CN110247438B - Active power distribution network resource optimization configuration based on longicorn whisker algorithm - Google Patents
Active power distribution network resource optimization configuration based on longicorn whisker algorithm Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract
The invention discloses an active power distribution network resource optimization configuration based on a longicorn algorithm, which considers the time sequence of the output and load of wind power and a photovoltaic distributed power supply, maximumly consumes renewable energy through the optimization configuration of the resource, balances active power, utilizes the longicorn algorithm to carry out the resource optimization configuration, balances the active power in each time interval according to the system requirement and the economic principle, carries out reactive compensation to reduce the power loss of the system, reduces the voltage deviation and meets the economic requirement.
Description
Technical Field
The invention relates to an active power distribution network resource optimization configuration technology, in particular to active power distribution network resource optimization configuration of a Tianniu whisker algorithm under the time sequence characteristics of wind power, photovoltaic power and other distributed power supply output and loads.
Background
With the access of a large number of distributed power sources, conventional power distribution networks face new challenges. First of all, the structure and operation of the distribution network are greatly affected, bidirectional power flow between transmission and distribution networks is generated, loads and power supplies will have double uncertainties, and customers will have double identities of consumers and producers. Secondly, the traditional power distribution network mainly adopts a single-side power supply and radiation type power supply mode, and a series of problems of voltage level increase, short-circuit current increase, power supply reliability reduction, electric energy quality deterioration and the like can be caused when a high-permeability distributed power supply is connected into the power distribution network. After the economic development of China enters a new normal state, the power grid development mode is changed greatly, the original scale effect is changed into investment benefit, and the sustainable development of enterprises is difficult to support by the traditional planning method for improving the margin of equipment and replacing the reliability. Under the condition of high permeability, three challenges are brought by the power generation mode that new energy is connected to a power distribution network in a distributed mode: 1. challenge of power back-off: the phenomenon of mass delivery to the transformer substation is gradually increased. 2. Challenges in power generation characteristics: the voltage level is increased, the short-circuit current is increased, the power supply reliability is reduced, and the quality of electric energy is deteriorated. 3. Challenges in energy utilization: the value utilization rate of the distributed energy is not high, and the response capability of the demand side of the power grid is insufficient. Active power distribution networks are the core technology to address the above challenges.
The active power distribution network can comprehensively control distributed energy sources (distributed power generation, flexible load and energy storage), and can flexibly use the network to realize effective management of power flow. The distributed energy sources play a certain supporting role on the system on the basis of meeting the supervision and access criteria. With the wide application of new energy power generation technology, an active power distribution network containing numerous regulated and controlled resources becomes a necessary trend for power distribution network development. The access of the distributed power supply can change the structure of the power distribution network, and brings difficulty to the voltage regulation and operation of the power distribution network. The active power distribution network can solve the problem of renewable energy consumption, and a mutual standby pattern of the distributed power supply and the power distribution network is formed. The research on the optimized allocation of the active power distribution network resources is beneficial to improving the safe and stable operation level of the system and the capability of the active power distribution network for absorbing renewable energy, and the aims of saving energy and reducing consumption are achieved.
The problem of resource optimization configuration of the active power distribution network needs to be solved in an important way to improve the economic benefit of the distributed power supply access to the power distribution network and ensure the safe, flexible and economic operation of the active power distribution network under the condition of access of a large number of distributed power supplies. For a power distribution network, an early power distribution system is a passive network, and the grid structure and the load characteristics are relatively fixed, while a large number of intermittent distributed power sources are connected under the condition of an active power distribution network, so that a lot of uncertain factors are brought to the whole power system. Therefore, according to the output of the distributed power supply and the time sequence of the load, the resource allocation is reasonably optimized, and the method becomes an important measure for improving the safe, stable and economic operation of the active power distribution network and promoting the consumption of renewable energy.
