Method and device for optimizing bearing capacity of renewable distributed power supply in power distribution network

Abstract

The invention relates to the technical field of power grid operation, in particular to a method and device for optimizing the bearing capacity of a renewable distributed power supply in a power distribution network. Respectively calculating a reverse load rate, a voltage fluctuation value, a harmonic verification result and a three-phase unbalance degree when the target renewable distributed power supply supplies power to the target power distribution network; carrying out bearing capacity evaluation on the target power distribution network according to the reverse load rate, the voltage fluctuation value, the harmonic verification result and the three-phase unbalance degree so as to obtain the current bearing capacity of the target power distribution network; and based on a pre-constructed bearing capacity optimization model, optimizing the target power distribution network according to the current bearing capacity to obtain a comprehensive optimization operation scheme. Therefore, the problems that the output uncertainty of photovoltaic power is high, the randomness of operation of a power distribution network system is increased, and line overload, voltage fluctuation, load shortage and the like are likely to happen are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation, and in particular to a method and device for optimizing the carrying capacity of a renewable distributed power source in a distribution network. Background Art

[0002] With the increasing maturity of new energy power generation technology and the continuous reduction of costs, renewable distributed generation (DG) has been widely used around the world, especially the increasing access to DG such as wind power and photovoltaic power in distribution networks. However, the access of a large number of DGs has also brought new challenges to the stable dispatching of distribution networks.

[0003] As the end of the power system, the distribution network has a complex structure, many points and a large amount of data, which makes the centralized dispatching dimension high and the solution complex. Especially when a high proportion of DG is connected, the operation status of the distribution network becomes more complex and uncertain. The output of photovoltaic power is affected by many factors such as weather, season and geographical location, and has high uncertainty, which increases the randomness of the operation of the distribution network system and is prone to risks such as line overload, voltage fluctuation and load shortage. These problems not only threaten the safe and stable operation of the distribution network, but also affect the effective use of clean energy and the operation efficiency of the distribution network.

[0004] Therefore, there is an urgent need for a method for evaluating the carrying capacity of renewable distributed power sources in the distribution network and an optimized operation method to ensure that the renewable distributed power sources can operate safely and stably after being connected to the distribution network. Summary of the invention

[0005] The present invention provides a method and device for optimizing the carrying capacity of a renewable distributed power source in a distribution network, so as to solve the problems that the output uncertainty of photovoltaic power is high, the randomness of the operation of the distribution network system is increased, and the problems of line overload, voltage fluctuation and load shortage are prone to occur.

[0006] The first aspect of the present invention provides a method for optimizing the carrying capacity of a renewable distributed power source in a distribution network, comprising the following steps: based on a pre-constructed carrying capacity evaluation platform, respectively calculating the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance when the target renewable distributed power source supplies power to the target distribution network; performing a carrying capacity evaluation on the target distribution network according to the reverse load rate, the voltage fluctuation value, the harmonic verification result and the three-phase imbalance to obtain the current carrying capacity of the target distribution network; based on a pre-constructed carrying capacity optimization model, optimizing the target distribution network according to the current carrying capacity to obtain a comprehensive optimization operation plan.

[0007] Optionally, the respectively calculating the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance after the target renewable distributed power source supplies power to the target distribution network includes:

[0008] When the target renewable distributed power source supplies power to the target distribution network, the output power and equivalent electric load power of the target renewable distributed power source, the voltage value of each node of the target distribution network at different times, the actual current value of the preset harmonic, the positive sequence current component and the negative sequence current component are collected;

[0009] Calculating the reverse load rate according to the output power and the equivalent electric load power;

[0010] Selecting the voltage values ​​of the same node in two adjacent sampling periods from the voltage values ​​of each node at different times to calculate the voltage fluctuation;

[0011] Compare the actual current value of each harmonic with the preset current limit value to obtain the harmonic verification result;

[0012] The three-phase unbalance degree is calculated according to the positive-sequence current component and the negative-sequence current component.

