

The example of the present embodiment based on Proton Exchange Membrane Fuel Cells (40kW IFC PC-29), its η _fC(t) and P _fCt the function of () is see document " Study on economical operation of microgrid ".

3, battery model

Storage battery t dump energy and storage battery the dump energy in t-1 moment, the t-1 moment to the discharge and recharge of t storage battery and electricity attenuation hourly relevant.

During battery discharging, P _sB(t)>=0, the residual capacity of t is:

$\mathrm{SOC}\left(t\right)=\mathrm{SOC}(t-1)-P_{\mathrm{SB}}\left(t\right)/{\mathrm{\&eta;}}_D-D_B{Q}_B^S$

During charge in batteries, P _sB(t)≤0, the residual capacity of t is:

$\mathrm{SOC}\left(t\right)=\mathrm{SOC}(t-1)-P_{\mathrm{SB}}\left(t\right){\mathrm{\&eta;}}_C-D_B{Q}_B^S$

In formula: SOC (t) is the residual capacity of t storage battery; P _sBt () is the charge-discharge electric power of t storage battery: η _c, η _dbe respectively charge and discharge efficiency; D _bfor storage battery self discharge ratio hourly; for the total capacity of storage battery.

In the present embodiment, the model of exerting oneself of photovoltaic cell see document " Study on economical operation during micro-grid connection ", (engrave by ox, Huang Wei, Guo Jiahuan, Deng. electric power network technique, 2010, 34 (11): 38-42), the model of exerting oneself of blower fan is see document " System modeling and optimization of microgrid using Genetic Algorithm " (QijunDeng, Xing Gao, Hong Zhou.The2nd International Conference on Intelligent Controland Information Processing, 2011:540-544).

The multiple target economic dispatch model of this method comprises target function and constraints, and described objective function F (t) is intraday cost of electricity-generating F ₁t () is minimum, pollutant is to the Environmental costs F of environmental impact simultaneously ₂t () is minimum, be specially:

minF(t)＝{F ₁(t)，F ₂(t)}t＝1，2...24 (1)

F ₁(t)＝C _f(t)+C _DP，(t)+C _OM(t)+C _grid(t)-C _sh(t) (2)

F ₂(t)＝C _c(t) (3)

In formula:

$C_f\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{f}_i\left(P_i\left(t\right)\right)---\left(4\right)$

$C_{\mathrm{DP}}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}[\frac{C_{\mathrm{az},i}}{{8706k}_i}\&CenterDot;\frac{r{(1+r)}^{n_i}}{{(1+r)}^{n_i}-1}\&CenterDot;P_i\left(t\right)]---\left(5\right)$

$C_{\mathrm{OM}}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{K}_{\mathrm{OM},i}{\&CenterDot;P}_i\left(t\right)---\left(6\right)$

C _grid(t)＝CP(t)×PR _grid(t) (7)

C _sh(t)＝Q _he(t)×K _ph(8)

$C_e\left(t\right)=\underset{i=1}{\overset{n+1}{\mathrm{\&Sigma;}}}\left(\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}((V_{\mathrm{ej}}+V_j){\&CenterDot;Q}_{\mathrm{ij}}\&CenterDot;P_i\left(t\right))\right)---\left(9\right)$

C _f(t), C _dP(t), C _oMt () is respectively fuel cost, the investment depreciable cost and operation expense in each micro-source of t; C _gridt () is for t microgrid is to the purchases strategies of outer net; C _sht () heats income for t co-generation unit; C _et Environmental costs that () is each micro-source of t and the Environmental costs summation of the outside net purchase electricity of microgrid; f _iit is the fuel cost function in i-th micro-source; P _it () is that the active power of i-th micro-source t exports; N is the number in micro-source, and (n+1)th is dominant eigenvalues; C _{az, i}it is the installation cost as per machine capacity in i-th micro-source; k _ibe the capacity factor in i-th micro-source, k _iannual energy output/(rated power in 8760 × this micro-source) in=the i-th micro-source; R is Annual Percentage Rate; n _iit is the pay back period of investment in i-th micro-source; K _{oM, i}it is the unit quantity of electricity operation expense coefficient in i-th micro-source; CP (t) is for t microgrid is to the purchase electricity price of outer net; PR _gridt () is for t microgrid is to the purchase of electricity of outer net; Q _het () is the heating capacity of t microgrid; K _phfor the price of unit heating capacity; V _ejfor the environmental value of jth item pollutant; V _jimpose a fine suffered by jth item pollutant; Q _ijit is the jth item pollutant discharge amount of i-th micro-source unit quantity of electricity; M is the kind of pollutant;

Described constraints comprises:

A, trend retrain:

${\mathrm{\&Delta;P}}_f=P_{\mathrm{fs}}-\underset{g\&Element;f}{\overset{h}{\mathrm{\&Sigma;}}}{V}_f{V}_g(G_{\mathrm{fg}}\cos {\mathrm{\&theta;}}_{\mathrm{fg}}+B_{\mathrm{fg}}\sin {\mathrm{\&theta;}}_{\mathrm{fg}})=0---\left(10\right)$