Disclosure of Invention
The invention aims to solve the problems caused by operation and optimization of an active power distribution network by considering the characteristic that the output and load of a distributed power supply have time sequence and the consumption of the active power distribution network, provides the resource optimization configuration of the active power distribution network based on a Tianniu algorithm, optimizes the resource configuration according to the economic benefit principle under the condition of maximally consuming renewable energy sources, balances active power, optimizes the reactive power output of each resource by using the Tianniu algorithm, further improves the consumption of the renewable resources of the active power distribution network and the operation level and economy of the power distribution network, establishes a mathematical model taking network loss and voltage deviation as objective functions, and performs multi-objective optimization.
The invention adopts the following technical scheme to achieve the purpose. The active power distribution network resource optimization configuration based on the longicorn whisker algorithm is characterized by comprising the following steps:
1) In the case of full-scale consumption of renewable energy, active power is balanced, and the power balance model is as follows:
Net_load＝P load (t)-p PV (t)-P WG (t)-P b (t) (2)
in the formula: p DR (i, t) represents the net power of the ith flexible adjustment resource in the t time period; net _ load (t) is Net active load which is used for flexibly adjusting resources to be compensated in the t-th time period; grid _ loss is a network loss coefficient of the active power distribution network; p load (t) is the original load of the active distribution network; p PV (t) photovoltaic power generation output of the active power distribution network in the tth time period; p WG (t) wind power generation output of the active power distribution network in the tth time period; p b (t) providing power to the upper stage of the transmission grid;
2) Converting multiple targets into a single target model by using a linear weighting method, namely the fitness function is as follows:
in the formula: w is a i The method is characterized in that a trade-off relation between multi-target weight value, reaction economy and voltage stability becomes a preference factor, m is the number of target functions, and
the multi-objective function is as follows:
a) Objective function 1:
in the formula: i =1,2,3, \ 8230, N, N is the total number of nodes of the power distribution network; v N Is a rated voltage; i. j is the head end and tail end nodes of the branch respectively; Δ V ij (x) The voltage drop longitudinal component of the branch i and j is shown; delta V ij The voltage drop transverse component of the branch i and j is shown; x is a control variable, so that the reactive power output of the resource can be flexibly adjusted;
b) The objective function 2: AND minimum loss:
in the formula: i =1,2,3,. Ang., N b Is the total number of branches in the network;
and &>
Respectively a flow-through branch b i Active power and reactive power of; />
Branch b i The branch resistance of (1); v bi Is a branch b i The terminal voltage of (1);
3) Initializing algorithm parameters;
4) Initializing the control position of the longicorn, namely initializing the reactive power output of each flexibly adjustable resource, wherein the formula is as follows:
X i ＝X imin +(X imax -X imin )×rand (6)
in the formula: i denotes the dimension, X imin Represents the ith minimum value of reactive power output of the flexibly adjustable resource, X imax The maximum value of the reactive power output of the ith flexibly adjustable resource is represented;
5) The positions of the left and right tentacles of the longicorn are obtained according to the positions of the longicorn, and the fitness values corresponding to the positions of the left and right tentacles of the longicorn are obtained according to the fitness function, wherein the calculation formula is as follows:
a) Calculating the positions of the left and right tentacles of the longicorn:
dir＝rands(k，1)
in the formula: k represents the total dimensionality of the control variables; dir represents a random vector of the orientation of the longicorn; xleft represents the position vector of the left whisker; d represents the fixed distance between the longicorn tentacles, namely the size of the longicorn; x is a control variable vector, so that the reactive power output of the resource can be flexibly adjusted; xright represents the position vector of the right whisker;
the formula (7) is used for generating a random vector to represent the orientation of the longicorn whiskers, the formula (8) normalizes the orientation of the longicorn whiskers, and the formula (9) and the formula (10) are respectively used for calculating the positions of the left and right longicorn whiskers;
b) Calculating the adaptability value of the left and right tentacles:
f(X)＝af 1 (X)+bf 2 (X) (11)
in the formula: a. b are respectively the objective function f 1 (X) and f 2 (X) a weight value;
6) And comparing the sizes of the adaptability values of the left and right tentacles, and updating the position of the longicorn, wherein the mathematical model is as follows:
X＝X-step×dir×sign(fleft-fright)+w (12)
in the formula: step represents the step size; sign is a sign function, fleft and fright are fitness values corresponding to the left and right tentacles respectively, and w is a random moving minimum step length;
7) Calculating a new fitness value according to the position of the longicorn updated in the step 6), and comparing the new fitness value with the fitness value corresponding to the last longicorn to obtain an optimal fitness value;
8) Judging whether a set iteration number termination condition is met or not, and if not, repeating the step 5); and if so, selecting a resource optimal configuration strategy corresponding to the optimal fitness value, namely the output of each flexible resource.