[0013] Optionally, the optimizing the target distribution network based on the pre-built carrying capacity optimization model according to the current carrying capacity to obtain a comprehensive optimized operation plan includes:

[0014] Setting a system power flow balance constraint, a target renewable distributed power generation power range constraint and a network security constraint for the pre-built load carrying capacity optimization model to obtain a comprehensive optimization scheduling model;

[0015] The target distribution network is optimized according to the comprehensive optimization scheduling model and the current carrying capacity.

[0016] Optionally, the optimizing the target distribution network based on the pre-built carrying capacity optimization model according to the current carrying capacity to obtain a comprehensive optimized operation plan includes:

[0017] Setting a system power flow balance constraint, a target renewable distributed power generation power range constraint and a network security constraint for the pre-built load carrying capacity optimization model to obtain a comprehensive optimization scheduling model;

[0018] The target distribution network is optimized according to the comprehensive optimization scheduling model and the current carrying capacity to obtain the comprehensive optimization operation plan.

[0019] Optionally, the target renewable distributed power source power range constraint is:

[0020]

[0021] in, is the actual output of the target renewable distributed generation at node i at time t, is the maximum allowed output of the target renewable distributed generation at node i at time t.

[0022] Optionally, the network security constraint is:

[0023]

[0024] I x,h h

[0025]

[0026] Where, λ is the reverse load rate, is the maximum reverse load rate, U i,d is the voltage fluctuation value, is the maximum voltage fluctuation value, I x,h is the actual current value of the hth harmonic, I h is the maximum actual current value, ε1 is the three-phase current unbalance, is the maximum three-phase current unbalance.

[0027] The second aspect of the present invention provides a device for optimizing the carrying capacity of a renewable distributed power source in a distribution network, including: a calculation module, which is used to calculate the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance when the target renewable distributed power source supplies power to the target distribution network based on a pre-built carrying capacity evaluation platform; an evaluation module, which is used to evaluate the carrying capacity of the target distribution network according to the reverse load rate, the voltage fluctuation value, the harmonic verification result and the three-phase imbalance to obtain the current carrying capacity; an optimization module, which is used to optimize the target distribution network according to the current carrying capacity based on a pre-built carrying capacity optimization model to obtain a comprehensive optimization operation plan.

[0028] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing the carrying capacity of renewable distributed power sources in a distribution network as described in the above embodiments.

[0029] A fourth aspect of the present invention provides a computer program product, which, when executed by a processor, implements the above method for optimizing the carrying capacity of renewable distributed power sources in a distribution network.

[0030] ​A fifth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above method for optimizing the carrying capacity of renewable distributed power sources in a distribution network.

[0031] The method and device for optimizing the carrying capacity of renewable distributed power sources in a distribution network proposed in an embodiment of the present invention quantitatively analyze the impact of the access of a high proportion of renewable distributed power sources on the distribution network and the load generated by it, including analyzing the impact of factors such as DG output characteristics, load characteristics and network topology on the operation of the distribution network, and adopts a combination of mathematical modeling and simulation analysis to evaluate the carrying capacity of the distribution network under different scenarios, thereby evaluating the carrying capacity of the distribution network for renewable distributed power sources; in addition, an improved niche particle swarm optimization is used to optimize the distribution network to obtain a comprehensive optimization operation plan. By implementing a reasonable optimization strategy, the carrying capacity of the distribution network is significantly improved, thereby enhancing the access of renewable distributed power sources, and also enhancing the carrying capacity and operation efficiency of the distribution network, promoting the efficient use of clean energy, and further promoting green and sustainable electricity growth.

[0032] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 A schematic diagram of a bearing capacity analysis platform provided according to an embodiment of the present invention;

[0035] Figure 2 A flowchart of a method for optimizing the carrying capacity of a renewable distributed power source in a distribution network according to an embodiment of the present invention;

[0036] Figure 3 A block diagram of a device for optimizing the carrying capacity of a renewable distributed power source in a distribution network according to an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0039] The following describes a method and device for optimizing the carrying capacity of a renewable distributed power source in a distribution network according to an embodiment of the present invention with reference to the accompanying drawings.

