CN105610202A - Multi-agent system-based active power control method for autonomous AC/DC micro-grid - Google Patents
Multi-agent system-based active power control method for autonomous AC/DC micro-grid Download PDFInfo
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Abstract
The invention provides a multi-agent system-based active power control method for an autonomous AC/DC micro-grid. The method comprises the following steps: (1) building an agent model, building a multi-agent system for the autonomous AC/DC micro-grid, enabling various entity agents to communicate with one another and obtaining power values of various agents in the next period of time; (2) judging whether power generation of renewable power supplies is sufficient or not in the micro-grid in the next period of time; (3) if the power generation of the renewable power supplies is not sufficient, carrying out economic dispatching conventional power smoothing short-term power balance; and (4) if the power generation of renewable power supplies is sufficient, calculating the power generation utilization rates of the renewable power supplies by a subgradient optimization algorithm. The active power control method solves the problem of convex optimization in coordinated control of the autonomous micro-grid and the problem of coordinated operation of distributed power supplies in the autonomous micro-grid, and improves the stability and the reliability of the micro-grid in autonomous operation.
Description
Technical field
The present invention relates to a kind of micro-electric network active control method, be specifically related to the micro-electric network active control method of a kind of autonomous alternating current-direct current based on multi-agent system.
Background technology
The micro-electric power network technique of alternating current-direct current is the emerging technology of reply high permeability distributed power source access, is the conventional development and progress that exchanges micro-electric power network technique. The micro-electrical network of alternating current-direct current has been taken into account the balance of DC load and DC characteristic power supply on the micro-electrical network of conventional AC basis, has reduced the loss in energy sources conversion, has better development prospect under the overall background of low-carbon economy.
The advantage that can give full play to local regenerative resource containing the micro-electrical network of alternating current-direct current of high permeability distributed power source in the time that autonomy moves, improves power supply capacity on the spot. When micro-electrical network is during in autonomous running status, must meet the active power equilibrium of supply and demand in micro-electrical network, although and the maximal power tracing control of distributed power source can ensure its generated output maximum. In the time that total maximum generation power of renewable distributed power source is greater than micro-electrical network demand power, needs normal power supplies to exert oneself and realize micro-electrical network alternating current-direct current side power equilibrium of supply and demand. In the time that total maximum generation power of renewable distributed power source is greater than micro-electrical network demand power, if adjust not in time the power output of distributed power source, can cause the power unbalanced supply-demand in micro-electrical network. Now, should reduce the generated energy of renewable distributed power source, to meet micro-electrical network internal power equilibrium of supply and demand. In the control of the micro-electrical network decentralized coordinating of autonomy, renewable distributed power source output characteristics can change along with external environmental condition, make its power output have intermittence and randomness, the object function that causes micro electric network coordination optimal control is a non-convex function that can be micro-.
Summary of the invention
In order to overcome above-mentioned the deficiencies in the prior art, the invention provides the micro-electric network active control method of a kind of autonomous alternating current-direct current based on multi-agent system, the invention solves the coordinated operation problem of each distributed power source in protruding optimization problem in autonomous micro electric network coordination control and autonomous micro-electrical network, stability and reliability while improving the autonomous operation of micro-electrical network.
In order to realize foregoing invention object, the present invention takes following technical scheme:
The micro-electric network active control method of autonomous alternating current-direct current based on multi-agent system, described method comprises the steps:
(1) set up agent model, build the micro-electrical network multi-agent system of autonomous alternating current-direct current, each entity agency intercommunication mutually, obtains next period performance number of each agency;
(2) judge next period, in micro-electrical network, whether renewable power supply generating is sufficient;
(3) if the generating of described renewable power supply is inadequate, carry out the level and smooth short-time rating difference of economic load dispatching normal power supplies;
(4) if the generating of described renewable power supply is sufficient, utilize Subgradient optimization algorithm to calculate each renewable power supply capacity factor.
