A distributed power source and energy storage collaborative optimization configuration method

Abstract

The application discloses a kind of distributed power and energy storage collaborative optimization configuration method, comprising the following steps: obtaining the load characteristics and output characteristics of power grid system, respectively draw load characteristic curve and output characteristic curve;Distributed power and energy storage are accessed to the optimal loss in simulation result by using OpenDss to build power grid simulation model, determine the structure and input parameter of power grid model;Line loss is taken as the loss target value of power grid, and the minimum line loss value is calculated by iterative algorithm;The minimum line loss in simulation result is the optimal loss of distributed power and energy storage access, and the corresponding distributed power and energy storage configuration is the optimal configuration under simulation model.The scheme uses OpenDss continuous time simulation and COM interface, can quickly obtain the simulation result under multivariable, multiple scene, take safety as constraint test, obtain the line loss power under each condition, select the optimal result and the optimal configuration scheme of corresponding distributed power and energy storage, to guide large-scale access of new energy, guide distribution network development.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and more specifically, to a method for the coordinated optimization configuration of distributed power sources and energy storage. Background Technology

[0002] With the introduction of the "dual carbon" target, building a new power system with new energy sources as the mainstay is imperative, and the distribution network will face the challenge of large-scale new energy integration. Traditional distribution network construction relies on rapid expansion of the grid scale to meet the continuous integration of new energy and diversified loads. This leads to problems such as "lack of interaction between power sources and loads, reliance on redundancy for security, reduced balancing capacity, and a lack of efficiency improvement methods," resulting in low grid resource utilization and high overall line loss rate. Therefore, energy storage is needed to improve the grid's economy and reliability. Thus, a method for the coordinated optimization of distributed power sources and energy storage is required to guide the integration of distributed power sources and energy storage into the distribution network. This will help reduce line losses in the energy power system and is an important means to promote the high-quality and efficient development of the new power system.

[0003] Existing distributed power generation and energy storage configurations prioritize "safety" as the primary access principle, while collaborative configuration remains in the exploratory stage. Current distributed power generation and energy storage configuration schemes require ensuring no impact on normal grid operation, specifying requirements for grid connection voltage levels, short-circuit current, voltage deviation, harmonic current, and power backfeed. In contrast, collaborative configuration of distributed power generation and energy storage refers to the proportion and duration of energy storage configurations required for each region's new energy projects, generally with a proportion of no less than 10% and a continuous energy storage duration of at least 2 hours.

[0004] Disadvantages of existing technology:

[0005] 1. Existing distributed power sources and energy storage are configured only for the safety of the power grid (short-circuit current, voltage deviation, harmonic current), without considering the optimal solution for grid utilization and overall line loss. The access of distributed power sources and energy storage is highly arbitrary.

[0006] 2. The existing configuration of distributed power generation and energy storage is only a basic configuration for the consumption of new energy. It fails to consider different load access methods and access locations. The collaborative configuration is not diversified or refined enough and cannot adapt to the large-scale access of new energy under the new power system. Summary of the Invention

[0007] The purpose of this invention is to propose a method for the coordinated optimization configuration of distributed power sources and energy storage. By utilizing OpenDSS continuous-time simulation and COM interface, simulation results under multiple variables and scenarios can be obtained quickly. With safety as a constraint check, the line loss power under each case is obtained, and the optimal result and the corresponding optimal configuration scheme of distributed power sources and energy storage are selected, thereby guiding the large-scale integration of new energy sources and the development of distribution networks.

[0008] To achieve the above technical objectives, the present invention provides a technical solution: a method for coordinated optimization configuration of distributed power sources and energy storage, comprising the following steps:

[0009] Obtain the load characteristics and output characteristics of the power grid system, and plot the load characteristic curve and output characteristic curve respectively;

[0010] OpenDSs was used to build a power grid simulation model, and the structure and input parameters of the power grid model were determined.

[0011] Line loss is used as the target loss value of the power grid, and the minimum line loss value is calculated through an iterative algorithm.

[0012] The minimum line loss in the simulation results is taken as the optimal loss for the integration of distributed power sources and energy storage, and the corresponding distributed power source and energy storage configuration is the optimal configuration under the simulation model.

