CN108599235B - Constant volume method for distributed photovoltaic network access - Google Patents
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Abstract
A constant volume method for distributed photovoltaic network access is characterized in that a model takes the maximum net loss gain of photovoltaic access, the maximum electricity purchasing gain from a main net after photovoltaic access and the minimum distributed photovoltaic input cost as objective functions, constraint conditions comprise node voltage constraint, branch transmission power constraint, node photovoltaic power constraint and main net electricity purchasing power constraint, load flow calculation is carried out on a combined system operation model, the objective functions are optimized, and the optimal network access power of each node can be obtained. The method provided by the invention not only avoids the defects of poor economic benefit and low stable contribution rate of the power system caused by small photovoltaic capacity, but also avoids the problems of overlarge photovoltaic power generation input capacity, low photovoltaic power generation utilization rate, out-of-limit node voltage, overhigh construction and maintenance cost and the like.
Description
Technical Field
The invention belongs to the field of optimized operation of power systems, and particularly relates to a constant volume method for distributed photovoltaic network access.
Background
With the strong popularization of rural photovoltaic poverty alleviation in China, the influence of small distributed photovoltaic output power on an electric power system is increasingly prominent, and an exact and feasible method is needed for guiding the network access scale of distributed photovoltaic power generation. The small distributed photovoltaic grid-connected system is close to the grid-connected system of the small distributed photovoltaic grid in the power distribution network at the power receiving end, so that the pressure of the power transmission of the power grid can be reduced, the transmission loss of a line is reduced, the voltage stability in the power distribution network is ensured, and the good economic benefit is achieved. However, when the capacity of the photovoltaic power generation equipment connected to a certain node is too large, the voltage of the node and the voltage of the peripheral nodes are out of limit, the line current is increased, the equipment investment cost is increased, and the service life of the power transmission equipment is shortened. If the capacity of the photovoltaic power generation equipment connected into the distribution network is smaller, the auxiliary effect of the distributed photovoltaic on the stability of the power grid cannot be fully exerted, and better economic benefit cannot be achieved.
The existing photovoltaic power stations mostly adopt centralized large-scale power generation, and the power generation mode needs a large continuous open space, so the power generation is mainly concentrated in remote areas with rare human smoke and far away from load centers, the electric energy generated by the power stations needs to be transmitted to the receiving end users in a large scale and a long distance, a high-power transformation device is also needed to process the electric energy generated by the photovoltaic power generation, and the fluctuation performance of the electric energy of the centralized photovoltaic power generation at a certain time point is particularly concentrated. At present, distributed photovoltaic power generation also appears in partial islands and mountainous areas, most of the photovoltaic power generation are in an island operation mode, the influence of factors such as weather is often large when the photovoltaic power generation device provides electric energy for users, sustainable power supply cannot be maintained, and a small-sized power generator with low cost performance is usually adopted to assist in guaranteeing the basic power utilization of the users.
At the present stage, the proportion of centralized photovoltaic power generation in domestic photovoltaic power generation is large. The large-scale photovoltaic power generation field is mainly concentrated in western regions with rare people or remote regions such as partial islands, and the land in the regions is relatively cheap and suitable for large-scale centralized utilization. However, areas suitable for large-scale concentrated photovoltaic power generation are far away from load centers, so that large-power transformation devices need to be invested and electric energy needs to be transmitted in a long distance, investment cost is greatly increased, and line loss and line accident rate are increased due to long-distance electric energy transmission. Therefore, the distributed photovoltaic power generation equipment is arranged in the distribution network close to the load side, so that the local consumption of energy is facilitated, and the line loss of electric energy transmission and the investment cost of the equipment can be reduced.