Preferably, described step (1) comprises the steps:
Step 1-1, according to the functional requirement of distributed power source in microgrid, adopt two coating control methods bottom distributed power source agent controller is carried out to modeling, its at the middle and upper levels for optimize coordinate key-course, lower floor is generator unit key-course; Described optimization is coordinated key-course and is comprised communication module, power measurement and prediction module and generating computing module, and described generator unit key-course comprises Generation Control module;
Step 1-2, according to the functional requirement of loading in microgrid, the modeling of bottom load agent controller adopts two coating control methods of load computation layer and load key-course; Upper strata is load computation layer, and lower floor is load key-course; Described load computation layer comprises communication module, load measurement and prediction module and load computing module; Described load key-course comprises load control module;
Step 1-3, according to the micro-electrical network framework of autonomy, build autonomous micro-electrical network multi-agent system model;
Step 1-4, according to environmental aspect and the Weather information of the micro-electrical network of autonomous alternating current-direct current, obtain next period each renewable power supply peak power output predicted valueWith always meritorious demand P of the micro-electrical network of autonomous alternating current-direct current of next periodD, wherein i=1,2 ..., n, n is the total number of micro-electrical network renewable power supply.
Preferably, in described step (2), judge that the standard of next period is:
A, whenTime, in micro-electrical network, renewable power supply always has work output to be less than always meritorious demand of micro-electrical network, represents renewable power supply generation deficiency in micro-electrical network;
B, whenTime, in micro-electrical network, renewable power supply always has work output to equal always meritorious demand of micro-electrical network, represents to meet the equilibrium of supply and demand in micro-electrical network;
C, whenTime, in micro-electrical network, renewable power supply always has work output to be greater than always meritorious demand of micro-electrical network, represents that in micro-electrical network, renewable power supply generating is sufficient.
Preferably, described step (3) comprises the steps:
Step 3-1, adopt scheduling model a few days ago, set up micro-grid generation cost objective function:
In formula, F1For the generating expense of normal power supplies, F2For the pollution treatment expense of micro-electrical network, T is micro-operation of power networks cycle,For normal power supplies i cost of electricity-generating, Pi(t) be normal power supplies i at the generated output in t moment, αiFor the pollution treatment cost of normal power supplies i unit generated output, wherein i=1,2 ..., m, m is the total number of micro-electrical network normal power supplies;
Step 3-2, the power output constraints that normal power supplies is set, battery SOC constraints, battery maximum discharge and recharge power constraint and micro-grid power equilibrium constraint;
The power output constraints of described normal power supplies is the constant interval that limits each normal power supplies power output in micro-electrical network, that is:
In formula, footmark " max ", " min " represent respectively maximum permissible value and the minimum permissible value of this variable,WithRepresent respectively normal power supplies i power output bound;
Described battery SOC constraints is the constant interval that limits battery SOC in micro-electrical network, that is:
In formula,WithRepresent respectively the SOC bound of energy-storage units i;
Battery maximum charge power constraint:
The maximum discharge power constraint of battery:
In formula, PBi.c.maxAnd P (t)Bi.d.max(t) maximum that represents respectively t moment battery i discharges and recharges power, and charge in batteries is got on the occasion of electric discharge and got negative value, PniThe rated power of battery i, ηciAnd ηdiBe respectively the efficiency for charge-discharge of battery i, δiAnd ECiBe respectively self-discharge rate and the rated capacity of battery i;
Described micro-grid power equilibrium constraint is that in the micro-electrical network interchange subnet of requirement and direct current subnet, each power supply always has work output to equal always meritorious demand of micro-electrical network, that is:
In formula, Pi(t) be t moment normal power supplies i generated output,For the t moment exchanges subnet renewable power supply j maximum generation power,For t moment direct current subnet renewable power supply j maximum generation power, PD_AC(t) for the t moment exchanges the meritorious demand of subnet, PD_DC(t) be the meritorious demand of t moment direct current subnet;
Step 3-3, adopt particle cluster algorithm to carry out micro-grid generation cost objective function and solve, obtain in micro-electrical network the distributed power source optimum scheme of exerting oneself.
