CN108964099B - Distributed energy storage system layout method and system - Google Patents
Distributed energy storage system layout method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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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
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Abstract
A distributed energy storage system layout method, the method comprising: based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the number of optional states of the individual distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, solving a preset objective function with the minimum investment cost of the distributed energy storage systems as a target by adopting the artificial fish swarm algorithm to obtain an optimal solution; and laying out the system based on the optimal solution. The invention adopts the artificial fish swarm algorithm, has the characteristics of simple operation, small control parameter, higher search precision and stronger robustness, can reduce the solving complexity, and solves the problems of complex layout and large calculation amount of the distributed energy storage system.
Description
Technical Field
The invention relates to the technical field of energy storage, in particular to a distributed energy storage system layout method and system.
Background
In recent years, with a large number of grid connection of distributed energy sources and a large number of problems brought by the grid connection to stable operation of a power distribution network, the energy storage system can be connected to play a role in supporting and adjusting the system to a certain extent, and the reasonable planning layout in the power distribution network can effectively realize demand side management, eliminate peak-valley difference and smooth load, and can also be used as a means for improving the operation stability and reliability of the power distribution network, adjusting frequency and compensating load fluctuation, however, the research on the layout method of the distributed energy storage system is single.
The existing phase of the subsidy policy is not clear and energy storage devices are often expensive. On the integral coordination layout of the distributed energy storage, the distributed energy storage system is considered, the layout is complex, and the calculated amount is large.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a comprehensive energy system grading regulation method and system for balancing the supply and demand of cold and hot electricity.
The technical scheme provided by the invention is as follows:
a distributed energy storage system layout method, the method comprising:
based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the number of optional states of the individual distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, solving a preset objective function with the minimum investment cost of the distributed energy storage systems as a target by adopting the artificial fish swarm algorithm to obtain an optimal solution;
and laying out the system based on the optimal solution.
Preferably, the solving, based on the obtained number of individuals of the distributed energy storage system, a maximum distance that the distributed energy storage system moves in the power grid, a distance that no influence exists between the distributed energy storage systems, a selectable number of states of the individuals of the distributed energy storage system in a field of view, and a density of the number of individuals of the distributed energy storage system, by using the artificial fish swarm algorithm, a preset objective function that aims at a minimum investment cost of the distributed energy storage system, includes:
setting the number of individuals of the distributed energy storage system into an artificial fish swarm algorithm: the number of individuals of the artificial fish school;
setting the maximum distance of the position movement of the distributed energy storage system in the power grid as an artificial fish swarm algorithm: the maximum step length of the movement of the human fish shoal;
setting the distance without influence among the distributed energy storage systems as an artificial fish swarm algorithm: sensing distance;
locating the density of the individual numbers of the distributed energy storage system as in an artificial fish swarm algorithm: a degree of congestion;
performing iterative computation on the objective function based on an artificial fish swarm algorithm to obtain a bulletin board value;
and taking the value of the bulletin board as the optimal layout of the distributed energy storage system when the investment cost is minimum.
Preferably, the iterative calculation is performed on the objective function based on the artificial fish swarm algorithm to obtain a bulletin board value, including:
taking the objective function as input of an artificial fish swarm algorithm, and performing iterative computation to obtain an fitness function of each individual;
selecting an execution method of the objective function according to the fitness function and the individual position of the individual to obtain an optimized value, wherein the optimized value is the investment cost of the distributed energy storage system;
comparing the optimized value with the value on the bulletin board, if the optimized value is superior to the value of the bulletin board, updating the bulletin board according to the optimized value, otherwise, not updating the value of the bulletin board;
and outputting the current value of the bulletin board when the artificial fish swarm algorithm reaches the maximum iteration number, otherwise, continuing to iterate the calculation.
Preferably, the objective function is represented by the following formula:
minf＝C dess +C mainainace +C rce
wherein: and (3) minf: the distributed energy storage system is at investment cost; c (C) dess The installation cost of the energy storage device; c (C) mainainace The operation and maintenance cost of the energy storage device; c (C) rce For reactive compensation equipment costs.
Preferably, the installation cost C of the reactive compensation equipment rce Calculated as follows:
wherein C is 0 The unit reactive capacity price is corrected for the reactive compensation equipment, and the unit is yuan/kvar; q (Q) gi Reactive compensation capacity for the ith node in kvar, Q gi >0 means to install a capacitor or a camera, Q gi <0 represents that a shunt reactor is arranged; n is the number of nodes.
