CN115545465A - Multi-target joint planning method and system for distributed photovoltaic and energy storage - Google Patents
Multi-target joint planning method and system for distributed photovoltaic and energy storage Download PDFInfo
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Abstract
The invention belongs to the technical field of power systems, and particularly relates to a distributed photovoltaic and energy storage multi-target joint planning method, which comprises the following steps: performing cluster division on the power distribution network by taking the system modularity based on the space electrical distance as a target; constructing a multi-objective combined planning model integrating distributed photovoltaic and energy storage operation planning by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions; and solving the multi-target joint planning model by using the optimization framework to obtain a group of planning solution sets, and selecting the optimal compromise solution from the planning solution sets as a planning result. The method provided by the invention is based on the cluster division result, considers the optimal operation scheme of distributed energy storage, provides an offset cross operation method aiming at the position variable in the decision variable, realizes multi-target joint planning of distributed photovoltaic and energy storage, and has the characteristics of short solving time, wide range and high planning reliability.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a distributed photovoltaic and energy storage multi-target joint planning method and system.
Background
With the aggravation of the crisis of fossil energy, the installed capacity of distributed power supplies is rapidly increased in recent years, the permeability of new energy in a power distribution network is also gradually increased, the intermittent and fluctuating output of the distributed power supplies is easily influenced by weather environment, the voltage stability of a power grid is impacted, the system tide is changed, lines are blocked, and the phenomenon that wind and light are abandoned is more frequent due to the anti-peak regulation characteristic of the output. The distributed energy storage has the advantages of bidirectional power output, energy time shifting, quick response and the like, and is one of the most preferable items for solving the problems of a power distribution network with high new energy ratio.
Distributed photovoltaic access distribution network can provide power and voltage support for remote nodes, and distributed energy storage can alleviate the problems of network loss increase, voltage out-of-limit and the like brought by the distributed photovoltaic access distribution network to a certain extent. However, most of the existing researches in the current distributed photovoltaic and energy storage planning only consider the economic objective or the safety objective, and cannot realize the two objectives; in order to process variables with different time scales, a double-layer optimization model is adopted in most of the existing researches, so that the problems of long optimization time and difficulty in model convergence exist, and the solving precision cannot be guaranteed; in addition, in order to simplify the double-layer model, some existing researches do not specifically consider an optimal operation strategy of distributed energy storage, and effective utilization of the distributed energy storage cannot be effectively achieved in a planning stage.
Disclosure of Invention
Aiming at the problems in the existing distributed photovoltaic and energy storage combined planning scheme, the invention provides a distributed photovoltaic and energy storage multi-target combined planning method and system, so as to improve the access rate of distributed photovoltaic and improve the electric energy quality of a power distribution network. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to a first aspect of the embodiments of the present invention, a multi-objective joint planning method for distributed photovoltaic and energy storage is provided.
In one embodiment, a multi-objective joint planning method for distributed photovoltaic and energy storage includes:
carrying out cluster division on the power distribution network by taking the system modularity based on the space electrical distance as a target;
constructing a distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions;
and solving the multi-target joint planning model by using the optimization framework to obtain a group of planning solution sets, and selecting the optimal compromise solution from the planning solution sets as a planning result.
Optionally, the step of performing cluster division on the power distribution network with the system modularity based on the spatial electrical distance as a target includes:
and performing cluster division on the power distribution network by taking the selected interconnected lines of each cluster as variables and taking the system modularity as a target.
Optionally, the system modularity is defined as:
wherein f represents the system modularity, D ij Representing the space electrical distance matrix, m representing the sum of network side weights, k i Representing the sum of the node edge weights, k, connected to node i j Represents the sum of the node edge weights connected to node j, and δ (i, j) =1 represents that node i and node j are located in the same cluster.
Optionally, the optimization objective of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model includes: annual average comprehensive cost, voltage fluctuation and network loss of the power distribution network.
