CN110690719B - Micro-grid battery energy storage configuration method and readable storage medium - Google Patents
Micro-grid battery energy storage configuration method and readable storage medium Download PDFInfo
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Abstract
The invention discloses a micro-grid battery energy storage configuration method and a readable storage medium, and relates to the technical field of micro-grids, wherein the method comprises the following steps: determining power mileage according to the structure of the power grid branch; establishing a linear optimization model, and carrying out optimization configuration on energy storage parameters according to the power mileage; and controlling the output of the energy storage battery according to the optimized configuration result. According to the method, the linear optimization model is established, the energy storage parameters are optimally configured according to the power mileage, and the output of the energy storage battery is controlled according to the optimal configuration result, so that the problem of voltage crossing of a power grid is relieved, and the voltage of the power grid is smoothed. Meanwhile, the linear optimization solution based on the power mileage is simple and quick, and is beneficial to the quick response and control of the power grid.
Description
Technical Field
The invention relates to the technical field of micro-grids, in particular to a micro-grid battery energy storage configuration method and a readable storage medium.
Background
In order to solve the energy crisis and improve the environmental quality, the efficient utilization of renewable energy has become a social development trend. However, renewable energy sources, such as wind, light and the like, have the disadvantages of strong intermittency and large fluctuation. The output curves of the energy sources have extremely high fluctuation, and the tidal current of the power grid flows in a two-way mode when the energy sources are directly connected into the power grid, so that the current flow frequently changes, the voltage locally rises, the voltage is magical, the power grid quality is influenced, and the electricity utilization safety is threatened. The Battery Energy Storage System (Battery Energy Storage System) can better solve the problem. The characteristic that the energy can be translated on a time scale is utilized, the peak clipping and valley filling of a power grid can be realized, and the voltage quality is ensured.
At present, many researches on the application of a centralized energy storage system in voltage regulation of a microgrid exist, but most of the researches are focused on establishment of an optimization model and an intelligent algorithm for solving the optimization model. The existing intelligent algorithm has the problems that the convergence rate of the existing intelligent algorithm to a large-scale problem is low, the solving time is long, and meanwhile, the optimal solution can not be found necessarily.
Disclosure of Invention
In view of the foregoing defects in the prior art, an object of the present invention is to provide a microgrid battery energy storage configuration method and a readable storage medium, wherein a linear optimization model is established, energy storage parameters are optimally configured according to the power mileage, and the output of an energy storage battery is controlled according to the optimal configuration result, so that the problem of voltage crossing of a power grid is alleviated, the voltage of the power grid is smoothed, and the microgrid battery energy storage configuration method and the readable storage medium are beneficial to fast response and control of the power grid.
One of the objectives of the present invention is achieved by such a technical solution, which is a microgrid battery energy storage configuration method, comprising the following steps:
determining power mileage according to the structure of the power grid branch;
establishing a linear optimization model, and carrying out optimization configuration on energy storage parameters according to the power mileage;
controlling the output of the energy storage battery according to the optimized configuration result;
the determining of the power mileage according to the power grid branch structure comprises the following steps:
calculating branch voltage drop according to the power grid branch structure to define power mileage, and satisfying the following conditions:
where Δ U represents the branch voltage drop, ρPRepresenting the resistance per unit length of the branch, pQRepresenting reactance value per unit length of branch, P representing grid node power, PMPRepresenting branch active power mileage, PMQRepresenting the reactive power mileage of the branch;
active power mileage PMP：
Reactive power mileage PMQ：
In the formula, Pi、QiRespectively the active and reactive power of branch I, IiIs the length of branch i, n is the number of branches;
establishing a linear optimization model, comprising:
taking the absolute values of the power mileage and summing to obtain an optimized objective function:
f＝min(∑Node_plus+N+S*N)
Node_plus＝|Node|＝|M*(P+Battery)|
wherein Node _ plus represents the absolute value of the power mileage, N represents the number of installation positions of energy storage sources, and S represents the capacity of a single energy storage source; m represents a mileage matrix;
determining constraint conditions according to the optimization objective function to obtain a final optimization objective function;
and solving the final optimization objective function to obtain an optimization configuration result.