Preferably, described step 3-3 comprises the steps:
Step 3-3-1, a particle population of initialization: the position and the speed that produce at random in allowed limits n particle, calculate the fitness of each particle as local optimum fitness, the relatively local optimum fitness of n particle, select wherein the optimum global optimum's fitness of charging to, this particle is designated as global optimum's vector;
Step 3-3-2, renewal weight factor w and study factor c1、c2：
In formula, wmax、wminFor the minimum and maximum value of the inertia weight factor, get wmax＝0.9，wmin=0.4; F is current fitness value, favgAnd fminBe respectively average fitness value and the fitness minimum of a value of current all particles;
In formula, Iter represents the now number of times of iteration, ItermaxRepresent total iterations, c1fAnd c1iC1End value and initial value, c2fAnd c2iC2End value and initial value, get c1i＝c2f＝2.5，c1f＝c2i＝0.5；
Step 3-3-3, object function is calculated, obtain the fitness value of each particle;
Step 3-3-4, renewal local optimum fitness, upgrade body local optimum vector;
Step 3-3-5, renewal global optimum fitness, upgrade global optimum's vector;
Step 3-3-6, the position of upgrading each particle and speed;
In formula, i=1,2 ..., n, the scale that n is population, d is the dimension that constant represents particle,Represent the speed of the d dimension of particle i particle in the k time iteration;Represent the position of the d dimension of particle i particle in the k time iteration; ω represents inertia weight; c1、c2Represent the study factor,Represent the individual extreme value of particle i d dimension in the k time iteration;Represent the global extremum of whole population d dimension in the k time iteration;For (0,1) the interval random number distributing;
If step 3-3-7 iterations arrives maximum, stop search, Output rusults; Otherwise return to step 3-3-2 and continue iterative computation.
Preferably, described step (4) comprises the steps:
Step 4-1, define the object function H (u of autonomous micro electric network coordination operationi(k)) be supply and demand difference minimum:
In formula, ui(k) be distributed power source i capacity factor;
Renewable power supply i capacity factor ui(k+1) iterative computation formula:
In formula, aij(t) be that the k time iteration power supply i acts on behalf of the right of correspondence coefficient between power supply j agency, di(k) calculate the iteration step length of the k time iteration for subgradient, si(k) be object function H (ui(k)) at ui(k) the deflection subgradient of locating, ui(k+1) be the k+1 time iteration renewable power supply i capacity factor;
Step 4-2, the k time iteration renewable power supply i agency of calculating and renewable power supply j agent communication weight coefficient aij(t)：
In formula, ni(k) be the renewable power supply agency sum of the k time iteration and renewable distributed power source i agent communication;
Step 4-3, the k time iteration deflection subgradient s of calculatingi(k)：
In formula, δkFor deflection factor,For object function H (ui(k)) at ui(k) subgradient of locating;
At a ui(k) iteration direction si(k) be a linear combination of current subgradient and last iteration direction;
The iteration step length d of step 4-4, the k time iteration of calculatingi(k)：
In formula, r is constant;
Step 4-5, calculate each power supply reference output power of next periodEach agency issues generating information, completes meritorious coordination and controls;
Subgradient optimization algorithm iteration stop condition:
Calculate the reference output power of each renewable distributed power source
Step 4-6, each power supply agency issue generating information to each power supply, and each power supply, by each power supply proxy information generating, completes autonomous micro-electric network active and coordinates to control.
Compared with prior art, beneficial effect of the present invention is:
The present invention is in order to accelerate optimizing ability and convergence rate, adopt adaptive weighting coefficient, improve part and global optimizing ability, adopt the method for the dynamic regularized learning algorithm factor, add strong algorithms extensive search to global scope in the time that optimizing starts, and in the time that optimizing soon finishes the precise search to subrange. to the non-smooth object function in supply and demand difference least model, adopt Subgradient optimization algorithm to solve the protruding optimization problem of autonomous micro electric network coordination control, on traditional Subgradient Algorithm, coordinate to control in conjunction with autonomous micro-electric network active, capacity factor and the weight coefficient of communicating by letter are added, and determine adaptive iteration step-length according to the micro-grid power difference of the autonomy after each iteration, power difference relation in direct ratio after Subgradient optimization algorithm iteration step-length and each Subgradient optimization algorithm iteration, improve the direction of search and the step-size in search of each Subgradient optimization algorithm iteration, improve Subgradient optimization algorithm the convergence speed, thereby solve the protruding optimization problem in autonomous micro electric network coordination control, and solved the coordinated operation problem of each distributed power source in autonomous micro-electrical network, stability and reliability while improving the autonomous operation of micro-electrical network.
