CN106100002A - A kind of optimizing operation method of alternating current-direct current mixing microgrid - Google Patents

Abstract

The optimizing operation method of a kind of alternating current-direct current mixing microgrid, for the mixing microgrid containing multiple micro-source, the alternating current-direct current mixing microgrid optimization establishing meter and Financial cost and environmental benefit runs mathematical model.Based on the architectural characteristic containing communication area and direct current region while alternating current-direct current mixing microgrid, improve black hole algorithm by coevolution and the subregion that complicated mixing microgrid is split as two relative opposition is coordinated calculating, can effectively solve the Solve problems of the alternating current-direct current mixing microgrid that variable is more, structure is more complicated.With contain wind, light, fuel cell, accumulator and miniature combustion engine concrete alternating current-direct current mixing microgrid as embodiment, be analyzed.A kind of optimizing operation method of the alternating current-direct current mixing microgrid of the present invention, from the architectural characteristic of microgrid, based on coevolution framework economic operation problem decoupled and coordinate, use simultaneously and improve black hole searching algorithm and carry out the optimizing of sub-district, reduce alternating current-direct current mixing microgrid economic operation problem solve difficulty.

Description

A kind of optimizing operation method of alternating current-direct current mixing microgrid

Technical field

The present invention relates to the optimizing operation method of a kind of microgrid.Particularly relate to a kind of based on coevolution black hole algorithm The optimizing operation method of alternating current-direct current mixing microgrid.

Background technology

Current energy and environment problem highlights day by day, and development utilization regenerative resource has become common recognition.Microgrid is as one Comprise the meta-synthetic engineering of regenerative resource distributed generation technology, by its to the high degree of compatibility of regenerative resource and Flexible modulation ability to distributed power generation, has obtained most attention in the industry in the today day by day emphasizing energy-conserving and environment-protective.Hand over straight Stream mixing microgrid can comprehensively play exchange microgrid and the complementary advantage of direct-current micro-grid, has suitable application area more widely.

Alternating current-direct current mixing microgrid economic operation problem is a complicated optimum problem high-dimensional, non-linear, multiobject.Pass System Mathematics Optimization Method and innovatory algorithm thereof calculate accurately, but generally have high requirements problem model, and it is big to solve difficulty； Intelligent algorithm, such as genetic algorithm, particle cluster algorithm etc., by collective search to be quickly found out engineering feasible solution, this type of method pair Model needs is relatively low, also can ensure to meet permissible accuracy, but solve when dimension is more and still can become difficulty.Intelligent algorithm When the problem dimension solved is more, solving difficulty and strengthen, this weakness can be by decomposition reasonable to problem total tune Method overcomes.

Summary of the invention

The technical problem to be solved is to provide a kind of economical operation that can reduce alternating current-direct current mixing microgrid and asks Topic solves the optimizing operation method of the alternating current-direct current mixing microgrid of difficulty.

The technical solution adopted in the present invention is: the optimizing operation method of a kind of alternating current-direct current mixing microgrid, including walking as follows Rapid:

1) communication area and the information on load data in direct current region, the weather information data of alternating current-direct current mixing microgrid are gathered, The data of following one day are predicted by the historical data that comprehensive microgrid runs, and obtain alternating current-direct current mixing microgrid in following a day Communication area load, direct current region load, wind energy and solar power prediction data；

2) the micro battery characteristic of alternating current-direct current mixing microgrid, the mathematics of exerting oneself of all controllable type micro battery in setting up microgrid are added up Model；

3) following for alternating current-direct current mixing microgrid intraday economical operation is divided into 24 periods, complete with alternating current-direct current mixing microgrid It runs fuel cost, operation expense, purchases strategies and the minimum object function of Environmental costs, it is considered to each within microgrid The constraint of period electric energy balance, grid-connected interconnection capacity-constrained, the restriction of exerting oneself in controlled micro-source, the shortest start-stop time in controlled micro-source Constraint, the charge capacity constraint of accumulator, power capacity constraint, the capacity continuity constraint of accumulator and the calculating of accumulator store The electric energy balance constraint at the whole story in cycle of battery, sets up alternating current-direct current mixing microgrid optimization and runs mathematical model；

4) based on step 1) in communication area load, direct current region load, wind energy and solar power prediction data, adopt Improve black hole optimized algorithm to step 3 with coevolution) in alternating current-direct current mixing microgrid optimization operation mathematical model solve, Obtain exerting oneself and mixing microgrid purchase of electricity prioritization scheme of day part controllable type micro battery.

Step 2) described in mathematical model of exerting oneself include:

(1) model of exerting oneself of fuel cell:

$\left\{\begin{array}{c}{f}_{fuel,FC}=C_{gas,FC}\frac{1}{{\mathrm{LHV}}_{gas,FC}}\frac{P_{FC}}{{\mathrm{\η}}_{FC}}\\ {\mathrm{\η}}_{FC}=-0.0023P_{FC}+0.6735\end{array}\right.$

Wherein, each symbol is defined as follows: f_fuel,FCFor the fuel cost of fuel cell, C_gas,FCUse for fuel cell Fuel price, LHV_gas,FCFor the low heat value of fuel, P_FCFor the size of exerting oneself of fuel cell, η_FCEfficiency for fuel cell.

(2) model of exerting oneself of miniature gas turbine:

$\left\{\begin{array}{c}{f}_{fuel,MT}=C_{gas}\frac{1}{LHV}\frac{P_{MT}}{{\mathrm{\η}}_{MT}}\\ {\mathrm{\η}}_{MT}=0.0753{\left(\frac{P_{MT}}{65}\right)}^3-0.3095{\left(\frac{P_{MT}}{65}\right)}^2+0.4174\left(\frac{P_{MT}}{65}\right)+0.1068\end{array}\right.$

Wherein, each symbol is defined as follows: f_fuel,MTFor the fuel cost of miniature gas turbine, C_gas,MTFor miniature gas The fuel price that turbine uses, LHV_gas,MTFor the low heat value of the fuel of miniature gas turbine, P_MTFor miniature gas turbine go out Power size, η_MTFor miniature gas turbine efficiency.

Step 3) described in alternating current-direct current mixing microgrid optimization run mathematical model include object function and constraints, its In, object function is:

$\left\{\begin{array}{c}\min F=C_{fuel}+C_{om}+C_{buy}+C_w\\ C_{fuel}=\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{N}{\Σ}}{f}^t\[P_i\left(t\right)\]\\ C_{om}=\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{N}{\Σ}}{k}_i{P}_i\left(t\right)\\ C_{buy}=\underset{t=1}{\overset{24}{\Σ}}{\mathrm{\α}}^t{P}_{buy}\left(t\right)\\ C_w=\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{N}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}\[{\mathrm{\β}}_j\·{\mathrm{\λ}}_{i,j}\·P_i\left(t\right)\]\end{array}\right.$

Wherein, each symbol is defined as follows: F is object function, C_fuelFor fuel cost, C_omFor operation expense, C_buyFor Purchases strategies, C_wFor polluting conversion cost；T is the period, and N is the total number in micro-source, f^t() is the i-th micro-source fuel in the t period Cost function, P_iT () is the i-th micro-source real output in the t period, k_iOperation expense system for the micro-source of i-th Number, α^tFor the purchase electricity price during t period, P_buyT () is the mixing microgrid purchase of electricity when the t period, M is pollutant sums, β_jFor The environmental evaluation standard of jth kind pollutant, λ_i,jDischarge coefficient for the jth kind pollutant of i-th unit；