At present, a small amount of distributed photovoltaic equipment also appears in a power distribution network, but the power generation equipment is lack of planning and management, and the power distribution network is accessed in an improper place or the network access capacity is improper, so that the problems that the voltage of a part of nodes in the power distribution network is out of limit, the power of a load end is reversely transmitted, certain branches of the power distribution network are overloaded to transmit electric energy and the like can be caused. The unreasonable distributed photovoltaic power generation equipment is connected to the network, so that not only can the investment cost be increased to influence the economic operation of the power distribution network, but also the safe operation of the power system can be influenced, and the scheduling cost of the operation of the power distribution network is increased. The random photovoltaic network access capacity does not reach the expected economic target, but negatively affects the safe operation of the power grid. This indicates the need for rational distribution of distributed photovoltaic grid-access capacity in the distribution grid, which severely impacts the economics and safety of grid operation.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a constant volume method for a distributed photovoltaic network. On the basis of neglecting photovoltaic prediction and load prediction errors, determining the access power of distributed photovoltaic of each node in a distribution network, purchasing the distributed photovoltaic of each node from a main network through the distribution network to achieve power balance if photovoltaic power supply cannot meet the load power, and optimizing the access power of the distributed photovoltaic of each node through a group intelligent algorithm, so that more photovoltaic electric energy can be input near the periphery of the heavy load node, the pressure of electric energy transportation of the distribution network is reduced, and the purpose of reducing line loss is achieved. Meanwhile, less photovoltaic electric energy is connected to a node far away from a load center, the node voltage can be prevented from exceeding the limit on the premise of meeting the load requirement, and the nearby consumption of energy is achieved as much as possible. On the basis, the investment cost and the maintenance cost of the photovoltaic power generation equipment and the operation cost of the power system need to be considered.
The technical scheme adopted by the invention is as follows:
a constant volume method for distributed photovoltaic network access is characterized in that a model takes the maximum net loss gain of photovoltaic access, the maximum electricity purchasing gain from a main net after photovoltaic access and the minimum distributed photovoltaic input cost as objective functions, constraint conditions comprise node voltage constraint, branch transmission power constraint, node photovoltaic power constraint and main net electricity purchasing power constraint, load flow calculation is carried out on a combined system operation model, the objective functions are optimized, and the optimal network access power of each node can be obtained.
A constant volume method for distributed photovoltaic networking comprises the following steps:
step 1: obtaining a next-day power distribution network load predicted value from a power supply company dispatching center, and calculating parameters as follows:
load predicted value and node voltage of power distribution network in 24 time periods of next dayMaximum voltage value VmaxMinimum voltage value VminMaximum transmission power S of branchline,maxMinimum transmission power Sline,minNode photovoltaic minimum network access power Ppv,minMaximum photovoltaic power-on-grid Ppv,maxFrom the main network minimum purchase power Pbuy,minMaximum amount of electricity purchased Pbuy,maxThe grid power price is selected 24 hours in the future;
step 2: setting optimization variables as photovoltaic network access capacity P of each nodepv,i. Main network electricity purchasing quantity PbuyNode voltage ViThe system load flow equation is calculated;
and step 3: determining an objective function of the optimization model:
the method comprises the following steps that the network loss income of photovoltaic access and the income of electricity purchasing from a main network after photovoltaic access are maximum, the distributed photovoltaic input cost is minimum, and a constructed objective function is as shown in a formula (1):
f＝Eloss+Ebuy-Cpv (1)
network loss gain E of photovoltaic accesslossCan be represented by the formulae (2) and (3), Closs,nonpvFor no grid loss in photovoltaic power generation access, Closs,pvFor the network loss after photovoltaic power generation access, RijAnd ZijResistance and impedance of the branch formed by nodes i and j, respectively, Δ Vij,tIs the voltage difference between the i and j nodes in the t period, KelcSelling electricity prices for the units.
Eloss＝Closs,nonpv-Closs,pv (2)
Electricity income E is purchased from main network after photovoltaic accessbuyRepresented by the formulae (4) and (5) wherein Cbuy，nonpvFor electricity purchase costs without photovoltaic power generation access, Cbuy，pvThe electricity purchasing cost is the electricity purchasing cost when the photovoltaic power generation is connected.
