Data system for photovoltaic consumption of power distribution network and layered mapping data model thereof

Abstract

The invention discloses a data system for photovoltaic consumption of a power distribution network and a hierarchical mapping data model of the data system, and the data model takes the effective network access property of distributed photovoltaic and the energy consumption optimization of the distributed photovoltaic as data construction guidance. The method comprises the following steps of: hierarchically constructing power grid operation data associated with distributed photovoltaic effective network access and / or distributed photovoltaic network access energy consumption into a plurality of independent data layers; at least photovoltaic power generation related data, power distribution network load related data, power grid stability related data and associated data thereof are integrated and constructed into a plurality of independent data layers, a layered mapping architecture is further constructed among the plurality of independent data layers, and the layered mapping architecture comprises two data paths. According to the method, layered construction and interlayer mapping processing can be efficiently carried out on photovoltaic power generation data, power distribution network load data, power grid stability data and the like, and the method has a brand new data structure and good photovoltaic network access optimization data processing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy smart grid, and in particular to a data system for photovoltaic consumption in a distribution network and a hierarchical mapping data model thereof. Background Art

[0002] The Technical Provisions for Distributed Generation Access to Distribution Networks, which was promulgated and implemented in 2014, stipulates the general principles and technical requirements that distributed generation access to the grid should follow. It is applicable to new, rebuilt and expanded distributed generation accessed to the grid at voltage levels of 35kV and below.

[0003] In recent years, with the maturity and application of new energy power generation technology, in addition to following the basic technical regulations of distributed access to distribution networks, different levels of research have emerged on the impact of photovoltaic output prediction and grid absorption on distribution networks. According to the search and analysis of the technical team of this study, the current technical research in China that is closer to this study includes the following aspects.

[0004] Research on "Monte Carlo-based Evaluation of Photovoltaic Absorption Capacity of Distribution Network" of Inner Mongolia Power Grid. Large-capacity distributed photovoltaic centralized access to the distribution system can cause local overvoltage problems at the grid connection point. To solve the overvoltage problem, starting from the two aspects of planning and operation control, a Monte Carlo-based multi-scenario photovoltaic absorption capacity evaluation method is first given, with voltage qualification rate and active network loss rate as evaluation indicators. Considering that each scenario requires flow calculation, an estimation method based on voltage sensitivity factor is introduced to reduce the calculation overhead. In terms of operation, based on the analysis of the control strategy of existing inverters participating in voltage regulation, a method for active and reactive coordinated control is given. To maximize the active power of photovoltaics and minimize the reactive power, this method transforms voltage control into a problem of solving the optimal solution of the objective function, and solves the problem through particle swarm optimization. Finally, the Python programming language is used to implement the relevant algorithm, and it is tested on the IEEE 33 model. The test results prove the effectiveness of the method. This project aims at the overvoltage problem, starting from the two aspects of grid planning and operation control, which has certain reference significance for this project. At the same time, there is also a certain reference to the photovoltaic access to the grid in other regions. It is known that the Monte Carlo method can simulate and evaluate different scenarios and uncertain factors, and more accurately evaluate the absorption capacity of photovoltaic systems under different circumstances. This can help determine the maximum accessible capacity of photovoltaic access to the distribution network in other regions, as well as the operation strategy and control method of photovoltaic systems. However, when the Monte Carlo method is used to evaluate the photovoltaic absorption capacity, the following situations need to be solved when substituting other regions: 1) It is necessary to collect and organize meteorological data, grid load data, etc. in other regions, and establish an accurate model to simulate the operation of photovoltaic systems under different scenarios. This involves the reliability of data and the difficulty of collection. 2) It is necessary to estimate and analyze various parameters, including the performance parameters of the photovoltaic system, changes in grid load, etc. This involves the accuracy of parameters and the handling of uncertainty. 3) A large number of simulation calculations are required, and multiple simulations may need to be run when evaluating the photovoltaic absorption capacity. Therefore, it is necessary to solve the problems of computational complexity and efficiency to improve the speed and accuracy of the evaluation. In summary, the Monte Carlo multi-scenario photovoltaic absorption capacity evaluation method can provide more accurate and comprehensive evaluation results, but in the application process, problems such as data collection, parameter estimation, computational complexity and result application need to be solved.