[0040] It should be noted that in order to ensure the safe operation of the target distribution network, it is necessary to ensure that the thermally stable reverse load rate of the equipment line after the renewable distributed generation DG is connected does not exceed the allowable range. The output fluctuation of the renewable distributed generation DG will affect the node voltage of the distribution network and may cause the node voltage to exceed the standard. Therefore, when evaluating the carrying capacity of the renewable distributed generation DG, it is necessary to verify the node voltage to ensure that it is within the allowable range. The access of renewable distributed generation DG will change the fault current level of the distribution network and affect the operating performance of the protection equipment. Therefore, it is necessary to evaluate the effectiveness of the protection configuration after the access of the renewable distributed generation DG to ensure that the protection equipment can work normally in the event of a fault. The fluctuations and harmonic pollution output of the renewable distributed generation DG will affect the power quality of the distribution network. Therefore, in order to ensure the power quality of users, it is necessary to verify the harmonic level after the access of the renewable distributed generation DG and take corresponding control measures. Such as Figure 1 As shown, in response to the above-mentioned influencing factors, the embodiment of the present invention adopts microservice technology to pre-build a capacity analysis platform including a data resource layer, a platform service layer, and an application management layer to achieve business module division and standardized services.

[0041] The data resource layer is used to obtain the basic data and real-time operation data of the target distribution network. It collects real-time operation parameters such as voltage, current, and harmonics through sensors installed at each node of the target distribution network, and stores and communicates data securely through servers and security gateways. The data resource layer uses distributed database technology to ensure high availability and consistency of data, providing reliable data support for other layers of the platform.

[0042] The platform service layer is used to process and analyze the data collected by the data resource layer. By building a model data platform and using big data analysis algorithms, the data of renewable distributed power equipment and power collection systems are processed to identify potential problems and hidden dangers in the power grid. In particular, for problems such as reverse load rate, voltage fluctuations, harmonics, and three-phase current imbalance, the platform service layer calculates these key indicators through real-time monitoring and historical data analysis, and evaluates their impact on power grid stability and security. The topology analysis component is used to analyze the power grid structure and identify the uneven distribution of electricity that may cause these problems. The data parsing component is used to decode and convert complex power data to meet the input requirements of the evaluation model. In addition, this layer also includes services such as file management, log management, data security, and scheduled tasks to ensure the integrity and traceability of the data processing process.

[0043] The application management layer is used to perform specific load-carrying capacity assessment tasks based on the data and services provided by the data resource layer and the platform service layer. This layer integrates different evaluation algorithms and visualization tools to conduct multi-dimensional analysis of the load-carrying capacity of renewable distributed power sources. In particular, combined with the geographic information system (GIS), the load-carrying capacity in different time periods and regions is refined. For the problems of reverse load rate, voltage fluctuation, harmonics and three-phase current imbalance, the statistical analysis module of the application management layer will conduct a comprehensive evaluation based on the data provided by the platform service layer, generate an analysis report, and put forward optimization suggestions. The information query module allows users to customize query conditions and obtain detailed operating data of specified nodes or devices. The accessible capacity module is used to calculate the maximum renewable distributed power DG capacity that can be connected to the current distribution network under different conditions to provide decision support.

[0044] Figure 2 A schematic flow chart of a method for optimizing the carrying capacity of a renewable distributed power source in a distribution network provided in an embodiment of the present invention.

[0045] like Figure 2 As shown, the method for optimizing the carrying capacity of the renewable distributed power source in the distribution network includes the following steps:

[0046] In step S201, the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance degree when the target renewable distributed power source supplies power to the target distribution network are calculated respectively.

[0047] In some embodiments, respectively calculating the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance after the target renewable distributed power source supplies power to the target distribution network includes:

[0048] When the target renewable distributed power source supplies power to the target distribution network, the output power and equivalent electric load power of the target renewable distributed power source, the voltage value of each node of the target distribution network at different times, the actual current value of the preset harmonic, the positive sequence current component and the negative sequence current component are collected;

[0049] Calculate the reverse load rate based on the output power and the equivalent electric load power;

[0050] The voltage value of the same node in two adjacent sampling periods is selected from the voltage values ​​of each node at different times to calculate the voltage fluctuation;

[0051] Compare the actual current value of each harmonic with the preset current limit value to obtain the harmonic verification result;

[0052] The three-phase unbalance is calculated based on the positive-sequence current component and the negative-sequence current component.