Eloss＝Closs,nonpv-Closs,pv (4)
Distributed photovoltaic input cost CpvCan be represented by formula (6) -formula (8), wherein C in formula (6)inv、CmainRespectively the photovoltaic investment cost and the maintenance cost, y in the formula (7) is the economic service life of the photovoltaic cell, k is the current rate, C0For unit installation cost, C in formula (8)main0Is the unit maintenance cost;
Cpv＝Cinv+Cmain (6)
and 4, step 4: determining the constraint conditions of the optimization model:
the constraint conditions comprise node voltage constraint, branch transmission power constraint, node photovoltaic power constraint and main network purchase electric quantity power constraint, and the power balance constraint is respectively as follows:
node voltage constraint: vmin<Vi<Vmax；
Branch transmission power constraint: sline,min<Sline,ij<Sline,max；
Third, node photovoltaic power constraint: ppv,min<Ppv,i<Ppv,max；
Power constraint of purchasing electric quantity from the main network: pbuy,min<Pbuy,i<Pbuy,max；
Power balance constraint: sigma Pload,i＝∑PPV,i+Pbuy+Ploss；
And 5: solving an optimized operation model:
and solving the optimization model by using an optimization algorithm and calculation software to obtain the optimal power of each node distributed photovoltaic power generation network.
The constant volume method for the distributed photovoltaic network access has the following beneficial effects:
1. on the basis of the known load prediction value in advance, the distributed photovoltaic power stations at a plurality of nodes are directly connected to the power distribution network, and the traditional power plants bear other part of load requirements in the power distribution network. And under the set economic index target function of the distributed photovoltaic capacity, determining the optimal network access capacity of the distributed photovoltaic by using an adaptive weight krill cluster algorithm according to the load flow calculation step and the load flow constraint of the power distribution network. The defects of poor economic benefit and low stable contribution rate of an electric power system caused by small photovoltaic capacity are overcome, and the problems of overlarge input capacity of photovoltaic power generation, low photovoltaic power generation utilization rate, out-of-limit node voltage, overhigh construction and maintenance cost and the like are solved.
2. When the distributed photovoltaic power generation system is used, load prediction of each node in a distribution network is combined, the krill group algorithm of the self-adaptive weight is used for optimizing, the capacity of distributed photovoltaic power generation equipment which is connected to the network in the distribution network is reasonably configured, and the functions of distributed photovoltaic in the distribution network are exerted to the maximum extent on the premise that a power grid meets the constraints of system stability, power flow constraint, node voltage and the like, so that the aims of reducing network loss, reducing investment of photovoltaic equipment and purchasing power to a main network as little as possible are achieved.
Drawings
Fig. 1 is a diagram of a distributed power generation distribution network system according to the present invention.
In fig. 1: 1-a first photovoltaic cell array, 2-a apartment, 3-a first inverter, 4-a control center, 5-a step-down substation, 6-a factory, 7-a communication network, 8-an ac bus, 9-a second photovoltaic cell array, 10-a second inverter, 11-an office building, 12-a third photovoltaic cell array, 13-a school, 14-a third inverter.
Fig. 2 is a flow chart of the adaptive weighted krill population algorithm of the present invention.
Fig. 3 is a graph showing voltage changes at nodes before and after DG is connected.
Fig. 4 is a graph showing the change in the current of each branch before and after DG is connected.
Fig. 5 is a graph showing a comparison of network loss before and after DG access.
Fig. 6 is a comparison graph of the system accumulated revenue before and after DG access.
Detailed Description
In order to make the technical advantages of the invention more apparent, the invention will be further described in detail by way of example with reference to the accompanying drawings. Fig. 1 is a system structure diagram of a distribution network of the present invention, which is composed of various power loads, distributed photovoltaic power generation, inverters, a distribution network, a step-down transformer substation 5 connected to a main network, and a control center 4. When the photovoltaic power generation is not enough to meet the load requirement in the power distribution network, the main network transmits electric energy to the power distribution network through the transformer.
A constant volume method for distributed photovoltaic network access is characterized in that a model takes the maximum net loss gain of photovoltaic access, the maximum electricity purchasing gain from a main net after photovoltaic access and the minimum distributed photovoltaic input cost as objective functions, constraint conditions comprise node voltage constraint, branch transmission power constraint, node photovoltaic power constraint and main net electricity purchasing power constraint, load flow calculation is carried out on a combined system operation model, the objective functions are optimized, and the optimal network access power of each node can be obtained.