Preferably, the objective function further includes a constraint condition; wherein the constraint comprises: node voltage
Constraint, branch current constraint, equality constraint, energy storage system active force constraint, reactive force constraint and state of charge constraint;
the node voltage constraint is as follows:
U imin ≤U′ i ≤U imax
in U imin : the upper voltage limit of node i; u (U) imax : the lower voltage limit of the node i, wherein i is an energy storage installation node; u's' i : the voltage of node i;
the branch current constraint comprises:
I′ k ≤I kmax
wherein I is k The current for branch k; i kmax Maximum allowable passing current for branch k;
the equality constraint includes: active power and reactive power balance constraints;
the active power balance constraint is as follows:
wherein: p (P) Gi : active power P generated by node i generator Di The method comprises the steps of carrying out a first treatment on the surface of the Active power consumed by the load; v (V) i The voltage of the node i; v (V) j The voltage of the node j; g ij Conductance on line ij; b (B) ij Susceptance on line ij;
the reactive power balance constraint is as follows:
wherein V is i The voltage of the node i; v (V) j The voltage of the node j; g ij Conductance on line ij; b (B) ij Susceptance on line ij; q (Q) i Reactive power of node i; q (Q) Di Reactive power consumed by the load;
the energy storage system active force constraint is shown as follows:
P cmin ≤P c ≤P cmax
wherein: p (P) c Is the active output of the energy storage system; wherein P is cmax P cmin The upper limit and the lower limit of the active force of the energy storage device are respectively;
Q cmin ≤Q c ≤Q cmax
wherein Q is c Reactive power output of the energy storage system; wherein Q is cmax Q cmin The upper limit and the lower limit of reactive power output of the energy storage device are respectively set.
Preferably, the execution method comprises foraging, clustering, rear-end collision and random behaviors.
A system of distributed energy storage system layout, the system comprising:
the acquisition module is used for: the method comprises the steps of obtaining the number of individuals of the distributed energy storage systems, wherein the maximum distance of the position movement of the distributed energy storage systems in a power grid, the distance without influence among the distributed energy storage systems and the density of the number of individuals of the distributed energy storage systems are obtained;
the calculation module: solving an objective function by adopting an artificial fish swarm algorithm based on the minimum investment cost of the distributed energy storage system as a target to obtain an optimal solution;
layout module: and laying out the system based on the optimal solution.
Preferably, the computing module includes:
and a calculation sub-module: the method comprises the steps of performing iterative computation by taking an objective function and constraint conditions as input of an artificial fish swarm algorithm to obtain an fitness function of each individual;
and (3) an optimization sub-module: the execution method for the objective function is selected according to the fitness function and the individual position of the individual, and an optimized value is obtained;
a first judging sub-module: comparing the optimized value with the value on the bulletin board, if the optimized value is superior to the value of the bulletin board, updating the bulletin board according to the optimized value, otherwise, not updating the value of the bulletin board;
and a second judging sub-module: and outputting the current value of the bulletin board when the artificial fish swarm algorithm reaches the maximum iteration number, otherwise, continuing to iterate the calculation.
Preferably, the optimization submodule comprises a function calculation unit and an execution unit;
the function calculation unit calculates an objective function by:
minf＝C dess +C mainainace +C rce
wherein: and (3) minf: is an objective function; c (C) dess The installation cost of the energy storage device; c (C) mainainace The operation and maintenance cost of the energy storage device; c (C) rce For reactive compensation equipment costs.
The execution unit includes:
a foraging behavior subunit, configured to randomly select a state in a field of view of an individual, respectively calculate objective function values of the individual and the state, and if the objective function value of the state is found to be better than the objective function value of the individual, move the individual one step in a direction of the state;
the group behavior subunit is used for searching the number and the central position of other individuals in the current visual field by the individuals, and if the central position of the individuals exists, the group behavior subunit moves towards the central position by one step;
a rear-end collision behavior subunit, configured to: searching the function optimal individual in the current visual field by the individual, and if the optimal individual exists, moving Xi to the optimal individual by one step;
the random behavior subunit is used for enabling the individual to randomly move one step to reach a new state.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a distributed energy storage system layout method, which is characterized by comprising the following steps: based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the number of optional states of the individual distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, solving a preset objective function with the minimum investment cost of the distributed energy storage systems as a target by adopting the artificial fish swarm algorithm to obtain an optimal solution; the system is laid out based on the optimal solution, and the method has the characteristics of simplicity in operation, small control parameters, higher search precision and stronger robustness, can reduce the complexity of the solution, realizes the overall coordination layout of the distributed energy storage, and solves the problems of complex layout and large calculation amount of the distributed energy storage system.