${\mathrm{\&Delta;Q}}_f=Q_{\mathrm{fs}}-\underset{g\&Element;f}{\overset{h}{\mathrm{\&Sigma;}}}{V}_f{V}_g(G_{\mathrm{fg}}\sin {\mathrm{\&theta;}}_{\mathrm{fg}}+B_{\mathrm{fg}}\cos {\mathrm{\&theta;}}_{\mathrm{fg}})=0---\left(11\right)$

In formula: h is the nodes of microgrid, f=1,2 ..., h; G _fg, B _fg, θ _fgbe respectively the admittance between node f and node g and phase angle difference, g ∈ f represents and the node that node f is connected;

B, working voltage retrain:

V _f，min≤V _f≤V _f，max(12)

In formula; V _{f, min}, V _{f, max}be respectively minimum, the maximum of working voltage on node f;

The active power constraint of c, photovoltaic, blower fan and miniature gas turbine:

P _i，min≤P _i≤P _i，max(13)

In formula: P _{i, min}, P _{i, max}be respectively minimum, the maximum of i-th micro-source active power;

D, miniature gas turbine climbing rate retrain:

P during increasing load _mT(t)-P _mT(t-1)≤R _{up, MT}(14)

P during load shedding _mT(t-1)-P _mT(t)≤R _{down, MT}(15)

In formula: P _mTt active power that () is t miniature gas turbine exports, R _{up, MT}, R _{down, MT}be respectively the limit value that miniature gas turbine increases and reduces active power;

E, microgrid and outer net allow mutual through-put power to retrain:

0≤P _Grid(t)≤P _Grid，max(16)

0≤Q _Grid(t)≤Q _Grid，max(17)

In formula: P _grid(t), Q _gridt () is respectively t microgrid and outer net allows mutual active power and reactive power; P _{grid, max}, Q _{grid, max}be respectively and allow mutual maximum active power and reactive power; for the power factor of tie-line power transmission; C is the power factor limit value of tie-line power transmission;

Formula (16 ~ 18) shows the controllable burden of microgrid as external electrical network, ensure that interconnection remains on certain scope to the power that microgrid transmits, and there is higher power factor, ensure that the fail safe of tie-line power transmission and more meet electric company and dock requirement into microgrid;

F, storage battery run constraint:

P _SB，min≤P _SB(t)≤P _SB，max(19)

-S _inv，SB≤P _SB(t)≤S _inv，SB(20)

$-\sqrt{S_{\mathrm{inv},\mathrm{SB}}^2-P_{\mathrm{SB}}^2\left(t\right)}\&le;Q_{\mathrm{SB}}\left(t\right)\&le;\sqrt{S_{\mathrm{inv},\mathrm{SB}}^2-P_{\mathrm{SB}}^2\left(t\right)}---\left(21\right)$

SOC _min≤SOC(t)≤SOC _max(22)

In formula: P _{sB, max}, P _{sB, min}be respectively the minimum and maximum active power of storage battery; S _{inv, SB}for the capacity of storage battery inverter; P _sB(t), Q _sBt (), SOC (t) are respectively the discharge and recharge active power of t battery AC side, reactive power and remaining battery capacity; SOC _min, SOC _maxbe respectively the minimum and maximum residual capacity of storage battery;

Formula (21) shows that the reactive power that storage battery exports only holds quantitative limitation by its inverter;

G, fuel cell operation retrain:

P _FC，min≤P _FC(t)≤P _FC，max(23)

P _FC(t)≤S _inv，FC(24)

$0\&le;Q_{\mathrm{FC}}\left(t\right)\&le;\sqrt{S_{\mathrm{inv},\mathrm{FC}}^2-P_{\mathrm{FC}}^2\left(t\right)}---\left(25\right)$

In formula: P _{fC, min}, P _{fC, max}be respectively minimum, the maximum of fuel cell active power; P _fC(t), Q _fCt () is respectively active power and the reactive power of t fuel cell AC; S _{inv, FC}for the capacity of fuel cell inverter;

Formula (20 ~ 21) and (24 ~ 25) show that the volume priority of storage battery and fuel cell inverter distributes to meritorious exerting oneself;

H, spinning reserve retrain:

$P_{\mathrm{PV}}\left(t\right)+P_{\mathrm{WT}}\left(t\right)+P_{\mathrm{MT}}\left(t\right)+\min \{P_{\mathrm{SB},\max },\sqrt{{\left(S_{\mathrm{inv},\mathrm{SB}}\right)}^2-{\left(Q_{\mathrm{SB}}\left(t\right)\right)}^2},(\mathrm{SOC}(t-1)-\mathrm{SO}{C}_{\min }-$

(26)

$D_B{Q}_B^S){\mathrm{\&eta;}}_D\}+\min \{P_{\mathrm{FC},\max },\sqrt{{\left(S_{\mathrm{inv},\mathrm{FC}}\right)}^2-{\left(Q_{\mathrm{FC}}\left(t\right)\right)}^2}\}+P_{\mathrm{Grid},\max }>=P_D\left(t\right)+P_{\mathrm{loss}}\left(t\right)+R\left(t\right)$

In formula: P _pV(t), P _wTt () is respectively the active power of t photovoltaic, blower fan; P _d(t), P _loss(t), R _dt () is respectively the total load of t micro-grid system, total network loss and required reserve capacity.