Optionally, the step of optimizing the annual average combined cost of the power distribution network includes:
F 1 for the annual average of the distribution networkThe comprehensive cost comprises the cost for purchasing electricity from the power distribution network to the main network
Distributed photovoltaic annual investment cost
And operating maintenance costs
Annual investment cost for distributed energy storage
And maintenance costs
Where T is the time scale of the planning scene for one day, e t And P L,t Respectively purchasing real-time electricity price and power of the electricity from the main network for the time period t,
for the annual investment cost of the distributed photovoltaic system,
for distributed photovoltaic annual average operating cost, C invest Annual investment costs for distributed energy storage, C op For the distributed energy storage annual average operating cost, gamma is the bank interest rate, y PV And y ESS Service life, N, of distributed photovoltaic and stored energy, respectively PV And N ESS For the number of distributed photovoltaics and stored energy respectively,
and
respectively configuring cost, maintenance cost and internet access subsidy price for unit capacity of the distributed photovoltaic,
and
respectively the rated power of the d-th distributed photovoltaic system and the output power in the t period,
and
respectively the unit installation cost and maintenance cost of the power and capacity of the distributed energy storage,
rated power and rated capacity of the b-th distributed energy storage are respectively set;
and storing the power of the seat b in a distributed mode in the time period t.
Optionally, the step of optimizing the voltage fluctuation includes:
F 2 for voltage fluctuations of the distribution network, N bus For the number of nodes, U, in the distribution network i,t Is the voltage at node i during time t,
is the average voltage at node i.
Optionally, the step of optimizing the network loss includes:
F 3 for network losses of the distribution network, N lb The number of branches of the power distribution network,
the line loss power of branch lb in time period T is shown, and T is the time scale of one day of the planning scene.
Optionally, the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model include:
node voltage deviation range constraint, power flow constraint, power balance constraint, charge and discharge constraint of distributed energy storage and cluster quantity constraint.
Optionally, the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model include:
|SoC b,0 -SoC b,T |≤σ (7)
wherein,
the power in the time period t is stored for the b-th seat in a distributed mode,
is the b-th seatRated power, soC, of distributed energy storage b,t The state of charge of the b-th distributed energy storage at the end of the t period,
the maximum value of the state of charge of the b-th distributed energy storage,
state of charge minimum, soC, for the b-th distributed energy storage b,0 For the initial state of charge values of the distributed energy storage,
the power of the b-th distributed energy storage in the time period tau is obtained, eta is the charge-discharge efficiency of the distributed energy storage,
the power of the distributed energy storage of the b-th seat in a time period T is obtained, and sigma is the maximum deviation amplitude of the first and last charge states; eta c 、η d Respectively the charging efficiency and the discharging efficiency of the distributed energy storage,
rated capacity for the b-th distributed energy storage
Using maximum cumulative charge-discharge quantity E b ' and maximum single charge-discharge amount E b "to calculate; n is a radical of hydrogen g As is the number of clusters in the power distribution network,
and
respectively the number of distributed photovoltaics and stored energy, N, in the g-th cluster PV And N ESS Respectively the number of distributed photovoltaics and stored energy.
Optionally, the step of solving the multi-objective joint planning model by using the optimization framework to obtain a set of planning solution sets, and then selecting an optimal compromise solution from the planning solution sets as a planning result includes:
solving the multi-target joint planning model by using an optimization framework based on the NSGA-III algorithm to obtain a group of planning solution sets, and selecting an optimal compromise solution from the group of planning solution sets by using a TOPSIS-based multi-attribute decision method as a planning result.
According to a second aspect of the embodiments of the present invention, a multi-objective joint planning system for distributed photovoltaic and energy storage is provided.
In one embodiment, a distributed photovoltaic and energy storage multi-objective joint planning system includes:
the cluster division module is used for carrying out cluster division on the power distribution network by taking the system modularity based on the space electrical distance as a target;
the model building module is used for building a distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions;
and the solving module is used for solving the multi-target joint planning model by using the optimization framework to obtain a group of planning solution sets, and then selecting the optimal compromise solution from the planning solution sets as a planning result.
