Power distribution network distributed power generation resource allocation method and system based on multi-objective optimization

Abstract

The invention discloses a distribution network distributed power generation resource allocation method and system based on multi-objective optimization. The method comprises the following steps: determining a test system and objective variables in a distributed power supply distribution network; constructing a multi-objective optimization model taking the electric energy transmission cost, the electric energy loss cost and the voltage deviation minimization as optimization objectives; solving the multi-objective optimization model by using a multi-objective bidirectional coevolution algorithm to obtain a non-dominated solution set; and the optimal compromise solution is selected from the non-dominated solution set by using a fuzzy set theory, and optimal configuration of the distributed power generation resources of the power distribution network is realized. According to the method, the effectiveness and accuracy of identifying the optimal distributed network by the power distribution network are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power system resource configuration, and in particular relates to a method and system for configuring distributed power generation resources in a distribution network based on multi-objective optimization. Background Art

[0002] In the past decade, due to the increase in electricity demand and the alarmingly rapid depletion of fossil fuels, people's interest in alternative energy sources has increased significantly. Therefore, the research on integrating distributed generation (DG) into the distribution network has received great attention. At the same time, placing DG at the optimal location with the right size can reduce the demand for reactive power, improve voltage distribution, and reduce active power loss and line load.

[0003] The reorganization of distribution networks and the integration of distributed generation usually require different evaluations. However, combining these two sub-problems will be a better choice for the entire power system. But most authors consider the objective function of minimizing power loss in distributed networks as a single one. The power consumption is relatively small when combining the reconstruction and integration of distributed generation and optimizing to find the optimal solution. Only a few scholars have tried to optimize this challenging problem with more objectives.

[0004] Most studies focus on single objectives or weighted and multi-objectives, mainly when considering costs, power loss costs, and voltage deviations. However, power system optimization often ignores the simultaneous consideration of non-dispatchable and dispatchable generation, lacking attention to dual-objective and triple-objective functions. In addition, the inclusion of controversial generation resources in the optimization formulation is limited, probably due to their huge investment and operating costs. Most of the reviewed studies optimize the system on a single-period basis, and only a few studies involve multi-period optimization. Another noteworthy point is that although the integration of intermittent and volatile renewable energy sources in modern power systems is increasing, the prevalence of deterministic models in the literature and the limited examples of probabilistic models are still very limited. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a method and system for allocating distributed power generation resources in a distribution network based on multi-objective optimization. The scheme is applicable to distribution networks of various sizes and has significant effects in reducing energy supply costs, reducing energy losses, improving voltage deviations and improving system reliability.

[0006] Technical solution: A method for allocating distributed generation resources in a distribution network based on multi-objective optimization of the present invention comprises:

[0007] Identify test systems and objective variables in distributed generation distribution networks;

[0008] According to the test system and objective variables in the distributed power distribution network, a multi-objective optimization model is constructed with the minimization of power transmission cost, power loss cost and voltage deviation as the optimization objectives;

[0009] The multi-objective bidirectional co-evolutionary algorithm is used to solve the multi-objective optimization model to obtain a set of non-dominated solutions.

[0010] The fuzzy set theory is used to select the best compromise solution from the set of non-dominated solutions to achieve the optimal configuration of distributed generation resources in the distribution network.

[0011] Furthermore, a multi-objective optimization model is constructed with the optimization objectives of minimizing power transmission cost, power loss cost and voltage deviation, including:

[0012] Taking the power transmission cost as the optimization target, the calculation formula is as follows:

[0013]

[0014] Where t and i represent specific time intervals and the number of generators; T and N DG Represents the total cycle and total number of distributed power sources; C sub and C DG A cost parameter representing the active power provided by substations and distributed generation in US dollars per megawatt-hour; A cost parameter representing the power supply from the substation; represents the active power provided by the substation at time t; represents the cost parameter of distributed power generation; Represents the active power provided by the i-th distributed generation device at time t.