[0053] In the actual implementation process, in the process of renewable distributed power generation capacity analysis, it is particularly important to deeply explore the thermal stability margin of equipment and lines while ensuring that the transformer does not exceed the thermal stability. The reverse load rate refers to the relative ratio of the actual load rate of the power grid to the maximum load rate when the distributed power generation supplies power to the power grid. Accurate calculation is the key to the reverse load rate to ensure the stable operation of the power grid.

[0054] When the target renewable distributed power source supplies power to the target distribution network, the output power and equivalent electric load power of the target renewable distributed power source are collected, and the actual operating limit of the transformer or line is obtained, and then the reverse load rate is calculated. The expression is as follows:

[0055]

[0056] Where P L , P D are the output power and equivalent electric load of renewable distributed power sources, respectively. The equivalent electric load refers to the output of other power sources excluding renewable distributed power sources. S e It is the actual operating limit of the transformer or line.

[0057] When the output power and load consumption of the power grid change, voltage deviation and voltage fluctuation will occur at each node of the target distribution network. The access of the target renewable distributed power source DG will further aggravate this voltage fluctuation. Voltage fluctuation can be quantified by comparing the change of voltage amplitude at the same node in two adjacent sampling periods. Therefore, it is necessary to collect the voltage value of each node of the target distribution network at different times, and quantify the voltage fluctuation value by comparing the change of voltage amplitude at the same node in two adjacent sampling periods. The expression is as follows:

[0058]

[0059] In the formula, are the voltage values ​​of node i at time t and t+T respectively, U i,d is the voltage fluctuation value of node i, and T is the voltage measurement period.

[0060] When the renewable distributed power source DG is connected to the grid, the high-frequency switching operation of the inverter switching device will generate harmonic components that match the system switching frequency, significantly aggravating the harmonic pollution in the distribution network. In order to ensure the stable operation of the power grid, harmonic verification becomes a key task. The principle of harmonic verification is to ensure that the harmonic voltage content generated by each node does not exceed the specified limit value after the renewable distributed power source is connected. Therefore, it is necessary to obtain the actual current value of the preset harmonic order and the limit value of the harmonic current of the corresponding order specified in GB / T14549 "Harmonics of Power Quality of Public Grids", and compare the two. The expression is as follows:

[0061] I x,h>I h (3)

[0062] Among them, I x,h Indicates the actual current value of the hth harmonic, I h It is the limit value of the h-th harmonic current specified in GB / T14549 "Harmonics of Power Quality in Public Grids".

[0063] When the output imbalance and randomness of the renewable distributed power source DG reaches a serious level, it may significantly cause the balance of the three-phase power supply in the distribution network to be destroyed. In order to quantify this imbalance, the embodiment of the present invention uses a three-phase current imbalance index to describe it, collects the positive sequence current component and the negative sequence current component to calculate the three-phase imbalance degree, and the specific expression is as follows:

[0064]

[0065] Where I2 is the negative sequence current component, I1 is the positive sequence current component, ε i It is the three-phase unbalance.

[0066] Calculating reverse load rate, voltage fluctuation, harmonics and three-phase current imbalance is a key step in the entire distribution network carrying capacity assessment process, in order to quantify the load characteristics of the power grid after the access of renewable distributed power sources, and thus ensure the safe and stable operation of the power grid after the access of renewable distributed power sources.

[0067] It should be noted that the calculation results of the reverse load rate can also be used to construct the subsequent load-carrying capacity optimization model. In this case, the reverse load rate will be used as a key input parameter to define the constraints of the power grid. The reverse load rate can help identify nodes in the power grid that may be overloaded or unbalanced, thereby optimizing the layout and scheduling strategy of renewable distributed power sources. The optimization model will comprehensively consider the relationship between the reverse load rate and other important indicators, and formulate the optimal power access plan to maximize the power grid's carrying capacity while ensuring the safety and stability of its operation.