2. The distributed energy storage system layout method combines distributed energy storage layout with economy, so that the investment cost is minimum, and the power grid added with energy storage can be safely, stably and reliably operated.
3. The invention provides a distributed energy storage system layout method, which takes investment minimization as an objective function to greatly improve the practical feasibility of adding an energy storage system into a power distribution network. Meanwhile, the energy storage system has the advantages of the energy storage system, plays a role in supporting and adjusting the power grid system to a certain extent, and can be used as a means for improving the running stability and reliability of the power grid, adjusting the frequency and compensating the load fluctuation.
Drawings
FIG. 1 is a flow chart of a distributed energy storage system layout method of the present invention;
fig. 2 is a schematic diagram of a distributed energy storage system layout process according to an embodiment of the invention.
Detailed Description
On the integral coordination layout of the distributed energy storage, the distributed energy storage system is considered, the layout is complex, the calculated amount is large, and the artificial fish swarm algorithm has the characteristics of simplicity in operation, small control parameters, higher search precision and stronger robustness, so that the solving complexity is greatly reduced. The method is based on an artificial fish swarm algorithm, the investment cost is minimized as a target value, and meanwhile, the layout of the energy storage system is realized by taking node voltage and branch current, power balance of each node, active and reactive power output of the energy storage system and charge state of the energy storage system as constraint conditions.
For a better understanding of the present invention, reference is made to the following description, drawings and examples.
Detailed description of the preferred embodiments
According to the distributed energy storage system layout method shown in fig. 1, based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in a power grid is not provided, the distance of influence among the distributed energy storage systems is not provided, the individual states of the distributed energy storage systems in the visual field are selected, the density of the individual number of the distributed energy storage systems is selected, and the artificial fish swarm algorithm is adopted to solve a preset objective function with the minimum investment cost of the distributed energy storage systems as a target, so that an optimal solution is obtained;
and laying out the system based on the optimal solution.
Based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the selectable state number of the individual of the distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, the artificial fish swarm algorithm is adopted to solve a preset objective function aiming at the minimum investment cost of the distributed energy storage systems, and the method comprises the following steps:
setting the number of individuals of the distributed energy storage system into an artificial fish swarm algorithm: the number of individuals of the artificial fish school;
setting the maximum distance of the position movement of the distributed energy storage system in the power grid as an artificial fish swarm algorithm: the maximum step length of the movement of the human fish shoal;
setting the distance without influence among the distributed energy storage systems as an artificial fish swarm algorithm: sensing distance;
locating the density of the individual numbers of the distributed energy storage system as in an artificial fish swarm algorithm: a degree of congestion;
performing iterative computation on the objective function based on an artificial fish swarm algorithm to obtain a bulletin board value;
and taking the value of the bulletin board as the optimal layout of the distributed energy storage system when the investment cost is minimum.
The iterative calculation is carried out on the objective function based on the artificial fish swarm algorithm to obtain the value of the bulletin board, and the method comprises the following steps:
taking the objective function as input of an artificial fish swarm algorithm, and performing iterative computation to obtain an fitness function of each individual;
selecting an execution method of the objective function according to the fitness function and the individual position of the individual to obtain an optimized value, wherein the optimized value is the investment cost of the distributed energy storage system;
comparing the optimized value with the value on the bulletin board, if the optimized value is superior to the value of the bulletin board, updating the bulletin board according to the optimized value, otherwise, not updating the value of the bulletin board;
and outputting the current value of the bulletin board when the artificial fish swarm algorithm reaches the maximum iteration number, otherwise, continuing to iterate the calculation.
The objective function is represented by the following formula:
minf＝C dess +C mainainace +C rce
wherein: and (3) minf: the distributed energy storage system is at investment cost; c (C) dess The installation cost of the energy storage device; c (C) mainainace The operation and maintenance cost of the energy storage device; c (C) rce For reactive compensation equipment costs.
The cost C of the reactive compensation equipment rce Calculated as follows:
wherein C is 0 The unit reactive capacity price is corrected for the reactive compensation equipment, and the unit is yuan/kvar; q (Q) gi Reactive compensation capacity for the ith node in kvar, Q gi >0 means to install a capacitor or a camera, Q gi <0 represents that a shunt reactor is arranged; n is the number of nodes.