In multi-objective optimization question, multiobjective optimization solution and each sub-goal optimal solution are closely-related, should comprise the contribution of each sub-goal.But often contradiction each other between each sub-goal, is generally difficult to make it reach optimum simultaneously; Correlation between each sub-goal optimal solution and multiobjective optimization solution is all fuzzy, is difficult to a boundary determined, and directly cannot compare its quality, therefore this method adopts fuzzy mathematics to solve multi-objective optimization question.

Solution throughway: first obtain the optimal solution of each sub-goal under institute's Prescribed Properties, recycle these optimal solutions by each sub-goal Function Modules gelatinization (namely determining membership function), then ask the solution making the membership function of common factor get maximum, this solution is exactly the optimal solution of multi-objective optimization question.

Fuzzy mathematics is by accurate mathematical method performance and the blooming processing actual objective reality, reach this object, first will determine membership function u.The size of u reflects the satisfaction of optimum results, represent the most satisfied, and u=0 represents least satisfaction.

How setting up membership function, there is no unified approach so far can follow, and it is sought fado and depends on practical experience, finally to meet objective reality for standard.Conventional membership function is divided into type less than normal and type bigger than normal, and for the former, target function is less, and satisfaction is larger, and the latter is just in time contrary.The multiple target of this method optimization is under the prerequisite of the Prescribed Properties meeting, and reduces cost of electricity-generating and reduces environmental pollution, expect to have the upper limit and without lower limit, therefore select the membership function of type less than normal.Conventional type membership function less than normal has: fall half distributed rectangular, lower semi-trapezoid distribution, fall half Γ shape distribution, and to fall half distributed rectangular be 2 distributions, is unsuitable for solving of continuity optimization problem; Lower semi-trapezoid distribution needs the minimum and maximum value first determining target function, but target function maximum fixes in actual engineering meaningless really; Therefore this method select fall half Γ shape distribution, for described target function, membership function is:

$u_k\left(t\right)=\left\{\begin{array}{cc}1& F_k\left(t\right){\&le;F}_{k\min \left(t\right)}\\ \exp \left(\frac{F_{k\min }\left(t\right)-F_k\left(t\right)}{F_{k\min }\left(t\right)}\right)& F_k\left(t\right){>F}_{k\min \left(t\right)}\end{array}\right.---\left(27\right)$

In formula: F _kmint () is single-goal function F _k(t) minimum value under described constraints, k=1,2; T=1,2 ..., 24.

After introducing fuzzy membership variable, the non-linear single goal microgrid economic dispatch model after obfuscation is:

In formula: u (t), u ₁(t), u ₂t () is respectively the satisfaction of the satisfaction of fuzzy optimization, the satisfaction of cost of electricity-generating and Environmental costs, wherein $u\left(t\right)=\underset{k=1}{\overset{2}{\mathrm{\&cap;}}}{u}_k\left(t\right).$

Genetic algorithm is a kind of random paralleling searching algorithm based on natural selection and gene genetics principle, is a kind of Finding Global Optimization and without any need for the effectively optimizing algorithm of initialization information.Crossover probability P in the parameter of genetic algorithm _cwith mutation probability P _mselection be the key affecting performance of genetic algorithms, P _clarger, the new individual speed produced is faster, but P _cthe possibility that time excessive, hereditary pattern is destroyed is larger, P _ctoo small, search procedure can be made slow.For mutation probability P _m, P _mtoo small, be not easy to produce new individual configurations, and P _mexcessive, genetic algorithm becomes pure random search algorithm.M.Srinvivas etc. propose a kind of adaptive algorithm, P _cand P _mautomatically can change with fitness, but this algorithm to ideal adaptation degree close to or equal maximum adaptation spend time, P _cand P _mclose to or equal zero, this is disadvantageous to the evolution initial stage, makes the defect individual at evolution initial stage be in a kind of state do not changed, cause evolving move towards locally optimal solution possibility increase.

This method adopts Improving Genetic Algorithm: the crossover probability P in genetic algorithm _cwith mutation probability P _maccording to following formulae discovery:

$P_c=\left\{\begin{array}{c}{P}_{c2}-\frac{(P_{c2}-P_{c3})(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}}{f}^{\&prime;}{\&GreaterEqual;f}_{\mathrm{avg}}\\ P_{c1}-\frac{(P_{c1}-P_{c2})(f^{\&prime;}-f_{\min })}{f_{\mathrm{avg}}-f_{\min }}{f}^{\&prime;}{<f}_{\mathrm{avg}}\end{array}\right.---\left(34\right)$

$P_m=\left\{\begin{array}{c}{P}_{m2}-\frac{(P_{m2}-P_{m3})(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}}{f}^{\&prime;}{\&GreaterEqual;f}_{\mathrm{avg}}\\ P_{m1}-\frac{(P_{m1}-P_{m2})(f^{\&prime;}-f_{\min })}{f_{\mathrm{avg}}-f_{\min }}{f}^{\&prime;}{<f}_{\mathrm{avg}}\end{array}\right.---\left(35\right)$

In formula: f _max, f _minand f _avgbe respectively maximum, the minimum and average fitness in population; F ' is fitness larger in two individualities that will intersect, P _c1, P _c2, P _c3, P _m1, P _m2, P _m3be the constant of setting, and P _c1, P _c2, P _c3∈ (0,1), P _m1, P _m2, P _m3∈ (0,0.1).