Optionally, the cluster dividing module includes: and performing cluster division on the power distribution network by taking the selected interconnected lines of each cluster as variables and taking the system modularity as a target.
Optionally, the system modularity is defined as:
wherein f represents the system modularity, D ij Representing the space electrical distance matrix, m representing the sum of network side weights, k i Represents the sum of the node edge weights, k, connected to node i j Represents the sum of the node edge weights connected to node j, δ (i, j) =1 represents nodei is located in the same cluster as node j.
Optionally, the optimization objective of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model includes: annual average comprehensive cost, voltage fluctuation and network loss of the power distribution network.
Optionally, the step of optimizing the annual average comprehensive cost of the power distribution network includes:
F 1 the annual average comprehensive cost of the power distribution network comprises the cost of purchasing power from the power distribution network to the main network
Distributed photovoltaic annual investment cost
And operating maintenance costs
Annual investment cost for distributed energy storage
And maintenance costs
Where T is the time scale of the planning scene for one day, e t And P L,t Respectively purchasing real-time electricity price and power of the electricity from the main network for the time period t,
for the annual investment cost of the distributed photovoltaic system,
for distributed photovoltaic annual average operating cost, C invest Annual investment cost for distributed energy storage, C op For the distributed energy storage annual average operating cost, gamma is the bank interest rate, y PV And y ESS Service life, N, of distributed photovoltaic and stored energy, respectively PV And N ESS For the number of distributed photovoltaics and stored energy respectively,
and
respectively configures cost, maintenance cost and internet subsidy price for unit capacity of the distributed photovoltaic,
and
respectively the rated power of the d-th distributed photovoltaic system and the output power in the t period,
and
respectively the unit installation cost and the maintenance cost of the power and the capacity of the distributed energy storage,
rated power and rated capacity of the b-th distributed energy storage are respectively set;
and storing the power of the seat b in a distributed mode in the time period t.
Optionally, the step of optimizing the voltage fluctuation comprises:
F 2 for voltage fluctuations of the distribution network, N bus For the number of nodes, U, in the distribution network i,t Is the voltage at node i during time t,
is the average voltage at node i.
Optionally, the step of optimizing the network loss includes:
F 3 for network losses of the distribution network, N lb The number of branches of the power distribution network,
the line loss power of branch lb in time period T is shown, and T is the time scale of one day of the planning scene.
Optionally, the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model include:
node voltage deviation range constraint, power flow constraint, power balance constraint, charge and discharge constraint of distributed energy storage and cluster quantity constraint.
Optionally, the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model include:
|SoC b,0 -SoC b,T |≤σ (7)
wherein,
the power in the time period t is stored for the b-th seat in a distributed mode,
rated power, soC, for distributed energy storage of the b-th seat b,t The state of charge of the b-th distributed energy storage at the end of the t period,
the maximum value of the state of charge of the seat b distributed energy storage,
state of charge minimum for the b-th distributed energy storage, soC b,0 For the initial state of charge value of the distributed energy storage,
the power of the b-th distributed energy storage in the time period tau is obtained, eta is the charge-discharge efficiency of the distributed energy storage,
the power of the distributed energy storage of the b-th seat in a time period T is obtained, and sigma is the maximum deviation amplitude of the first and last charge states; eta c 、η d Respectively the charging and discharging efficiency of the distributed energy storage,
rated capacity for the b-th seat distributed energy storage
Using the maximum accumulated charge-discharge quantity E b ' and maxCharge and discharge capacity per time E " b To calculate; n is a radical of hydrogen g As is the number of clusters in the power distribution network,
and
respectively the number of distributed photovoltaics and stored energy, N, in the g-th cluster PV And N ESS Respectively the number of distributed photovoltaics and stored energy.