The constraint conditions include: the upper limit of the capacity of the battery, the upper limit and the lower limit of the charge and discharge power of the battery, the residual energy storage initial capacity, the charge and discharge balance and the initial capacity limit.
Solving the final optimization objective function to obtain an optimized configuration result, comprising:
and adding a node constraint matrix to solve the final optimization objective function to obtain an optimization configuration result.
Optionally, before the linear optimization model is established, the method includes:
obtaining and determining a node power demand matrix according to the active power matrix and the battery variable matrix, and satisfying the following conditions:
Pg＝P+Battery
wherein Battery represents a Battery variable matrix;
determining a power mileage matrix, and satisfying the following conditions:
Node＝M*Pg
wherein Node represents the power mileage of the common Node.
Optionally, the energy storage parameters include the location, number, and capacity of energy storage.
Optionally, controlling the output of the energy storage battery according to the optimized configuration result includes:
dividing the time period, determining the output of the power grid node according to the divided time interval, and satisfying the following conditions:
wherein, BatteryijRepresents the average power, Ba, of battery i in scheduling period ji(t) represents the real-time power at time t, PijRepresenting the average power flow, P, calculated from historical power flow data when no battery is storing energyi(t) is the average power requirement, and j represents the scheduling period.
The second object of the present invention is achieved by the technical solution, which is a computer-readable storage medium, wherein an implementation program for information transfer is stored on the computer-readable storage medium, and the implementation program implements the steps of the foregoing method when executed by a processor.
Due to the adoption of the technical scheme, the invention has the following advantages:
according to the method, the linear optimization model is established, the energy storage parameters are optimally configured according to the power mileage, and the output of the energy storage battery is controlled according to the optimal configuration result, so that the problem of voltage crossing of a power grid is relieved, and the voltage of the power grid is smoothed. Meanwhile, the linear optimization solution based on the power mileage is simple and quick, and is beneficial to the quick response and control of the power grid.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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The drawings of the invention are illustrated as follows:
FIG. 1 is a flow chart of a first embodiment of the present invention;
FIG. 2 is a diagram illustrating a graph-theory transformation of the power grid according to the first embodiment of the present invention;
FIG. 3 is a monthly active and reactive power flow graph of a first node of the grid in accordance with a second embodiment of the present invention;
FIG. 4 is a graph showing the output curve of the energy storage optimally obtained according to the second embodiment of the present invention
FIG. 5 is a comparison of the grid voltage with or without stored energy after the second embodiment of the present invention has been set to average level
Fig. 6 is a power consumption and output curve of a second embodiment of the present invention, photovoltaic and hydroelectric, wherein fig. 6a is the load, fig. 6b is the photovoltaic and fig. 6c is the hydroelectric;
FIG. 7 shows the voltage at the 17 th node with or without energy storage according to the second embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
A first embodiment of the present invention provides a method for configuring energy storage of a microgrid battery, as shown in fig. 1, the method includes the following steps:
determining power mileage according to the structure of the power grid branch;
establishing a linear optimization model, and carrying out optimization configuration on energy storage parameters according to the power mileage;
and controlling the output of the energy storage battery according to the optimized configuration result.
According to the method, the linear optimization model is established, the energy storage parameters are optimally configured according to the power mileage, and the output of the energy storage battery is controlled according to the optimal configuration result, so that the problem of voltage crossing of a power grid is relieved, and the voltage of the power grid is smoothed. Meanwhile, the linear optimization solution based on the power mileage is simple and quick, and is beneficial to the quick response and control of the power grid.
Optionally, in an optional embodiment of the present invention, the determining the power mileage according to the branch structure of the power grid includes:
calculating branch voltage drop according to the power grid branch structure to define power mileage, and satisfying the following conditions:
where Δ U represents the branch voltage drop, ρPRepresenting the resistance per unit length of the branch, pQRepresenting reactance value per unit length of branch, P representing grid node power, PMPRepresenting branch active power mileage, PMQAnd the reactive power mileage of the branch is represented.