Further, the multi-objective function is voltage deviation and system grid loss.
The invention considers the output of renewable resources and the time sequence of load, can maximally consume the renewable resources such as wind power, photoelectricity and the like under the condition of not abandoning light and wind as much as possible by optimizing the resource allocation, optimizes the resource allocation by relatively accurate global optimization of a Tianniu Lexu algorithm, balances the active power in each time interval according to the system requirement and the economic principle, performs reactive compensation to reduce the power loss of the system, reduce the voltage deviation and meet the economic requirement, and has certain practical value, and the safe and stable operation level of the system and the capability of actively distributing the renewable energy sources in a power distribution network can be improved. The method is suitable for the resource optimization configuration of the active power distribution network, which considers the time sequence of the output and the load of the renewable resources, maximally consumes the renewable resources and can keep the safe and stable operation of the system.
Drawings
Fig. 1 is a flow chart of the active power distribution network resource optimization configuration based on the longicorn whisker algorithm.
Detailed Description
The invention is further illustrated by the following figures and examples. Referring to fig. 1, the active power distribution network resource optimization configuration based on the longicorn whisker algorithm optimizes resource configuration according to the economic benefit principle under the condition of maximally absorbing renewable energy, balances active power, and optimizes reactive power output of each resource by using the longicorn whisker algorithm, and the method comprises the following steps:
the first step is as follows: active power balance. Under the condition of fully consuming renewable energy, resource allocation optimization is carried out according to the principle that the minimum adjustable resource allocation cost is economic, active power is balanced, and a power balance model is as follows:
Net_load＝P l o ad (t)-P PV (t)-P WG (t)-P b (t) (2)
in the formula: p DR (i, t) represents the net power of the ith flexible adjustment resource in the t time period; net _ load (t) is the Net active load which can be adjusted at the tth time and needs to be compensated by the flexible resource; grid _ loss is a network loss coefficient of the active power distribution network; p load (t) is the original load of the active power distribution network; p PV (t) photovoltaic power generation output of the active power distribution network at the tth time period; p WG (t) wind power generation output of the active power distribution network in the t-th time period; p b (t) providing power to the upper stage of the transmission grid;
the second step is that: converting multiple targets into a single target model by using a linear weighting method, namely the fitness function is as follows:
in the formula: w is a i A trade-off relationship of multi-target weight value, reaction economy and voltage stability becomes a preferenceThe factor m being the number of objective functions, in general
The multi-objective function is as follows:
a) Objective function 1:
in the formula: i =1,2,3, \ 8230, N, N is the total number of nodes of the power distribution network; v N Is a rated voltage; i. j is the head end and tail end nodes of the branch respectively; Δ V ij (x) The voltage drop longitudinal component of the branch i and j is shown; delta V ij The voltage drop transverse component of the branch i and j is shown; x is a control variable, so that the reactive power output of the resource can be flexibly adjusted;
b) Objective function 2:
in the formula: i =1,2,3, \ 8230;, N b Is the total number of branches in the network;
and &>
Respectively a flow-through branch b i Active power and reactive power of; />
Is a branch b i The branch resistance of (1); />
Is a branch b i The terminal voltage of (1);
the third step: the algorithm parameter initialization comprises parameters such as the maximum iteration times of the longicorn beards, the dimension of the longicorn, the size of the minimum longicorn, the random moving minimum step length, the size of the longicorn, the random moving minimum step length attenuation factor, the original data of the active power distribution network and the like;
the fourth step: initializing the longicorn position, namely initializing the reactive power output of each flexibly adjustable resource. The formula for initialization of the longicorn position is as follows:
X i ＝X imin +(X i，max -X imin )×rand (6)
in the formula: i denotes the dimension, X imin Represents the ith minimum value of reactive power output of the flexibly adjustable resource, X imax The maximum value of the reactive power output of the ith flexibly adjustable resource is represented;