[0068] In step S202, based on the pre-built carrying capacity evaluation platform, the carrying capacity of the target distribution network is evaluated according to the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance to obtain the current carrying capacity of the target distribution network.

[0069] In the actual implementation process, the reverse load rate, voltage fluctuation value, harmonic verification results and three-phase imbalance are applied as an input variable to the platform service layer of the aforementioned capacity analysis platform. The platform service layer will process the data and conduct a comprehensive evaluation. During the processing, the platform service layer will analyze the impact of the reverse load rate, voltage fluctuation value, harmonic verification results and three-phase imbalance on the overall carrying capacity of the target distribution network through big data analysis and simulation tools, and evaluate the security risks that the distribution network may face through modules such as topology analysis to obtain the current carrying capacity of the target distribution network.

[0070] In step S203, based on the pre-built carrying capacity optimization model, the target distribution network is optimized according to the current carrying capacity to obtain a comprehensive optimized operation plan.

[0071] In some embodiments, based on a pre-built carrying capacity optimization model, the target distribution network is optimized according to the current carrying capacity to obtain a comprehensive optimization operation plan, including:

[0072] The system power flow balance constraint, target renewable distributed generation power range constraint and network security constraint are set for the pre-built load capacity optimization model to obtain a comprehensive optimization dispatch model;

[0073] The target distribution network is optimized according to the comprehensive optimization dispatching model and the current carrying capacity to obtain a comprehensive optimization operation plan.

[0074] In the actual implementation process, the embodiment of the present invention pre-constructs a carrying capacity optimization model with the goal of maximizing the acceptance of photovoltaic power generation. In other words, the maximum total capacity of renewable distributed power generation that the distribution network can accept is determined. The model directly reflects the upper limit of the distribution network's ability to accept renewable distributed power generation, ensuring that the power grid maintains stability and safety while efficiently utilizing clean energy. The specific expression is:

[0075]

[0076] Where F(x) is the total capacity of renewable distributed generation that the distribution network can accommodate, and x i is the decision variable, which refers to the photovoltaic power generation acceptance capacity of a single region, and n is the number of connected photovoltaic power stations.

[0077] After obtaining the current carrying capacity of the target distribution network and the carrying capacity optimization model, the current carrying capacity and the carrying capacity optimization model are passed to the application management layer of the carrying capacity analysis platform, and the system flow balance constraints, the target renewable distributed power generation power range constraints and the network security constraints are set for the carrying capacity optimization model to construct a comprehensive optimization scheduling model. The improved improved niche particle swarm optimization is used to solve the comprehensive optimization scheduling model to support the comprehensive analysis and decision-making of the carrying capacity.

[0078] Among them, the system power flow balance constraint is:

[0079]

[0080] Where P Gi is the active power generated at node i, P Di is the load active power at node i, V i 、V j is the voltage amplitude, N s is the number of all nodes, G ij is the conductivity, B ij is the susceptance, θ ij is the voltage phase angle difference, Q Gi is the power generation at node i, Q Di is the reactive power of the load at node i.

[0081] When the renewable distributed generation is connected to node i, its output at time t should meet the target renewable distributed generation power range constraint:

[0082]

[0083] In the formula, is the actual output of the target renewable distributed generation at node i at time t, is the maximum allowed output of the target renewable distributed generation at node i at time t.

[0084] The calculated results of reverse load rate, voltage fluctuation, harmonics and three-phase current imbalance must all be less than the maximum values ​​required for network safety operation, constituting the network safety constraints as follows:

[0085]

[0086] I x,h h (11)

[0087]

[0088] Where λ is the reverse load rate, is the maximum reverse load rate, U i,d ​is the voltage fluctuation value, is the maximum voltage fluctuation value, I x,h is the actual current value of the hth harmonic, I h is the maximum actual current value, ε1 is the three-phase current unbalance, is the maximum three-phase current unbalance.

[0089] Furthermore, the comprehensive optimization scheduling model exhibits significant complexity due to its multi-objective, multi-variable, multi-constraint and high-dimensional characteristics. In order to effectively cope with these challenges, the embodiment of the present invention proposes an improved modified microhabitat particle swarm optimization (MNPSO). The MNPSO algorithm is an improved version of the traditional particle swarm optimization algorithm (PSO). The traditional PSO simulates the foraging behavior of bird flocks and relies on the position and speed of each particle in the flock to find the optimal solution.