[0015] Furthermore, a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets is constructed, including:

[0016] Taking the power loss cost as the optimization target, the calculation formula is as follows:

[0017]

[0018] in, P represents the cost parameter of branch network loss; loss represents the total active power loss within the entire time range; t represents a specific time interval; and T represents the total cycle of the distributed power source.

[0019] Furthermore, the total active power loss P in the entire time range is loss The expression is as follows:

[0020]

[0021] Among them, N l represents the total number of branches; (m,k) represents a specific branch between busbars m and k; V m Indicates bus voltage m; V k Indicates bus k voltage; G mk represents the branch conductance between busbars m and k; θ mk Indicates the generatrix angle θ m and θ k The branch voltage angle difference between them.

[0022] Furthermore, a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets is constructed, including:

[0023] Taking voltage deviation as the optimization target, the calculation formula is as follows:

[0024]

[0025] Where m' represents a specific bus number; N b Indicates the number of buses in the network; V ref Represents the voltage on the relaxed bus; V m represents the voltage on a specific bus; b represents the index of each bus; t represents a specific time interval; and T represents the total cycle of the distributed generation.

[0026] Furthermore, a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets is constructed, including:

[0027] Equality constraints and inequality constraints are established, where the equality constraints include matching the active power and reactive power generated by the power grid with the load demand and network losses, and the inequality constraints include the operating restrictions of equipment and components in the power system, as well as safety constraints on the lines and load buses.

[0028] Furthermore, the multi-objective bidirectional co-evolutionary algorithm is used to solve the multi-objective optimization model to obtain a set of non-dominated solutions, including:

[0029] The constraint dominance principle is used to deal with the constraints in the multi-objective optimization problem, which uses the following rules to compare individuals randomly selected from the current population and

[0030] If the solution and Neither works, so compare and if Then choose a solution for the objective function

[0031] If the solution feasible No, then choose a feasible solution for the objective function

[0032] If the solution and Both are feasible and Then choose a solution for all objective functions

[0033] From the infeasible solution and feasible search space, the population is co-evolved. First, the objective function space is normalized, and the combined population u is used t The ideal Point and Lowest Point Get the objective function F i ′(v j )=(f1′(v j ),f2′(v j ),…,f′ m (v j )) th untie;

[0034]

[0035] The population is updated using the angle-based density selection technique, which will be updated from F′(x j ) and from F′(x k ) t The vector angle between the solutions is calculated as:

[0036]

[0037] Generating non-dominated infeasible solutions increases the diversity of the Pareto front, and the results are as follows:

[0038] min F(x)=(f1(x),f2(x),…,f m (x),CV(x)) T .

[0039] Based on the same inventive concept, a distribution network distributed generation resource configuration system based on multi-objective optimization of the present invention comprises:

[0040] A variable determination module, used for determining a test system and objective variables in a distributed power distribution network;

[0041] A multi-objective optimization model building module is used to build a multi-objective optimization model with the minimization of power transmission cost, power loss cost and voltage deviation as optimization objectives;

[0042] A model solving module is used to solve the multi-objective optimization model using a multi-objective bidirectional co-evolutionary algorithm to obtain a non-dominated solution set;

[0043] The best compromise solution determination module is used to select the best compromise solution from the non-dominated solution set using fuzzy set theory to achieve optimal configuration of distributed generation resources in the distribution network.

[0044] Based on the same inventive concept, a distribution network distributed power generation resource configuration device based on multi-objective optimization of the present invention includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned distribution network distributed power generation resource configuration method based on multi-objective optimization.

[0045] Based on the same inventive concept, a computer-readable storage medium of the present invention is characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned distribution network distributed power generation resource configuration method based on multi-objective optimization.