Constraints is:

(1) generator unit power allowances:

$P_i^{\min }\≤P_i\left(t\right)\≤P_i^{\max }$

(2) the shortest start-off time constraints:

$\left\{\begin{array}{c}{T}_{on}\≥T_{on}^{\min }\\ T_{off}\≥T_{off}^{\min }\end{array}\right.$

If when the iteration improving black hole algorithm tends to convergence, part of generating units does not meet the constraint of start-stop time, to not having There is the unit meeting start-off time constraints to be adjusted as follows: if to start the time continuously too short for unit, then force to prolong The long running time；If downtime is too short, then forces to change into running with lowest power downtime by violation, then adjust other Unit output is to meet other constraints of system；

(3) accumulator electric-quantity capacity and power capacity constraint:

$\left\{\begin{array}{c}{E}_{min}\≤E_{ES}\left(t\right)\≤E_{max}\\ P_{ES,min}\≤P_{ES}\left(t\right)\≤P_{ES,max}\end{array}\right.$

(4) accumulator capacity continuity constraint:

$\left\{\begin{array}{c}E\left(t\right)=E(t-1)+P_{ES}\left(t\right){\mathrm{\&eta;}}_{ES1}\mathrm{\&Delta;}T,P_{ES}\left(t\right)<0\\ E\left(t\right)=E(t-1)+\frac{P_{ES}\left(t\right)}{{\mathrm{\&eta;}}_{ES2}}\mathrm{\&Delta;}T,P_{ES}\left(t\right)\&GreaterEqual;0\end{array}\right.$

(5) electric energy balance at the whole story in calculating accumulator cycle constraint:

E (0)=E (24) E (t)

(6) total system real-time electric energy balance constraint:

∑P_i(t)+P_buy(t)+P_ES(t)=P_D(t)+P_Loss(t)

(7) grid-connected interconnection capacity-constrained:

$0\&le;P_{buy}\left(t\right)\&le;P_{buy}^{\max }$

Wherein:WithRespectively export upper and lower bound, P_iT () is the actual output in the t period of the micro-source of i-th Power；T_onFor the time that continuously opens of controllable type micro battery, T_offFor the continuous downtime of controllable type micro battery, controllable type is micro- The lower limit with downtime that continuously opens of power supply is respectivelyWithE_EST () is that the electricity of the accumulator of t period holds Amount, E_min、E_maxIt is respectively lower limit and the upper limit of accumulator electric-quantity capacity；P_EST () is the accumulator output of t period, with output For positive direction, P_ES,min、P_ES,maxIt is respectively lower limit and the upper limit of accumulator output；；E (t) is the electricity of accumulator t period, E (t-1) it is the electricity of accumulator t-1 period；η_ES1For accumulator charge efficiency；η_ES2For battery discharging efficiency；△ T is unit Period, △ T=1 in the present invention；P_buyT () is the power purchase power of alternating current-direct current mixing microgrid t period, P_DT () is that alternating current-direct current mixing is micro- The electrical load requirement of net t period, P_LossT () is the active power loss of alternating current-direct current mixing microgrid t period；For interconnection capacity about Maximum power purchase power under Shu.

Step 4) described in alternating current-direct current mixing microgrid optimization is run mathematical model solve and include:

(1) initialize: input micro battery, load parameter；Input algorithm parameter include celestial body scope, maximum optimizing number of times, The number of celestial body, the minimum range in newly generated distance of celestial bodies black hole and the dimension of celestial body；Interregional mutual of day part AC and DC Power；The mutual power calculation iterations pp=1 that AC and DC is interregional；Set the interregional mutual power of AC and DC to change Amount ε；

(2) each optimized variable of alternating current-direct current mixing microgrid is divided in communication area and direct current region according to position；

(3) mutual power is counted in each area power Constraints of Equilibrium, integrating step 3) described in constraints, provide merit Rate Constraints of Equilibrium is as follows:

$\left\{\begin{array}{c}{P}_{AC}\left(t\right)+P_{ES}^{AC}\left(t\right)=P_D^{AC}\left(t\right)+P_{Loss}^{AC}\left(t\right)+P_M\left(t\right)+u_0\&CenterDot;P_{Loss}^M\left(t\right)\\ P_{DC}\left(t\right)+P_{ES}^{DC}\left(t\right)+P_M\left(t\right)+(1-u_0)\&CenterDot;P_{Loss}^M\left(t\right)=P_D^{DC}\left(t\right)+P_{Loss}^{DC}\left(t\right)\end{array}\right.$

Wherein, each symbol is defined as follows: P_ACT () is the generated output output total amount of t period communication area micro battery, including Power purchase power；Output for t period communication area accumulator；For t period communication area load；For t period communication area network loss；P_MT () is the mutual power in t period AC and DC region；Cross, straight for the t period Flow the loss of interregional bi-directional inverter；u₀For the loss factor of bi-directional inverter, work as P_M(t) > 0 time, u₀=1, otherwise u₀= 0；P_DCT () is the generated output output total amount of t period direct current region micro battery；For t period direct current region accumulator Output；For t period direct current region load；For t period direct current region network loss；

(4) the iterations iter=1 of improvement black hole algorithm is set；

(5) in the p of region, N number of celestial body is randomly generated；

(6) calculate the self adaptation angle value of initial celestial body, select optimum self adaptation angle value to determine initial black hole；

(7) position of celestial body is updated；

(8) the self adaptation angle value of celestial body after calculating updates, if the self adaptation angle value of celestial body is better than the self adaptation in initial black hole Angle value, exchanges the position of described celestial body and initial black hole, and described celestial body is new black hole, and other celestial bodies move towards new black hole Updating, otherwise continue the position near initial black hole location updating celestial body, celestial body is towards the speed of black hole mobile update and inertia For:

$\left\{\begin{array}{c}{v_i}^t=\mathrm{\&omega;}{v_i}^{t-1}+r_1(x_{iP}-{x_i}^t)+r_2(x_{BH}-{x_i}^t)\\ \mathrm{\&omega;}={\mathrm{\&omega;}}_{max}-({\mathrm{\&omega;}}_{\max }-{\mathrm{\&omega;}}_{min})N_{iter}/N_{itermax}\end{array}\right.$

Wherein, each symbol is defined as follows: v_i ^tFor the speed of celestial body t, v_i ^t-1For the speed in celestial body t-1 moment, define t When=1, speed is 0；x_iPHistory adaptive optimal control value for i-th celestial body；x_i ^tFor i-th celestial body in the position of t；x_BHFor black The position in hole；r₁And r₂For the random number between [0,1]；ω is the inertia parameter that celestial body moves towards black hole, ω_min、ω_maxFor The lower limit of the inertia parameter that celestial body moves towards black hole and the upper limit, take ω_max=0.9, ω_min=0.4；N_iterBlack for current improvement The iterations of hole algorithm, N_itermaxFor improving the maximum iteration time of black hole algorithm；

The constraint of velocity that celestial body moves towards black hole is:

$\left\{\begin{array}{c}{v}_{idmin}\&le;v_{id}\&le;v_{idmax}\\ v_{id\max }=(x_{idmax}-x_{id\min })/L\\ v_{id\min }=-v_{id\max }\end{array}\right.$

Wherein, each symbol is defined as follows: v_idThe speed moved towards black hole for celestial body i, v_idminWith v_idmaxIt is respectively celestial body The minimum moved towards black hole and maximal rate；x_idmax、x_idminIt is respectively celestial body i and moves minimum once, maximum towards black hole Distance；It is fixed that definite value parameter L takes according to example, is used for limiting the amplitude of speed；

(9) judging whether celestial body meets phagocytosis condition, if meeting, celestial body is swallowed by black hole, and random in feas ible space Position regenerates new celestial body, and in feas ible space, random site regenerates new celestial body, specifically uses Logistic to map Producing Chaos Variable, mapping equation is as follows:

x_k+1=μ x_k(1-x_k)

Wherein, each symbol is defined as follows: x_k、x_k+1For celestial body position in kth, k+1 optimizing；μ is constant, takes 4 orders Map and enter chaos state；Described it is mapped with fixed point 0,0.25,0.50,0.75,1, if the initial position of celestial body is described Fixed point, then select the position of the new celestial body of common generating random number；

If not meeting phagocytosis condition, celestial body continues to the position near position, black hole mobile update celestial body；

(10) judging whether current iterations reaches maximum iteration time, if reaching, terminating to calculate, output calculates As a result, step (11) is entered；If being unsatisfactory for, setting the iterations iter=iter+1 improving black hole algorithm, returning (7th) Step；

(11) AC and DC subregion produces several optimum individuals respectively；

(12) individuality that sub-for AC and DC district submits to is combined with each other, obtains multiple the whole network unit output plan, due to possible Exist by the cooperation across sub-district complementary, therefore optimize as follows by the period:

min(C_fuel(t)+C_om(t)+C_buy(t)+C_w(t))

Wherein, each symbol is defined as follows: C_fuelT () is the fuel cost of t period, C_omT () is that the operation maintenance of t period becomes This, C_buyT () is the purchases strategies of t period, C_wT () is the pollution conversion cost of t period, constraints and step 3) consistent；

(13) all 24 periods all carry out the optimization described in (12nd) step process, obtain complete period totle drilling cost；

(14) the complete period totle drilling cost obtained in (13rd) step is found out the combination that integrated cost is minimum, calculate day part The mutual power that AC and DC is interregional, as the calculating iterations pp=1 of the interregional mutual power of day part AC and DC, If the interregional mutual power of the day part AC and DC mutual power interregional relative to initial day part AC and DC changes Amount average less than given ε, then turns (15th) step；Calculating iterations when the interregional mutual power of day part AC and DC During pp ＞ 1, if if the interregional mutual power of day part AC and DC is interregional relative to last day part AC and DC Mutual power knots modification average less than given ε, then turns (15th) step；

Otherwise using combination minimum for integrated cost as new border transmission power value, it is assigned to alternating current and direct current region, calculates The mutual power in day part alternating current-direct current subinterval, formula is as follows:

$P_M\left(t\right)=P_{AC}\left(t\right)+P_{ES}^{AC}\left(t\right)-P_D^{AC}\left(t\right)-P_{Loss}^{AC}\left(t\right)-u_0\&CenterDot;P_{Loss}^M\left(t\right)$

Wherein, each symbol is defined as follows: P_MT () is the mutual power in t period AC and DC region；P_ACT () is to hand over the t period The generated output output total amount of stream region micro battery, including power purchase power；Output for t period communication area accumulator Power；For t period communication area load；For t period communication area network loss；For t period AC and DC The loss of interregional bi-directional inverter；u₀For the loss factor of bi-directional inverter, work as P_M(t) > 0 time, u₀=1, otherwise u₀=0； The mutual power calculation iterations pp=pp+1 that AC and DC is interregional, turns (3rd) step and continues to calculate；

(15) stop calculating, export result.

The optimizing operation method of a kind of alternating current-direct current mixing microgrid of the present invention, from the architectural characteristic of microgrid, based on association With Evolution lines economic operation problem decoupled and coordinate, using simultaneously and improve black hole searching algorithm and carry out the optimizing of sub-district, Reduce alternating current-direct current mixing microgrid economic operation problem solve difficulty.The invention have the advantages that:

(1) according to the architectural characteristic of alternating current-direct current mixing microgrid, coevolution framework is introduced, by micro-for complicated alternating current-direct current mixing Network optimization operation problem splits into two relatively simple communication area optimization problems and direct current region optimization problem, reduces and asks The difficulty that topic solves.

(2), after using coevolution framework to split complicated alternating current-direct current mixing microgrid optimization operation problem, use Improve black hole algorithm it is solved.Cause algorithm precocious owing to traditional black hole algorithm is easily trapped into local optimum, and If being directly usually unsatisfactory for constraint by the celestial body that original method stochastic generation is new producing new celestial body when.For improving these Shortcoming, adds mechanism of chaos, adds inertia and constraint of velocity that celestial body is attracted when generating new celestial body, and to being unsatisfactory for opening The unit stopping constraint adjusts its start-stop time, improves solution efficiency.

Accompanying drawing explanation

Fig. 1 is the flow chart of the optimizing operation method of the present invention a kind of alternating current-direct current mixing microgrid；

Fig. 2 is alternating current-direct current mixing microgrid structure chart of the present invention；

Fig. 3 is that embodiment of the present invention area typical case's day wind power generating set, photovoltaic cell capable of generating power be pre-, AC load, direct current Load measuring and tou power price curve chart；

Fig. 4 is the fuel cost in controlled micro-source in the present invention-machine end power；

Fig. 5 is the optimum results figure of 24 periods of alternating current-direct current mixing microgrid exchanging area after the present invention optimizes；

Fig. 6 is the optimum results figure of 24 periods of alternating current-direct current mixing microgrid DC area after the present invention optimizes；

Fig. 7 is the optimum results figure of alternating current-direct current mixing microgrid overall 24 periods after the present invention optimizes；

Fig. 8 is the mutual power optimization knot of alternating current-direct current mixing microgrid exchanging area and 24 periods of DC area after the present invention optimizes Fruit figure.

Detailed description of the invention

Below in conjunction with embodiment and accompanying drawing, the optimizing operation method of a kind of alternating current-direct current mixing microgrid of the present invention is made in detail Describe in detail bright.

The optimizing operation method traditional method to be overcome of a kind of alternating current-direct current mixing microgrid of the present invention and intelligent algorithm are multiple Drawback on miscellaneous problem solving, combines coevolution method with black hole algorithm, proposes coevolution black hole algorithm.

The present invention is with the Financial cost of alternating current-direct current mixing microgrid and the minimum target of Environmental costs, it is contemplated that alternating current-direct current mixes Day part electric energy balance constraint within microgrid, grid-connected interconnection capacity-constrained, the restriction of exerting oneself in controlled micro-source, controlled micro-source The shortest start-off time constraints, the charge capacity constraint of accumulator, the power capacity constraint of accumulator, the capacity seriality of accumulator Constraint, the electric energy balance constraint at the whole story in calculating cycle etc. of accumulator, set up alternating current-direct current mixing microgrid optimal operation model, and propose A kind of coevolution black hole algorithm, solves day part micro battery and exerts oneself and the mutual power in AC and DC two region.To contain The concrete alternating current-direct current mixing microgrid of wind, light, fuel cell, accumulator and miniature combustion engine is embodiment, for specific embodiment, to excellent Change result to be analyzed.