Optionally, the solving module includes:
solving the multi-target combined planning model by using an optimization framework based on an NSGA-III algorithm to obtain a group of planning solution sets, and selecting an optimal compromise solution from the group of planning solution sets by using a TOPSIS (technique for order preference by similarity to zero) multi-attribute decision-making method as a planning result.
According to a third aspect of embodiments of the present invention, there is provided a computer apparatus.
In some embodiments, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the distributed photovoltaic and energy storage multi-target joint planning method is based on cluster division results, considers an optimal operation scheme of distributed energy storage, provides an offset cross operation method for position variables in decision variables, achieves distributed photovoltaic and energy storage multi-target joint planning, and has the advantages of being short in solving time, wide in consideration range and high in planning reliability.
And while controlling the diversity group, considering an optimal operation scheme of distributed energy storage, realizing the joint planning of distributed photovoltaic and energy storage in the power distribution network to obtain a reasonable planning result and guide the configuration and construction of new energy and energy storage of the power distribution network.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating a method for multi-objective joint planning of distributed photovoltaic and energy storage in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram of an optimization framework based on the NSGA-III algorithm, shown in accordance with an exemplary embodiment;
FIG. 3 is a diagram illustrating the results of a cluster partitioning of a power distribution network in accordance with an exemplary embodiment;
FIG. 4 is a diagram illustrating the results of a power distribution network voltage fluctuation scenario in a planning scenario, in accordance with an exemplary embodiment;
FIG. 5 is a schematic diagram illustrating optimal operating results of distributed energy storage in a planning scenario, according to an exemplary embodiment;
FIG. 6 is a schematic diagram of a distributed photovoltaic and energy storage multi-objective joint planning system in accordance with an exemplary embodiment;
FIG. 7 is a schematic diagram illustrating a configuration of a computer device, according to an example embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another element without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a structure, device, or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on those shown in the drawings, merely for convenience of description and to simplify description, and are not intended to indicate or imply that the device or element so referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.
Herein, the term "plurality" means two or more, unless otherwise specified.
Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Fig. 1 shows an embodiment of a distributed photovoltaic and energy storage multi-objective joint planning method according to the present invention.
In this optional embodiment, the multi-objective joint planning method for distributed photovoltaic and energy storage includes the following steps:
the method comprises the following steps that S1, cluster division is carried out on a power distribution network by taking system modularity based on space electrical distance as a target;
s2, constructing a distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions;
and S3, solving the multi-target combined planning model by using the optimization frame to obtain a group of planning solution sets, and selecting an optimal compromise solution from the planning solution sets as a planning result.
It should be understood that, although the steps in the flowchart are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
Optionally, the step of performing cluster division on the power distribution network with the system modularity based on the spatial electrical distance as a target includes: and performing cluster division on the power distribution network by taking the selected interconnected lines of each cluster as variables and taking the system modularity as a target.
Optionally, the system modularity is defined as:
wherein f represents the system modularity, D ij Representing a space electrical distance matrix, obtained by extracting a voltage/active power sensitivity matrix from a power flow calculation correction equation and calculating the voltage/active power sensitivity matrix, wherein the voltage/active power sensitivity matrix is used as a network edge weight adjacent matrix, m represents the sum of network edge weights, k i Representing the sum of the node edge weights, k, connected to node i j Represents the sum of the node edge weights connected to node j, and δ (i, j) =1 represents that node i and node j are located in the same cluster.
Optionally, the optimization objective of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model includes: annual average comprehensive cost, voltage fluctuation and network loss of the power distribution network.