[0013] Preferably, the load characteristics include the daily charging status with energy storage and the user's daily load demand.

[0014] The output characteristics include daily discharge with energy storage and daily output of distributed power sources.

[0015] The load characteristic curves include energy storage charging curves and typical user load curves;

[0016] The output characteristic curves include the daily discharge curve with energy storage and the typical curve of distributed power generation.

[0017] As a preferred method, building a power grid simulation model using OpenDSs includes the following steps:

[0018] Set the power grid structure and input parameters, including voltage level, base frequency, line and transformer impedance admittance, and user load; connect distributed power sources and energy storage elements according to the variable access configuration principle. The initial access location is the first end by default, and the initial access capacity is 0 by default. If the access location or capacity is determined, it is manually modified and will no longer be used as a variable in subsequent steps; use the load characteristic curve and output characteristic curve as input parameters for the power grid simulation model.

[0019] As a preferred method, calculating the minimum line loss value using an iterative algorithm includes the following steps:

[0020] Using the OpenDSS COM interface, the power grid simulation model is iteratively simulated using Python. The access capacity and location of distributed generation and energy storage are used as variables. In each simulation, the parameters of the distributed generation or energy storage are adjusted, either by increasing the access capacity by S or shifting the access location by L. After each simulation, constraint verification is performed. If the verification fails, the parameters of the distributed generation or energy storage are adjusted again. If the verification succeeds, the current line loss value is compared with the previous line loss value. If the current line loss value is less than the previous line loss value, the current line loss value is taken as the optimal loss, and the current adjustment result of the distributed generation or energy storage parameters is output. The next iteration continues until the variables exceed the parameter boundaries, at which point the iteration process stops.

[0021] As a preferred option, the following principles apply to the configuration of distributed power supply access:

[0022] Connection method selection: When connecting small-capacity photovoltaics, choose centralized or decentralized connection based on cost and actual space resources; when the photovoltaic capacity is large, decentralized connection should be given priority; when the photovoltaic capacity is too large and there is a large amount of active power backfeed, centralized connection is not advisable.

[0023] Optimal configuration for centralized access: When the load is concentrated, the closer the distributed power source access point is to the concentrated load point, the smaller the line loss; when the load is evenly distributed, the line loss is minimized when the photovoltaic access line is 2 / 3 of the way through.

[0024] Optimal configuration for distributed access: When photovoltaics are accessed in a distributed manner, the distributed power sources should be accessed near the load concentration point or in the middle of the segment; for lines with uniform loads, the accessed photovoltaic capacity should be distributed as evenly as possible, and the photovoltaic installed capacity of the first segment should be slightly smaller than that of other segments.

[0025] As a preferred option, the following principles apply to energy storage access configuration:

[0026] Access method selection: Choose centralized access or distributed access based on cost, space resources, and actual management situation. When the peak shaving ratio is below 15%, centralized access is adopted.

[0027] Centralized access optimal configuration: When the load is evenly distributed, the optimal specific access location for energy storage is selected according to different peak shaving ratios; among them, when connected to the line from 2 / 3 to the end, the loss reduction effect of energy storage can be maximized, and as the energy storage capacity continues to increase, the optimal installation point is continuously moved forward.

[0028] Optimal configuration for distributed access: For residential, commercial loads and industrial loads with small peak shaving, the energy storage capacity is configured in an increasing manner, with the energy storage capacity mainly configured in the later part of the line. As the peak shaving ratio increases, the focus of energy storage configuration begins to shift forward. For industrial loads with large peak shaving, when energy storage is mainly configured in the middle and earlier part of the line, the line loss is smaller.