As it is shown in figure 1, the optimizing operation method of a kind of alternating current-direct current mixing microgrid of the present invention, comprise the steps:

1) communication area and the information on load data in direct current region, the weather information data of alternating current-direct current mixing microgrid are gathered, The data of following one day are predicted by the historical data that comprehensive microgrid runs, and obtain alternating current-direct current mixing microgrid in following a day Communication area load, direct current region load, wind energy and solar power prediction data；

2) the micro battery characteristic of alternating current-direct current mixing microgrid, the mathematics of exerting oneself of all controllable type micro battery in setting up microgrid are added up Model；

Described mathematical model of exerting oneself includes:

(1) model of exerting oneself of fuel cell:

$\left\{\begin{array}{c}{f}_{fuel,FC}=C_{gas,FC}\frac{1}{{\mathrm{LHV}}_{gas,FC}}\frac{P_{FC}}{{\mathrm{\&eta;}}_{FC}}\\ {\mathrm{\&eta;}}_{FC}=-0.0023P_{FC}+0.6735\end{array}\right.$

Wherein, each symbol is defined as follows: f_fuel,FCFor the fuel cost of fuel cell, C_gas,FCUse for fuel cell Fuel price, LHV_gas,FCFor the low heat value of fuel, P_FCFor the size of exerting oneself of fuel cell, η_FCEfficiency for fuel cell.

(2) model of exerting oneself of miniature gas turbine:

$\left\{\begin{array}{c}{f}_{fuel,MT}=C_{gas}\frac{1}{LHV}\frac{P_{MT}}{{\mathrm{\&eta;}}_{MT}}\\ {\mathrm{\&eta;}}_{MT}=0.0753{\left(\frac{P_{MT}}{65}\right)}^3-0.3095{\left(\frac{P_{MT}}{65}\right)}^2+0.4174\left(\frac{P_{MT}}{65}\right)+0.1068\end{array}\right.$

Wherein, each symbol is defined as follows: f_fuel,MTFor the fuel cost of miniature gas turbine, C_gas,MTFor miniature gas The fuel price that turbine uses, LHV_gas,MTFor the low heat value of the fuel of miniature gas turbine, P_MTFor miniature gas turbine go out Power size, η_MTFor miniature gas turbine efficiency.

3) following for alternating current-direct current mixing microgrid intraday economical operation is divided into 24 periods, complete with alternating current-direct current mixing microgrid It runs fuel cost, operation expense, purchases strategies and the minimum object function of Environmental costs, it is considered to each within microgrid The constraint of period electric energy balance, grid-connected interconnection capacity-constrained, the restriction of exerting oneself in controlled micro-source, the shortest start-stop time in controlled micro-source Constraint, the charge capacity constraint of accumulator, power capacity constraint, the capacity continuity constraint of accumulator and the calculating of accumulator store The electric energy balance constraint at the whole story in cycle of battery, sets up alternating current-direct current mixing microgrid optimization and runs mathematical model；

Described alternating current-direct current mixing microgrid optimization runs mathematical model and includes object function and constraints, wherein, mesh Scalar functions is:

$\left\{\begin{array}{c}\min F=C_{fuel}+C_{om}+C_{buy}+C_w\\ C_{fuel}=\underset{t=1}{\overset{24}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{f}^t\&lsqb;P_i\left(t\right)\&rsqb;\\ C_{om}=\underset{t=1}{\overset{24}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{k}_i{P}_i\left(t\right)\\ C_{buy}=\underset{t=1}{\overset{24}{\&Sigma;}}{\mathrm{\&alpha;}}^t{P}_{buy}\left(t\right)\\ C_w=\underset{t=1}{\overset{24}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}\underset{j=1}{\overset{M}{\&Sigma;}}\&lsqb;{\mathrm{\&beta;}}_j\&CenterDot;{\mathrm{\&lambda;}}_{i,j}\&CenterDot;P_i\left(t\right)\&rsqb;\end{array}\right.$

Wherein, each symbol is defined as follows: F is object function, C_fuelFor fuel cost, C_omFor operation expense, C_buyFor Purchases strategies, C_wFor polluting conversion cost；T is the period, and N is the total number in micro-source, f^t() is the i-th micro-source fuel in the t period Cost function, P_iT () is the i-th micro-source real output in the t period, k_iOperation expense system for the micro-source of i-th Number, α^tFor the purchase electricity price during t period, P_buyT () is the mixing microgrid purchase of electricity when the t period, M is pollutant sums, β_jFor The environmental evaluation standard of jth kind pollutant, λ_i,jDischarge coefficient for the jth kind pollutant of i-th unit；

Constraints is:

(1) generator unit power allowances:

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

(2) the shortest start-off time constraints:

$\left\{\begin{array}{c}{T}_{on}\&GreaterEqual;T_{on}^{\min }\\ T_{off}\&GreaterEqual;T_{off}^{\min }\end{array}\right.$

If when the iteration improving black hole algorithm tends to convergence, part of generating units does not meet the constraint of start-stop time, to not having There is the unit meeting start-off time constraints to be adjusted as follows: if to start the time continuously too short for unit, then force to prolong The long running time；If downtime is too short, then forces to change into running with lowest power downtime by violation, then adjust other Unit output is to meet other constraints of system；

(3) accumulator electric-quantity capacity and power capacity constraint:

$\left\{\begin{array}{c}{E}_{min}\&le;E_{ES}\left(t\right)\&le;E_{max}\\ P_{ES,min}\&le;P_{ES}\left(t\right)\&le;P_{ES,max}\end{array}\right.$

(4) accumulator capacity continuity constraint:

$\left\{\begin{array}{c}E\left(t\right)=E(t-1)+P_{ES}\left(t\right){\mathrm{\&eta;}}_{ES1}\mathrm{\&Delta;}T,P_{ES}\left(t\right)<0\\ E\left(t\right)=E(t-1)+\frac{P_{ES}\left(t\right)}{{\mathrm{\&eta;}}_{ES2}}\mathrm{\&Delta;}T,P_{ES}\left(t\right)\&GreaterEqual;0\end{array}\right.$

(5) electric energy balance at the whole story in calculating accumulator cycle constraint:

E (0)=E (24) E (t)

(6) total system real-time electric energy balance constraint:

∑P_i(t)+P_buy(t)+P_ES(t)=P_D(t)+P_Loss(t)

(7) grid-connected interconnection capacity-constrained:

$0\&le;P_{buy}\left(t\right)\&le;P_{buy}^{\max }$

Wherein:WithRespectively export upper and lower bound, P_iT () is the actual output in the t period of the micro-source of i-th Power；T_onFor the time that continuously opens of controllable type micro battery, T_offFor the continuous downtime of controllable type micro battery, controllable type is micro- The lower limit with downtime that continuously opens of power supply is respectivelyWithE_EST () is the charge capacity of the accumulator of t period, E_min、E_maxIt is respectively lower limit and the upper limit of accumulator electric-quantity capacity；P_EST () is the accumulator output of t period, to be just output as Direction, P_ES,min、P_ES,maxIt is respectively lower limit and the upper limit of accumulator output；；E (t) is the electricity of accumulator t period, E (t- 1) it is the electricity of accumulator t-1 period；η_ES1For accumulator charge efficiency；η_ES2For battery discharging efficiency；When △ T is unit Section, △ T=1 in the present invention；P_buyT () is the power purchase power of alternating current-direct current mixing microgrid t period, P_DT () is alternating current-direct current mixing microgrid The electrical load requirement of t period, P_LossT () is the active power loss of alternating current-direct current mixing microgrid t period；For interconnection capacity-constrained Under maximum power purchase power.