The objective function further includes a constraint; wherein the constraint comprises: node voltage constraint, branch current constraint, equality constraint, energy storage system active power output constraint, reactive power output constraint and state of charge constraint;
the node voltage constraint is as follows:
U imin ≤U′ i ≤U imax
in U imin : the upper voltage limit of node i; u (U) imax : the lower voltage limit of the node i, wherein i is an energy storage installation node; u's' i : the voltage of node i;
the branch current constraint comprises:
I′ k ≤I kmax
wherein I is k The current for branch k; i kmax Maximum allowable passing current for branch k;
the equality constraint includes: active power and reactive power balance constraints;
the active power balance constraint is as follows:
wherein: p (P) Gi : active power P generated by node i generator Di The method comprises the steps of carrying out a first treatment on the surface of the Active power consumed by the load; v (V) i The voltage of the node i; v (V) j The voltage of the node j; g ij Conductance on line ij; b (B) ij Susceptance on line ij;
the reactive power balance constraint is as follows:
wherein V is i The voltage of the node i; v (V) j The voltage of the node j; g ij Conductance on line ij; b (B) ij Susceptance on line ij; q (Q) i Reactive power of node i; q (Q) Di Reactive power consumed by the load;
the energy storage system active force constraint is shown as follows:
P cmin ≤P c ≤P cmax
wherein: p (P) c Is the active output of the energy storage system; wherein P is cmax P cmin The upper limit and the lower limit of the active force of the energy storage device are respectively;
Q cmin ≤Q c ≤Q cmax
wherein Q is c Reactive power output of the energy storage system; wherein Q is cmax Q cmin The upper limit and the lower limit of reactive power output of the energy storage device are respectively set.
The execution method comprises foraging, clustering, rear-end collision and random behaviors.
Second embodiment
A system for distributed energy storage system layout, an acquisition module: the method comprises the steps of obtaining the number of individuals of the distributed energy storage systems, wherein the maximum distance of the position movement of the distributed energy storage systems in a power grid, the distance without influence among the distributed energy storage systems and the density of the number of individuals of the distributed energy storage systems are obtained;
the calculation module: solving an objective function by adopting an artificial fish swarm algorithm based on the minimum investment cost of the distributed energy storage system as a target to obtain an optimal solution;
layout module: and laying out the system based on the optimal solution.
The computing module comprises:
and a calculation sub-module: the method comprises the steps of performing iterative computation by taking an objective function and constraint conditions as input of an artificial fish swarm algorithm to obtain an fitness function of each individual;
and (3) an optimization sub-module: the execution method for the objective function is selected according to the fitness function and the individual position of the individual, and an optimized value is obtained;
a first judging sub-module: comparing the optimized value with the value on the bulletin board, if the optimized value is superior to the value of the bulletin board, updating the bulletin board according to the optimized value, otherwise, not updating the value of the bulletin board;
and a second judging sub-module: and outputting the current value of the bulletin board when the artificial fish swarm algorithm reaches the maximum iteration number, otherwise, continuing to iterate the calculation.
The optimization submodule comprises a function calculation unit and an execution unit;
the function calculation unit calculates an objective function by:
minf＝C dess +C mainainace +C rce
wherein: and (3) minf: is an objective function; c (C) dess The installation cost of the energy storage device; c (C) mainainace The operation and maintenance cost of the energy storage device; c (C) rce For reactive compensation equipment costs.
The execution unit includes:
a foraging behavior subunit, configured to randomly select a state in a field of view of an individual, respectively calculate objective function values of the individual and the state, and if the objective function value of the state is found to be better than the objective function value of the individual, move the individual one step in a direction of the state;
the group behavior subunit is used for searching the number and the central position of other individuals in the current visual field by the individuals, and if the central position of the individuals exists, the group behavior subunit moves towards the central position by one step;
a rear-end collision behavior subunit, configured to: searching the function optimal individual in the current visual field by the individual, and if the optimal individual exists, moving Xi to the optimal individual by one step;
the random behavior subunit is used for enabling the individual to randomly move one step to reach a new state.