Brief description of the drawings
Fig. 1 is the flow chart of a kind of micro-electric network active control method of autonomous alternating current-direct current based on multi-agent system provided by the invention,
Fig. 2 is provided by the invention based on particle swarm optimization algorithm iterative process figure,
Fig. 3 is provided by the invention based on adaptive step Subgradient optimization algorithm iteration flow chart.
Detailed description of the invention
Below in conjunction with accompanying drawing, the present invention is described in further detail.
As shown in Figure 1, the micro-electric network active control method of a kind of autonomous alternating current-direct current based on multi-agent system provided by the invention, concrete steps are as follows:
Step 1, set up agent model, build the micro-electrical network multi-agent system of autonomous alternating current-direct current, each entity agency (Agent) intercoms mutually, obtains next period performance number of each agency;
According to the functional requirement of distributed power source in microgrid, adopt two coating control methods to carry out modeling to bottom distributed power source agent controller, it coordinates key-course for optimizing at the middle and upper levels, and lower floor is generator unit key-course; Described optimization is coordinated key-course and is comprised communication module, power measurement and prediction module, generating computing module, and described generator unit key-course comprises Generation Control module. According to the functional requirement of loading in microgrid, the modeling of bottom load agent controller adopts two coating control methods of load computation layer and load key-course; Upper strata is load computation layer, and lower floor is load key-course; Described load computation layer comprises communication module, load measurement and prediction module, load computing module; Described load key-course comprises load control module. According to the micro-electrical network framework of autonomy, build autonomous micro-electrical network multi-agent system model.
According to environmental aspect and the Weather information of the micro-electrical network of autonomous alternating current-direct current, obtain next period each renewable power supply peak power output predicted valueWith always meritorious demand P of the micro-electrical network of autonomous alternating current-direct current of next periodD, wherein i=1,2 ..., n, n is the total number of micro-electrical network renewable power supply;
Step 2, judge next period, in micro-electrical network, whether renewable power supply generating is sufficient;
WhenTime, in micro-electrical network, renewable power supply always has work output to be less than always meritorious demand of micro-electrical network, renewable power supply generation deficiency in micro-electrical network.
WhenTime, in micro-electrical network, renewable power supply always has work output to equal always meritorious demand of micro-electrical network, in micro-electrical network, meets the equilibrium of supply and demand.
WhenTime, in micro-electrical network, renewable power supply always has work output to be greater than always meritorious demand of micro-electrical network, and in micro-electrical network, renewable power supply generating is sufficient.
If the generating of step 3 renewable power supply is inadequate, the level and smooth short-time rating difference of economic load dispatching normal power supplies;
1. adopt scheduling model a few days ago, set up micro-grid generation cost objective function:
In formula (1), F1For the generating expense of normal power supplies, F2For the pollution treatment expense of micro-electrical network, T is micro-operation of power networks cycle,For normal power supplies i cost of electricity-generating, Pi(t) be normal power supplies i at the generated output in t moment, αiFor the pollution treatment cost of normal power supplies i unit generated output, wherein i=1,2 ..., m, m is the total number of micro-electrical network normal power supplies.
Power output constraints, battery SOC constraints, battery maximum that 2. normal power supplies is set discharge and recharge power constraint and micro-grid power equilibrium constraint;
The power output constraints of described normal power supplies is the constant interval that limits each normal power supplies power output in micro-electrical network, that is:
In formula (2), footmark " max ", " min " represent respectively maximum permissible value and the minimum permissible value of this variable,WithRepresent respectively normal power supplies i power output bound.
Described battery SOC constraints is the constant interval that limits battery SOC in micro-electrical network, that is:
In formula (3),WithRepresent respectively the SOC bound of energy-storage units i.
Described battery maximum charge power constraint:
The maximum discharge power constraint of described battery:
In formula (4), (5), PBi.c.maxAnd P (t)Bi.d.max(t) maximum that represents respectively t moment battery i discharges and recharges power, and charge in batteries is got on the occasion of electric discharge and got negative value, Pni---the rated power of battery i, ηciAnd ηdiBe respectively the efficiency for charge-discharge of battery i, δiAnd ECiBe respectively self-discharge rate and the rated capacity of battery i.