Specifically, the scheme can be described as: the power mileage is deduced through a power grid branch voltage equation, the power mileage of the scheme provided by the embodiment is in a linear relation with the output power of the power grid node, the calculation of voltage drop is greatly simplified, and the accuracy is high.
More specifically, as shown in fig. 2, Root represents a Root Node, Node1 to Node5 represent common nodes, Load1 to Load3 represent loads, and DG1 to DG3 represent distributed generators. A node refers to an upstream grid (with transformer) or a combination of a distributed generator and a load at the same location, where the node representing the upstream grid is the root node, also referred to as node 0. The root node is labeled 0, a branch refers to a line connecting two nodes, and the label of the branch is equal to the larger of the two nodes. (in the implementation step), therefore, node and branch are corresponding, and node i corresponds to branch i. As shown in the right side of fig. 2, the branch corresponding to NODE1 is 1, and the NODE2 is 2, in this embodiment, the NODEs are sorted according to a left-order traversal manner, and fig. 2 shows a sample grid and its corresponding graph representation.
A branch refers to a line connecting two nodes, the branch being numbered equal to the larger of the two nodes. The parameters of each branch have the characteristics of length, resistance, reactance and the like.
Further, power mileage is derived and solved, and according to a voltage drop equation of the power grid branch:
wherein, Delta UiRepresenting the variation of the voltage of the ith branch, UiIs the voltage on the line, Pi，QiRepresenting the active and reactive power, R, flowing through this branchi，XiIt is the resistance and reactance of that branch.
Therefore, the voltage drop of the ith line is determined by the active power, the reactive power, the line voltage level and the line impedance transmitted on the line. The resistance and reactance are converted in this embodiment as follows:
where ρ isP,iIs the resistance per unit length of the branch, which is determined by the characteristics of the wire itself, pQ,iIs the reactance per unit length of the branch,/iIs the length of the branch.
For a branch line of a micro-grid, the voltage of each node of the branch line is kept at a voltage level UNNearby, while for the same line, pP,iAnd rhoQ,iAre all fixed values.
The branch voltage drop equation can thus be converted into:
wherein Δ UiFor the voltage change on branch i, ρP,iIs the resistance per unit length of the branch, which is determined by the characteristics of the wire itself, pQ,iIs the reactance per unit length of the branch,/iIs the length of the branch.
If the balanced node of the grid, i.e. the voltage reference node, is 0, there is one, and only one (for radial grids, there is only one) line to node n from 0-1-2 … n. If the line is labeled as the larger of the nodes at both ends, then the voltage at node n is.
Where Δ U is the voltage change from node to reference point, Δ UiFor the voltage drop on branch i, ρP,iIs the resistance per unit length of the branch, which is determined by the characteristics of the wire itself, pQ,iIs the reactance per unit length of the branch,/iIs the length of the branch.
The formula is arranged to obtain:
wherein PMPAnd PMQThe active and reactive power mileage defined for the method of the invention. Can seeIn the case of neglecting the power loss, in this embodiment, the power mileage is completely calculated from the power injected from the node, and the linear combination of the power mileage and the power injected from the node can obtain the voltage drop value.
Optionally, in an optional embodiment of the present invention, before the linear optimization model is established, the method includes:
extracting active power mileage to perform matrix representation:
wherein, PiRepresenting the active power of branch i,/iRepresenting the length of the branch i, and n representing the number of branches;
obtaining and determining a node power demand matrix according to the active power matrix and the battery variable matrix, and satisfying the following conditions:
Pg＝P+Battery
wherein Battery represents a Battery variable matrix;
determining a power mileage matrix, and satisfying the following conditions:
Node＝M*Pg
wherein Node represents the power mileage of the common Node.
Specifically, in this embodiment, a linear optimization model is provided to configure energy storage parameters, where the energy storage parameters include the location, quantity, and capacity of energy storage.