[0090] The particle speed and position update formula is as follows:

[0091] v i (t+1)=ω·v i (t)+c1·r1·(p best,i -x i (t))+c2·r2·(g best -x i (t))(13)

[0092] x i (t+1)=x i (t)+v i (t+1)(14)

[0093] Among them, v i (t) is the velocity of particle i at the tth iteration, x i (t) is the position of particle i at the tth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, p best,i is the individual optimal position of particle i, g best is the global best position.

[0094] However, in complex multi-objective optimization problems, particle swarms are prone to fall into local extremes in the early stages, resulting in the optimization results failing to reach the global optimum. MNPSO overcomes this problem by introducing niche technology and a dynamic inertia weight adjustment mechanism. Niche technology is used in MNPSO to maintain the diversity of the population and prevent particles from falling into local extremes. Specifically, the niche technology allows particles in different regions to evolve and optimize independently by dividing the search space into multiple niche regions. This approach prevents the entire particle swarm from concentrating in a certain area, thereby enhancing the global search capability and ensuring that the algorithm can find the global optimal solution in a complex high-dimensional space. Through specific mechanisms and strategies, the algorithm can cleverly prevent particles from falling into local extremes in the early stages, thereby strengthening the exploration and search capabilities of global extremes. In the later stages, the algorithm further strengthens the fine search for local extremes to ensure that it can quickly and accurately converge to the final optimal solution.

[0095] In the MNPSO algorithm, the adjustment of the inertia weight ω is crucial. The inertia weight affects the update of the particle velocity and thus affects its search range. The formula is as follows:

[0096]

[0097] In the formula, ω start is the initial inertia weight value, ω end is the end inertia weight value, k is the adjustment coefficient, t is the current iteration number of the algorithm, and t max is the maximum number of iterations. In the early stages of optimization, the larger the inertia weight ω start This allows particles to have a larger search range, thereby enhancing the global search capability. As the number of iterations increases, the inertia weight gradually decreases to ω end , which makes the particles gradually concentrate on the local area for fine search to ensure rapid convergence to the optimal solution. In formula (13), the speed of change of the inertia weight is controlled by adjusting the parameter k, thereby controlling the global and local balance of the search process.

[0098] NPSO adopts a Pareto optimal solution strategy when dealing with multi-objective optimization problems. In multi-objective optimization, the goal of the embodiment of the present invention is to find a set of solutions that achieve a balance between all objectives, rather than just optimizing a single objective. i , the embodiment of the present invention defines multiple objective functions

[0099] f1(x i ),f2(x i ),…,f m (x i ) to measure its performance. A solution x iIt is called a Pareto optimal solution if and only if there is no other solution x j , so that:

[0100]

[0101] This means that x i It is not possible to improve on at least one objective, and it is not possible to get worse on other objectives. By this strategy, the algorithm can generate a Pareto frontier where each solution is an equilibrium state that cannot be further improved.

[0102] In order to deal with multi-constraint problems, MNPSO introduces the penalty function method. For each objective function f k (x i ), the embodiment of the present invention introduces the constraint condition g j (x i )≤0 and h j (x i )=0. In this case, the optimization problem can be transformed into an unconstrained optimization problem, where each objective function f k (x i ) is modified to:

[0103]

[0104] Among them, λ j and μ j is the penalty coefficient, which is used to control the severity of constraint violation. In this way, the algorithm can consider multiple objectives and constraints simultaneously during the optimization process to ensure that the final solution is optimal while satisfying the constraints.

[0105] Through the above mechanism, the MNPSO algorithm can effectively avoid falling into local extreme values ​​in the early stage and quickly converge to the global optimal solution in the later stage.