the fifth step: the positions of the left and right tentacles of the longicorn are obtained according to the positions of the longicorn, and the fitness values corresponding to the positions of the left and right tentacles of the longicorn are obtained according to the fitness function, wherein the calculation formula is as follows:
a) Calculating the positions of the left and right tentacles of the longicorn:
dir＝rands(k，1) (7)
in the formula: k represents the total dimensionality of the control variables; dir represents a random vector of the orientation of the longicorn; xleft represents the position vector of the left whisker; d represents the fixed distance between the longicorn tentacles, namely the size of the longicorn; x is a control variable vector, so that the reactive power output of the resource can be flexibly adjusted; xright represents the position vector of the right whisker; the formula (7) is used for generating a random vector to represent the orientation of the longicorn whiskers, the formula (8) is used for normalizing the orientation of the longicorn whiskers, and the formulas (9) and (10) are respectively used for calculating the positions of the left and right longicorn whiskers;
b) Left and right whisker fitness value calculation
f(X)＝af 1 (X)+bf 2 (X) (11)
In the formula: a, b are the objective function f 1 (X) and f 2 (X) a weight value;
and a sixth step: and comparing the sizes of the adaptability values of the left and right tentacles, and updating the position of the longicorn, wherein the mathematical model is as follows:
X＝X-step×dir×sign(fleft-fright)+w (12)
d＝d×eta_d+d 0 (13)
L＝L×eta_L+L 0 (14)
w＝L×rands(k，1) (15)
step＝step×eta_step (16)
in the formula: step represents the step size; sign is a sign function; fleft and fright are the fitness values corresponding to the left and right tentacles respectively; w is the minimum step length of random movement, and d is the size of a longicorn; d 0 A smallest longicorn; eta _ d is a longicorn size attenuation factor; eta _ step is the step attenuation factor; l is a random moving minimum step length control parameter; l is 0 A minimum step size control parameter for starting random movement; k is the longicorn dimension. Formula (12) is to update the position of the longicorn; equation (13) is a size update for a longicorn; equations (14) and (15) are the update of the random move minimum step size; equation (16) is to update the step size;
the seventh step: calculating a new fitness value according to the updated position of the longicorn, and comparing the new fitness value with the fitness value corresponding to the last longicorn to obtain an optimal fitness value;
eighth step: judging whether a termination condition is met, if not, repeating the fifth step and continuing iteration; if the resource optimization configuration strategy is satisfied, selecting a resource optimization configuration strategy corresponding to the optimal fitness value, namely the output of each flexible resource;
the ninth step: the software is realized, the whole program is based on a windows operating system, and matlab2017a of mathwork company is adopted as a programming development platform.
According to the steps, the time sequence characteristics of the output and the load of the distributed power supply such as wind power, photovoltaic power and the like are considered, renewable energy is maximally consumed through the optimal configuration of resources, active power is balanced, the requirement of the system for maximally consuming the renewable energy is met, and the reactive output of the adjustable resources is optimally configured through a longicorn algorithm, so that the network loss is reduced, and the economic requirement is met; and the voltage deviation of the node is reduced, and the requirement of safe and stable operation of the system is met.
Example (b): a certain power distribution system: the typical daily maximum load is 4.74MW, the minimum load is 2.45MW, the installed photovoltaic power generation capacity is 2MW, the installed wind power generation capacity is 2MW, and the transformer substation transmits power to the system according to the fixed power of 1.5 MW. The system load and the output of the renewable energy source can show the time sequence, in order to ensure the balance of power supply and demand in the system and the full consumption of the renewable energy source power generation, a micro-gas turbine, an energy storage battery, a charging pile, a capacitor and other flexibly adjustable resources are configured, wherein the daily average charging quantity of the charging pile is 0.1MW. An improved standard IEEE33 node system is used for simulation calculation, an energy storage battery is connected to a node 5, a micro-combustion turbine is connected to a node 33, a charging electric pile is connected to a node 22, a photovoltaic power plant is connected to a node 18, a wind power plant is connected to a node 28, and capacitor banks are connected to nodes 6, 11 and 31.