Determining an optimized objective function
Because energy storage system only improves the active power condition, simultaneously, the pressure drop of low voltage distribution network mainly causes because the transmission of the active of circuit, consequently, in this embodiment, only consider the active power mileage:
for a power grid, the matrix P can characterize all its states if only the voltage variations of its bus are considered. The rows of the matrix P represent grid nodes, the columns represent time, and P (i, t) is the average power demand of node i at time t (positive numbers represent absorbed real power). The value is the average load demand-the average power generation of the power generation device at the moment t of the ith node. Namely, it is
p(i，j)＝ps(i，t)-pd(i，t)
Consider a variable Battery matrix, whose magnitude is consistent with P, whose value represents the charge and discharge capacity of the Battery at the i-th node at time t. If no Battery exists at the node i, the ith row of the Battery matrix is all 0.
Therefore, in the embodiment of the present invention, P + Battery is proposed as a power requirement matrix of each node at each time point.
Further, a mileage matrix M is defined, wherein the ith row and the jth column are the length of the branch i (if the branch i is a must-pass branch from node 0 to node j), and otherwise, the length is 0. For example for the first behavior l in fig. 21，l1，l1，l1，0]Then consider the formula Node M (P + Battery), i.e.
Not counting the power loss, the right side of the above formula represents the power flowing through branch i, and the length of the upper branch represents the total power mileage transmitted by the ith branch at the time t.
Optionally, establishing a linear optimization model includes:
taking the absolute values of the power mileage and summing to obtain an optimized objective function:
f＝min(∑Node_plus+N+S*N)
Node_plus＝|Node|＝|M*(P+Battery)|
wherein Node _ plus represents the absolute value of the power mileage, N represents the number of installation positions of energy storage sources, and S represents the capacity of a single energy storage source;
determining constraint conditions according to the optimization objective function to obtain a final optimization objective function;
and solving the final optimization objective function to obtain an optimization configuration result.
Optionally, the constraint condition includes: the upper limit of the capacity of the battery, the upper limit and the lower limit of the charge and discharge power of the battery, the residual energy storage initial capacity, the charge and discharge balance and the initial capacity limit.
Specifically, in this embodiment, the power mileage value has a positive value or a negative value, and it is desirable that the voltage is closer to the standard value as better as possible and the deviation is as small as possible in the specific implementation process. Taking the absolute value of Node, and obtaining Node _ plus from it, the sum of Node _ plus is the total power mileage, i.e. one of the targets that needs to be optimized.
Meanwhile, in the specific implementation process, the total number and the capacity of the storage batteries are expected to be as small as possible, so that the total optimization target is obtained in a weighting mode in the embodiment:
namely:
f＝min(∑Node_plus+N+S*N)
Node_plus＝|Node|＝|M*(P+Battery)|
further determining constraints
For a battery charge-discharge matrix, it needs to satisfy certain constraints.
In order to facilitate the calculation of the constraint, in this embodiment, the accumulated charging matrix is calculated first:
Battery_accumulate(i，j)＝Battery*U2
it represents the sum of the power charged and discharged cumulatively for the accumulator at node i at the moment of time j. Wherein U is2Is the upper right triangular matrix.
Similarly, in the initial situation, the battery may have a certain amount of residual power, and the Int vector is set to represent the initial active residual of the battery at each node.
Then the following constraints need to be satisfied:
firstly, for a battery, the charging and discharging cannot be too fast, so the charging and discharging power needs to meet certain upper and lower limits: low _ limit is less than or equal to battery (i, j) is less than or equal to high _ limit
Wherein battery (i, j) is the average amount of charge released or absorbed by the battery;
the battery capacity cannot exceed the upper limit of the battery capacity at any time, so an upper limit constraint is added. The initial capacity residual of the stored energy needs to be taken into account:
battery_accumulate(i，j)≤S(i)*N(i)-Int(i).
the battery capacity cannot be below 0 at any time, so a lower limit constraint is added. The initial capacity residual of the stored energy needs to be taken into account:
battery_accumulate(i，j)≥-Int(i)
in a larger period (one year), the charge and discharge of the stored energy need to be balanced, otherwise, due to the periodic change of the power condition, if the charge and discharge of the stored energy in one period are unbalanced, the electric quantity of the stored energy will continuously rise or fall after each period:
battery_accumulate(i，tmax)≤limit
initial capacity limit:
0≤Int(i)≤S(i)*N(i)
optionally, solving the final optimization objective function to obtain an optimization configuration result includes:
and adding a node constraint matrix to solve the final optimization objective function to obtain an optimization configuration result.