According to the load of each node in the power distribution network, the capacity of the equipment accessing the distributed photovoltaic at the corresponding position becomes a key influence factor for determining the operation economy and stability of the power distribution network. The method establishes an economic objective function taking photovoltaic investment cost and system fuel cost as consideration, considers constraint conditions such as photovoltaic capacity constraint, frequency modulation power plant output capacity constraint, node voltage constraint, transmission line transmission capacity constraint and the like in a system, takes photovoltaic access capacity accessed by each node as a dependent variable, adopts a latest group artificial intelligence algorithm-an adaptive weight phophius chinensis group algorithm to carry out optimization, and finally obtains the appropriate photovoltaic access capacity of each node in the power distribution network.
The method specifically comprises the following steps:
step 1: obtaining a next-day power distribution network load predicted value from a power supply company dispatching center, and calculating parameters as follows:
load prediction of power distribution network in 24 time periods of next dayValue, maximum voltage value V of node voltagemaxMinimum voltage value VminMaximum transmission power S of branchline,maxMinimum transmission power Sline,minNode photovoltaic minimum network access power Ppv,minMaximum photovoltaic power-on-grid Ppv,maxFrom the main network minimum purchase power Pbuy,minMaximum amount of electricity purchased Pbuy,maxAnd the grid power price is selected 24 hours in the future.
Step 2: setting optimization variables as photovoltaic network access capacity P of each nodepv,i. The photovoltaic access amount is subtracted from the original load amount of the i node to obtain the input power of the i node, the input power of the i node and the access point of the main network are used as balance nodes, and the electricity purchasing amount P from the main network can be calculated by utilizing a Newton-Raphson load flow calculation methodbuyAnd PQ node voltage Vi。
And step 3: the objective function of the optimization model is determined as equation (1), i.e.:
f＝Eloss+Ebuy-Cpv (1)
in the formula ElossNetwork loss gain for photovoltaic access, EbuyFor photovoltaic access subsequent purchase of electricity from the mains, CpvThe investment cost of the distributed photovoltaic power supply. And gradually iterating and optimizing by using a krill group intelligent optimization algorithm to obtain a maximum function adaptive value f by taking the network loss profit of photovoltaic access and the profit of electricity purchase from a main network after photovoltaic access as the maximum and the distributed photovoltaic input cost as the minimum.
Network loss gain E of photovoltaic accesslossCan be represented by the formulae (2) and (3), Closs,nonpvFor no grid loss in photovoltaic power generation access, Closs,pvFor the network loss after photovoltaic power generation access, RijAnd ZijResistance and impedance of the branch formed by nodes i and j, respectively, Δ Vij,tIs the voltage difference between the i and j nodes in the t period, KelcSelling electricity prices for the units.
Eloss＝Closs,nonpv-Closs,pv (2)
Electricity income E is purchased from main network after photovoltaic accessbuyRepresented by the formulae (4) and (5) wherein Cbuy，nonpvFor electricity purchase costs without photovoltaic power generation access, Cbuy，pvFor the purchase of electricity in the presence of photovoltaic power generation access, Cbuy,TElectricity price at time T, Pbuy，iPower is input for the ith node.
Eloss＝Cbuy,nonpv-Cbuy,pv (4)
Distributed photovoltaic input cost CpvCan be represented by formula (6) -formula (8), wherein C in formula (6)invFor photovoltaic projection
Capital cost, CmainFor photovoltaic maintenance cost, y in the formula (7) is the economic service life of the photovoltaic cell, and k is the label
Current rate, n is the number of nodes, Cbuy,TElectricity price at time T, Pbuy，iFor the i-th node input power, C0
For unit installation cost, C in formula (8)main0For unit maintenance cost, Ppv,iPower is accessed for the inode.