[0029] The beneficial effects of this invention are as follows: The distributed power source and energy storage collaborative optimization configuration method proposed in this invention fills the gap in the refined configuration of distributed power sources and energy storage, providing power supply companies with quantifiable, multi-dimensional optimization schemes for distributed power sources, energy storage, and collaborative configuration, thereby improving grid utilization and overall line loss. Specifically: a) Through the distributed power source and energy storage collaborative optimization configuration method, the optimal access scheme for distributed power source and energy storage projects can be quickly obtained through multi-faceted comparison, ensuring the safe operation of the distribution network, reducing equipment line loss, and improving the grid energy transmission efficiency. Simultaneously, it reduces the redundancy margin in distribution network construction, improves equipment utilization, and avoids unnecessary grid investment; b) The configuration scheme obtained through this method will effectively guide the access of distributed power sources and energy storage in the distribution network according to different load characteristics, including the optimal access method, access location, and access capacity. This can reduce grid investment, improve the accuracy of grid investment, reduce line loss, improve company management level and economic benefits, and contribute to the construction of new power systems. Attached Figure Description

[0030] Figure 1 This is a flowchart of a distributed power source and energy storage collaborative optimization configuration method according to the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0032] Example:

[0033] like Figure 1 The flowchart shown illustrates a method for coordinated optimization configuration of distributed power sources and energy storage, comprising the following steps:

[0034] Obtain the load and output characteristics of the power grid system, and plot the load characteristic curve and output characteristic curve respectively; including the following steps:

[0035] The load characteristics include the daily charging status of energy storage and the daily load demand of users. The output characteristics include the daily discharging status of energy storage and the daily output status of distributed power sources. Daily discharge curves for energy storage, typical curves for distributed power sources, energy storage charging curves, and typical user load curves are plotted. The daily output status of distributed power sources, the charging and discharging status of energy storage, and user electricity consumption are obtained, and corresponding characteristic curves are plotted. Distributed power source output and different user load characteristics will have a certain impact on line loss, so using typical daily curves as parameters is closer to the actual line loss. The typical user load curves include four main types of loads: industrial, commercial, residential, and administrative office. The charging and discharging curves of energy storage do not have a fixed curve and change with the combined daily load of distributed power sources and users. To ensure "peak shaving and valley filling," they can be generated using a pre-defined program.

[0036] A power grid simulation model is built using OpenDSs to determine the model's structure and input parameters. This includes the following steps: setting the power grid structure and input parameters, including voltage level, base frequency, line and transformer impedance admittance, and user load; connecting distributed power sources and energy storage components according to variable access configuration principles, where the initial access location is the head end by default, and the initial access capacity is 0 by default. If the access location or capacity is determined, it is manually modified and will not be used as a variable in subsequent steps; and using load characteristic curves and output characteristic curves as input parameters for the power grid simulation model.

[0037] The principles for configuring distributed power supply access are as follows:

[0038] Connection method selection: When connecting small-capacity photovoltaics, centralized or decentralized connection can be selected based on actual conditions such as cost and space resources; when the photovoltaic capacity is large, decentralized connection is preferred, and the location of centralized connection needs to be carefully selected, otherwise it will lead to an increase in line loss instead of a decrease; when the photovoltaic capacity is too large and a large amount of active power is fed back, it is not recommended to choose centralized connection. The photovoltaic connection selection for different loads is shown in Table 1.

[0039] Centralized access optimal configuration: When the load is concentrated, the closer the distributed power source access point is to the load concentration point, the smaller the line loss; when the load is evenly distributed, the optimal distributed power source installed capacity for different access locations and different loads is shown in Table 2, and the line loss is minimized when the photovoltaic access line is 2 / 3 of the way through.

[0040] Optimal Configuration for Distributed Grid Connection: When connecting photovoltaic (PV) grids in a distributed manner, the distributed power sources should be connected near the load concentration point or in the middle of the grid segment. For lines with uniform loads, the connected PV capacity should be distributed as evenly as possible, with the first segment having a slightly smaller installed capacity than the other segments. The optimal installed capacity configuration for distributed grid connection is shown in Table 3.

[0041] Table 1. Photovoltaic grid connection options for different loads

[0042] Centralized access or distributed access Distributed access live Capacity ≤ 0.42 Maximum Load Capacity > 0.42 Maximum Load Business Capacity ≤ 0.58 Maximum Load Capacity > 0.58 Maximum Load industry Capacity ≤ 0.61 Maximum Load Capacity > 0.61 Maximum Load

[0043] Table 2. Centralized Access Locations and Optimal Installed Capacity Configurations for Distributed Power Generation

[0044]

[0045]

[0046] Table 3. Optimal Installed Capacity Configuration for Distributed Power Supply Decentralized Access

[0047] Optimal capacity / maximum load Maximum output / maximum load live 0.833 0.708 Business 1.306 1.11 industry 1.417 1.204

[0048] The principles for energy storage access configuration are as follows:

[0049] Access method selection: Choose centralized access or distributed access based on actual conditions such as cost, space resources, and management. When the peak shaving ratio is below 15%, centralized access is recommended.