[0005] Research on "Reliability Assessment of Distribution Network Considering Distributed Photovoltaic Grid Connection under New Power System in Ningxia". Under the new power system, a large number of distributed photovoltaics are connected to the distribution network. Considering the random characteristics of their output and load, the security of the distribution network must be comprehensively evaluated from multiple angles. In view of the impact of the flexible access of distributed photovoltaics to the distribution network on its reliability, a model of distributed photovoltaic output probability is first established based on the typical output characteristics of distributed photovoltaics. Subsequently, the indicators of photovoltaic grid connection assessment are determined to quantify the risks of the distribution network, including voltage over-limit, power over-limit and load loss risk indicators. Finally, a distribution network model with distributed photovoltaic grid connection is established on the MATLAB simulation platform, and the impact of different photovoltaic output scenarios, grid connection methods and grid connection capacity on the distribution network is considered to evaluate the reliability of the distribution network with distributed photovoltaic grid connection. In this study, considering different photovoltaic output scenarios, grid connection methods and grid connection capacity to evaluate the reliability of the distribution network has certain reference significance for this project. By considering the operating characteristics and influencing factors of the photovoltaic system, the reliability level of photovoltaic access to the distribution network in other regions can be more comprehensively evaluated. This method can analyze the impact of the grid connection of the photovoltaic system on the reliability of the distribution network. For example, the uncertainty and volatility of the photovoltaic system will affect the voltage stability and power balance of the distribution network, and it is necessary to evaluate the degree of its impact on the reliability indicators of the distribution network. By evaluating the reliability of the photovoltaic system connected to the grid, the operation strategy of the photovoltaic system can be optimized to improve the reliability of the distribution network. For example, the operation mode and control strategy of the photovoltaic system can be adjusted to reduce its adverse impact on the reliability of the distribution network.

[0006] Guangdong "Research on the calculation model of short-circuit current of photovoltaic system for different application scenarios". Distributed photovoltaic systems have begun to be connected to the distribution network on a large scale. At the same time, the complex fault characteristics of photovoltaic systems have also brought new problems to the traditional distribution network short-circuit current calculation and relay protection. When the distributed photovoltaic system is connected, the distribution network changes from the original single power supply network to a multi-power supply network. This change will affect the normal operation and abnormal operation of the distribution network. When analyzing the short-circuit current of the photovoltaic system, different scenarios have different requirements for simulation analysis accuracy, calculation speed, etc. This project proposes three photovoltaic system short-circuit current calculation models suitable for accurate simulation analysis, small-scale distribution network system simulation analysis, and large-scale distribution network system engineering calculation. First, based on the device-level main circuit and the complete control algorithm, a device-level photovoltaic system short-circuit transient model is proposed; on the basis of retaining the low-frequency transient characteristics, the main circuit and control algorithm are simplified, and a photovoltaic system short-circuit low-frequency transient equivalent model is proposed; on this basis, the phase-locked loop is removed, and the mathematical model of the equivalent model is derived, and the mathematical model is further simplified into a calculation model for calculating the steady-state effective value and sub-transient peak value of the short-circuit current. The power flow calculation software was written in the MATLAB environment, and the three models were simulated and compared to verify the effectiveness of the model. In terms of the short-circuit current of the photovoltaic access distribution network system, it has certain reference significance for this project.

[0007] In addition, the current status of foreign research is summarized as follows.

[0008] India's "Distributed Energy Sharing Program (DESP)". The Indian government's Interdisciplinary Cyber ​​Physical Systems (ICPS) proposed a distributed energy sharing program (DESP) to share energy among PV users in smart hybrid microgrids (SHMs) to enable PV users to use energy efficiently. The difficulties faced by PV users include the intermittent nature of renewable energy sources (RES), and PV users who consume their own PV power face power fluctuation problems. PV users have a time slot with excess PV power (power loss) and another time slot with insufficient PV power (shortage). In addition, sometimes, there is no PV power at all (affecting the daily life of users). PV users are directly connected to the utility grid, resulting in a moderate price for selling PV power, but PV users buy power from the utility grid at a high price during a period of time. According to the plan, PV users can increase their profits. Therefore, DESP was proposed. SHM requires a DESP to manage the battery storage system (BSS) and use an internal pricing scheme to minimize the price of PV users to effectively use RES. The Internet of Things (IoT) communicates SHM with PV power users (and vice versa), enabling efficient and cost-effective PV energy exchange. This approach improves PV inclusion rates through DESP and maximizes profits for PV power users.