Detailed description of the preferred embodiments
As shown in fig. 2, the flow chart of the distributed energy storage system layout method provided by the invention is characterized in that the complexity of solving is greatly reduced by utilizing the characteristics of simple operation, small control parameters, higher search precision, stronger robustness and the like of an artificial fish swarm algorithm under the condition that the power grid added with energy storage can run safely, stably and reliably, and simultaneously, the optimal layout of energy storage when investment cost is minimized is also satisfied. The concrete introduction is as follows:
1. a distributed energy storage system layout method, the method comprising the steps of:
step 1: initializing and setting in an artificial fish swarm algorithm;
step 2: inputting an objective function and a network constraint condition in an artificial fish swarm algorithm;
step 3: the fish swarm algorithm starts to iterate, the fitness function of each individual is calculated, and the selection behavior is evaluated, namely, the optimal execution method for the objective function is selected according to the individual position. These methods of execution include foraging, clustering, rear-end collisions, and random behavior;
step 4: completing the optimization of one-time target address selection, evaluating the optimized value, and if the optimized value is better than the bulletin board, updating the bulletin board to the individual;
step 5: and if the fish swarm algorithm reaches the maximum iteration number, outputting the value of the bulletin board, and if the value of the bulletin board does not reach the maximum iteration number, returning to the step 3, and finally realizing the optimal layout of energy storage when the investment cost is minimum.
2. According to the distributed energy storage system layout method, the step 1 is used for initializing and setting the individual number, the maximum moving step length, the sensing distance, the maximum number of foraging attempts, the crowding degree and the maximum iteration number of the artificial fish swarm algorithm.
3. According to the distributed energy storage system layout method, the step 2 establishes a distributed energy storage construction mode with the minimum total cost as an objective function, and the optimal position of the energy storage layout is given according to the comparison of the total objective function. The specific calculation method is as follows:
the objective function, i.e. the minimum total cost, comprises the installation cost of the energy storage device, the operation maintenance cost of the energy storage device and the cost of installing reactive compensation equipment, and is expressed by the following formula
minf＝C dess +C mainainace +C rce (1)
Wherein:
C dess the installation cost of the energy storage device;
C mainainace the operation and maintenance cost of the energy storage device;
C rce to take account of the costs of installing reactive compensation equipment, wherein,
C 0 (meta/kvar) is a price per reactive capacity which is corrected taking account of the installation of reactive compensation equipment. Q (Q) gi (kvar) reactive compensation capacity for the ith load point, qgi>0 means to install a capacitor or a camera Qgi<0 represents that a shunt reactor is arranged;
n is the number of nodes;
constraint conditions
Node voltage constraint
U imin ≤U' i ≤U imax (2)
Branch current constraint
I' k ≤I kmax (3)
i is an energy storage installation node; wherein U is imin U imax The upper limit and the lower limit of the voltage of the node i are respectively;
I kmax maximum allowable passing current for branch k;
the equality constraint is to inject active power and reactive power balance constraint, namely power flow constraint, into each node:
active and reactive power output constraint of energy storage system
P cmin ≤P c ≤P cmax
Q cmin ≤Q c ≤Q cmax (5)
P c Is the active output of the energy storage system; wherein P is cmax P cmin The upper limit and the lower limit of the active force of the energy storage device are respectively;
Q c reactive power output of the energy storage system; wherein Q is cmax Q cmin The upper limit and the lower limit of reactive power output of the energy storage device are respectively;
state of charge constraints:
SOC xmin ≤SOC x ≤SOC xmax (6)
SOC xmin 、SOC xmax the minimum and maximum states of charge of the energy storage system x, respectively.
4. According to the distributed energy storage system layout method, in the step 3, the fitness function of each individual is calculated, the optimal position which meets the minimum investment is taken, and the optimal position is given to the bulletin board. Then, according to the individual position, selecting the optimization method of the corresponding objective function, wherein the execution methods comprise foraging, clustering, rear-end collision and random behaviors, and the method is characterized in that:
(1) Foraging behavior: the individual Xi randomly selects a state Xj in the visual field, and objective function values Yi and Yj of the Xi and Xj are calculated respectively, and if the Yi is found to be better than the Yi, the Xi moves one step in the direction of the Xj.
(2) Clustering behavior: the individual Xi searches for the number nf of other individuals in the current field of view (dij < Vaisual) and the center position Xc, if Yc/nf > δyi, there is a center position due to the individual, xi moves one step toward the center position. (3) rear-end collision behavior: individual Xi searches for the optimal individual Xj of the (dij < Vaisual) function Yj in the current field of view, if Yj/nf > δyi, then Xi moves one step towards the optimal individual
(4) Random behavior in which an individual Xi moves randomly one step to a new state
5. According to the distributed energy storage system layout method, the optimization mode is selected in the step 3, then the optimization of the primary target address selection in the step four is completed, and if the objective function of the optimized individual is better than the objective function value corresponding to the individual on the bulletin board, the bulletin board is updated to the individual.