[0046] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are:

[0047] A new multi-objective bidirectional co-evolutionary algorithm (BICO) is applied to optimize the allocation and reconstruction of multi-period distributed power sources while minimizing energy supply, energy consumption cost and voltage deviation. For better exploration and exploitation, the algorithm combines the constraint dominance principle and evolves the population from feasible and infeasible search spaces through a new angle-based density section. The proposed method outperforms the published multi-objective evolutionary algorithms and improves the effectiveness and accuracy of identifying the optimal distributed network for distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a method for configuring distributed generation resources in a distribution network based on multi-objective optimization disclosed in an embodiment of the present invention;

[0049] Figure 2 It is a basic configuration diagram of the IEEE33 node network disclosed in the embodiment of the present invention;

[0050] Figure 3 It is a basic configuration diagram of a 118-node network disclosed in an embodiment of the present invention;

[0051] Figure 4 It is the final non-dominated solution of the algorithm disclosed in the embodiment of the present invention in the 118-node network;

[0052] Figure 5is a graph comparing the objective functions of all cases of the embodiment of the present invention and the objective functions of the basic case;

[0053] Figure 6 It is a structural schematic diagram of a distribution network distributed generation resource configuration system based on multi-objective optimization disclosed in an embodiment of the present invention;

[0054] Figure 7 It is a structural schematic diagram of a distribution network distributed power generation resource configuration device based on multi-objective optimization disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It will be understood by those skilled in the art that the purposes and advantages that can be achieved with the present invention are not limited to the specific description of the above beneficial effects, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description.

[0056] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software or a combination of the two. Whether it is implemented in hardware or software depends on the specific application and design of the technical solution. Professional and technical personnel can use different methods to implement the functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0057] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] Example 1

[0059] See also Figure 1 , Figure 1 1 is a flow chart of a method for configuring distributed generation resources in a distribution network based on multi-objective optimization disclosed in an embodiment of the present invention. Figure 1 The described method for configuring distributed generation resources in a distribution network is applied to a power system, such as power system resource configuration, etc., which is not limited in the embodiments of the present invention. Figure 1 As shown, the method for configuring distributed generation resources in a distribution network based on multi-objective optimization may include the following operations:

[0060] S1. Determine the test system and objective variables in the distributed power distribution network. Specifically include the following:

[0061] S1.1. The test systems used in the study include a small IEEE 33-node test system and a large 118-node test system;

[0062] S1.2. Objective variables considered when designing the distribution network test system, specifically the temporal fluctuations of sunshine and load.

[0063] In this embodiment, if Figure 2 and Figure 3 As shown, the IEEE 33-node test system is a 12.66 kV radial distribution network, while the 118-node network is a 11KV large radial distribution network.

[0064] S2. Objective variables considered in the design of the distribution network test system, and a multi-objective optimization model with the minimization of power transmission cost, power loss cost and voltage deviation as the optimization objectives. Specifically including the following:

[0065] S2.1. Taking the power transmission cost as the optimization target, the calculation formula is as follows:

[0066]

[0067] Where t and i represent specific time intervals and the number of generators; T and N DG Indicates the total cycles and total number of distributed power sources; represents the cost parameter of power supply from the substation; C sub and C DG A cost parameter representing the active power provided by substations and distributed generation in US dollars per megawatt-hour; represents the active power provided by the substation at time t; represents the cost parameter of distributed power generation; represents the active power provided by the i-th distributed generation device at time t; and Both are measured in megawatts.

[0068] It can be seen that the first objective function is based on an economic perspective, which minimizes the cost of energy supply over the entire time frame.

[0069] S2.2, taking the power loss cost as the optimization target, the calculation formula is as follows:

[0070]

[0071] in, P represents the cost parameter of branch network loss; loss represents the total active power loss within the entire time range; t represents a specific time interval; and T represents the total cycle of the distributed power source.

[0072] In this embodiment, the total active power loss P in the entire time range is loss The expression is as follows:

[0073]

[0074] Among them, N l represents the total number of branches; (m,k) represents a specific branch between busbars m and k; V m Indicates bus voltage m; V k Indicates bus k voltage; G mk represents the branch conductance between busbars m and k; θ mk Indicates the generatrix angle θ m and θ k The branch voltage angle difference between them. The branches mentioned in this paragraph refer to the power lines in the distribution network, that is, the wires or cables connecting different electrical nodes. These branches can be transmission lines or distribution lines, which transmit electrical energy in the power grid.