4) based on step 1) in communication area load, direct current region load, wind energy and solar power prediction data, adopt Improve black hole optimized algorithm to step 3 with coevolution) in alternating current-direct current mixing microgrid optimization operation mathematical model solve, Obtain exerting oneself and mixing microgrid purchase of electricity prioritization scheme of day part controllable type micro battery.Described is excellent to alternating current-direct current mixing microgrid Change operation mathematical model to carry out solving including:

(1) initialize: input micro battery, load parameter；Input algorithm parameter include celestial body scope, maximum optimizing number of times, The number of celestial body, the minimum range in newly generated distance of celestial bodies black hole and the dimension of celestial body；Interregional mutual of day part AC and DC Power；The mutual power calculation iterations pp=1 that AC and DC is interregional；Set the interregional mutual power of AC and DC to change Amount ε；

(2) each optimized variable of alternating current-direct current mixing microgrid is divided in communication area and direct current region according to position；

(3) mutual power is counted in each area power Constraints of Equilibrium, integrating step 3) described in constraints, provide merit Rate Constraints of Equilibrium is as follows:

$\left\{\begin{array}{c}{P}_{AC}\left(t\right)+P_{ES}^{AC}\left(t\right)=P_D^{AC}\left(t\right)+P_{Loss}^{AC}\left(t\right)+P_M\left(t\right)+u_0\&CenterDot;P_{Loss}^M\left(t\right)\\ P_{DC}\left(t\right)+P_{ES}^{DC}\left(t\right)+P_M\left(t\right)+(1-u_0)\&CenterDot;P_{Loss}^M\left(t\right)=P_D^{DC}\left(t\right)+P_{Loss}^{DC}\left(t\right)\end{array}\right.$

Wherein, each symbol is defined as follows: P_ACT () is the generated output output total amount of t period communication area micro battery, including Power purchase power；Output for t period communication area accumulator；For t period communication area load；For t period communication area network loss；P_MT () is the mutual power in t period AC and DC region；Cross, straight for the t period Flow the loss of interregional bi-directional inverter；u₀For the loss factor of bi-directional inverter, work as P_M(t) > 0 time, u₀=1, otherwise u₀= 0；P_DCT () is the generated output output total amount of t period direct current region micro battery；For t period direct current region accumulator Output；For t period direct current region load；For t period direct current region network loss；

(4) the iterations iter=1 of improvement black hole algorithm is set；

(5) in the p of region, N number of celestial body is randomly generated；

(6) calculate the self adaptation angle value of initial celestial body, select optimum self adaptation angle value to determine initial black hole；

(7) position of celestial body is updated；

(8) the self adaptation angle value of celestial body after calculating updates, if the self adaptation angle value of celestial body is better than the self adaptation in initial black hole Angle value, exchanges the position of described celestial body and initial black hole, and described celestial body is new black hole, and other celestial bodies move towards new black hole Updating, otherwise continue the position near initial black hole location updating celestial body, celestial body is towards the speed of black hole mobile update and inertia For:

$\left\{\begin{array}{c}{v_i}^t=\mathrm{\&omega;}{v_i}^{t-1}+r_1(x_{iP}-{x_i}^t)+r_2(x_{BH}-{x_i}^t)\\ \mathrm{\&omega;}={\mathrm{\&omega;}}_{max}-({\mathrm{\&omega;}}_{\max }-{\mathrm{\&omega;}}_{min})N_{iter}/N_{itermax}\end{array}\right.$

Wherein, each symbol is defined as follows: v_i ^tFor the speed of celestial body t, v_i ^t-1For the speed in celestial body t-1 moment, define t When=1, speed is 0；x_iPHistory adaptive optimal control value for i-th celestial body；x_i ^tFor i-th celestial body in the position of t；x_BHFor black The position in hole；r₁And r₂For the random number between [0,1]；ω is the inertia parameter that celestial body moves towards black hole, ω_min、ω_maxFor The lower limit of the inertia parameter that celestial body moves towards black hole and the upper limit, take ω_max=0.9, ω_min=0.4；N_iterBlack for current improvement The iterations of hole algorithm, N_itermaxFor improving the maximum iteration time of black hole algorithm；

The constraint of velocity that celestial body moves towards black hole is:

$\left\{\begin{array}{c}{v}_{idmin}\&le;v_{id}\&le;v_{idmax}\\ v_{id\max }=(x_{idmax}-x_{id\min })/L\\ v_{id\min }=-v_{id\max }\end{array}\right.$

Wherein, each symbol is defined as follows: v_idThe speed moved towards black hole for celestial body i, v_idminWith v_idmaxIt is respectively celestial body The minimum moved towards black hole and maximal rate；x_idmax、x_idminIt is respectively celestial body i and moves minimum once, maximum towards black hole Distance；It is fixed that definite value parameter L takes according to example, is used for limiting the amplitude of speed, in embodiments of the invention, L=5；

(9) judging whether celestial body meets phagocytosis condition, if meeting, celestial body is swallowed by black hole, and random in feas ible space Position regenerates new celestial body, and the ergodic only using the new celestial body of generating random number is not ideal enough, uses chaotic maps feasible In space, random site regenerates new celestial body, specifically uses Logistic to map and produces Chaos Variable, and mapping equation is as follows:

x_k+1=μ x_k(1-x_k)

Wherein, each symbol is defined as follows: x_k、x_k+1For celestial body position in kth, k+1 optimizing；μ is constant, takes 4 orders Map and enter chaos state；Described it is mapped with fixed point 0,0.25,0.50,0.75,1, if the initial position of celestial body is described Fixed point, then select the position of the new celestial body of common generating random number；

If not meeting phagocytosis condition, celestial body continues to the position near position, black hole mobile update celestial body；

(10) judging whether current iterations reaches maximum iteration time, if reaching, terminating to calculate, output calculates As a result, step (11) is entered；If being unsatisfactory for, setting the iterations iter=iter+1 improving black hole algorithm, returning (7th) Step；

(11) AC and DC subregion produces several optimum individuals respectively；

(12) individuality that sub-for AC and DC district submits to is combined with each other, obtains multiple the whole network unit output plan, due to possible Exist by the cooperation across sub-district complementary, therefore optimize as follows by the period:

min(C_fuel(t)+C_om(t)+C_buy(t)+C_w(t))

Wherein, each symbol is defined as follows: C_fuelT () is the fuel cost of t period, C_omT () is that the operation maintenance of t period becomes This, C_buyT () is the purchases strategies of t period, C_wT () is the pollution conversion cost of t period, constraints and step 3) consistent；Institute The adjustable unit of period t is only calculated by optimizing of stating, do not change the power of Unit Commitment state and energy storage, therefore this step Optimization problem variable and constraint are the most less.