Described micro-grid power equilibrium constraint is that in the micro-electrical network interchange subnet of requirement and direct current subnet, each power supply always has work output to equal always meritorious demand of micro-electrical network, that is:
In formula (6), (7), Pi(t) be t moment normal power supplies i generated output,For the t moment exchanges subnet renewable power supply j maximum generation power,For the t moment exchanges subnet renewable power supply j maximum generation power, PD_AC(t) for the t moment exchanges the meritorious demand of subnet, PD_DC(t) be the meritorious demand of t moment direct current subnet.
3. adopt particle cluster algorithm to carry out micro-grid generation cost objective function and solve, obtain in micro-electrical network the distributed power source optimum scheme of exerting oneself, as shown in Figure 2, concrete steps are as follows:
1) initialize a particle population: the position and the speed that produce at random in allowed limits n particle, calculate the fitness of each particle as local optimum fitness, the relatively local optimum fitness of n particle, select wherein the optimum global optimum's fitness of charging to, this particle is designated as global optimum's vector;
2) upgrade weight factor w and study factor c1、c2：
In formula (8), wmax、wminFor the minimum and maximum value of the inertia weight factor, get wmax＝0.9，wmin=0.4; F is current fitness value, favgAnd fminBe respectively average fitness value and the fitness minimum of a value of current all particles.
In formula (9), Iter represents the now number of times of iteration, ItermaxRepresent total iterations, c1fAnd c1iC1End value and initial value, c2fAnd c2iC2End value and initial value, get c1i＝c2f＝2.5，c1f＝c2i＝0.5。
3) object function is calculated, obtain the fitness value of each particle;
4) upgrade local optimum fitness, upgrade body local optimum vector;
5) upgrade global optimum's fitness, upgrade global optimum's vector;
6) upgrade position and the speed of each particle;
In formula (10), i=1,2 ..., n, the scale that n is population, d is the dimension that constant represents particle,Represent the speed of the d dimension of particle i particle in the k time iteration;Represent the position of the d dimension of particle i particle in the k time iteration; ω represents inertia weight; c1、c2Represent the study factor,Represent particlei?kThe individual extreme value of d dimension in inferior iteration;Represent that whole population is in the k time iterationdThe global extremum of dimension;For (0,1) the interval random number distributing.
7) if iterations arrives maximum, stop search, Output rusults. Otherwise return to step 2 and continue iterative computation.
If the generating of step 4 renewable power supply is sufficient, utilize Subgradient optimization algorithm to calculate each renewable power supply capacity factor;
As shown in Figure 3, for based on adaptive step Subgradient optimization algorithm iteration method, step is as follows:
Be greater than micro-electrical network always when meritorious demand when renewable power supply always has work output, utilize Subgradient optimization algorithm to carry out renewable power supply output distribution, define the object function H (u of autonomous micro electric network coordination operationi(k)) be supply and demand difference minimum:
In formula (11), ui(k) be distributed power source i capacity factor.
1. renewable power supply i capacity factor ui(k+1) iterative computation formula:
In formula (12), aij(t) be that the k time iteration power supply i acts on behalf of the right of correspondence coefficient between power supply j agency, di(k) calculate the iteration step length of the k time iteration for subgradient, si(k) be object function H (ui(k)) at ui(k) the deflection subgradient of locating, ui(k+1) be the k+1 time iteration renewable power supply i capacity factor.
2. calculating the k time iteration renewable power supply i acts on behalf of and renewable power supply j agent communication weight coefficient aij(t)：
In formula (13), ni(k) be the renewable power supply agency sum of the k time iteration and renewable distributed power source i agent communication.
3. calculate iteration deflection subgradient s the k timei(k)：
In formula (17), δkFor deflection factor,For object function H (ui(k)) at ui(k) subgradient of locating.
At a ui(k) iteration direction si(k) be a linear combination of current subgradient and last iteration direction.
4. calculate the iteration step length d of the k time iterationi(k)：
In formula (16), r is constant.
5. calculate each power supply reference output power of next periodEach agency issues generating information, completes meritorious coordination and controls.
Subgradient optimization algorithm iteration stop condition:
Calculate the reference output power of each renewable distributed power source
Now, each power supply agency issues generating information to each power supply, and each power supply, by each power supply proxy information generating, completes autonomous micro-electric network active and coordinates to control.
Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.
Claims (6)
1. the micro-electric network active control method of the autonomous alternating current-direct current based on multi-agent system, is characterized in that, described method comprises the steps:
(1) set up agent model, build the micro-electrical network multi-agent system of autonomous alternating current-direct current, each entity agency intercommunication mutually, obtains next period performance number of each agency;
(2) judge next period, in micro-electrical network, whether renewable power supply generating is sufficient;
(3) if the generating of described renewable power supply is inadequate, carry out the level and smooth short-time rating difference of economic load dispatching normal power supplies;
(4) if the generating of described renewable power supply is sufficient, utilize Subgradient optimization algorithm to calculate each renewable power supply capacity factor.
2. control method according to claim 1, is characterized in that, described step (1) comprises the steps:
Step 1-1, according to the functional requirement of distributed power source in microgrid, adopt two coating control methods bottom distributed power source agent controller is carried out to modeling, its at the middle and upper levels for optimize coordinate key-course, lower floor is generator unit key-course; Described optimization is coordinated key-course and is comprised communication module, power measurement and prediction module and generating computing module, and described generator unit key-course comprises Generation Control module;
Step 1-2, according to the functional requirement of loading in microgrid, the modeling of bottom load agent controller adopts two coating control methods of load computation layer and load key-course; Upper strata is load computation layer, and lower floor is load key-course; Described load computation layer comprises communication module, load measurement and prediction module and load computing module; Described load key-course comprises load control module;
Step 1-3, according to the micro-electrical network framework of autonomy, build autonomous micro-electrical network multi-agent system model;
Step 1-4, according to environmental aspect and the Weather information of the micro-electrical network of autonomous alternating current-direct current, obtain next period each renewable power supply peak power output predicted valueWith always meritorious demand P of the micro-electrical network of autonomous alternating current-direct current of next periodD, wherein i=1,2 ..., n, n is the total number of micro-electrical network renewable power supply.
3. control method according to claim 2, is characterized in that, in described step (2), judges that the standard of next period is:
A, whenTime, in micro-electrical network, renewable power supply always has work output to be less than always meritorious demand of micro-electrical network, represents renewable power supply generation deficiency in micro-electrical network;
B, whenTime, in micro-electrical network, renewable power supply always has work output to equal always meritorious demand of micro-electrical network, represents to meet the equilibrium of supply and demand in micro-electrical network;
C, whenTime, in micro-electrical network, renewable power supply always has work output to be greater than always meritorious demand of micro-electrical network, represents that in micro-electrical network, renewable power supply generating is sufficient.
4. control method according to claim 1, is characterized in that, described step (3) comprises the steps:
Step 3-1, adopt scheduling model a few days ago, set up micro-grid generation cost objective function:
In formula, F1For the generating expense of normal power supplies, F2For the pollution treatment expense of micro-electrical network, T is micro-operation of power networks cycle,For normal power supplies i cost of electricity-generating, Pi(t) be normal power supplies i at the generated output in t moment, αiFor the pollution treatment cost of normal power supplies i unit generated output, wherein i=1,2 ..., m, m is the total number of micro-electrical network normal power supplies;
Step 3-2, the power output constraints that normal power supplies is set, battery SOC constraints, battery maximum discharge and recharge power constraint and micro-grid power equilibrium constraint;
The power output constraints of described normal power supplies is the constant interval that limits each normal power supplies power output in micro-electrical network, that is:
In formula, footmark " max ", " min " represent respectively maximum permissible value and the minimum permissible value of this variable,WithRepresent respectively normal power supplies i power output bound;
Described battery SOC constraints is the constant interval that limits battery SOC in micro-electrical network, that is:
In formula,WithRepresent respectively the SOC bound of energy-storage units i;
Battery maximum charge power constraint:
The maximum discharge power constraint of battery:
In formula, PBi.c.maxAnd P (t)Bi.d.max(t) maximum that represents respectively t moment battery i discharges and recharges power, and charge in batteries is got on the occasion of electric discharge and got negative value, PniThe rated power of battery i, ηciAnd ηdiBe respectively the efficiency for charge-discharge of battery i, δiAnd ECiBe respectively self-discharge rate and the rated capacity of battery i;
Described micro-grid power equilibrium constraint is that in the micro-electrical network interchange subnet of requirement and direct current subnet, each power supply always has work output to equal always meritorious demand of micro-electrical network, that is:
In formula, Pi(t) be t moment normal power supplies i generated output,For the t moment exchanges subnet renewable power supply j maximum generation power,For t moment direct current subnet renewable power supply j maximum generation power, PD_AC(t) for the t moment exchanges the meritorious demand of subnet, PD_DC(t) be the meritorious demand of t moment direct current subnet;
Step 3-3, adopt particle cluster algorithm to carry out micro-grid generation cost objective function and solve, obtain in micro-electrical network the distributed power source optimum scheme of exerting oneself.