[0075] It can be seen that the second objective function takes into account economic and technical aspects and calculates the cost of energy losses over the entire time frame using the above formula.

[0076] S2.3, taking voltage deviation as the optimization target, the calculation formula is as follows:

[0077]

[0078] Where m' represents a specific bus number; N b Indicates the number of buses in the network; V ref represents the voltage on the relaxed busbar, which is set to 1p.u in this embodiment; V m represents the voltage on a specific bus; b represents the index of each bus; t represents a specific time interval; and T represents the total cycle of the distributed power source. and is the cost parameter of substation power supply, distributed generation and branch network loss; N DG 、N l and N b They represent the number of schedulable and unschedulable DGs, branches and buses, respectively.

[0079] S2.4. Establishing equality constraints: The power balance equation requires that the active power and reactive power generated by the grid match the load demand and network losses, as follows:

[0080]

[0081] Among them, P sub and Q sub is the active and reactive power provided by the substation, PD and Q D are the active and reactive power demands, δ bj is the voltage angle of the branch connected between busbars b and j; G bj and B bj are the transmission conductance and susceptance of the branch between busbars b and j; is the active power provided by the i-th distributed generation device, is the reactive power provided by the i-th distributed generation device.

[0082] S2.5. Establish inequality constraints, operating restrictions on equipment and components in the power system, and safety constraints on lines and load buses, as follows:

[0083]

[0084] N l =N b -1 (8)

[0085]

[0086] in, are the minimum and maximum active power of primary substation respectively, are the minimum and maximum reactive power of the primary substation, respectively. are the minimum and maximum active power of distributed generation, respectively. are the minimum and maximum values ​​of the reactive power of distributed generation, respectively. are the minimum and maximum values ​​of the PV bus voltage, are the minimum and maximum values ​​of the PQ bus voltage respectively, It is the maximum value of MVA branch flow.

[0087] Formula (1) shows the active and reactive limits of the primary substation, which in this case is the reference bus. Formula (2) represents the active and reactive constraints of dispatchable and non-dispatchable distributed generation. The cumulative power constraints of DGS are formulas (3)-(4), which stipulate that the total amount of active and reactive power generated by distributed generation must be less than or equal to the total demand. Formulas (5)-(6) are the voltage constraints of the PV bus and the PQ bus, respectively, and formula (7) is the MVA branch flow limit. Formulas (8)-(9) ensure radial constraints of the distribution system. Matrix A is the connection matrix, also called the branch and bus association matrix, where mg=1 means that the branch is linked from bus j, while mg=-1 means that the i-th branch is linked to the bus, and mg=0 ensures that the i-th branch is not connected to the j-th bus.

[0088] S3. Use a multi-objective bidirectional co-evolutionary algorithm (BiCo) to solve the multi-objective optimization model to obtain a set of non-dominated solutions. Specifically, it includes the following:

[0089] S3.1. Use the constraint dominance principle to deal with constraints in multi-objective optimization problems. It uses the following rules to compare individuals randomly selected from the current population. and

[0090] If the solution and Neither works, so compare and if Then choose a solution for the objective function

[0091] If the solution feasible No, then choose a feasible solution for the objective function

[0092] If the solution and Both are feasible and Then choose a solution for all objective functions

[0093] Among them, the CV function usually refers to the "constraint violation measure" function. This function is used to quantify the degree to which a solution violates the constraints in a multi-objective optimization problem, and is used to screen individuals in this scheme. Represents individual x u 、x v Voltage deviation, cost, and energy loss under the objective function.