Cpv＝Cinv+Cmain (6)
And 4, step 4: determining the constraint conditions of the optimization model:
the constraint conditions comprise node voltage constraint, branch transmission power constraint, node photovoltaic power constraint and main network purchase electric quantity power constraint, and the power balance constraint is respectively as follows:
node voltage constraint: vmin<Vi<Vmax；
Branch transmission power constraint: sline,min<Sline,ij<Sline,max；
Third, node photovoltaic power constraint: ppv,min<Ppv,i<Ppv,max；
Power constraint of purchasing electric quantity from the main network: pbuy,min<Pbuy<Pbuy,max；
Power balance constraint: sigma Pload,i＝∑PPV,i+Pbuy+Ploss,ij。
Vmax、VminRespectively the maximum and minimum node voltages, Sline,max、Sline,minMaximum and minimum branch transmission power, Ppv,max、Ppv,minRespectively a constraint upper and lower limit value, P, for the photovoltaic power accessed by the nodebuy,max、Pbuy,minMaximum and minimum power, P, respectively, for purchasing electric power from the main networkload,iIs the i-node load, PPV,iConnecting photovoltaic power, P, to i-nodebuyFor purchasing power from the main network, Ploss,ijThe active loss of the line between the node i and the node j is obtained.
And 5: solving an optimized operation model:
and solving the optimization model by using an intelligent krill swarm optimization algorithm and MATLAB simulation calculation software to obtain the optimal power of each node distributed photovoltaic power generation network.
As can be seen from fig. 3, the voltage of each node connected to the distributed photovoltaic is obviously higher than the voltage of a node not connected to the DG, and the per unit minimum voltage value (node 14) of the node voltages in the system is increased from 0.9889 to 0.9943, which indicates that the connected distributed photovoltaic can well support the power distribution network. Fig. 4 is a comparison graph of branch current after the distributed photovoltaic is connected to and branch current before the distributed photovoltaic is connected to, and it can be known from the graph that as the total capacity of the distributed photovoltaic increases, the network loss of the distribution network and the node voltage offset are both reduced, but when the total capacity of the distributed photovoltaic is greater than the total load capacity of the system, some branch currents are negative, but the branch currents in the simulation calculation process cannot be negative due to the directionality of relay protection, and it can be known from the graph that the currents are positive, so that the feasibility of the krill group intelligent optimization algorithm is verified.
Fig. 2 shows a specific process of calculating the capacity of each node accessing the distribution network through a krill group intelligent algorithm. Calculating a specific flow:
step 1): firstly, determining access nodes of distributed photovoltaic power generation in a distribution network, and setting photovoltaic access quantity of the nodes as a control variable X ═ X (X)1、X2……Xn) The load of each node is a constant, and the voltage V of the nodeiBranch transmission power SijOutput power S of frequency modulation power plantgen,iIs a state variable.
Step 2): forming initial P-shrimp group X ═ (X)1、X2……Xn)。
Step 3): subtracting the initial krill group control variable from the load prediction quantity to obtain the input power of each node, calculating by using a Newton Raphson load flow calculation formula on the premise that the active and reactive power of a PQ node, the active and voltage of a PV node and the access node of a main power network are known as balance nodes to obtain a state variable Vi、Sline,ij、Pbuy,i。
Step 4): by data in step 3) and known RijAnd ZijBranch resistance and impedance, K, formed by nodes i, jelcUnit selling price of electricity, Cbuy,TElectricity price at time T, C0Cost per unit installation, Cmain0The unit maintenance cost and the discount rate k are substituted into equations (1) to (8) to solve the value of the objective function f, and the optimal solution bestX and the optimal objective function bestf are obtained for the first time.