Innovatory algorithm makes the P of maximum adaptation degree individuality in colony _cand P _mnon-vanishing, bring up to P respectively _c3and P _m3, make defect individual not be in a kind of dead state, thus make algorithm jump out locally optimal solution.And when individual fitness is less than average fitness, fitness value is more concentrated, makes algorithm easily be absorbed in locally optimal solution, and now this improvement increases P _cand P _m, thus add the diversity of population.

The present embodiment, for micro-grid system as shown in Figure 2, further illustrates the application of this method.The present embodiment uses improving expert inquiry method and takes the variation of roulette selection, multiple-spot detection and multiple spot and solves following example.Population size is taken as 30, and iterations is 300.Because genetic algorithm belongs to heuristic value, itself has randomness, and every suboptimization has different results, therefore the result hereafter provided is all the mean value that double counting obtains for 20 times.

In Fig. 2, A is resident load, maximum active power is 15kW, B is Commercial Load, and maximum active power is 30kW, C is industrial load, maximum active power is 2 × 15kW, belong to interruptible load, D is industrial load, and maximum active power is 40kW, it is heat load that the power factor of three kinds of loads all gets 0.85, E.Consider the feature of the radial network configuration of distribution and low-voltage circuit parameter, get line resistance R=0.64 Ω/km, X=0.1 Ω/km.In Fig. 2,1 ~ 7 is node.

1.1 basic data

Heat income in the present embodiment and be taken as 0.1 yuan/kWh, voltage permissible variation is-5% ~+5%, meritorious and the idle upper limit that microgrid and outer net transmit is taken as 50kW and 30.987kVar respectively, the power factor limit value of tie-line power transmission is 0.85, the capacity of storage battery inverter is 60kVA, maximum, least residue capacity, initial capacity are respectively 100%, 30%, 70% rated capacity, and the rated capacity of storage battery is 900kWh, and the capacity of fuel cell inverter is 40kVA.The relevant information in each micro-source is as shown in table 1, and the daily load curve (with this day maximum electric loading percentage) of WT, PV exert oneself, heat load, the MT that obtained by heat load gain merit power curve (MT takes " electricity determining by heat " mode) and 3 kinds of character electric loadings as shown in Figure 3.Each micro-source pollutants emissions data, the pollutant standard of value, fine grade are see document, and Spot Price is see document.

The parameter in each micro-source of table 1

1.2 single object optimization results

The present embodiment preferentially utilizes exerting oneself of WT and PV unit, tracing control maximum power output, supposes that MT does not provide idle exerting oneself.

1.2.1 cost of electricity-generating is minimum

When being incorporated into the power networks, because node 1 connects distribution, regarded as balance node during Load flow calculation, get its reference voltage of voltage as microgrid simultaneously, MT, FC and SB adopt power (PQ) to control, and with the minimum active optimization result for target of cost of electricity-generating as shown in Figure 4, idle work optimization result as shown in Figure 5, wherein meritorious vacancy=total burden with power-PV-WT-MT is meritorious exerts oneself, no-power vacancy=total load or burden without work.

In Fig. 4,1 ~ 7 period, in microgrid, load is comparatively light, and the dump energy that PV, WT and MT send charges a battery, 8 ~ 17 periods, there is meritorious vacancy in system, preferentially call the meritorious of storage battery and exert oneself, therefore storage battery is almost in full capacity generating state, 17 period Mo storage battery residual capacity reach lower limit, still there is meritorious vacancy in 18 ~ 20 period microgrids, what storage battery exported gains merit is that 0,21 ~ 24 period dump energies that send of PV, WT and MT charge a battery.1 ~ 9 and 21 ~ 24 periods, storage battery just can meet microgrid safe and reliable operation (on the basis of not cutting load in the scope of exerting oneself, microgrid can run under Prescribed Properties meeting), therefore outwards net purchase is not electric, 10 ~ 13 and 15 ~ 20 periods, can not meet microgrid safe and reliable operation in the scope of exerting oneself of storage battery, and now purchase electricity price, lower than the cost of electricity-generating of FC, therefore preferentially meets microgrid power shortage from outer net power purchase.14 the period PV, WT, MT and SB cannot meet microgrid safe and reliable operation, and now purchase electricity price higher than the cost of electricity-generating of FC, preferentially call FC to provide meritorious vacancy, the outside net purchase electricity of insufficient section meets, other periods do not need to call FC provides meritorious exerting oneself just can meet microgrid safe and reliable operation, therefore only calls FC and provide idle and exert oneself.