5. control method according to claim 4, is characterized in that, described step 3-3 comprises the steps:
Step 3-3-1, a particle population of initialization: the position and the speed that produce at random in allowed limits n particle, calculate the fitness of each particle as local optimum fitness, the relatively local optimum fitness of n particle, select wherein the optimum global optimum's fitness of charging to, this particle is designated as global optimum's vector;
Step 3-3-2, renewal weight factor w and study factor c1、c2：
In formula, wmax、wminFor the minimum and maximum value of the inertia weight factor, get wmax＝0.9，wmin=0.4; F is current fitness value, favgAnd fminBe respectively average fitness value and the fitness minimum of a value of current all particles;
In formula, Iter represents the now number of times of iteration, ItermaxRepresent total iterations, c1fAnd c1iC1End value and initial value, c2fAnd c2iC2End value and initial value, get c1i＝c2f＝2.5，c1f＝c2i＝0.5；
Step 3-3-3, object function is calculated, obtain the fitness value of each particle;
Step 3-3-4, renewal local optimum fitness and optimal vector;
Step 3-3-5, renewal global optimum's fitness and optimal vector;
Step 3-3-6, the position of upgrading each particle and speed;
In formula, i=1,2 ..., n, the scale that n is population, d is the dimension that constant represents particle,Represent the speed of the d dimension of particle i particle in the k time iteration;Represent the position of the d dimension of particle i particle in the k time iteration; ω represents inertia weight; c1、c2Represent the study factor,Represent the individual extreme value of particle i d dimension in the k time iteration;Represent the global extremum of whole population d dimension in the k time iteration;For (0,1) the interval random number distributing;
If step 3-3-7 iterations arrives maximum, stop search, Output rusults; Otherwise return to step 3-3-2 and continue iterative computation.
6. control method according to claim 2, is characterized in that, described step (4) comprises the steps:
Step 4-1, define the object function H (u of autonomous micro electric network coordination operationi(k)) be supply and demand difference minimum:
In formula, ui(k) be distributed power source i capacity factor;
Renewable power supply i capacity factor ui(k+1) iterative computation formula:
In formula, aij(t) be that the k time iteration power supply i acts on behalf of the right of correspondence coefficient between power supply j agency, di(k) calculate the iteration step length of the k time iteration for subgradient, si(k) be object function H (ui(k)) at ui(k) the deflection subgradient of locating, ui(k+1) be the k+1 time iteration renewable power supply i capacity factor;
Step 4-2, the k time iteration renewable power supply i agency of calculating and renewable power supply j agent communication weight coefficient aij(t)：
In formula, ni(k) be the renewable power supply agency sum of the k time iteration and renewable distributed power source i agent communication;
Step 4-3, the k time iteration deflection subgradient s of calculatingi(k)：
In formula, δkFor deflection factor,For object function H (ui(k)) at ui(k) subgradient of locating;
At a ui(k) iteration direction si(k) be a linear combination of current subgradient and last iteration direction;
The iteration step length d of step 4-4, the k time iteration of calculatingi(k)：
In formula, r is constant;
Step 4-5, calculate each power supply reference output power of next periodEach agency issues generating information, completes meritorious coordination and controls;
Subgradient optimization algorithm iteration stop condition:
Calculate the reference output power of each renewable distributed power source
Step 4-6, each power supply agency issue generating information to each power supply, and each power supply, by each power supply proxy information generating, completes autonomous micro-electric network active and coordinates to control.
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