[0106] Based on the algorithm, the embodiment of the present invention optimizes the target distribution network according to the comprehensive optimization scheduling model and the current carrying capacity to obtain a comprehensive optimization operation plan. The algorithm not only improves the efficiency of optimization, but also enhances the ability of the algorithm to solve complex problems. It can be applied to comprehensive optimization scheduling problems with multiple objectives, multiple variables, and multiple constraints in distribution networks.

[0107] According to the method for optimizing the carrying capacity of renewable distributed power sources in the distribution network proposed in the embodiment of the present invention, the impact of the access of a high proportion of renewable distributed power sources on the distribution network and the load generated by it is quantitatively analyzed, including the analysis of the impact of factors such as DG output characteristics, load characteristics and network topology on the operation of the distribution network, and a combination of mathematical modeling and simulation analysis is used to evaluate the carrying capacity of the distribution network under different scenarios, thereby evaluating the carrying capacity of the distribution network for renewable distributed power sources; in addition, the distribution network is optimized by using an improved niche particle swarm optimization to obtain a comprehensive optimization operation plan. By implementing a reasonable optimization strategy, the carrying capacity of the distribution network is significantly improved, thereby enhancing the access of renewable distributed power sources, and also enhancing the carrying capacity and operation efficiency of the distribution network, promoting the efficient use of clean energy, and further promoting green and sustainable electricity growth.

[0108] Next, a device for optimizing the carrying capacity of a renewable distributed power source in a distribution network according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0109] Figure 3 It is a block diagram of a device for optimizing the carrying capacity of a renewable distributed power source in a distribution network according to an embodiment of the present invention.

[0110] like Figure 3 As shown, the device 30 for optimizing the carrying capacity of the renewable distributed power source in the distribution network includes: a calculation module 301 , an evaluation module 302 and an optimization module 303 .

[0111] Among them, the calculation module 301 is used to calculate the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance when the target renewable distributed power supply supplies power to the target distribution network based on the pre-built carrying capacity evaluation platform. The evaluation module 302 is used to evaluate the carrying capacity of the target distribution network according to the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance to obtain the current carrying capacity. The optimization module 303 is used to optimize the target distribution network according to the current carrying capacity based on the pre-built carrying capacity optimization model to obtain a comprehensive optimization operation plan.

[0112] It should be noted that the above explanation of the embodiment of the method for optimizing the carrying capacity of renewable distributed power sources in the distribution network is also applicable to the device for optimizing the carrying capacity of renewable distributed power sources in the distribution network of this embodiment, and will not be repeated here.

[0113] According to the device for optimizing the carrying capacity of renewable distributed power sources in the distribution network proposed in the embodiment of the present invention, the impact of the access of a high proportion of renewable distributed power sources on the distribution network and the load generated by it is quantitatively analyzed, including the analysis of the impact of factors such as DG output characteristics, load characteristics and network topology on the operation of the distribution network, and a combination of mathematical modeling and simulation analysis is used to evaluate the carrying capacity of the distribution network under different scenarios, thereby evaluating the carrying capacity of the distribution network for renewable distributed power sources; in addition, the distribution network is optimized by using an improved niche particle swarm optimization to obtain a comprehensive optimization operation plan. By implementing a reasonable optimization strategy, the carrying capacity of the distribution network is significantly improved, thereby enhancing the access of renewable distributed power sources, and also enhancing the carrying capacity and operation efficiency of the distribution network, promoting the efficient use of clean energy, and further promoting green and sustainable electricity growth.

[0114] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0115] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0116] When the processor 402 executes the program, the method for optimizing the carrying capacity of the renewable distributed power source in the distribution network provided in the above embodiment is implemented.

[0117] Furthermore, the electronic device further comprises:

[0118] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0119] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0120] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0121] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0122] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0123] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0124] An embodiment of the present invention further provides a computer program product, and when the computer program / instructions are executed by a processor, the above method for optimizing the carrying capacity of renewable distributed power sources in a distribution network is implemented.

[0125] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above method for optimizing the carrying capacity of renewable distributed power sources in a distribution network.