As can be seen from Figure 4, before 17 periods, the meritorious vacancy of microgrid provides primarily of SB, after 17 period Mo SB dump energies arrive lower limit, meritorious vacancy provides primarily of outer net, tie-line power transmission mainly serves the effect of peak regulation here, and FC only exerts oneself lower than just providing meritorious during purchase electricity price in cost of electricity-generating.

As can be seen from Figure 5, it is idle that SB and FC provides on preferential satisfied meritorious Demand Base, coordinates the idle reactive requirement meeting microgrid of interconnection.The reactive requirement of micro-grid system provides primarily of SB and FC, microgrid is only needing outer net to provide the meritorious period (10 ~ 20 period) meeting microgrid safe and reliable operation of exerting oneself just to absorb a small amount of idle from outer net, and outer net is higher to the power factor of microgrid through-put power, access distribution with microgrid as a controllable burden, the power factor command of distribution to access load conforms to.

1.2.2 Environmental costs are minimum

With the minimum active optimization result for target of Environmental costs as shown in Figure 6, idle work optimization result as shown in Figure 7.

In Fig. 6,1 ~ 7 period, in microgrid, load is lighter, the dump energy that PV, WT and MT send charges a battery, 8 ~ 20 periods, and system exists meritorious vacancy, preferentially call the meritorious of storage battery to exert oneself, 21 ~ 24 period dump energies that send of PV, WT and MT charge a battery.1 ~ 9 and 17 ~ 24 periods, storage battery can meet microgrid safe and reliable operation in the scope of exerting oneself, therefore outside net purchase electricity or call the meritorious of FC and exert oneself, 10 ~ 16 periods, microgrid safe and reliable operation cannot be met in the scope of exerting oneself of storage battery, the lower-cost FC of preferential transfer environment provides meritorious and exerts oneself, therefore FC is almost in full capacity generating state, and the outside net purchase electricity of insufficient section meets.

As can be seen from Figure 6, the meritorious vacancy of microgrid provides primarily of SB, and FC and dominant eigenvalues mainly serve the effect of peak regulation here, and the good FC of environmental benefit assume responsibility for main peak regulation task.

As can be seen from Figure 7, it is idle that SB and FC provides on preferential satisfied meritorious Demand Base, coordinates the idle reactive requirement meeting microgrid of interconnection transmission.The reactive requirement of micro-grid system provides primarily of SB and FC, because the Environmental costs of outside net purchase electricity are higher, therefore microgrid is only needing outer net to provide the meritorious period (10 ~ 16 period) meeting microgrid safe and reliable operation of exerting oneself just to absorb the idle of minute quantity from outer net.

1.3 multiple-objection optimization results

1.3.1 generating optimization result

By the active optimization result after multi objective fuzzy as shown in Figure 8, idle work optimization result as shown in Figure 9.

Under multiple target, the charging and discharging state of day part storage battery is similar to single goal as can be seen from Figure 8.1 ~ 7 period, in microgrid, load is comparatively light, and the dump energy that PV, WT and MT send charges a battery, 8 ~ 20 periods, there is meritorious vacancy in system, preferentially calls the meritorious of storage battery and exert oneself, and 21 ~ 24 period dump energies that send of PV, WT and MT charge a battery.1 ~ 9 and 17 ~ 24 periods, storage battery can meet microgrid safe and reliable operation in the scope of exerting oneself, therefore outside net purchase electricity or call the meritorious of FC and exert oneself, 10 ~ 16 periods, microgrid safe and reliable operation cannot be met in the scope of exerting oneself of storage battery, consider cost of electricity-generating and Environmental costs to call FC and dominant eigenvalues to meet microgrid safe and reliable operation.

As can be seen from Figure 8, the meritorious vacancy of microgrid provides primarily of SB, FC and the dominant eigenvalues shared task of peak regulation.

As can be seen from Figure 9, it is idle that SB and FC provides on preferential satisfied meritorious Demand Base, coordinates the idle reactive requirement meeting microgrid of interconnection transmission.The reactive requirement of micro-grid system provides primarily of SB and FC, and the idle of SB with FC substantially became complementary fit to meet the reactive requirement of microgrid in one day.Microgrid is only needing outer net to provide the meritorious period (10 ~ 16 period) meeting microgrid safe and reliable operation of exerting oneself just to absorb a small amount of idle from outer net.

1.3.2 single goal compares with multiple-target system optimal value

Every total cost that single goal and multiple-objection optimization draw is as shown in table 2.

Expense under table 2 different target

As can be seen from Table 2, higher than minimum Environmental costs by 15.6% with the minimum Environmental costs drawn for target of cost of electricity-generating, environmental benefit is poor; And higher than minimum cost of electricity-generating by 6.2% with the minimum cost of electricity-generating drawn for target of Environmental costs, economy is poor; The cost of electricity-generating that multi-target fuzzy optimal draws is higher than minimum cost of electricity-generating by 3.9%, and Environmental costs are higher than minimum Environmental costs by 1.1%, synthesized and coordinated economy and the feature of environmental protection.