Optionally, the step of optimizing the annual average combined cost of the power distribution network includes:
F 1 the annual average comprehensive cost of the power distribution network comprises the cost of purchasing power from the power distribution network to the main network
Distributed photovoltaic annual investment cost
And operating maintenance costs
Annual investment cost for distributed energy storage
And maintenance costs
Where T is the time scale of the planning scene for one day, e t And P L,t Respectively being dominant for a period of tThe real-time electricity price and power of the power purchased by the grid,
for the annual investment cost of the distributed photovoltaic system,
annual average operating cost for distributed photovoltaics, C invest Annual investment costs for distributed energy storage, C op For the annual average operating cost of distributed energy storage, gamma is the bank interest rate, y PV And y ESS Service life, N, of distributed photovoltaic and stored energy, respectively PV And N ESS For the number of distributed photovoltaics and stored energy respectively,
and
respectively configuring cost, maintenance cost and internet access subsidy price for unit capacity of the distributed photovoltaic,
and
respectively the rated power of the d distributed photovoltaic and the output power in the t period,
and
respectively the unit installation cost and the maintenance cost of the power and the capacity of the distributed energy storage,
rated power and rated capacity of the b-th distributed energy storage are respectively set;
and storing the power of the seat b in a distributed mode in the time period t.
Optionally, the step of optimizing the voltage fluctuation comprises:
F 2 for voltage fluctuations of the distribution network, N bus For the number of nodes, U, in the distribution network i,t For the voltage at node i in time t, U i Is the average voltage at node i.
Optionally, the network loss optimizing step includes:
F 3 for network losses of the distribution network, N lb The number of branches of the power distribution network,
the line loss power of branch lb in time period T is shown, and T is the time scale of one day of the planning scene.
Optionally, the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model include: node voltage deviation range constraint, power flow constraint, power balance constraint, charge and discharge constraint of distributed energy storage and cluster quantity constraint.
Specifically, the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-objective joint planning model include:
|SoC b,0 -SoC b,T |≤σ (7)
wherein,
the power in the time period t is stored for the b-th seat in a distributed mode,
rated power, soC, for distributed energy storage of the b-th base b,t For the b-th distributed energy storage at the charge state at the end of the t period,
the maximum value of the state of charge of the b-th distributed energy storage,
state of charge minimum, soC, for the b-th distributed energy storage b,0 For the initial state of charge values of the distributed energy storage,
the power of the distributed energy storage of the b-th seat in the time period tau is obtained, eta is the charge-discharge efficiency of the distributed energy storage,
the power of the b-th distributed energy storage in a time period T is obtained, and sigma is the maximum deviation amplitude of the head and tail state of charge; eta c 、η d Respectively the charging efficiency and the discharging efficiency of the distributed energy storage,
rated capacity for the b-th distributed energy storage
Adopting maximum accumulated charge-discharge quantity E' b And maximum single charge-discharge quantity E' b To calculate; n is a radical of hydrogen g As is the number of clusters in the power distribution network,
and
the number of distributed photovoltaic and stored energy, N, in the g-th cluster respectively PV And N ESS For the quantity of distributed photovoltaic and stored energy, respectively, the present embodiment sets up
Optionally, the step of solving the multi-objective joint planning model by using the optimization framework to obtain a set of planning solution sets, and then selecting an optimal compromise solution from the planning solution sets as a planning result includes: solving the multi-target combined planning model by using an optimization framework based on an NSGA-III algorithm to obtain a group of planning solution sets, and selecting an optimal compromise solution from the group of planning solution sets by using a TOPSIS (technique for order preference by similarity to zero) multi-attribute decision-making method as a planning result.
Considering that the position variables in the decision variables are 0-1 variables, but the 0-1 symbols of the decision variables represent that corresponding nodes have/have no/have configuration energy storage instead of binary 0-1 concepts of cross operation in the genetic algorithm, in order to ensure the solving precision and accelerate the solving speed, an improved offset cross operation method is provided for the position variables so as to promote the variable fusion.