[0050] Centralized Access Optimal Configuration: When the load is evenly distributed, the optimal specific access location for energy storage under different peak shaving ratios is shown in Appendix Table 4. Connecting to the line from 2 / 3 to the end maximizes the loss reduction effect of energy storage, and as the energy storage capacity continues to increase, the optimal installation point moves further forward.

[0051] Optimal configuration for distributed access: For residential, commercial loads and industrial loads with small peak shaving, the energy storage capacity is configured in an increasing manner, with the energy storage capacity mainly configured in the later part of the line. As the peak shaving ratio increases, the focus of energy storage configuration begins to shift forward. For industrial loads with large peak shaving, when energy storage is mainly configured in the middle and earlier part of the line, the line loss is smaller.

[0052] Table 4. Optimal Centralized Access Locations for Different Energy Storage Capacities

[0053] Peak reduction ratio ≤5％ 5％～15％ 15％～25％ ≥25％ Location End of line Line 5 / 6 3 / 4 of the line 2 / 3 of the way

[0054] Using line loss as the target loss value for the power grid, the minimum line loss value is calculated through an iterative algorithm. The calculation of the minimum line loss value through the iterative algorithm includes the following steps:

[0055] Using the OpenDSS COM interface, the power grid simulation model is iteratively simulated using Python. The access capacity and location of distributed generation and energy storage are used as variables. In each simulation, the parameters of the distributed generation or energy storage are adjusted, either by increasing the access capacity by S or shifting the access location by L. After each simulation, constraint verification is performed (constraint verification includes voltage deviation, harmonics, reverse load rate, etc.). If the verification fails, the parameters of the distributed generation or energy storage continue to be adjusted. If the verification succeeds, the current line loss value is compared with the previous line loss value. If the current line loss value is less than the previous line loss value, the current line loss value is taken as the optimal loss, and the current adjustment result of the distributed generation or energy storage parameters is output. The next iteration continues until the variables exceed the parameter boundaries, at which point the iteration process stops.

[0056] The minimum line loss in the simulation results is taken as the optimal loss for the integration of distributed power sources and energy storage, and the corresponding distributed power source and energy storage configuration is the optimal configuration under the simulation model.

[0057] Results of coordinated optimization of distributed power generation and energy storage configuration: Based on the existing energy storage quota requirements of distributed power generation, the standard quota is set at 10% of the distributed power generation capacity and 2 hours of energy storage configuration.

[0058] Under standard quotas, when renewable energy sources are centrally connected to the grid:

[0059] Centralized energy storage access: For industrial loads and commercial loads with large photovoltaic installed capacity, or when photovoltaics are connected after 2 / 3 of the line, the line loss is minimized when the centralized energy storage access location is the same as the photovoltaic access location; in other cases, the energy storage access location is closer to 5 / 6 of the line, resulting in smaller line losses, and it moves forward as the photovoltaic installed capacity increases. See Table 5 for specific results.

[0060] Distributed access to energy storage: When most energy storage is configured near the optimal point of centralized access, the line loss is minimized. The optimal distributed access to energy storage is actually equivalent to centralized access, and the line loss of centralized energy storage configuration is better than that of distributed configuration.

[0061] Table 5. Selection of Energy Storage Centralized Access for Photovoltaic Centralized Access under Standard Quota

[0062]

[0063] Under standard quotas, when renewable energy is distributed and connected in a decentralized manner:

[0064] Centralized energy storage access: The larger the photovoltaic capacity, the greater the impact of the energy storage access location on line loss. The closer to the end, the better the loss reduction effect. Distributed energy storage access: When most of the energy storage capacity is located at the end of the line, the line loss is minimal, which is consistent with the optimal configuration of centralized energy storage. Under the same conditions, the line loss is slightly less when the photovoltaic and energy storage access locations are different than when the photovoltaic and energy storage access locations are the same.