The present embodiment, by unified for the pollutant produced in the power generation process of the micro-source environmental benefit converting Environmental costs to and run to weigh microgrid, represents the integrated value of pollutant discharge amount.

As shown in Figure 10, Environmental costs as shown in figure 11 for each moment cost of electricity-generating that single goal and multiple-objection optimization draw.

As can be seen from Figure 10 and Figure 11, in multi-target fuzzy optimal draw a day, cost of electricity-generating and Environmental costs are a little more than minimum cost of electricity-generating and minimum Environmental costs.And with the minimum cost of electricity-generating that draws for target of Environmental costs and with the minimum Environmental costs drawn for target of cost of electricity-generating compared to minimum cost of electricity-generating and minimum Environmental costs all higher.Multi-target fuzzy optimal has coordinated ecnomics and enviroment balance between the two preferably, interests and the social benefit of microgrid owner and user are taken into account, not only increase power supply quality, and taken into account the utilization of environmental protection and regenerative resource, achieve economic dispatch and the Appropriate application of distributed power source.In sum, multi-objective Model more can reflect the practical operation situation of microgrid definitely than single goal model, and this model can reach good environmental protection effect with little Financial cost of trying one's best, and achieves energy-conserving and environment-protective power generation dispatching.

1.3.3 satisfaction optimum results

After multi objective fuzzy, the satisfaction of each sub-goal and the satisfaction of multi objective fuzzy are as shown in figure 12.

As can be seen from Figure 12, each sub-goal satisfaction of multiple-objection optimization on the whole and fuzzy optimization satisfaction higher.As can be seen from the satisfaction curve of each sub-goal, what have appreciable impact to the satisfaction of multi-target fuzzy optimal is the satisfaction of cost of electricity-generating, is secondly the satisfaction of Environmental costs.This means to affect in all factors of microgrid power generation dispatching, economy is decisive factor, is secondly environmental factor.This is because the power supply in microgrid is mostly clean energy resource, although the micro-source emission pollutant had, relative to conventional power source, produce the pollutant that every kilowatt hour electricity discharges lower.

Energy control method of the present invention establishes considers that micro-source provides meritorious and idle simultaneously and exerts oneself and take into account the cogeneration of heat and power type micro-grid system multiple target economic dispatch model heating income, wind is comprised with one, light, storage, miniature gas turbine, the concrete microgrid of fuel cell and thermoelectricity load is example, the meritorious of each micro-source under the mode that is incorporated into the power networks considering Spot Price is optimized by fuzzy theory and improved adaptive GA-IAGA, idlely to exert oneself and the satisfaction of multiple-objection optimization, comparative analysis single goal and multiple-target system optimal value, simulation example shows that multi-objective Model more can the actual motion state of precise reaction microgrid than single goal model, good environmental benefit can be realized with little operating cost of trying one's best, and the unit (as storage battery) of good combination property has obvious advantage when considering the multiple target such as economy and the feature of environmental protection, demonstrate the validity of put forward model and algorithm.

Meritorious and the idle output in each micro-source in microgrid is controlled by control method of the present invention, micro-source can be made itself to provide idle idle the exerting oneself coordinating outer net to provide to microgrid of exerting oneself to meet System Reactive Power demand, fall less and the additional investment of reactive power compensator has been installed, guarantee that outer net is without the need to providing excessive reactive power support to microgrid and ensureing that the power transmitted between outer net and microgrid has higher power factor, meet electric company better and dock requirement into microgrid.

Claims (2)

1. one kind based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model, described microgrid comprises multiple micro-source and load, each micro-source is connected by interconnection with load, described load comprises electric loading and heat load, it is characterized in that, described method specifically comprises the following steps:

1) processor obtains the information of each micro-source and interconnection in microgrid, with cost of electricity-generating with Environmental costs are minimum sets up multiple target economic dispatch model for target;

2) set up membership function, obfuscation is carried out to multiple target economic dispatch model, obtain non-linear single goal microgrid economic dispatch model;

3) adopt Improving Genetic Algorithm to be optimized non-linear single goal microgrid economic dispatch model to solve, the optimum obtaining each micro-source and interconnection is gained merit and reactive power data, and these data are transferred to microgrid power controller;

4) microgrid power controller is gained merit according to the optimum received and the meritorious and reactive power of each micro-source of reactive power Data Control and interconnection exports;

Described micro-source comprises photovoltaic, blower fan, miniature gas turbine, fuel cell and storage battery;

Described multiple target economic dispatch model comprises target function and constraints, and described objective function F (t) is intraday cost of electricity-generating F ₁t () is minimum, pollutant is to the Environmental costs F of environmental impact simultaneously ₂t () is minimum, be specially:

minF(t)＝{F ₁(t),F ₂(t)} t＝1,2...24 (1)

F ₁(t)＝C _f(t)+C _DP(t)+C _OM(t)+C _grid(t)-C _sh(t) (2)