Specifically, the above constraints are linear constraints, but due to the existence of the absolute value removal, the whole optimization model exhibits nonlinearity and is not easy to solve, so the Sen matrix (0-1 matrix of n × t) is added in the embodiment to solve the problem.
1000*sen(i,j)≥node(i,j)＞1000*(sen(i,j)-1)
Then sen (i, j) is 1 when node (i, j) (value between-1000 and 1000) is greater than 0, and sen (i, j) is 0 at other times.
Then there are:
node_plus(i,j)＝node(i,j)*(sen(i,j)-0.5)*2
wherein the Sen matrix is a 0-1 matrix of n x t, and the node (i, j) is the charging and discharging power of the i battery at the time of j.
Therefore, the final optimization model is:
f＝min(∑Node_plus+N+S*N)
s.t.
Node＝M*(P+Battery)
1000*Sen≥Node＞1000*(Sen-1)
Node_plus＝node.*(Sen-0.5)*2
battery_accumulate(i,j)≤S(i)*N(i)-Int(i)
battery_accumulate(i,j)≥-Int(i)
battery_accumulate(i,tmax)≤limit
0≤Int(i)≤S(i)*N(i)
low_limit≤battery(i,j)≤high_limit
therefore, the final optimization objective function can be solved to obtain the optimization configuration result, specifically in this embodiment, lingo can be adopted to perform solving.
Optionally, controlling the output of the energy storage battery according to the optimized configuration result includes:
dividing the time period, determining the output of the power grid node according to the divided time interval, and satisfying the following conditions:
wherein, BatteryijRepresents the average power, Ba, of battery i in scheduling period ji(t) represents the real-time power at time t, PijRepresenting the average power flow, P, calculated from historical power flow data when no battery is storing energyi(t) is the average power requirement, and j represents the scheduling period.
Specifically, the embodiment further provides a control method based on the configuration result, and the control method is used for controlling the output curve of the stored energy.
The requirements of the control method are determined.
battery (i, j) reflects the average power absorbed by the battery over a period of time (e.g., one month) during which the actual renewable energy output may vary dramatically, resulting in voltage spikes and fluctuations. A control method is therefore required to adapt the stored energy output to this variation while meeting this average.
In formulating the control strategy for node i in time period j, the time period is divided into small time intervals (15 minutes) in the present embodiment, and for each time interval, the constant output of the battery at node i in a given time interval is calculated.
First, in the present embodiment, it is defined that battery (i, j) is the average amount of electricity discharged or absorbed by the battery, which is a result obtained by optimizing the power mileage. P (i, j) is the average injected power without battery calculated from historical data. And, Pi(t) is the case of the injected power at the inode, at smaller time intervals during the larger time period j.
The control method should satisfy two requirements.
First, it should follow the actual power flow situation Pi(t) and its value P (i, j) varies with time, although the average power flow of node i is equal to P (i, j). If P (i, j) becomes larger, the battery should absorb more energy to allow the grid to become balanced.
Secondly, the average power of the node i in the period j should be as close to battery (i, j) as possible to achieve the optimal state of the solution.