Due to the time sequence of the load and the output of the renewable energy, the system has unbalanced supply and demand, insufficient power supply or excessive power supply occurs in different periods, and the loss of the power grid is large. According to the implementation method and steps of the invention, the resource optimization configuration is carried out on the system.
The first step is as follows: and configuring and optimizing the flexibly adjustable resources according to the principle that the minimum cost of the adjustable resource configuration is economical, balancing the active power, wherein the power balancing formula is as follows:
Net_load＝p load (t)-p PV (t)-P WG (t)-P b (t) (2)
the active balance calculation is performed according to the above formula, and the power supply state in each hour of 24 hours, that is, the state
The active power of the resource to be adjusted can be adjusted, and then the energy storage battery with the lowest operation cost and the charging pile are preferentially used for adjustment. The active power supply and demand of the computing system reach balance, the daily average charging amount of the charging pile is 0.1MW, the micro gas turbine and the energy storage battery provide active power when the power supply is insufficient, the energy storage battery and the charging pile absorb the active power when the power supply is excessive, and the power output range of the micro gas generator is [0,1.59 ]]MW, power output range of energy storage battery is [ -0.85,1.0]MW, the charging power of the charging pile is 0.00833MW/h when the charging time of the charging pile is 1 hour to 9 hours and 22 hours to 24 hours, and the active balance condition of 24 hours is shown in Table 1;
TABLE 1
| Time/h | Active output of energy storage battery (MW) | Active power output (MW) of micro-gas turbine | Charger peg (MW) |
| At 1 hour | 0 | 0.996 | -0.00833 |
| At 2 time | 0 | 0.687 | -0.00833 |
| At 3 hour | -0.522 | 0 | -0.00833 |
| At 4 th hour | -0.623 | 0 | -0.00833 |
| At 5 th hour | -0.241 | 0 | -0.00833 |
| At 6 th hour | 0.083 | 0 | -0.00833 |
| At 7 th hour | -0.718 | 0 | -0.00833 |
| At 8 th hour | 0.196 | 0 | -0.00833 |
| At time 9 | -0.491 | 0 | -0.00833 |
| At 10 hours | -0.010 | 0 | 0 |
| At 11 th hour | 0.156 | 0 | 0 |
| At 12 th hour | -0.258 | 0 | 0 |
| At 13 th hour | -0.449 | 0 | 0 |
| At time 14 | -0.540 | 0 | 0 |
| At 15 time | -0.735 | 0 | 0 |
| At 16 hours | -0.857 | 0 | 0 |
| At 17 time | -0.408 | 0 | 0 |
| At 18 hours | 0.231 | 0 | 0 |
| At 19 time | 1.000 | 0.784 | 0 |
| At 20 hours | 1.000 | 0.439 | 0 |
| At time 21 | 1.000 | 1.541 | 0 |
| At time 22 | 1.000 | 1.595 | -0.00833 |
| At 23 time | 0.720 | 0.000 | -0.00833 |
| At 24 hours | 0.465 | 0.693 | -0.00833 |
The second step is that: converting multiple targets into a single-target model by using a linear weighting method, namely, the fitness function is as follows:
a) Objective function 1:
b) Objective function 2:
converting the multi-objective function into a single-objective model according to the above linear weighting method, ensuring the quality of voltage under the condition of reducing network loss, and carrying out non-dimensionalization treatment on the objective function before using the linear weighting method, namely f 1 /f 1min ，f 2 /f 2min ，f 1min And f 2min Respectively, the minimum value of the objective function 1 and the objective function 2 when the two are optimized independently, and the value of the corresponding weight coefficient is w 1 ＝0.2，w 2 ＝0.8；
The third step: and (5) initializing algorithm parameters. The iteration number is set to be n =100, and the minimum longicorn d is taken 0 =0.0001, the initial size d =0.02 of the longicorn, the attenuation factor eta _ d =0.95 of the longicorn size, the random move minimum step control parameter L =0, and the initial random move minimum step control parameter L 0 =0.0001, longitudal dimension k =5, step attenuation factor eta _ step =0.95, etc.;
the fourth step: and initializing control variables of the longicorn, namely initializing reactive power output of each flexibly adjustable resource. The formula is as follows:
X i ＝X imin +(X imax -X imin )×rand (6)
the micro-gas turbine, the energy storage battery and the three groups of capacitors can be used for carrying out reactive power regulation, so that the position of the longicorn stigma is determined by the output of the five flexibly-regulated resources, the power factors of the energy storage battery and the micro-gas turbine can be regulated, the minimum power factor is 0.9, the reactive power output range of the energy storage battery is determined by the active power output of the energy storage battery, the micro-gas turbine only sends out reactive power and does not absorb reactive power, the reactive power output range is determined by the active power output of the micro-gas turbine, and the reactive power output range of the capacitor group is Mvar (0, 1.2). Random reactive power output of each flexibly adjustable resource in the corresponding output range can be obtained by using an initialized longicorn position formula;
the fifth step: the positions of the left and right tentacles of the longicorn are obtained according to the positions of the longicorn, and the fitness values corresponding to the positions of the left and right tentacles of the longicorn are obtained according to the fitness function, wherein the calculation formula is as follows:
a) Calculating the positions of the left and right tentacles of the longicorn:
dir＝rands(k，1) (7)
calculating the positions of the left and right tentacles according to the longicorn position obtained in the fourth step;
b) Calculating the adaptability value of the left and right tentacles:
f(X)＝af 1 (X)+bf 2 (X) (11)
the left and right whisker position values determined in a) are respectively taken into the formula (11) to obtain the objective function f 1 (X) and f 2 The value of (X) can be obtained by network load flow calculation after flexible adjustment resources are added, wherein a =0.2, b =0.8, f 1 (X) and f 2 (X) after calculation, carrying out non-dimensionalization treatment and then using a linear weighting method to obtain a first iteration optimal fitness value;
and a sixth step: and comparing the sizes of the adaptability values of the left and right tentacles, and updating the position of the longicorn, wherein the mathematical model is as follows:
X＝X-step×dir×sign(fleft-fright)+w (12)
d＝d×eta_d+d 0 (13)
L＝L×eta_L+L 0 (14)
w＝L×rands(k，1) (15)
step＝step×eta_step (16)
updating the position of the longicorn according to the fitness values corresponding to the left and right tentacles obtained in the fifth step, wherein the size d and the step length step of the longicorn are continuously reduced in order to improve the convergence precision of the iteration later stage in the updating process, but the minimum size d of the longicorn is also added 0 And the shortest step length w prevents the search from getting into local optimum;
the seventh step: calculating a new fitness value according to the updated longicorn position, and comparing the new fitness value with the fitness value corresponding to the last longicorn to obtain an optimal fitness value;
eighth step: judging whether a termination condition of iteration times n =100 is met, if not, repeating the fifth step to continue iteration; and if so, selecting a resource optimization configuration strategy corresponding to the optimal fitness value, namely the reactive power output of each flexible resource.
According to the eight steps, the optimal resource optimal allocation of each time interval can be obtained, the corresponding active and reactive power output, the grid loss and the voltage deviation of each flexible resource are obtained, and the data shown in the table 2 are obtained through calculation.
TABLE 2
Through optimization calculation, the 24-hour average network loss of the system is 65.62kW, the voltage deviation is 0.378, and compared with the 24-hour average network loss of 79.44kW without reactive power output optimization, the voltage deviation is 0.561, the network loss and the voltage deviation are both obviously reduced, and the economical efficiency and the safety of the operation of the power distribution network are met.