[0094] S3.2. Evolve the population from the infeasible solution and feasible search space together. First, normalize the objective function space and use the combined population u t The ideal Point and Lowest Point Get the objective function F i ′(v j )=(f1′(v j ),f2′(v j ),…,f′ m (v j )) th untie;

[0095]

[0096] S3.3, using the angle-based density selection technique to update the population, from F′(x j ) and from F′(x k ) t The vector angle between the solutions is calculated as:

[0097]

[0098] S3.4, generating non-dominated infeasible solutions increases the diversity of the Pareto frontier. The multi-objective bidirectional co-evolutionary algorithm regards CV as an additional objective function M+1 th , so that the original constraint problem becomes an unconstrained multi-objective problem, and the following results are given:

[0099] min F(x)=(f1(x),f2(x),…,f m (x),CV(x)) T .

[0100] S4. Use fuzzy set theory to select the best compromise solution from the set of non-dominated solutions to achieve optimal configuration of distributed generation resources in the distribution network.

[0101] In multi-objective optimization, there is usually no single optimal solution, but rather a set of Pareto front solutions that offer different trade-offs between multiple objectives. Fuzzy set theory provides a mathematical tool for dealing with such multi-criteria decision-making problems, thereby selecting the best compromise.

[0102] The system of this embodiment is composed of IEEE118 node system, such as Figure 2 shown.

[0103] According to the characteristics and objectives of the system, a multi-objective optimization problem including three conflicting objective functions is established. The final non-dominated solution of the algorithm proposed in the embodiment of the present invention in the 118-node network is as follows: Figure 4 As shown. The first subfigure shows the relationship between the power transmission cost f1 and the power loss cost f2 during the optimization process. This relationship is usually manifested as a trade-off because costs may increase when reducing energy losses. The second subfigure shows the relationship between the power transmission cost f1 and the voltage deviation f3. Reducing the voltage deviation can improve the stability of the power grid and the power quality. The last subfigure shows the distribution of the final non-dominated solutions, distinguishing different solutions by shape to show their distribution characteristics in the objective function space. This helps to observe whether the algorithm can explore the Pareto front evenly. The objective functions of all cases in the embodiments of the present invention and the comparison results with the basic case objective functions are shown in the following figure. Figure 5 shown. Figure 5The optimization results of different cases are shown in three sub-figures and compared with the base case. Figure 5 (a), (b), and (c) respectively show the optimization results of power transmission cost, power loss cost, and voltage deviation under different cases. The improved efficiency of the embodiment of the present invention compared with the basic case is shown in Table 1.

[0104] Table 1

[0105] Compare Projects Improve efficiency Voltage distribution 22％ Voltage deviation and power loss More than 80% Energy supply costs 92.99％ Energy loss costs 86％

[0106] Obviously, compared with the traditional multi-objective evolutionary algorithm, the method proposed in the present invention can optimize multiple objectives at the same time, showing faster convergence speed and better solutions within given computing resources, and provides an efficient, flexible and reliable optimization tool for the planning and management of distribution networks.

[0107] The technical solution of the present invention determines the potential location and expected scale of distributed power sources in the distribution network; constructs a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets; applies a multi-objective bidirectional co-evolutionary algorithm to solve the multi-objective optimization model to obtain a set of non-dominated solutions; uses fuzzy set theory to select the best compromise solution from the set of non-dominated solutions, and develops a system to achieve optimal configuration of distributed power generation resources in the distribution network. This method uses a multi-objective bidirectional co-evolutionary algorithm to minimize energy supply, energy consumption cost and voltage deviation, and combines the constraint dominance principle to evolve the population from feasible and infeasible search spaces through angle-based density sections, which is superior to the published multi-objective evolutionary algorithms and improves the effectiveness and accuracy of identifying the best distributed network in the distribution network.