Step 5): iterative adherence of krill populations
Rule of where Ni，Fi，DiRespectively representing induced motion, foraging motion and random diffusion, wherein the induced motion NiForaging movement FiAnd followMechanical diffusion DiThe functional forms are respectively as follows:
inducing motion NiIs represented by Ni,new＝Nmaxαi+ωnNi,oldWherein ω isnTo induce inertial weight, NmaxAt maximum induction rate, Ni,newAnd Ni,oldThe values of new and old induced exercise are obtained. In order to avoid the algorithm from falling into the limited optimum, the expressions of the induced inertia weight and the foraging inertia weight are
t and tmaxFor the number of iterations and the maximum number of iterations, αi＝αi,local+αi,target，αi,localIs the current position alphai,targetIs the position of the target, and is,
② foraging movement FiIs shown as Fi,new＝Vmaxβi+ωfFi,oldWherein ω isfFor foraging inertial weight, VmaxFor maximum foraging speed, Fi,newAnd Fi,oldFor new and old induced exercise value, betai＝βi,food+βi,best，
βi,best＝Ki,bestXi,best，Ki,foodShadow of food on current particleSound power, Ki,bestThe individual influence is optimized for the history of the current particle.
③ random motion DiIs shown as
DmaxThe maximum diffusion velocity range is usually [0.002,0.01 ]]And delta is [ -1,1]Random value in between.
Step 6): intelligent algorithm according to krill group
For krill group X ═ X (X)1、X2……Xn) And (6) updating.
Step 7): and checking whether the iteration termination times are reached, if the iteration termination times do not meet the conditions, repeating the operation according to the steps 5) to 6), and if the iteration termination times do not meet the conditions, outputting the optimal target value and the corresponding optimal solution.
As can be seen from fig. 5, the loss of each branch connected to the distributed photovoltaic system is lower than that of a branch not connected to the DG, the data of the network loss is 62.71kW and 273.29kW, respectively, and the network loss is reduced by 77.03%, so that it is known that it is necessary to optimize the capacity and location of the distributed photovoltaic system when the distributed photovoltaic system is connected to the network. As can be seen in fig. 6, the sum of the expected network loss revenue and the electricity purchasing revenue of the distribution network after the photovoltaic power generation is accessed is increased year by year; the benefit of not accessing the photovoltaic is lower than that of accessing the DG, so that the profitability of the power distribution network after the DG is accessed is obviously improved, and the improvement ratio is as high as nearly 40%.
Claims (2)
1. A constant volume method for distributed photovoltaic networking is characterized by comprising the following steps:
step 1: obtaining a next-day power distribution network load predicted value from a power supply company dispatching center, and calculating parameters as follows:
the load predicted value and the node voltage maximum voltage value V of the power distribution network in 24 time intervals on the next daymaxMinimum voltage value VminMaximum transmission power S of branchline,maxMinimum transmission power Sline,minNode photovoltaic minimum network access power Ppv,minMaximum photovoltaic networkingPower Ppv,maxFrom the main network minimum purchase power Pbuy,minMaximum amount of electricity purchased Pbuy,maxThe grid power price is selected 24 hours in the future;
step 2: setting optimization variables as photovoltaic network access capacity P of each nodepv,i(ii) a Main network electricity purchasing quantity PbuyNode voltage ViThe system load flow equation is calculated;
and step 3: determining an objective function of the optimization model:
the method comprises the following steps that the network loss income of photovoltaic access and the income of electricity purchasing from a main network after photovoltaic access are maximum, the distributed photovoltaic input cost is minimum, and a constructed objective function is as shown in a formula (1):
f＝Eloss+Ebuy-Cpv (1)
network loss gain E of photovoltaic accesslossCan be represented by the formulae (2) and (3), Closs,nonpvFor no grid loss in photovoltaic power generation access, Closs,pvFor the network loss after photovoltaic power generation access, RijAnd ZijResistance and impedance of the branch formed by nodes i and j, respectively, Δ Vij,tIs the voltage difference between the i and j nodes in the t period, KelcSelling electricity prices for units;
Eloss＝Closs,nonpv-Closs,pv (2)