[0009] "Analysis of Non-Household Photovoltaic Energy Absorption" in Japan. In Japan, the penetration rate of photovoltaics (PV) in the distribution network has increased significantly, and predicting the amount and location of PV installations will become increasingly important. Existing agent-based PV diffusion models only consider the popularity of residential PV, but in fact there is a large amount of non-residential PV such as industrial and commercial, and its impact should not be ignored. So far, agent-based models that consider optimization do not exist. The study of non-household PV energy absorption analysis incorporates the optimization problem into the distribution network PV-based model and analyzes how the indoor PV absorption results vary with the allocation of non-household PV. The results of this study show that non-user PV installations are more diffuse than residential PV in the terminal segment. Based on this model study, the distribution network can more effectively absorb PV energy by reasonably allocating the capacity and location of PV power generation equipment.

[0010] Combining the above-mentioned scientific and technological policies with the development and research of photovoltaic power generation and grid-connected technology, it can be seen that the development of a special data technology system for photovoltaic consumption in distribution networks is what is needed at present, which is also the technical topic faced by this development project. Summary of the invention

[0011] The technical problem to be solved by the present invention is to develop a special data technology system for photovoltaic absorption in distribution network based on in-depth research on distributed photovoltaic access and absorption capacity of distribution network.

[0012] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0013] A hierarchical mapping data model for photovoltaic power consumption in distribution networks. The data model takes the effective accessibility of distributed photovoltaic power generation and its energy consumption optimization as data construction guidance, and hierarchically constructs the grid operation data associated with the effective accessibility of distributed photovoltaic power generation and / or the distributed photovoltaic access energy consumption into multiple independent data layers.

[0014] As a preferred technical solution of the present invention, data collection is performed on at least photovoltaic power generation, distribution network load, grid stability and grid operation data related to the above three items. The data collection includes importing relevant data from an existing grid operation database and obtaining structured or unstructured data from a newly created special data acquisition port; and integrating photovoltaic power generation related data, distribution network load related data, grid stability related data and their related data into multiple independent data layers.

[0015] As a preferred technical solution of the present invention, further, a layered mapping architecture is constructed between multiple independent data layers, and the layered mapping architecture includes two data paths.

[0016] As a preferred technical solution of the present invention, the first data path is: the integrated data of each layer is retrieved from the existing basic database or the newly added special data port according to the needs of each level, allowing the same data to be repeatedly loaded into different layers and independently called and processed for each data layer;

[0017] As a preferred technical solution of the present invention, the second data path is: the data structure and data algorithm built in any data layer are mapped compatibly to any other data layer, so that common data structures and algorithms only need to be built in one data layer, and other data layers only need to call the constructed data structure and algorithm framework as needed.

[0018] As a preferred technical solution of the present invention, for the second data path, firstly, multiple groups of specific data models are constructed with the photovoltaic power generation data layer as the object, and data layer mapping is performed for other data layers.

[0019] As a preferred technical solution of the present invention, the multiple groups of specific data models may optionally include:

[0020] 2-A. The photovoltaic power generation data layer allows for secondary segmentation, which can be segmented into independent data layers with a serialized internal secondary hierarchical structure according to time, geographic information, network access direction or other indicators;

[0021] Mapping this secondary segmentation data structure to other data layers besides the photovoltaic power generation data layer means that the other data layers can also be secondary stratified according to their internal data characteristics.

[0022] As a preferred technical solution of the present invention, the multiple groups of specific data models further include:

[0023] 2-B. Construct a data extraction and return processing layer next to the photovoltaic power generation data layer, and perform unified algorithm application and data processing for the same type of data processing in the data layer;

[0024] By mapping this data extraction and return processing layer to other data layers outside the photovoltaic power generation data layer, it is possible to uniformly call this side data extraction and return processing layer for the data processing types that are common in each data layer, thereby avoiding repeatedly distributing the same algorithm to different data layers, resulting in low data storage and computing efficiency.