[0108] After adopting the above scheme, the present invention realizes the optimal site selection and scale determination of distributed generation resources (including dispatchable and non-dispatched resources) and the optimal reconfiguration of network structure by comprehensively considering multiple objectives such as power transmission cost, power loss cost and voltage deviation. The present invention adopts a novel multi-objective bidirectional co-evolutionary algorithm (BiCo), which combines the constraint dominance principle (CDP) and the angle-based density selection technology to improve the search efficiency and the quality of the solution. The algorithm effectively explores and utilizes the search space by co-evolving strategies in feasible and infeasible solution spaces to generate a series of uniformly distributed non-dominated solutions; and combines fuzzy set theory to identify the best compromise solution from the obtained non-dominated solution set to achieve the best balance between technical and economic goals; through case studies conducted on IEEE 33-node and 118-node distribution networks, the present invention has been demonstrated to have significant effects in reducing energy supply costs, reducing energy losses, improving voltage deviations and improving system reliability. The present invention is applicable to distribution networks of various sizes and provides an efficient, flexible and reliable optimization tool for the planning and management of distribution networks.

[0109] Example 2

[0110] See also Figure 6 , Figure 6 The present invention discloses a distributed power generation resource configuration system for a distribution network based on multi-objective optimization, which can realize power system resource configuration, and specifically includes:

[0111] A variable determination module, used for determining a test system and objective variables in a distributed power distribution network;

[0112] A multi-objective optimization model building module is used to build a multi-objective optimization model with the minimization of power transmission cost, power loss cost and voltage deviation as optimization objectives;

[0113] A model solving module is used to solve the multi-objective optimization model using a multi-objective bidirectional co-evolutionary algorithm to obtain a non-dominated solution set;

[0114] The best compromise solution determination module is used to select the best compromise solution from the non-dominated solution set using fuzzy set theory to achieve optimal configuration of distributed generation resources in the distribution network.

[0115] In an optional embodiment, a method for configuring distributed power generation resources in a distribution network based on multi-objective optimization includes: a) determining a test system and objective variables in a distributed power distribution network; b) constructing a multi-objective optimization model with minimization of power transmission cost, power loss cost and voltage deviation as optimization objectives; c) solving the multi-objective optimization model using a multi-objective bidirectional collaborative evolutionary algorithm to obtain a set of non-dominated solutions; d) selecting the best compromise solution from the set of non-dominated solutions using fuzzy set theory to achieve optimal configuration of distributed power generation resources in the distribution network.

[0116] Example 3

[0117] See also Figure 7 , Figure 7 Schematic diagram of a distributed power generation resource configuration device for a distribution network based on multi-objective optimization disclosed in an embodiment of the present invention. Figure 7 The described device can be applied in an electric power system, such as for configuring electric power system resources, etc., and the embodiments of the present invention do not limit this.

[0118] like Figure 7 As shown, the device may include a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiment and can achieve the technical effect consistent with the above method.

[0119] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0120] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0121] Example 4

[0122] Embodiment 4 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the above embodiment are implemented and the technical effect consistent with the above method can be achieved.

[0123] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0124] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0125] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0126] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] Of course, the storage medium containing computer executable instructions provided by an embodiment of the present invention, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0128] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for allocating distributed generation resources in a distribution network based on multi-objective optimization, characterized in that: include: Identify test systems and objective variables in distributed generation distribution networks; According to the test system and objective variables in the distributed power distribution network, a multi-objective optimization model is constructed with the minimization of power transmission cost, power loss cost and voltage deviation as the optimization objectives; The multi-objective bidirectional co-evolutionary algorithm is used to solve the multi-objective optimization model to obtain a set of non-dominated solutions. The fuzzy set theory is used to select the best compromise solution from the set of non-dominated solutions to achieve the optimal configuration of distributed generation resources in the distribution network.

2. The method for configuring distributed generation resources in a distribution network based on multi-objective optimization according to claim 1, characterized in that: Construct a multi-objective optimization model with the optimization objectives of minimizing power transmission cost, power loss cost and voltage deviation, including: Taking the power transmission cost as the optimization target, the calculation formula is as follows: Where t and i represent specific time intervals and the number of generators; T and N DG Represents the total cycle and total number of distributed power sources; C sub and C DG A cost parameter representing the active power provided by substations and distributed generation in US dollars per megawatt-hour; A cost parameter representing the power supply from the substation; represents the active power provided by the substation at time t; represents the cost parameter of distributed power generation; Represents the active power provided by the i-th distributed generation device at time t.