6. According to the distributed energy storage system layout method, in the step 5, if the iteration number of the fish swarm algorithm reaches the set maximum iteration number, the value of the bulletin board is output, if the iteration number of the fish swarm algorithm does not reach the set maximum iteration number, the step 3 is repeated, and finally the optimal layout of energy storage when the investment cost is minimum is achieved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (7)
1. A distributed energy storage system layout method, the method comprising:
based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the number of optional states of the individual distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, solving a preset objective function with the minimum investment cost of the distributed energy storage systems as a target by adopting an artificial fish swarm algorithm to obtain an optimal solution;
laying out a system based on the optimal solution;
based on the acquired individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the selectable state number of the individual of the distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, the artificial fish swarm algorithm is adopted to solve a preset objective function aiming at the minimum investment cost of the distributed energy storage systems, and the method comprises the following steps:
setting the number of individuals of the distributed energy storage system into an artificial fish swarm algorithm: the number of individuals of the artificial fish school;
setting the maximum distance of the position movement of the distributed energy storage system in the power grid as an artificial fish swarm algorithm: the maximum moving step length of the artificial fish school;
setting the distance without influence among the distributed energy storage systems as an artificial fish swarm algorithm: sensing distance;
locating the density of the individual numbers of the distributed energy storage system as in an artificial fish swarm algorithm: a degree of congestion;
performing iterative computation on the objective function based on an artificial fish swarm algorithm to obtain a bulletin board value;
taking the value of the bulletin board as the optimal layout of the distributed energy storage system when the investment cost is minimum; the iterative calculation is carried out on the objective function based on the artificial fish swarm algorithm to obtain the value of the bulletin board, and the method comprises the following steps:
taking the objective function as input of an artificial fish swarm algorithm, and performing iterative computation to obtain an fitness function of each individual;
selecting an execution method of the objective function according to the fitness function and the individual position of the individual to obtain an optimized value, wherein the optimized value is the investment cost of the distributed energy storage system;
comparing the optimized value with the value on the bulletin board, if the optimized value is superior to the value of the bulletin board, updating the bulletin board according to the optimized value, otherwise, not updating the value of the bulletin board;
and outputting the current value of the bulletin board when the artificial fish swarm algorithm reaches the maximum iteration number, otherwise, continuing to iterate the calculation.
2. A distributed energy storage system layout method in accordance with claim 1 wherein said objective function is represented by the formula:
min f＝C dess +C mainainace +C rce
wherein: min f: the distributed energy storage system is at investment cost; c (C) dess The installation cost of the energy storage device; c (C) mainainace The operation and maintenance cost of the energy storage device; c (C) rce For reactive compensation equipment costs.
3. A distributed energy storage system layout method according to claim 2 wherein reactive compensation equipment cost C is installed rce Calculated as follows:
wherein C is 0 The unit reactive capacity price is corrected for the reactive compensation equipment, and the unit is yuan/kvar; q (Q) gi Reactive compensation capacity for the ith node in kvar, Q gi >0 means to install a capacitor or a camera, Q gi <0 represents that a shunt reactor is arranged; n is the number of nodes.
4. A distributed energy storage system layout method in accordance with claim 1 wherein said objective function further comprises constraints; wherein the constraint comprises: node voltage constraint, branch current constraint, equality constraint, energy storage system active power output constraint, reactive power output constraint and state of charge constraint;
the node voltage constraint is as follows:
U imin ≤U′ i ≤U imax
in U imin : the lower voltage limit of node i; u (U) imax : the upper voltage limit of the node i, wherein i is an energy storage installation node; u's' i : the voltage of node i;
the branch current constraint comprises:
I′ k ≤I kmax
in the formula, I' k The current for branch k; i kmax Maximum allowable passing current for branch k;
the equality constraint includes: active power and reactive power balance constraints;
the active power balance constraint is as follows:
wherein: p (P) Gi : active power generated by the generator at the node i; p (P) Di : active power consumed by the load; v (V) i The voltage of the node i; v (V) j The voltage of the node j; g ij Conductance on line ij; b (B) ij Susceptance on line ij;
the reactive power balance constraint is as follows:
wherein V is i The voltage of the node i; v (V) j The voltage of the node j; g ij Conductance on line ij; b (B) ij : susceptance on line ij; q (Q) i : reactive power of node i; q (Q) Di : reactive power consumed by the load;
the energy storage system active force constraint is shown as follows:
P cmin ≤P c ≤P cmax
wherein: p (P) c Is the active output of the energy storage system; wherein P is cmax 、P cmin The upper limit and the lower limit of the active force of the energy storage device are respectively;
Q cmin ≤Q c ≤Q cmax
wherein Q is c Reactive power output of the energy storage system; wherein Q is cmax 、Q cmin The upper limit and the lower limit of reactive power output of the energy storage device are respectively set.