[0025] As a preferred technical solution of the present invention, the multiple groups of specific data models further include:

[0026] 2-C. Constructing a photovoltaic power generation prediction data model, which specifically includes a basic prediction layer and an optimized prediction layer that introduces secondary fluctuation factors on the basic prediction layer;

[0027] 2-Ca, basic prediction layer, this data layer is based on the existing time series prediction data algorithm, and further adds the difference operation under the time series to make the non-stationary time series stable; set adjustable parameters for it: difference order parameter, order parameter of moving average item, based on the adjustability of parameters to adapt to different specific photovoltaic grid access scenarios;

[0028] 2-Cb, optimized prediction layer, above the basic prediction layer, incorporates the fluctuation factors that have a consistent impact on the overall data processing paradigm of the basic prediction layer. The optimized prediction layer for the annual cycle fluctuation factors of photovoltaics contains four sets of adjustable parameters to introduce the fluctuation factors into the model and adapt to different specific photovoltaic grid access scenarios. The four sets of parameters are: annual cycle volatility autoregressive parameters, annual cycle volatility difference order parameters, annual cycle volatility moving average order parameters, annual cycle volatility period and / or fluctuation amplitude;

[0029] Finally, when mapping the above photovoltaic power generation prediction data model to other data layers such as photovoltaic power generation related data, distribution network load related data, and grid stability related data, specifically:

[0030] For 2-Ca, the structure of the basic prediction layer remains basically unchanged (because it directly refers to the existing time series algorithm). It only needs to replace the prediction data with distribution network load-related data and grid stability-related data, and set the difference order parameters and the order parameters of the moving average term to adapt to the specific scenario.

[0031] For 2-Cb, on the one hand, the optimization data is replaced by distribution network load-related data and grid stability-related data. On the other hand, the structure of the four groups of parameters is kept unchanged. Volatility autoregressive parameters, volatility difference order parameters, volatility moving average order parameters, volatility period and / or volatility amplitude parameters are still used, but they are adjusted to the corresponding period interval according to the actual data environment of the target data layer.

[0032] As a preferred technical solution of the present invention, the multiple groups of specific data models further include:

[0033] 2-D. A global connection layer for all data layers is further constructed within the second data path. On the one hand, global coordination is performed on the data orientations of different data layers, including weighted integration of linearly associated data orientations to form a global data orientation, selective coordination or compromise allocation coordination of conflicting data orientations, and verification and coordination of common data orientations.

[0034] The beneficial effects of adopting the above technical solution are: mainly including two aspects. First, on the whole, this research project has developed a set of special data technology systems for photovoltaic absorption in distribution networks based on in-depth research on distributed photovoltaic access and absorption capacity of distribution networks (see the implementation examples below for details); on the other hand, it has also jointly developed a new hierarchical mapping data model for photovoltaic absorption in distribution networks with multiple scientific research and technical teams. It takes the effective access to the grid of distributed photovoltaics and its energy absorption optimization as the data construction guide, and carries out global and consistent hierarchical & mapping construction of distributed photovoltaic grid-related data. It can efficiently construct and map photovoltaic power generation data, distribution network load data, grid stability data, etc. in layers, and has a new data structure and photovoltaic grid-connection optimization data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the overall framework of the research project of this invention.

[0036] Figure 2 The four-season solar irradiance variation curves are periodically associated with photovoltaic power generation; in the figure: (a) spring solar irradiance variation curve; (b) summer solar irradiance variation curve; (c) autumn solar irradiance variation curve; (d) winter solar irradiance variation curve.

[0037] Figure 3 Schematic diagram of the relationship between photovoltaic output and meteorological characteristics under different weather types.

[0038] Figure 4 Schematic diagram of node power flow optimized for 10kV photovoltaic grid access and consumption assessment.

[0039] Figure 5 Schematic diagram of the node process for evaluating the absorption capacity of low-voltage photovoltaic access to the distribution network. DETAILED DESCRIPTION

[0040] The following examples describe the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than limitation, so as to provide a thorough understanding of the present application embodiments. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application.

[0041] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the items listed in association and all possible combinations, and includes these combinations. As used in the present specification and the appended claims, the term "if" can be interpreted as "when..." or "once" or "in response to determination" or "in response to detection" according to the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to determination" or "once [described condition or event] is detected" or "in response to detection [described condition or event]" according to the context.