3. The method for allocating distributed generation resources in a distribution network based on multi-objective optimization according to claim 1, characterized in that: Construct a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets, including: Taking the power loss cost as the optimization target, the calculation formula is as follows: in, P represents the cost parameter of branch network loss; loss It represents the total active power loss in the whole time range; t represents a specific time interval; T represents the total cycle of distributed power source.

4. The method for configuring distributed generation resources in a distribution network based on multi-objective optimization according to claim 3 is characterized in that: The total active power loss P in the entire time range is loss The expression is as follows: Among them, N l represents the total number of branches; (m,k) represents a specific branch between busbars m and k; V m Indicates bus voltage m; V k Indicates bus k voltage; G Mk represents the branch conductance between busbars m and k; θ mk Indicates the generatrix angle θ m and θ k The branch voltage angle difference between them.

5. The method for configuring distributed generation resources in a distribution network based on multi-objective optimization according to claim 1, characterized in that: Construct a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets, including: Taking voltage deviation as the optimization target, the calculation formula is as follows: Among them, m ′ Indicates a specific bus number; N b Indicates the number of buses in the network; V ref Represents the voltage on the relaxed bus; V m represents the voltage on a specific bus; b represents the index of each bus; t represents a specific time interval; and T represents the total cycle of the distributed generation.

6. The method for allocating distributed generation resources in a distribution network based on multi-objective optimization according to claim 1, characterized in that: Construct a multi-objective optimization model with power transmission cost, power loss cost and voltage deviation as optimization targets, including: Equality constraints and inequality constraints are established, where the equality constraints include matching the active power and reactive power generated by the power grid with the load demand and network losses, and the inequality constraints include the operating restrictions of equipment and components in the power system, as well as safety constraints on the lines and load buses.

7. The method for allocating distributed generation resources in a distribution network based on multi-objective optimization according to claim 1, characterized in that: The multi-objective bidirectional co-evolutionary algorithm is used to solve the multi-objective optimization model to obtain a set of non-dominated solutions, including: The constraint dominance principle is used to deal with the constraints in the multi-objective optimization problem, which uses the following rules to compare individuals randomly selected from the current population and If the solution and Neither works, so compare and if Then choose a solution for the objective function If the solution feasible No, then choose a feasible solution for the objective function If the solution and Both are feasible and Then choose solutions for all objective functions From the infeasible solution and feasible search space, the population is co-evolved. First, the objective function space is normalized, and the combined population u is used t The ideal Point and Lowest Point Get the objective function F i ′ (v j )=(f1 ′ (v j ),f2 ′ (v j ),…,f ′ m (v j )) th untie; The population is updated using the angle-based density selection technique, which will be updated from F ′ (x j ) and from F ′ (x k ) t The vector angle between the solutions is calculated as: Generating non-dominated infeasible solutions increases the diversity of the Pareto front, and the results are as follows: min F(x)=(f1(x),f2(x),…,f m (x),CV(x)) T 。 8. A distributed generation resource configuration system for a distribution network based on multi-objective optimization, characterized in that: include: A variable determination module, used for determining a test system and objective variables in a distributed power distribution network; A multi-objective optimization model building module is used to build a multi-objective optimization model with the minimization of power transmission cost, power loss cost and voltage deviation as optimization objectives; A model solving module is used to solve the multi-objective optimization model using a multi-objective bidirectional co-evolutionary algorithm to obtain a non-dominated solution set; The best compromise solution determination module is used to select the best compromise solution from the non-dominated solution set using fuzzy set theory to achieve optimal configuration of distributed generation resources in the distribution network.

9. A distributed generation resource configuration device for a distribution network based on multi-objective optimization, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method for configuring distributed generation resources in a distribution network based on multi-objective optimization as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for configuring distributed generation resources in a distribution network based on multi-objective optimization as claimed in any one of claims 1 to 7.

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