5. A distributed energy storage system layout method in accordance with claim 1 wherein said execution method comprises foraging, clustering, rear-end collision and random behavior.
6. A system of distributed energy storage system layout, the system comprising:
the acquisition module is used for: the method comprises the steps of obtaining the number of individuals of the distributed energy storage systems, wherein the maximum distance of the position movement of the distributed energy storage systems in a power grid, the distance without influence among the distributed energy storage systems and the density of the number of individuals of the distributed energy storage systems are obtained;
the calculation module: solving an objective function by adopting an artificial fish swarm algorithm based on the minimum investment cost of the distributed energy storage system as a target to obtain an optimal solution;
layout module: laying out a system based on the optimal solution;
the acquisition module comprises: based on the obtained individual number of the distributed energy storage systems, the maximum distance of the position movement of the distributed energy storage systems in the power grid, the distance without influence among the distributed energy storage systems, the selectable state number of the individual of the distributed energy storage systems in the visual field, and the density of the individual number of the distributed energy storage systems, the artificial fish swarm algorithm is adopted to solve a preset objective function aiming at the minimum investment cost of the distributed energy storage systems, and the method comprises the following steps:
setting the number of individuals of the distributed energy storage system into an artificial fish swarm algorithm: the number of individuals of the artificial fish school;
setting the maximum distance of the position movement of the distributed energy storage system in the power grid as an artificial fish swarm algorithm: the maximum step length of the movement of the human fish shoal;
setting the distance without influence among the distributed energy storage systems as an artificial fish swarm algorithm: sensing distance;
locating the density of the individual numbers of the distributed energy storage system as in an artificial fish swarm algorithm: a degree of congestion;
performing iterative computation on the objective function based on an artificial fish swarm algorithm to obtain a bulletin board value;
taking the value of the bulletin board as the optimal layout of the distributed energy storage system when the investment cost is minimum;
the computing module comprises:
and a calculation sub-module: the method comprises the steps of performing iterative computation by taking an objective function and constraint conditions as input of an artificial fish swarm algorithm to obtain an fitness function of each individual;
and (3) an optimization sub-module: the execution method for the objective function is selected according to the fitness function and the individual position of the individual, and an optimized value is obtained;
a first judging sub-module: comparing the optimized value with the value on the bulletin board, if the optimized value is superior to the value of the bulletin board, updating the bulletin board according to the optimized value, otherwise, not updating the value of the bulletin board;
and a second judging sub-module: and outputting the current value of the bulletin board when the artificial fish swarm algorithm reaches the maximum iteration number, otherwise, continuing to iterate the calculation.
7. The system of claim 6, wherein the optimization sub-module comprises a function computation unit and an execution unit;
the function calculation unit calculates an objective function by:
minf＝C dess +C mainainace +C rce
wherein: minf is an objective function; c (C) dess The installation cost of the energy storage device; c (C) mainainace The operation and maintenance cost of the energy storage device; c (C) rce Cost for reactive compensation equipment;
the execution unit includes:
a foraging behavior subunit, configured to randomly select a state in a field of view of an individual, respectively calculate objective function values of the individual and the state, and if the objective function value of the state is found to be better than the objective function value of the individual, move the individual one step in a direction of the state;
the group behavior subunit is used for searching the number and the central position of other individuals in the current visual field by the individuals, and if the number and the central position of the individuals are better than the central position of the individuals, the group behavior subunit moves towards the central position by one step;
a rear-end collision behavior subunit, configured to: the individual searches the function optimal individual in the current visual field, and if the optimal individual exists, the individual moves towards the optimal individual by one step;
the random behavior subunit is used for enabling the individual to randomly move one step to reach a new state.
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