[0042] In addition, in the description of the present application specification and the attached claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The reference to "one embodiment" or "some embodiments" described in the present application specification means that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in the different parts of this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0043] Example 1: Overview of the research project on distributed photovoltaic access and absorption capacity of distribution network

[0044] See attached Figure 1 , which is the overall framework roadmap of the research project. From the substantive technical level, this research project mainly includes:

[0045] (1) Investigate the characteristics of distribution network operation scenarios, sort out the distribution network structure, and study the load characteristics, power consumption curve, peak load regulation capacity, and controllable load conditions of the distribution network.

[0046] (2) Based on (1), the constraint factors of distributed photovoltaic access are further analyzed by combining the interactive characteristics of the distribution network and distributed photovoltaic access.

[0047] (3) By analyzing the correlation between distributed photovoltaic access and the grid operation status such as distribution network voltage index, line loss index, protection index, etc., a specific calculation model of the index system constraining distributed photovoltaic access is proposed; combined with the tree-enhanced naive Bayes algorithm (TAN algorithm), the correlation and independence of various grid indicators are comprehensively considered to construct a comprehensive evaluation method for distributed photovoltaic access to the distribution network, and a scientific and universal evaluation is conducted on the strategies for the absorption capacity of distributed photovoltaics in the distribution network, and a distributed photovoltaic absorption strategy that is beneficial to the friendly interaction between photovoltaic and power grids and improves the absorption capacity is proposed.

[0048] (4) Construct a new data model, with the effective access to the grid of distributed photovoltaics and its energy consumption optimization as the data construction guide, and construct a dedicated data model to layer the grid operation data related to the effective access to the grid of distributed photovoltaics and / or the energy consumption of distributed photovoltaics into multiple independent data layers; this data model is oriented to the multi-layer and multi-dimensional construction and data mapping of distributed photovoltaic access and consumption in the distribution network, and at the same time has both basic versatility (data model architecture) and data processing depth (construction of data processing algorithms), and can develop a new model that has adjustable applicability to the photovoltaic access data accumulated by the power grid and various specific data optimization goals.

[0049] The above four groups of substantial technology development sub-items are respectively described in detail in the four embodiments below.

[0050] Example 2: Basic data research and data analysis sub-project

[0051] The sub-projects of this study are aimed at: (1) investigating the characteristics of distribution network operation scenarios, sorting out the distribution network structure, and studying the characteristics of distribution network load, power consumption curve, peak load regulation capacity, and controllable load conditions.

[0052] In this research sub-item, a specific data survey and analysis work is carried out by taking a certain city's power grid as an example. The details are as follows.

[0053] Regional overview. A certain city is located in the eastern part of central Shanxi Province, in the mountain basin at the western foot of Taihang Mountain. It borders Pingshan and Jingxing of Hebei Province to the east, Shouyang County and Yangqu County to the west, Xiyang County to the south, and Wutai and Dingxiang to the north. It is the gateway to the Three Shanxi Provinces and the key point of Shanxi and Hebei. It is located in the middle of the two provincial capitals of Taiyuan and Shijiazhuang. It is connected to the Bohai Economic Belt and the southeastern coastal areas in the east and the central and western regions in the west. It is an important channel for Beijing-Tianjin-Tangshan and the developed coastal areas to radiate to the inland areas. It is one of the best intersections between developed and underdeveloped areas. It has a strong absorption and radiation function and has an important influence and driving role on the economic development of the central and western regions. A certain city is the political, economic, cultural and information center of eastern Shanxi. A certain city currently has three districts and two counties, namely the urban area, mining area, suburbs, Pingding County, and Yu County. The total area of ​​the city is 4,558.93 square kilometers. At the end of 2022, the population will be 1.3185 million, including 913,500 urban residents and 405,000 rural residents. The urbanization rate is 69.48%, which is a medium-sized city with a high level of urbanization. The whole territory has a continental monsoon climate with distinct four seasons, with more wind and sand in spring, more rain in summer and autumn, and dry and cold winter. The average annual temperature is 8.7℃~10.7℃, and the annual precipitation is 572mm. A certain city is an industrial city in the eastern part of Shanxi Province and an important part of my country's energy and heavy chemical base. In 2022, the city's GDP will reach 74.22 billion yuan, a year-on-year increase of 3.6% at comparable prices, and the city's per capita GDP will reach 56,300 yuan.