F ₂(t)＝C _e(t) (3)

In formula:

$C_f\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{f}_i\left(P_i\left(t\right)\right)---\left(4\right)$

$C_{\mathrm{DP}}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}[\frac{C_{\mathrm{az},i}}{8760k_i}\&CenterDot;\frac{r{(1+r)}^{n_i}}{{(1+r)}^{n_i}-1}\&CenterDot;P_i\left(t\right)]---\left(5\right)$

$C_{\mathrm{OM}}\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{K}_{\mathrm{OM},i}\&CenterDot;P_i\left(t\right)---\left(6\right)$

C _grid(t)＝CP(t)×PR _grid(t) (7)

C _sh(t)＝Q _he(t)×K _ph(8)

$C_e\left(t\right)=\underset{i=1}{\overset{n+1}{\mathrm{\&Sigma;}}}\left(\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}((V_{\mathrm{ej}}+V_j)\&CenterDot;Q_{\mathrm{ij}}\&CenterDot;P_i\left(t\right))\right)---\left(9\right)$

C _f(t), C _dP(t), C _oMt () is respectively fuel cost, the investment depreciable cost and operation expense in each micro-source of t; C _gridt () is for t microgrid is to the purchases strategies of outer net; C _sht () heats income for t co-generation unit; C _et Environmental costs that () is each micro-source of t and the Environmental costs summation of the outside net purchase electricity of microgrid; f _iit is the fuel cost function in i-th micro-source; P _it () is that the active power of i-th micro-source t exports; N is the number in micro-source, and (n+1)th is dominant eigenvalues; C _{az, i}it is the installation cost as per machine capacity in i-th micro-source; k _ibe the capacity factor in i-th micro-source, k _iannual energy output/(rated power in 8760 × this micro-source) in=the i-th micro-source; R is Annual Percentage Rate; n _iit is the pay back period of investment in i-th micro-source; K _{oM, i}it is the unit quantity of electricity operation expense coefficient in i-th micro-source; CP (t) is for t microgrid is to the purchase electricity price of outer net; PR _gridt () is for t microgrid is to the purchase of electricity of outer net; Q _het () is the heating capacity of t microgrid; K _phfor the price of unit heating capacity; V _ejfor the environmental value of jth item pollutant; V _jimpose a fine suffered by jth item pollutant; Q _ijit is the jth item pollutant discharge amount of i-th micro-source unit quantity of electricity; M is the kind of pollutant;

Described constraints comprises:

A, trend retrain:

$\mathrm{\&Delta;}{P}_f=P_{\mathrm{fs}}-\underset{g\&Element;f}{\overset{h}{\mathrm{\&Sigma;}}}{V}_f{V}_g(G_{\mathrm{fg}}\cos {\mathrm{\&theta;}}_{\mathrm{fg}}+B_{\mathrm{fg}}\sin {\mathrm{\&theta;}}_{\mathrm{fg}})=0---\left(10\right)$

$\mathrm{\&Delta;}{Q}_f=Q_{\mathrm{fs}}-\underset{g\&Element;f}{\overset{h}{\mathrm{\&Sigma;}}}{V}_f{V}_g(G_{\mathrm{fg}}\sin {\mathrm{\&theta;}}_{\mathrm{fg}}-B_{\mathrm{fg}}\cos {\mathrm{\&theta;}}_{\mathrm{fg}})=0---\left(11\right)$

In formula: h is the nodes of microgrid, f=1,2 ..., h; G _fg, B _fg, θ _fgbe respectively the admittance between node f and node g and phase angle difference, g ∈ f represents and the node that node f is connected;

B, working voltage retrain:

V _f,min≤V _f≤V _f,max(12)

In formula: V _{f, min}, V _{f, max}be respectively minimum, the maximum of working voltage on node f;

The active power constraint of c, photovoltaic, blower fan and miniature gas turbine:

P _i,min≤P _i≤P _i,max(13)

In formula: P _{i, min}, P _{i, max}be respectively minimum, the maximum of i-th micro-source active power;

D, miniature gas turbine climbing rate retrain:

P during increasing load _mT(t)-P _mT(t-1)≤R _{up, MT}(14)

P during load shedding _mT(t-1)-P _mT(t)≤R _{down, MT}(15)

In formula: P _mTt active power that () is t miniature gas turbine exports, R _{up, MT}, R _{down, MT}be respectively the limit value that miniature gas turbine increases and reduces active power;

E, microgrid and outer net allow mutual through-put power to retrain:

0≤P _Grid(t)≤P _Grid,max(16)

0≤Q _Grid(t)≤Q _Grid,max(17)

In formula: P _grid(t), Q _gridt () is respectively t microgrid and outer net allows mutual active power and reactive power; P _{grid, max}, Q _{grid, max}be respectively and allow mutual maximum active power and reactive power; for the power factor of tie-line power transmission; C is the power factor limit value of tie-line power transmission;

F, storage battery run constraint:

P _SB,min≤P _SB(t)≤P _SB,max(19)

-S _inv,SB≤P _SB(t)≤S _inv,SB(20)