The improved offset crossover operation method comprises the following specific steps: selecting two individuals in pairs without returning from the population by adopting a binary tournament selection method, and sequencing the two individuals in pairs, wherein the two individuals in front and at back are in a cross probability p c And performing crossing. Selecting the waitingTwo crossed individuals are respectively assumed to be the nodes a, b and c, d in the cluster at the energy storage position of the cluster, namely the corresponding position variable s a ＝s b ＝1,s c ＝s d =1; referring to the SBX simulation binary crossing operation, the variables before and after crossing should satisfy a + b + c + d = a 1 +b 1 +c 1 +d 1 (ii) a Random offset probability of p g When rand () > p i The positions of the two bodies are directly crossed without offsetting the positions; when rand () < p i Then two are randomly generated to be in [ -2,2]Random integer of interval r 1 ,r 2 At a position after crossing a 1 ＝c+r 1 、b 1 ＝d-r 1 And c 1 ＝a+r 2 、d 1 ＝b-r 2 I.e. the cluster g of two individuals after the intersection,
and checking the crossed individuals again to prevent the position from overflowing the cluster.
The NSGA-III is a multi-objective optimization algorithm improved on the basis of a genetic algorithm, and has higher solving efficiency when solving a multi-objective problem of mixed integer nonlinearity; in the process of solving the model by using the algorithm, each individual is updated and calculated by adopting an embedded load flow calculation mode to obtain each fitness value, and different time scale variables are processed by adopting a mixed coding mode, wherein the variables comprise 0-1 variable of position variable and real variable of capacity and output; the method comprises the following steps that an improved offset crossing operation method is adopted to replace an original binary crossing mode aiming at the particularity of a variable 0-1, and an analog binary crossing method is adopted for a real variable; the above improvements and processing of the algorithm constitute an optimization framework based on the NSGA-III algorithm, as shown in fig. 2.
The problem solving difficulty can be reduced by performing combined planning based on cluster division, so that the distribution of the planning positions of distributed photovoltaic and energy storage is more uniform and reasonable; the distributed photovoltaic and energy storage operation planning integrated multi-objective combined planning method takes annual comprehensive operation cost, voltage fluctuation and network loss as optimization objectives, gives consideration to economic and safety indexes of the power distribution network, and considers the optimized operation of distributed energy storage in a planning stage, so that a planning result is more perfect and reasonable; an optimization framework based on the NSGA-III algorithm is built to solve the problem, an improved offset cross operation method is provided for the particularity of the position variable, and the solving speed and the convergence of the problem are improved.
A specific embodiment of the multi-objective joint planning method for distributed photovoltaic and energy storage according to the present invention is given below.
The method comprises the following steps: and inputting power distribution network parameters and simulation parameters.
The grid frame of the power distribution network adopts IEEE-33 node standard example parameters, the distributed energy storage device adopts a lithium battery substrate, and other simulation related parameters are shown in the following table:
table 1 example-related simulation parameters
Step two: the power distribution network is subjected to cluster division by taking the system modularity based on the space electrical distance as a target, and the cluster division result is shown in fig. 3.
Step three: and solving the multi-target combined planning model integrating distributed photovoltaic and energy storage operation planning by using an optimization framework based on NSGA-III based on cluster division results to obtain a planning result.
The combined planning result obtained by the solution is shown in table 2, the annual average comprehensive cost of the power distribution network corresponding to the planning result is 1242 ten thousand yuan, the voltage fluctuation is 6, and the network loss in the planning scene is 3.19MWh.
TABLE 2 Joint planning results
The voltage fluctuation is shown in fig. 4, the dotted line represents a voltage fluctuation curve of the power distribution network when the distributed energy storage is not contained, and it can be seen from the graph that the voltage fluctuation caused by the distributed photovoltaic access power distribution network can be greatly reduced by adding the distributed energy storage in the joint planning, and the voltage fluctuation is reduced by 66.49% in the embodiment.
The optimal operation result of the distributed energy storage under the planning scene is shown in fig. 5, and it can be seen that, in a distributed photovoltaic power generation peak period of 10 to 16, the distributed energy storage mainly absorbs the power which is over-generated and reduces the net load peak value; at other times, the distributed energy storage is mainly discharged to carry out energy arbitrage and balance the distributed photovoltaic power generation valley. Therefore, the combined planning considering distributed photovoltaic and energy storage can greatly reduce the net load peak-valley difference of the power distribution network from 8.17MW to 5.36MW, which is reduced by 34.3%.