Claims (1)
1. The active power distribution network resource optimization configuration based on the longicorn whisker algorithm is characterized by comprising the following steps:
1) In the case of full-scale consumption of renewable energy, active power is balanced, and the power balance model is as follows:
Net_load＝P load (t)-P PV (t)-P WG (t)-P b (t) (2)
in the formula: p DR (i, t) represents the net power of the ith flexible adjustment resource in the t time period; net _ load (t) is Net active load which is used for flexibly adjusting resources to be compensated in the t-th time period; grid _ loss is a network loss coefficient of the active power distribution network; p load (t) is the original load of the active power distribution network; p PV (t) photovoltaic power generation output of the active power distribution network in the tth time period; p WG (t) wind power generation output of the active power distribution network in the tth time period; p b (t) providing power to the upper stage of the transmission grid;
2) Converting multiple targets into a single target model by using a linear weighting method, namely the fitness function is as follows:
in the formula: w is a i The method is a multi-target weight value, reflects the trade-off relation between economy and voltage stability, becomes a preference factor, m is the number of target functions,
the multi-objective function is as follows:
a) Objective function 1: the ADN voltage deviation of the active power distribution network is minimum:
in the formula: i =1,2,3, \ 8230, N, N is distribution network sectionTotal number of dots; v N Is a rated voltage; i. j is the head end and tail end nodes of the branch respectively; Δ V ij (x) The voltage drop longitudinal component of the branch i and j is shown; delta V ij The voltage drop transverse components of the branches i and j are obtained; x is a control variable, so that the reactive power output of the resource can be flexibly adjusted;
b) The objective function 2: the ADN loss of the active power distribution network is minimum:
in the formula: i =1,2,3, \ 8230;, N b Is the total number of branches in the network;
and
respectively a flow-through branch b i Active power and reactive power of;
is a branch b i The branch resistance of (1); v bi Is a branch b i The terminal voltage of (1);
3) Initializing algorithm parameters;
4) Initializing the control position of the longicorn, namely initializing the reactive power output of each flexibly adjustable resource, wherein the formula is as follows:
X i ＝X imin +(X imax -X imin )×rand (6)
in the formula: i denotes the dimension, X imin Represents the ith minimum value of reactive power output of the flexibly adjustable resource, X imax The maximum value of the reactive power output of the ith flexibly adjustable resource is represented;
5) The positions of the left and right tentacles of the longicorn are obtained according to the positions of the longicorn, and the fitness values corresponding to the positions of the left and right tentacles of the longicorn are obtained according to the fitness function, wherein the calculation formula is as follows:
a) Calculating the positions of the left and right tentacles of the longicorn:
dir＝rands(k，1) (7)
in the formula: k represents the total dimensionality of the control variables; dir represents a random vector of the orientation of the longicorn; xleft represents the position vector of the left whisker; d represents the fixed distance between the longicorn tentacles, namely the size of the longicorn; x is a control variable vector, so that the reactive power output of the resource can be flexibly adjusted; xright represents the position vector of the right whisker;
the formula (7) is used for generating a random vector to represent the orientation of the longicorn whiskers, the formula (8) normalizes the orientation of the longicorn whiskers, and the formula (9) and the formula (10) are respectively used for calculating the positions of the left and right longicorn whiskers;
b) Calculating the adaptability value of the left and right tentacles:
f(X)＝af 1 (X)+bf 2 (X) (11)
in the formula: a. b are respectively the objective function f 1 (X) and f 2 (X) a weight value;
6) And comparing the sizes of the adaptability values of the left and right tentacles, and updating the position of the longicorn, wherein the mathematical model is as follows:
X＝X-step×dir×sign(fleft-fright)+w (12)
in the formula: step represents the step size; sign is a sign function, fleft and fright are fitness values corresponding to the left and right tentacles respectively, and w is a random moving minimum step length;
7) Calculating a new fitness value according to the position of the longicorn updated in the step 6), and comparing the new fitness value with the fitness value corresponding to the last longicorn to obtain an optimal fitness value;
8) Judging whether a set iteration number termination condition is met or not, and if not, repeating the step 5); and if so, selecting a resource optimal configuration strategy corresponding to the optimal fitness value, namely the output of each flexible resource.
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