$-\sqrt{S_{\mathrm{inv},\mathrm{SB}}^2-P_{\mathrm{SB}}^2\left(t\right)}\&le;Q_{\mathrm{SB}}\left(t\right)\&le;\sqrt{S_{\mathrm{inv},\mathrm{SB}}^2-P_{\mathrm{SB}}^2\left(t\right)}---\left(21\right)$

SOC _min≤SOC(t)≤SOC _max(22)

In formula: P _{sB, max}, P _{sB, min}be respectively the minimum and maximum active power of storage battery; S _{inv, SB}for the capacity of storage battery inverter; P _sB(t), Q _sBt (), SOC (t) are respectively the discharge and recharge active power of t battery AC side, reactive power and remaining battery capacity; SOC _min, SOC _maxbe respectively the minimum and maximum residual capacity of storage battery;

G, fuel cell operation retrain:

P _FC,min≤P _FC(t)≤P _FC,max(23)

P _FC(t)≤S _inv,FC(24)

$0\&le;Q_{\mathrm{FC}}\left(t\right)\&le;\sqrt{S_{\mathrm{inv},\mathrm{FC}}^2-P_{\mathrm{FC}}^2\left(t\right)}---\left(25\right)$

In formula: P _{fC, min}, P _{fC, max}be respectively minimum, the maximum of fuel cell active power; P _fC(t), Q _fCt () is respectively active power and the reactive power of t fuel cell AC; S _{inv, FC}for the capacity of fuel cell inverter;

H, spinning reserve retrain:

$\begin{array}{c}{P}_{\mathrm{PV}}\left(t\right)+P_{\mathrm{WT}}\left(t\right)+P_{\mathrm{MT}}\left(t\right)+\min \{P_{\mathrm{SB},\max },\sqrt{{\left(S_{\mathrm{inv},\mathrm{SB}}\right)}^2-{\left(Q_{\mathrm{SB}}\left(t\right)\right)}^2},(\mathrm{SOC}(t-1)-{\mathrm{SOC}}_{\min }-\\ D_B{Q}_B^S\left){\mathrm{\&eta;}}_D\right\}+\min \{P_{\mathrm{FC},\max },\sqrt{{\left(S_{\mathrm{inv},\mathrm{FC}}\right)}^2-{\left(Q_{\mathrm{FC}}\left(t\right)\right)}^2}\}+P_{\mathrm{Grid},\max }>=P_D\left(t\right)+P_{\mathrm{loss}}\left(t\right)+R\left(t\right)\end{array}---\left(26\right)$

In formula: P _pV(t), P _wTt () is respectively the active power of t photovoltaic, blower fan; η _dfor discharging efficiency; D _bfor storage battery self discharge ratio hourly; for the total capacity of storage battery; P _d(t), P _loss(t), R _dt () is respectively the total load of t micro-grid system, total network loss and required reserve capacity;

Described membership function adopts the membership function falling half Γ shape distribution, and for described target function, membership function is:

$u_k\left(t\right)=\left\{\begin{array}{cc}1& F_k\left(t\right)\&le;F_{k\min }\left(t\right)\\ \exp \left(\frac{F_{k\min }\left(t\right)-F_k\left(t\right)}{F_{k\min }\left(t\right)}\right)& F_k\left(t\right)>F_{k\min }\left(t\right)\end{array}\right.---\left(27\right)$

In formula: F _kmint () is single-goal function F _k(t) minimum value under described constraints, k=1,2; T=1,2 ..., 24;

Non-linear single goal microgrid economic dispatch model after described obfuscation is:

In formula: u (t), u ₁(t), u ₂t () is respectively the satisfaction of the satisfaction of fuzzy optimization, the satisfaction of cost of electricity-generating and Environmental costs, wherein

2. according to claim 1 a kind of based on microgrid energy control method under the mode that is incorporated into the power networks of Multiobjective Scheduling model, it is characterized in that, described Improving Genetic Algorithm refers to: the crossover probability P in genetic algorithm _cwith mutation probability P _maccording to following formulae discovery:

$P_c=\left\{\begin{array}{c}{P}_{c2}-\frac{(P_{c2}-P_{c3})(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}}{f}^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{c1}-\frac{(P_{c1}-P_{c2})(f^{\&prime;}-f_{\min })}{f_{\mathrm{avg}}-f_{\min }}{f}^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(29\right)$

$P_m=\left\{\begin{array}{c}{P}_{m2}-\frac{(P_{m2}-P_{m3})(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}}{f}^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{m1}-\frac{(P_{m1}-P_{m2})(f^{\&prime;}-f_{\min })}{f_{\mathrm{avg}}-f_{\min }}{f}^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(30\right)$

In formula: f _max, f _minand f _avgbe respectively maximum, the minimum and average fitness in population; F ' is fitness larger in two individualities that will intersect, P _c1, P _c2, P _c3, P _m1, P _m2, P _m3be the constant of setting, and P _c1, P _c2, P _c3∈ (0,1), P _m1, P _m2, P _m3∈ (0,0.1).