[0054] Table 2-1 Economic and social development of a city's urban area Unit: square kilometers, 100 million yuan, %, 10,000 people, 10,000 yuan / person, %

[0055] years Land Area GDP GDP growth Total population at the end of the year GDP per capita Urbanization rate 2019 44.82 352.303 3.5 45.2132 7.79 100 2020 44.82 371.487 3.4 44.8729 8.28 100 2021 44.82 449.67 7.3 44.8976 10.02 100 2022 44.82 509.11 4.6 44.8729 11.35 100

[0056] Overview of power grid construction. A certain power grid is a typical transmission power grid, with a 500kV station as the center. The west is connected to Houcun, Taiyuan with a 500kV Yanghou double circuit, the east is connected to Guishan, Hebei with a 500kV Yanggui double circuit, and the middle is connected to Songxi Station in Jinzhong with a 500kV Yangsong double circuit. It is connected to the Jinzhong power grid with six 220kV lines, namely Haibai double circuit, Yanglu double circuit, Yangxi line and Yangyan line. There are 8 220kV substations in the network, forming 8 power supply systems of Changling, Hailuwan, Hongwei, Suyu, Lianzhuang, Taodong, Wenchi and Yuxian. Except for the 220kV Haiyu line, all 220kV interconnection lines are double-circuit power supply, forming a grid structure of a certain station-Lianzhuang station-Hongwei station-Taodong station-Changling station-a certain station, a certain station-Wenchi station-Yuxian station-Hailuowan station-Suyu station-Yangguang power plant, and 220kV Yanghong double circuit charging operation. A certain power grid has initially formed a power grid structure with 500 kV as the support, 220 kV double-ring network as the backbone, 110 kV and below flexible distribution network as the basis, and coordinated development of various voltage levels. By the end of 2022, a certain power grid has one 500 kV substation (a certain), two main transformers, and a total capacity of 2 million kVA.

[0057] There are 8 220 kV substations and 16 main transformers with a total capacity of 2.64 million kVA; there are 28 220 kV lines with a total length of 449.59 km. In terms of 110 kV power grid, as of the end of 2022, a certain power grid has 28 110 kV substations and 59 main transformers with a total capacity of 2617.5 MVA. In terms of 35 kV power grid, as of the end of 2022, a certain power grid has 24 35 kV substations and 48 main transformers with a total capacity of 537.55 MVA. In terms of 10 (20) kV power grid, there are 337 10 (20) kV lines with a total length of 3846.43 km. There are a total of 471 switch stations and ring network rooms. In some areas, 10 kV lines have problems such as long power supply radius, low load, high line loss and poor power quality. At the same time, due to the lack of upper power supply points and low line connection rate, the grid structure is relatively weak. See the table below for details.

[0058] Table 2-2 Main transformer capacity of 220 kV and above substations in a certain city in 2022

[0059]

[0060]

[0061] Table 2-3 Main transformer capacity of a city's 110 kV substation in 2022

[0062] Serial number Device Name Voltage level Capacity(MVA) Commissioning date 1 110kV Chengbei Substation AC 110 kV 80 2009-09-17 2 110kV Yangjiazhuang Substation AC 110 kV 100 2010-12-15 3 110kV Canshi Substation AC 110 kV 100 1985-10-20 4 110kV Zhongshe Substation AC 110 kV 80 2008-12-31 5 110kV Xiyu Substation AC 110 kV 63 1980-07-24 6 110kV Wudu Substation AC 110 kV 80 1998-01-28 7 110kV Boli Substation AC 110 kV 100 1978-12-20 8 110kV Tuolingtou Substation AC 110 kV 90 2006-12-29 9 110kV Yinying Substation AC 110 kV 71.5 2001-01-24 10 110kV Xinxing Substation AC 110 kV 80 2007-06-19 11 110kV Weibo Substation AC 110 kV 126 1989-11-29 12 110kV City Substation AC 110 kV 80 2001-12-03 13 110kV Xichengwu Substation AC 110 kV 100 2015-02-12 14 110kV Niangziguan Substation AC 110 kV 32 1980-01-01 15 110kV Liujiacun Substation AC 110 kV 100 2012-12-05 16 110kV Development East Substation AC 110 kV 213 2014-01-24 17 110kV Maipo Substation AC 110 kV 100 2016-12-14 18 110kV Chenjiazhuang Substation AC 110 kV 71.5 2000-08-04 19 110kV Baiyangshu Substation AC 110 kV 81.5 1990-04-23 20 110kV Majiaping Substation AC 110 kV 163 1955-10-01 21 110kV Qingyatou Substation AC 110 kV 100 2013-08-01 22 110kV Chengxi Substation AC 110 kV 63 1966-08-06 23 110kV Xiushui Substation AC 110 kV 100 2000-12-01 24 110kV Xiling Substation AC 110 kV 63 1979-05-01 25 110kV Nanlou Substation AC 110 kV 80 2005-12-01 26 110kV Xincun Substation AC 110 kV 100 2013-06-22 27 110kV Nanshangzhuang Substation AC 110 kV 100 2013-08-20 28 110kV Huangshayan Substation AC 110 kV 100 2019-04-30