(13) all 24 periods all carry out the optimization described in (12nd) step process, obtain complete period totle drilling cost；

(14) the complete period totle drilling cost obtained in (13rd) step is found out the combination that integrated cost is minimum, calculate day part The mutual power that AC and DC is interregional, as the calculating iterations pp=1 of the interregional mutual power of day part AC and DC, If the interregional mutual power of the day part AC and DC mutual power interregional relative to initial day part AC and DC changes Amount average less than given ε, then turns (15th) step；Calculating iterations when the interregional mutual power of day part AC and DC During pp ＞ 1, if if the interregional mutual power of day part AC and DC is interregional relative to last day part AC and DC Mutual power knots modification average less than given ε, then turns (15th) step；

Otherwise using combination minimum for integrated cost as new border transmission power value, it is assigned to alternating current and direct current region, calculates The mutual power in day part alternating current-direct current subinterval, formula is as follows:

$P_M\left(t\right)=P_{AC}\left(t\right)+P_{ES}^{AC}\left(t\right)-P_D^{AC}\left(t\right)-P_{Loss}^{AC}\left(t\right)-u_0\&CenterDot;P_{Loss}^M\left(t\right)$

Wherein, each symbol is defined as follows: P_MT () is the mutual power in t period AC and DC region；P_ACT () is to hand over the t period The generated output output total amount of stream region micro battery, including power purchase power；Output for t period communication area accumulator Power；For t period communication area load；For t period communication area network loss；For t period AC and DC The loss of interregional bi-directional inverter；u₀For the loss factor of bi-directional inverter, work as P_M(t) > 0 time, u₀=1, otherwise u₀=0； The mutual power calculation iterations pp=pp+1 that AC and DC is interregional, turns (3rd) step and continues to calculate；

(15) stop calculating, export result.

Embodiment is given below

Considering alternating current-direct current mixing microgrid as shown in Figure 2, microgrid works in normal state.In alternating current-direct current mixing microgrid There are miniature gas turbine (MT), fuel cell (FC), accumulator (ES), wind-power electricity generation (WT), photovoltaic generation (PV) in micro-source.Its In, wind-powered electricity generation only accesses communication area, and photovoltaic only accesses direct current region.Electric energy is sent into communication area by PCC by bulk power grid, limits Power purchase power upper limit is 100kW, and lower limit is 0.Additionally, communication area is equipped with miniature combustion engine 1, fuel cell totally 3, energy storage device 1.Direct current region is furnished with the miniature combustion engine 2 of same model, fuel cell 3, energy storage device 1.Energy storage device maximum electricity 150kWh, minimum amount of power 20kWh, maximum charge-discharge electric power is 40kW, and efficiency for charge-discharge all takes 92%.Miniature combustion engine and fuel cell Using fuelled with natural gas, Gas Prices takes 2.80 yuan/m³。

The parameter in each micro-source of table 1

Table 2 tou power price

The controlled micro-source pollutants emission factor of table 3

Table 4 pollutant evaluation criterion

(1) weather information data in alternating current-direct current mixing microgrid, information on load data, comprehensive alternating current-direct current mixing microgrid are gathered The data of following one day are predicted by the historical data run, and obtain 24 period alternating current-direct current mixing microgrids in following a day Wind-power electricity generation, photovoltaic generation, AC load and DC load prediction data.Embodiment area typical case's day wind-power electricity generation prediction song Line, photovoltaic generation prediction curve, AC load prediction curve and DC load prediction curve are as shown in Figure 3；

(2) controlled micro-source operation characteristic in statistics alternating current-direct current mixing microgrid, sets up the fuel cost-machine in all controlled micro-sources End power function, obtains the fuel cost-machine end power curve in controlled micro-source as shown in Figure 4；

(3) with one hour for optimizing the period, following for alternating current-direct current mixing microgrid intraday optimization operation is divided into 24 Period, fuel cost, operation expense, purchases strategies and Environmental costs with alternating current-direct current mixing microgrid 24 hour operation are minimum Object function, it is considered to the day part power-balance within alternating current-direct current mixing microgrid, generator unit power allowances, generator unit are the shortest Start-stop time, accumulator electric-quantity capacity and power capacity constraint, accumulator capacity seriality, grid-connected interconnection capacity limit are about Bundle condition, sets up alternating current-direct current mixing microgrid optimal operation model；

(4) coevolution is used to improve the mathematics that the alternating current-direct current mixing microgrid optimization in step (3) is run by black hole algorithm Model solves, and obtain day part each region controllable type micro battery exerts oneself the prioritization scheme with two interregional mutual power such as Shown in Fig. 5-8.

In the present embodiment, set the maximum iteration time 100 improving black hole algorithm, and continuous 10 generations evolve to stagnate and then recognize For convergence；Each region optimizing number of individuals 50；In coevolution framework, τ takes 13.

Result of calculation is totle drilling cost 4483.84 yuan.Knowable to Fig. 5-8, in alternating current-direct current mixing microgrid running, due to combustion Material cell power generation cost is relatively low, preferentially uses fuel cell power generation, when fuel cell can not meet energy supply demand, re-uses micro- Fuel engine power generation；Accumulator is charged when low electricity price, discharges when high electricity price；Microgrid preferentially uses outer net purchase when outer net electricity price is low Electricity, mainly uses internal generator unit, reduces the operating cost of alternating current-direct current mixing microgrid during outer net electricity price height.

In sum, by the test result of the present embodiment, illustrate that the one that the present invention proposes is improved based on coevolution The alternating current-direct current mixing microgrid optimizing operation method of black hole algorithm can effectively realize the optimization of alternating current-direct current mixing microgrid and run, and fully sends out Wave the economic benefit of microgrid, environmental benefit.

Claims (4)

1. the optimizing operation method of an alternating current-direct current mixing microgrid, it is characterised in that comprise the steps:

1) communication area and the information on load data in direct current region, the weather information data of alternating current-direct current mixing microgrid are gathered, comprehensively The data of following one day are predicted by the historical data that microgrid runs, the friendship of alternating current-direct current mixing microgrid in obtaining following a day Stream region load, direct current region load, wind energy and solar power prediction data；

2) the micro battery characteristic of alternating current-direct current mixing microgrid, the mathematical modulo of exerting oneself of all controllable type micro battery in setting up microgrid are added up Type；

3) following for alternating current-direct current mixing microgrid intraday economical operation is divided into 24 periods, transports with alternating current-direct current mixing microgrid whole day The minimum object function of row fuel cost, operation expense, purchases strategies and Environmental costs, it is considered to the day part within microgrid Electric energy balance constraint, grid-connected interconnection capacity-constrained, the restriction of exerting oneself in controlled micro-source, controlled micro-source the shortest start-stop time about Bundle, the charge capacity constraint of accumulator, the power capacity constraint of accumulator, the capacity continuity constraint of accumulator and calculating electric power storage The electric energy balance constraint at the whole story in cycle in pond, sets up alternating current-direct current mixing microgrid optimization and runs mathematical model；

4) based on step 1) in communication area load, direct current region load, wind energy and solar power prediction data, use association Improve black hole optimized algorithm with evolving to step 3) in alternating current-direct current mixing microgrid optimization operation mathematical model solve, obtain The exerting oneself and mix microgrid purchase of electricity prioritization scheme of day part controllable type micro battery.