The embodiment of the invention provides a control method design
With the above two requirements, the present embodiment can design a simple and effective method to control the battery, and ensure that the power of the battery at any given time interval j is:
the invention provides a micro-grid energy storage configuration control method based on power mileage to solve the problems of grid voltage fluctuation and line crossing of a micro-grid caused by access of renewable energy sources. Then, a linear optimization model is provided to configure the position, the quantity and the capacity of the stored energy by using the power mileage. On the basis, a control method based on a configuration result is provided, and the output of the stored energy is controlled. By effectively utilizing the energy storage system, the power of the power grid is moved on the time axis, the problem of voltage crossing of the power grid is solved, and the voltage of the power grid is smoothed. Meanwhile, the linear optimization solution based on the power mileage is simple and quick, and is beneficial to the quick response and control of the power grid.
The second embodiment of the invention provides an implementation case of a microgrid battery energy storage configuration method
The system in this case is a practical three-phase 10kV power grid, and has 17 nodes in total, and the total nominal load is 1.08MW + j0.324MVar. Under standard conditions, all Photovoltaics (PV) generate 0.502MW together and all hydroelectric power stations (HS) generate 1.2MW + j0.59mvar together. The high permeability of hydroelectric and PV results in an imbalance of power supply and demand in different seasons. Data from the past year shows that energy excess in the 5 months reaches approximately 50 ten thousand hours and energy shortage in the 2 months reaches 85 ten thousand hours. This imbalance, coupled with the irregularity of the PV output, results in severe fluctuations and spikes in voltage. Tables 1 and 2 summarize the key parts of the case study.
TABLE 1 load and Generator information for each node
TABLE 2 parameters for each type of cable
Table 1 shows the nominal power and load of each generator and table 2 shows the cable parameters. Wherein cable 1 is 10 kilovolt copper lines with a cross-sectional area of 50mm2, cable 2 is 10 kilovolt copper lines with a cross-sectional area of 35mm2, and cable 3 is 380-V copper lines with a cross-sectional area of 16mm 2.
A key problem with this network is the imbalance of supply and demand from season to season. At months 5 and 9, the load is not heavy, but the HS and PV provide excess power to the grid. However, in winter, the load increases, while photovoltaic and HS sources have little output. Fig. 3 shows the average active and reactive demand of node 0 throughout the year.
In this example, the planning is performed at intervals of one month, and data having a total length of 1 year is used.
The solution is performed according to the steps described above.
Firstly, defining an optimization objective function:
f＝min(∑Node_plus+N+S*N)
Node_plus＝|Node|＝|M*(P+Battery)|
then determining the constraint conditions:
Node＝M*(P+Battery)
1000*Sen≥Node＞1000*(Sen-1)
Node_plus＝node.*(Sen-0.5)*2
battery_accumulate(i,j)≤S(i)*N(i)-Int(i)
battery_accumulate(i,j)≥-Int(i)
battery_accumulate(i,tmax)≤limit
0≤Int(i)≤S(i)*N(i)
low_limit≤battery(i,j)≤high_limit
and finally, determining a control method of the battery:
the global optimal solution is obtained through the steps. Table 3 shows the power mileage of the original grid and the grid using the energy storage system
TABLE 3 comparison of original network with stored energy network
With the assistance of stored energy, the total absolute power mileage is significantly reduced, showing an increase in the grid voltage quality. Table 4 shows the location, capacity and initial energy of all stored energy.
TABLE 4 location, capacity and initial energy of stored energy
As shown in fig. 4, the charging and discharging curves of the batteries at different nodes. The results show that the surplus energy of 5 months and 9 months is preserved and released in winter, and the peak clipping capability of the method is vividly demonstrated. In order to visualize the grid voltage distribution diagram, the present embodiment selects data of 12 pm (midnight) on the fifth day of each month, and calculates the average discharge or charge rate thereof, resulting in fig. 5.
In the figure, node 11 and node 17 are end nodes. The original grid voltage distribution condition is quite bad, the seasonal fluctuation is large, and the voltage of the node 17 in the month 5 exceeds the threshold value (7%). With the addition of stored energy, the voltage curve is greatly improved, becomes smoother, and remains within a safe operating range.
For the operation control, 5 months and 5 days of data are selected in the embodiment to prove the feasibility of the model of the method. Fig. 6 shows the total active and reactive conditions of the day, see fig. 6a, 6b and 6 c.