[0126] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0127] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0128] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0129] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0130] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0132] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0133] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the carrying capacity of renewable distributed power sources in a distribution network, characterized in that: The following steps are involved: Based on the pre-built load capacity assessment platform, the reverse load rate, voltage fluctuation value, harmonic verification results and three-phase imbalance degree when the target renewable distributed generation supplies power to the target distribution network are calculated respectively; Performing a carrying capacity evaluation on the target distribution network according to the reverse load rate, the voltage fluctuation value, the harmonic verification result and the three-phase imbalance degree to obtain a current carrying capacity of the target distribution network; Based on the pre-constructed carrying capacity optimization model, the target distribution network is optimized according to the current carrying capacity to obtain a comprehensive optimized operation plan.

2. The method for optimizing the carrying capacity of renewable distributed power sources in a distribution network according to claim 1, characterized in that: The respectively calculating the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance degree after the target renewable distributed power source supplies power to the target distribution network includes: When the target renewable distributed power source supplies power to the target distribution network, the output power and equivalent electric load power of the target renewable distributed power source, the voltage value of each node of the target distribution network at different times, the actual current value of the preset harmonic, the positive sequence current component and the negative sequence current component are collected; Calculating the reverse load rate according to the output power and the equivalent electric load power; Selecting the voltage values ​​of the same node in two adjacent sampling periods from the voltage values ​​of each node at different times to calculate the voltage fluctuation; Compare the actual current value of each harmonic with the preset current limit value to obtain the harmonic verification result; The three-phase unbalance degree is calculated according to the positive-sequence current component and the negative-sequence current component.

3. The method for optimizing the carrying capacity of renewable distributed power sources in a distribution network according to claim 1, characterized in that: The method of optimizing the target distribution network based on the pre-built carrying capacity optimization model according to the current carrying capacity to obtain a comprehensive optimization operation plan includes: Setting a system power flow balance constraint, a target renewable distributed power source power range constraint, and a network security constraint on the pre-built load carrying capacity optimization model to obtain a comprehensive optimization scheduling model; The target distribution network is optimized according to the comprehensive optimization scheduling model and the current carrying capacity to obtain the comprehensive optimization operation plan.

4. The method for optimizing the carrying capacity of renewable distributed power sources in a distribution network according to claim 3, characterized in that: The system power flow balance constraint is: Among them, P Gi is the active power generated at node i, P Di is the load active power at node i, V i 、V j is the voltage amplitude, N s is the number of all nodes, G ij is the conductivity, B ij is the susceptance, θ ij is the voltage phase angle difference, Q Gi is the power generation at node i, Q Di is the reactive power of the load at node i.

5. The method for optimizing the carrying capacity of renewable distributed power sources in a distribution network according to claim 3, characterized in that: The target renewable distributed power generation power range constraint is: in, is the actual output of the target renewable distributed generation at node i at time t, is the maximum allowed output of the target renewable distributed generation at node i at time t.

6. The method for optimizing the carrying capacity of renewable distributed power sources in a distribution network according to claim 3, characterized in that: The network security constraints are: I x,h <I h Where, λ is the reverse load rate, is the maximum reverse load rate, U i,d is the voltage fluctuation value, is the maximum voltage fluctuation value, I x,h is the actual current value of the hth harmonic, I h is the maximum actual current value, ε1 is the three-phase current unbalance, is the maximum three-phase current unbalance.

7. A device for optimizing the carrying capacity of a renewable distributed power source in a distribution network, characterized in that: include: A calculation module, for calculating the reverse load rate, voltage fluctuation value, harmonic verification result and three-phase imbalance when the target renewable distributed power source supplies power to the target distribution network based on a pre-built load capacity evaluation platform; An evaluation module, configured to evaluate the carrying capacity of the target distribution network according to the reverse load rate, the voltage fluctuation value, the harmonic verification result and the three-phase imbalance degree, so as to obtain a current carrying capacity; The optimization module is used to optimize the target distribution network according to the current carrying capacity based on a pre-built carrying capacity optimization model to obtain a comprehensive optimized operation plan.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing the carrying capacity of a renewable distributed power source in a distribution network as described in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the method for optimizing the carrying capacity of a renewable distributed power source in a distribution network as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for optimizing the carrying capacity of a renewable distributed power source in a distribution network as described in any one of claims 1 to 6.

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