The optimizing operation method of a kind of alternating current-direct current mixing microgrid the most according to claim 1, it is characterised in that step 2) institute The mathematical model of exerting oneself stated includes:

(1) model of exerting oneself of fuel cell:

$\left\{\begin{array}{c}{f}_{fuel,FC}=C_{gas,FC}\frac{1}{{\mathrm{LHV}}_{gas,FC}}\frac{P_{FC}}{{\mathrm{\&eta;}}_{FC}}\\ {\mathrm{\&eta;}}_{FC}=-0.0023P_{FC}+0.6735\end{array}\right.$

Wherein, each symbol is defined as follows: f_fuel,FCFor the fuel cost of fuel cell, C_gas,FCThe fuel used for fuel cell Price, LHV_gas,FCFor the low heat value of fuel, P_FCFor the size of exerting oneself of fuel cell, η_FCEfficiency for fuel cell.

(2) model of exerting oneself of miniature gas turbine:

$\left\{\begin{array}{c}{f}_{fuel,MT}=C_{gas}\frac{1}{LHV}\frac{P_{MT}}{{\mathrm{\&eta;}}_{MT}}\\ {\mathrm{\&eta;}}_{MT}=0.0753{\left(\frac{P_{MT}}{65}\right)}^3-0.3095{\left(\frac{P_{MT}}{65}\right)}^2+0.4174\left(\frac{P_{MT}}{65}\right)+0.1068\end{array}\right.$

Wherein, each symbol is defined as follows: f_fuel,MTFor the fuel cost of miniature gas turbine, C_gas,MTMake for miniature gas turbine Fuel price, LHV_gas,MTFor the low heat value of the fuel of miniature gas turbine, P_MTFor miniature gas turbine exert oneself big Little, η_MTFor miniature gas turbine efficiency.

The optimizing operation method of a kind of alternating current-direct current mixing microgrid the most according to claim 1, it is characterised in that step 3) institute State alternating current-direct current mixing microgrid optimization run mathematical model include object function and constraints, wherein, object function is:

$\left\{\begin{array}{c}\min F=C_{fuel}+C_{om}+C_{buy}+C_w\\ C_{fuel}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{f}^t\&lsqb;P_i\left(t\right)\&rsqb;\\ C_{om}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{k}_i{P}_i\left(t\right)\\ C_{buy}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}^t{P}_{buy}\left(t\right)\\ C_w=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}\&lsqb;{\mathrm{\&beta;}}_j\&CenterDot;{\mathrm{\&lambda;}}_{i,j}\&CenterDot;P_i\left(t\right)\&rsqb;\end{array}\right.$

Wherein, each symbol is defined as follows: F is object function, C_fuelFor fuel cost, C_omFor operation expense, C_buyFor power purchase Cost, C_wFor polluting conversion cost；T is the period, and N is the total number in micro-source, f^t() is the i-th micro-source fuel cost in the t period Function, P_iT () is the i-th micro-source real output in the t period, k_iFor the operation expense coefficient in the micro-source of i-th, α^t For the purchase electricity price during t period, P_buyT () is the mixing microgrid purchase of electricity when the t period, M is pollutant sums, β_jFor jth kind The environmental evaluation standard of pollutant, λ_i,jDischarge coefficient for the jth kind pollutant of i-th unit；

Constraints is:

(1) generator unit power allowances:

P_i ^min≤P_i(t)≤P_i ^max

(2) the shortest start-off time constraints:

$\left\{\begin{array}{c}{T}_{on}\&GreaterEqual;T_{on}^{\min }\\ T_{off}\&GreaterEqual;T_{off}^{\min }\end{array}\right.$

If when the iteration improving black hole algorithm tends to convergence, part of generating units does not meet the constraint of start-stop time, to the fullest The unit of foot start-off time constraints is adjusted as follows: if to start the time continuously too short for unit, then force to extend fortune The row time；If downtime is too short, then forces to change into running with lowest power downtime by violation, then adjust other units Exert oneself to meet other constraints of system；

(3) accumulator electric-quantity capacity and power capacity constraint:

$\left\{\begin{array}{c}{E}_{\min }\&le;E_{ES}\left(t\right)\&le;E_{max}\\ P_{ES,min}\&le;P_{ES}\left(t\right)\&le;P_{ES,max}\end{array}\right.$

(4) accumulator capacity continuity constraint:

$\left\{\begin{array}{c}E\left(t\right)=E(t-1)+P_{ES}\left(t\right){\mathrm{\&eta;}}_{ES1}\mathrm{\&Delta;}T,P_{ES}\left(t\right)<0\\ E\left(t\right)=E(t-1)+\frac{P_{ES}\left(t\right)}{{\mathrm{\&eta;}}_{ES2}}\mathrm{\&Delta;}T,P_{ES}\left(t\right)\&GreaterEqual;0\end{array}\right.$

(5) electric energy balance at the whole story in calculating accumulator cycle constraint:

E (0)=E (24) E (t)

(6) total system real-time electric energy balance constraint:

∑P_i(t)+P_buy(t)+P_ES(t)=P_D(t)+P_Loss(t)

(7) grid-connected interconnection capacity-constrained:

$0\&le;P_{buy}\left(t\right)\&le;P_{buy}^{\max }$

Wherein: P_i ^maxAnd P_i ^minRespectively export upper and lower bound, P_iT () is the i-th micro-source real output in the t period； T_onFor the time that continuously opens of controllable type micro battery, T_offFor the continuous downtime of controllable type micro battery, controllable type micro battery Continuously open the lower limit with downtime to be respectivelyWithE_EST () is the charge capacity of the accumulator of t period, E_min、 E_maxIt is respectively lower limit and the upper limit of accumulator electric-quantity capacity；P_EST () is the accumulator output of t period, to be output as pros To, P_ES,min、P_ES,maxIt is respectively lower limit and the upper limit of accumulator output；；E (t) is the electricity of accumulator t period, E (t-1) Electricity for the accumulator t-1 period；η_ES1For accumulator charge efficiency；η_ES2For battery discharging efficiency；△ T is the unit period, △ T=1 in the present invention；P_buyT () is the power purchase power of alternating current-direct current mixing microgrid t period, P_DWhen () is alternating current-direct current mixing microgrid t t The electrical load requirement of section, P_LossT () is the active power loss of alternating current-direct current mixing microgrid t period；For under interconnection capacity-constrained Maximum power purchase power.