Even within a short time, the fluctuations in photovoltaic output are considerable and variable. The demand also fluctuates, reaching peak in the morning and evening. Compared with hydroelectric power, the output of hydroelectric power is much more smooth and has small fluctuation. The complicated changes cause voltage fluctuations in a short time, and need to be controlled in time.
As shown in section 3, a control strategy is applied to the grid to control the stored energy to change with the load fluctuation, resulting in a curve of the voltage at node 17, as shown in fig. 7.
The new voltage at node 17 is stabilized around 1.01 per unit, with little fluctuation and is significantly smoother than before.
A third embodiment of the present invention proposes a computer-readable storage medium, on which an implementation program for information transfer is stored, which when executed by a processor implements the steps of the aforementioned method.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered thereby.
Claims (5)
1. A microgrid battery energy storage configuration method, characterized in that the method comprises the following steps:
determining power mileage according to the structure of the power grid branch;
establishing a linear optimization model, and carrying out optimization configuration on energy storage parameters according to the power mileage;
controlling the output of the energy storage battery according to the optimized configuration result;
the determining of the power mileage according to the power grid branch structure comprises the following steps:
calculating branch voltage drop according to the power grid branch structure to define power mileage, and satisfying the following conditions:
where Δ U represents the branch voltage drop, ρPRepresenting the resistance per unit length of the branch, pQRepresenting reactance value per unit length of branch, P representing active power matrix, PMPRepresenting branch active power mileage, PMQRepresenting the reactive power mileage of the branch; u shapeNRepresenting the nominal voltage of the branch;
active power mileage PMP：
Reactive power mileage PMQ：
In the formula, Pi、QiRespectively the active and reactive power of branch i, liIs the length of branch i, n is the number of branches;
establishing a linear optimization model, comprising:
taking the absolute values of the power mileage and summing to obtain an optimized objective function:
f＝min(∑Node_plus+N+S*N)
Node_plus＝|Node|＝|M*(P+Battery)|
wherein Node _ plus represents the absolute value of the power mileage, N represents the installation number of the energy storage sources, and S represents the capacity of a single energy storage source; battery represents a Battery variable matrix, and P represents an active power matrix; m represents a mileage matrix;
determining constraint conditions according to the optimization objective function to obtain a final optimization objective function;
solving the final optimization objective function to obtain an optimization configuration result;
the constraint conditions include: the upper limit of the capacity of the battery, the upper limit and the lower limit of the charge and discharge power of the battery, the residual initial capacity of the stored energy, the charge and discharge balance and the initial capacity limit;
solving the final optimization objective function to obtain an optimized configuration result, comprising:
and adding a node constraint matrix to solve the final optimization objective function to obtain an optimization configuration result.
2. The method of claim 1, wherein prior to establishing the linear optimization model, the method comprises:
obtaining and determining a node power demand matrix according to the active power matrix and the battery variable matrix, and satisfying the following conditions:
Pg＝P+Battery
wherein, Battery ═ Battery [ ]ij]n×tRepresenting a battery variable matrix, P representing an active power matrix, j representing a time variable, and t representing a scheduling period;
determining a power mileage matrix, and satisfying the following conditions:
Node＝M*Pg
wherein Node represents the power mileage of a common Node; m represents a mileage matrix.
3. The method of claim 1, wherein the energy storage parameters include location, amount, and capacity of energy storage.
4. The method of claim 3, wherein controlling the output of the energy storage battery based on the optimal configuration result comprises:
dividing the time period, determining the output of the power grid node according to the divided time interval, and satisfying the following conditions:
wherein, BatteryIjDenotes the average power, Ba, of the cell I over a time variable ji(t) represents the real-time power at time t, PijRepresenting the average power flow calculated from historical power flow data when no battery is storing energy,Pi(t) is the average power demand, and j represents a time variable.
5. A computer-readable storage medium characterized by: the computer-readable storage medium has stored thereon an implementation program for information transfer, which when executed by a processor implements the steps of the method according to any one of claims 1 to 4.
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