In another embodiment, a multi-objective joint planning system for distributed photovoltaic and energy storage is also provided.
As shown in fig. 6, the system includes: the cluster division module 100 is used for performing cluster division on the power distribution network by taking the system modularity based on the space electrical distance as a target; the model building module 200 is used for building a distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions; and the solving module 300 is configured to solve the multi-objective joint planning model by using the optimization framework to obtain a set of planning solution sets, and then select an optimal compromise solution from the planning solution sets as a planning result.
The various modules in the system of the present application may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The working principle of the distributed photovoltaic and energy storage multi-target joint planning system in this embodiment is the same as that of the above-described distributed photovoltaic and energy storage multi-target joint planning method embodiment, and details are not repeated here.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program is executed by a processor to carry out the steps in the above-described method embodiments.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the inventive arrangements and is not intended to limit the computing devices to which the inventive arrangements may be applied, as a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (17)
1. A multi-objective combined planning method for distributed photovoltaic and energy storage is characterized by comprising the following steps:
carrying out cluster division on the power distribution network by taking the system modularity based on the space electrical distance as a target;
constructing a distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions;
and solving the multi-target joint planning model by using the optimization framework to obtain a group of planning solution sets, and selecting the optimal compromise solution from the planning solution sets as a planning result.
2. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 1,
the method comprises the following steps of performing cluster division on the power distribution network by taking system modularity based on space electrical distance as a target, wherein the steps comprise:
and performing cluster division on the power distribution network by taking the selected interconnected lines of the clusters as variables and taking the system modularity as a target.
3. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 1,
the system modularity is defined as:
wherein f represents the system modularity, D ij Representing the space electrical distance matrix, m representing the sum of network side weights, k i Representing the sum of the node edge weights, k, connected to node i j Represents the sum of the node edge weights connected to node j, and δ (i, j) =1 represents that node i and node j are located in the same cluster.
4. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 1,
the optimization objective of the distributed photovoltaic and energy storage operation planning integrated multi-objective combined planning model comprises the following steps: annual average comprehensive cost, voltage fluctuation and network loss of the power distribution network.
5. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 4,
the optimization step of the annual average comprehensive cost of the power distribution network comprises the following steps:
F 1 the annual average comprehensive cost of the power distribution network comprises the cost of purchasing power from the power distribution network to the main network
Distributed photovoltaic annual investment cost
And operating and maintenance costs
Annual investment cost for distributed energy storage
And maintenance costs
Where T is the time scale of a day for planning a scene, e t And P L,t Respectively purchasing real-time electricity price and power of the electricity from the main network for the time period t,
for the annual investment cost of the distributed photovoltaic system,
annual average operating cost for distributed photovoltaics, C invest Annual investment cost for distributed energy storage, C op For the distributed energy storage annual average operating cost, gamma is the bank interest rate, y PV And y ESS Service life, N, of distributed photovoltaic and stored energy, respectively PV And N ESS For the number of distributed photovoltaics and stored energy respectively,
and
respectively configuring cost, maintenance cost and internet access subsidy price for unit capacity of the distributed photovoltaic,
and
respectively the rated power of the d distributed photovoltaic and the output power in the t period,
and
respectively the unit installation cost and the maintenance cost of the power and the capacity of the distributed energy storage,
rated power and rated capacity of the b-th distributed energy storage are respectively set;
and storing the power of the seat b in a distributed mode in the time period t.
6. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 4,
the step of optimizing the voltage fluctuation comprises:
F 2 for voltage fluctuations of the distribution network, N bus For the number of nodes, U, in the distribution network i,t Is the voltage at node i during time t,
is the average voltage at node i.
7. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 4,
the network loss optimization step comprises the following steps:
F 3 for network losses of the distribution network, N lb The number of branches of the power distribution network,
the line loss power of branch lb in time period T is shown, and T is the time scale of one day of the planning scene.
8. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 1,
the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model comprise:
node voltage deviation range constraint, power flow constraint, power balance constraint, charge and discharge constraint of distributed energy storage and cluster quantity constraint.
9. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 1,
the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model comprise:
|SoC b,0 -SoC b,T |≤σ (7)
wherein,
the power in the time period t is stored for the b-th seat in a distributed mode,
rated power, soC, for distributed energy storage of the b-th base b,t The state of charge of the b-th distributed energy storage at the end of the t period,
the maximum value of the state of charge of the seat b distributed energy storage,
state of charge minimum for the b-th distributed energy storage, soC b,0 For the initial state of charge value of the distributed energy storage,
the power of the b-th distributed energy storage in the time period tau is obtained, eta is the charge-discharge efficiency of the distributed energy storage,
the power of the b-th distributed energy storage in a time period T is obtained, and sigma is the maximum deviation amplitude of the head and tail state of charge; eta c 、η d Respectively the charging efficiency and the discharging efficiency of the distributed energy storage,
rated capacity for the b-th distributed energy storage
Adopting maximum accumulated charge-discharge quantity E' b And the maximum single charge-discharge capacity E ″) b To calculate; n is a radical of hydrogen g As is the number of clusters in the power distribution network,
and
the number of distributed photovoltaic and stored energy, N, in the g-th cluster respectively PV And N ESS Respectively the number of distributed photovoltaics and stored energy.
10. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 1,
the method comprises the following steps of solving the multi-target joint planning model by using an optimization framework to obtain a group of planning solution sets, and selecting an optimal compromise solution from the planning solution sets as a planning result, wherein the steps comprise:
solving the multi-target joint planning model by using an optimization framework based on the NSGA-III algorithm to obtain a group of planning solution sets, and selecting an optimal compromise solution from the group of planning solution sets by using a TOPSIS-based multi-attribute decision method as a planning result.
11. A multi-objective joint planning system for distributed photovoltaic and energy storage, comprising:
the cluster division module is used for carrying out cluster division on the power distribution network by taking the system modularity based on the space electrical distance as a target;
the model building module is used for building a distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model by taking the position and capacity of distributed photovoltaic access, the position of distributed energy storage access and time sequence output as optimized decision variables and optimizing targets and constraint conditions;
and the solving module is used for solving the multi-target joint planning model by using the optimization framework to obtain a group of planning solution sets, and then selecting the optimal compromise solution from the planning solution sets as a planning result.
12. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 11,
the cluster division module includes: and performing cluster division on the power distribution network by taking the selected interconnected lines of each cluster as variables and taking the system modularity as a target.
13. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 11,
the system modularity is defined as:
wherein f represents the system modularity, D ij Representing the space electrical distance matrix, m representing the sum of network side weights, k i Representing the sum of the node edge weights, k, connected to node i j Represents the sum of the node edge weights connected to node j, and δ (i, j) =1 represents that node i and node j are located in the same cluster.
14. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 11,
the optimization objective of the distributed photovoltaic and energy storage operation planning integrated multi-objective combined planning model comprises the following steps: annual average comprehensive cost, voltage fluctuation and network loss of the power distribution network.
15. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 11,
the constraint conditions of the distributed photovoltaic and energy storage operation planning integrated multi-target combined planning model comprise:
node voltage deviation range constraint, power flow constraint, power balance constraint, charge and discharge constraint of distributed energy storage and cluster quantity constraint.
16. The multi-objective joint planning method for distributed photovoltaic and energy storage as claimed in claim 11,
the solving module comprises:
solving the multi-target combined planning model by using an optimization framework based on an NSGA-III algorithm to obtain a group of planning solution sets, and selecting an optimal compromise solution from the group of planning solution sets by using a TOPSIS (technique for order preference by similarity to zero) multi-attribute decision-making method as a planning result.
17. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 10 when executing the computer program.
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