The optimizing operation method of a kind of alternating current-direct current mixing microgrid the most according to claim 1, it is characterised in that step 4) institute State solve alternating current-direct current mixing microgrid optimization operation mathematical model includes:

(1) initialize: input micro battery, load parameter；Input algorithm parameter includes celestial body scope, maximum optimizing number of times, celestial body Number, the minimum range in newly generated distance of celestial bodies black hole and the dimension of celestial body；The mutual merit that day part AC and DC is interregional Rate；The mutual power calculation iterations pp=1 that AC and DC is interregional；Set the mutual power knots modification that AC and DC is interregional ε；

(2) each optimized variable of alternating current-direct current mixing microgrid is divided in communication area and direct current region according to position；

(3) mutual power is counted in each area power Constraints of Equilibrium, integrating step 3) described in constraints, be given power put down Weighing apparatus constraint is as follows:

$\left\{\begin{array}{c}{P}_{AC}\left(t\right)+P_{ES}^{AC}\left(t\right)=P_D^{AC}\left(t\right)+P_{Loss}^{AC}\left(t\right)+P_M\left(t\right)+u_0\&CenterDot;P_{Loss}^M\left(t\right)\\ P_{DC}\left(t\right)+P_{ES}^{DC}\left(t\right)+P_M\left(t\right)+(1-u_0)\&CenterDot;P_{Loss}^M\left(t\right)=P_D^{DC}\left(t\right)+P_{Loss}^{DC}\left(t\right)\end{array}\right.$

Wherein, each symbol is defined as follows: P_ACT () is the generated output output total amount of t period communication area micro battery, including power purchase Power；Output for t period communication area accumulator；For t period communication area load；For t Period communication area network loss；P_MT () is the mutual power in t period AC and DC region；Interregional for t period AC and DC The loss of bi-directional inverter；u₀For the loss factor of bi-directional inverter, work as P_M(t) > 0 time, u₀=1, otherwise u₀=0；P_DC(t) Generated output for t period direct current region micro battery exports total amount；Output work for t period direct current region accumulator Rate；For t period direct current region load；For t period direct current region network loss；

(4) the iterations iter=1 of improvement black hole algorithm is set；

(5) in the p of region, N number of celestial body is randomly generated；

(6) calculate the self adaptation angle value of initial celestial body, select optimum self adaptation angle value to determine initial black hole；

(7) position of celestial body is updated；

(8) the self adaptation angle value of celestial body after calculating updates, if the self adaptation angle value of celestial body is better than the self adaptation angle value in initial black hole, Exchanging the position of described celestial body and initial black hole, described celestial body is new black hole, other celestial bodies towards new black hole mobile update, Otherwise continuing the position near initial black hole location updating celestial body, celestial body towards speed and the inertia of black hole mobile update is:

$\left\{\begin{array}{c}{v_i}^t={{\mathrm{\&omega;v}}_i}^{t-1}+r_1(x_{iP}-{x_i}^t)+r_2(x_{BH}-{x_i}^t)\\ \mathrm{\&omega;}={\mathrm{\&omega;}}_{\max }-({\mathrm{\&omega;}}_{\max }-{\mathrm{\&omega;}}_{min})N_{iter}/N_{itermax}\end{array}\right.$

Wherein, each symbol is defined as follows: v_i ^tFor the speed of celestial body t, v_i ^t-1For the speed in celestial body t-1 moment, define t=1 Shi Sudu is 0；x_iPHistory adaptive optimal control value for i-th celestial body；x_i ^tFor i-th celestial body in the position of t；x_BHFor black hole Position；r₁And r₂For the random number between [0,1]；ω is the inertia parameter that celestial body moves towards black hole, ω_min、ω_maxFor star The lower limit of the inertia parameter that body moves towards black hole and the upper limit, take ω_max=0.9, ω_min=0.4；N_iterFor currently improving black hole The iterations of algorithm, N_itermaxFor improving the maximum iteration time of black hole algorithm；

The constraint of velocity that celestial body moves towards black hole is:

$\left\{\begin{array}{c}{v}_{idmin}\&le;v_{id}\&le;v_{idmax}\\ v_{id\max }=(x_{idmax}-x_{idmin})/L\\ v_{id\min }=-v_{id\max }\end{array}\right.$

Wherein, each symbol is defined as follows: v_idThe speed moved towards black hole for celestial body i, v_idminWith v_idmaxBe respectively celestial body towards Minimum that black hole is moved and maximal rate；x_idmax、x_idminBe respectively celestial body i move towards black hole minimum once, maximum away from From；It is fixed that definite value parameter L takes according to example, is used for limiting the amplitude of speed；

(9) judging whether celestial body meets phagocytosis condition, if meeting, celestial body is swallowed by black hole, and random site in feas ible space Regenerating new celestial body, in feas ible space, random site regenerates new celestial body, specifically uses Logistic to map and produces Chaos Variable, mapping equation is as follows:

x_k+1=μ x_k(1-x_k)

Wherein, each symbol is defined as follows: x_k、x_k+1For celestial body position in kth, k+1 optimizing；μ is constant, takes 4 orders and maps Enter chaos state；Described it is mapped with fixed point 0,0.25,0.50,0.75,1, if the initial position of celestial body is described motionless Point, then select the position of the new celestial body of common generating random number；

If not meeting phagocytosis condition, celestial body continues to the position near position, black hole mobile update celestial body；

(10) judge whether current iterations reaches maximum iteration time, if reaching, terminating to calculate, exporting result of calculation, Enter step (11)；If being unsatisfactory for, setting the iterations iter=iter+1 improving black hole algorithm, returning (7th) step；

(11) AC and DC subregion produces several optimum individuals respectively；

(12) individuality that sub-for AC and DC district submits to is combined with each other, obtains multiple the whole network unit output plan, owing to there may be Complementary by the cooperation across sub-district, therefore optimize as follows by the period:

min(C_fuel(t)+C_om(t)+C_buy(t)+C_w(t))

Wherein, each symbol is defined as follows: C_fuelT () is the fuel cost of t period, C_omT () is the operation expense of t period, C_buyT () is the purchases strategies of t period, C_wT () is the pollution conversion cost of t period, constraints and step 3) consistent；

(13) all 24 periods all carry out the optimization described in (12nd) step process, obtain complete period totle drilling cost；

(14) the complete period totle drilling cost obtained in (13rd) step is found out the combination that integrated cost is minimum, calculating day part friendship, The mutual power that direct current is interregional, as the calculating iterations pp=1 of the interregional mutual power of day part AC and DC, if The mutual power knots modification that the interregional mutual power of day part AC and DC is interregional relative to initial day part AC and DC Average less than given ε, then turns (15th) step；Calculating iterations pp when the interregional mutual power of day part AC and DC During ＞ 1, if if the interregional mutual power of the day part AC and DC friendship interregional relative to last day part AC and DC Cross-power knots modification average less than given ε, then turns (15th) step；

Otherwise using combination minimum for integrated cost as new border transmission power value, it is assigned to alternating current and direct current region, when calculating each The mutual power in section alternating current-direct current subinterval, formula is as follows:

$P_M\left(t\right)=P_{AC}\left(t\right)+P_{ES}^{AC}\left(t\right)-P_D^{AC}\left(t\right)-P_{Loss}^{AC}\left(t\right)-u_0\&CenterDot;P_{Loss}^M\left(t\right)$

Wherein, each symbol is defined as follows: P_MT () is the mutual power in t period AC and DC region；P_ACT () is t period exchanging area The generated output output total amount of territory micro battery, including power purchase power；Output work for t period communication area accumulator Rate；For t period communication area load；For t period communication area network loss；For t period AC and DC district The loss of the bi-directional inverter between territory；u₀For the loss factor of bi-directional inverter, work as P_M(t) > 0 time, u₀=1, otherwise u₀=0； The mutual power calculation iterations pp=pp+1 that AC and DC is interregional, turns (3rd) step and continues to calculate；

(15) stop calculating, export result.