electricity income E is purchased from main network after photovoltaic accessbuyRepresented by the formulae (4) and (5) wherein Cbuy，nonpvFor electricity purchase costs without photovoltaic power generation access, Cbuy，pvFor the purchase of electricity in the presence of photovoltaic power generation access, Cbuy,TElectricity price at time T, Pbuy，iInputting power for the ith node;
Ebuy＝Cbuy,nonpv-Cbuy,pv (4)
distributed photovoltaic input cost CpvCan be represented by formula (6) -formula (8), wherein C in formula (6)inv、CmainRespectively the photovoltaic investment cost and the maintenance cost, y in the formula (7) is the economic service life of the photovoltaic cell, k is the current rate, C0Is unit installation cost, Ppv,iFor i-node photovoltaic access capacity, C in equation (8)main0Is the unit maintenance cost;
Cpv＝Cinv+Cmain (6)
and 4, step 4: determining the constraint conditions of the optimization model:
the constraint conditions comprise node voltage constraint, branch transmission power constraint, node photovoltaic power constraint and main network purchase electric quantity power constraint, and the power balance constraint is respectively as follows:
node voltage constraint: vmin<Vi<Vmax；
Branch transmission power constraint: sline,min<Sline,ij<Sline,max；
Third, node photovoltaic power constraint: ppv,min<Ppv,i<Ppv,max；
Power constraint of purchasing electric quantity from the main network: pbuy,min<Pbuy,i<Pbuy,max；
Power balance constraint: sigma Pload,i＝∑PPV,i+Pbuy+Ploss
And 5: solving an optimized operation model:
and solving the optimization model by using an intelligent krill swarm optimization algorithm and MATLAB simulation calculation software to obtain the optimal power of each node distributed photovoltaic power generation network.
2. A method for calculating the capacity of each node of distributed photovoltaic power generation accessed to a distribution network through a krill group intelligent algorithm is characterized by comprising the following steps:
step 1): firstly, determining access nodes of distributed photovoltaic power generation in a distribution network, and setting photovoltaic access quantity of the nodes as a control variable X ═ X (X)1、X2……Xn) The load of each node is a constant, and the voltage V of the nodeiBranch transmission power SijOutput power S of frequency modulation power plantgen,iIs a state variable;
step 2): forming initial P-shrimp group X ═ (X)1、X2……Xn)；
Step 3): performing load flow calculation according to the initial krill group control variable and the load prediction quantity to obtain a state variable Vi、Sline,ij、Pbuy,i；
Step 4): solving an objective function through the data in the step 3), and initially obtaining an optimal solution bestX and an optimal objective function bestf;
step 5): iterative adherence of krill populations
Rule of where Ni，Fi，DiRespectively representing induced motion, foraging motion and random diffusion, wherein the induced motion NiForaging movement FiAnd random diffusion DiThe functional forms are respectively as follows:
inducing motion NiIs represented by Ni,new＝Nmaxαi+ωnNi,oldWherein ω isnTo induce inertial weight, NmaxAt maximum induction rate, Ni,newAnd Ni,oldThe values of new and old induced motion are obtained; in order to avoid the algorithm from falling into the limited optimum, the expressions of the induced inertia weight and the foraging inertia weight are
t and tmaxFor the number of iterations and the maximum number of iterations, αi＝αi,local+αi,target，αi,localIs the current position alphai,targetIs the position of the target, and is,
Kbestand KworstFor best and worst krill adaptation values, KiFor the current fitness value or objective function of the ith krill,
αi,target＝Ki,best*Xi,best，Xiis the current position;
② foraging movement FiIs shown as Fi,new＝Vmaxβi+ωfFi,oldWherein ω isfFor foraging inertial weight, VmaxFor maximum foraging speed, Fi,newAnd Fi,oldFor new and old induced exercise value, betai＝βi,food+βi,best，
βi,best＝Ki,bestXi,best，Ki,foodIs the influence of the food on the present particle, Ki,bestThe historical optimal individual influence of the current particle is obtained;
③ random motion DiIs shown as
DmaxThe maximum diffusion velocity range is usually [0.002,0.01 ]]And delta is [ -1,1]A random value in between;
step 6): intelligent algorithm according to krill group
For krill group X ═ X (X)1、X2……Xn) Updating is carried out;
step 7): checking whether a termination condition is reached, if the termination condition is not met, repeating the operation according to the steps 5) to 6), and if the termination condition is reached, outputting an optimal target value and a corresponding optimal solution.
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