[0065] The specific embodiments described above are preferred embodiments of the distributed power source and energy storage collaborative optimization configuration method of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for coordinated optimization configuration of distributed power sources and energy storage, characterized in that, Includes the following steps: Obtain the load characteristics and output characteristics of the power grid system, and plot the load characteristic curve and output characteristic curve respectively; OpenDSs was used to build a power grid simulation model, and the structure and input parameters of the power grid model were determined. Line loss is used as the target loss value of the power grid, and the minimum line loss value is calculated through an iterative algorithm; The minimum line loss in the simulation results is taken as the optimal loss for the integration of distributed generation and energy storage. The corresponding distributed generation and energy storage configuration is the optimal configuration under the simulation model. Building a power grid simulation model using OpenDSs involves the following steps: Configure the power grid structure and input parameters, including voltage level, fundamental frequency, line and transformer impedance admittance, and user load; Distributed power sources and energy storage components are connected according to the variable access configuration principle. The initial access location is the first end by default, and the initial access capacity is 0 by default. If the access location or capacity is determined, it is manually modified and will no longer be used as a variable in subsequent steps. The load characteristic curve and the output characteristic curve are used as input parameters for the power grid simulation model; The minimum line loss value is calculated using an iterative algorithm, which includes the following steps: Using the OpenDSS COM interface, the power grid simulation model is iteratively simulated using Python. The access capacity and location of distributed generation and energy storage are used as variables. In each simulation, the parameters of the distributed generation or energy storage are adjusted, either by increasing the access capacity by S or shifting the access location by L. After each simulation, constraint verification is performed. If the verification fails, the parameters of the distributed generation or energy storage are adjusted again. If the verification succeeds, the current line loss value is compared with the previous line loss value. If the current line loss value is less than the previous line loss value, the current line loss value is taken as the optimal loss, and the current adjustment result of the distributed generation or energy storage parameters is output. The next iteration continues until the variables exceed the parameter boundaries, at which point the iteration process stops. The principles for configuring distributed power supply access are as follows: Connection method selection: When connecting small-capacity photovoltaics, choose centralized or decentralized connection based on cost and actual space resources; when the photovoltaic capacity is large, decentralized connection should be given priority; when the photovoltaic capacity is too large and there is a large amount of active power backfeed, centralized connection is not advisable. Optimal configuration for centralized access: When the load is concentrated, the closer the distributed power source access point is to the load concentration point, the smaller the line loss; when the load is evenly distributed, the line loss is minimized when the photovoltaic access line is 2 / 3 of the way through. Optimal configuration for distributed grid connection: When grid-connecting photovoltaics in a distributed manner, the distributed power sources should be connected near the load concentration point or in the middle of the segment; for lines with uniform loads, the connected photovoltaic capacity should be distributed as evenly as possible, and the installed capacity of the first segment should be slightly smaller than that of other segments; The principles for energy storage access configuration are as follows: Access method selection: Choose centralized access or distributed access based on cost, space resources, and actual management situation. When the peak shaving ratio is below 15%, centralized access is adopted. Centralized access optimal configuration: When the load is evenly distributed, the optimal specific access location for energy storage is selected according to different peak shaving ratios; among them, when connected to the line from 2 / 3 to the end, the loss reduction effect of energy storage can be maximized, and as the energy storage capacity continues to increase, the optimal installation point is continuously moved forward. Optimal configuration for distributed access: For residential, commercial loads and industrial loads with small peak shaving, the energy storage capacity is configured in an increasing manner, with the energy storage capacity mainly configured in the later part of the line. As the peak shaving ratio increases, the focus of energy storage configuration begins to shift forward. For industrial loads with large peak shaving, when energy storage is mainly configured in the middle and earlier part of the line, the line loss is smaller.

2. The method for coordinated optimization configuration of distributed power sources and energy storage according to claim 1, characterized in that, The load characteristics include the daily charging status of energy storage facilities and the daily load demand of users. The output characteristics include daily discharge with energy storage and daily output of distributed power sources. The load characteristic curves include energy storage charging curves and typical user load curves; The output characteristic curves include the daily discharge curve with energy storage and the typical curve of distributed power generation.

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