[0063] Table 2-4 Main transformer capacity of a 35 kV substation in a certain city in 2022

[0064]

[0065]

[0066] Furthermore, the sub-items of power and load forecasting are introduced as follows.

[0067] Regarding electricity forecast. We use mature forecasting methods such as time series forecasting, elasticity coefficient method, and per capita electricity consumption to calculate the total electricity consumption of a city from 2023 to 2027. In order to make full use of the useful information of each model, we use the optimal combination forecasting method to make a comprehensive forecast of electricity consumption. According to the actual economic and power development of a city, the recommended solution is to use a weighted combination of multiple forecasting models to improve the forecast accuracy. The comprehensive forecast results of electricity are shown in Table 2-5.

[0068] Table 2-5 Electricity forecast table for a city Unit: billion kWh

[0069]

[0070] Table 2-6 Electricity forecast results Unit: billion kWh

[0072] plan 2020 2021 2022 2023 2024 2025 2026 2027 2030 2035 High plan 81.85 90.08 99.11 104.70 110.62 116.89 125.52 134.80 148.15 178.16 Medium Solution 81.85 90.08 99.11 103.65 108.39 113.36 120.99 129.13 136.91 158.42 Low plan 81.85 90.08 99.11 102.88 106.80 110.86 117.80 125.17 127.02 140.96

[0073] About load forecasting. According to the historical load data and user capacity requirements and other relevant information, combined with the forecast results of electricity consumption in the whole society, the natural growth rate method, regression analysis method and maximum load utilization hours method are used to predict conventional loads, and then the method of increasing conventional users is adopted to predict the load of the whole society, and high, medium and low plans are proposed. After comprehensive analysis, a recommended plan is given. The results of the recent load forecast are an important reference for the recent power grid construction in the region. In order to effectively guide the recent power grid construction, the near- and medium-term load forecast adopts the regression analysis method, the maximum load utilization hours method and the large user method + natural growth rate method. Among them, the large user method mainly predicts the near- and medium-term load development of known new users, and the natural growth rate method mainly predicts the load of unknown users in the region. The large user method load forecast mainly predicts the near- and medium-term load development of known new users. The method is mainly based on the known user capacity and the load S-curve mathematical model to make year-by-year load forecasts for users with different load properties. The mathematical model of the S-curve is: Where: Y is the annual maximum load in the tth year, that is, the system maximum load on the maximum load day or typical load day. t is the number of years from the current year. See the table below.

[0074] Table 2-7 S-type curve load growth curve

[0075]

[0076]

[0077] The S-curve growth parameter A value is 0.25 for general industry, which means that the load will grow to 80% of the future load in the first year after completion. The commercial value is 0.7, which means that the load will grow to 80% of the future load in the second year after completion. The residential area with a good location is 2, which means that the load will grow to 80% of the future load in the third year after completion. The residential area with an average location is 5, which means that the load will reach 80% of the future load in the fourth year after completion.

[0078] A certain city is the largest anthracite producing area and an important mineral concentration area in the country, known as the "hometown of coal and iron". As a resource-based city, coal, electricity, aluminum, and chemical industry are the main pillar industries. The secondary industry accounts for 45.1% of the city's total industry, and the energy industry accounts for 81% of the industrial added value. Based on the prediction results of the per capita comprehensive electricity consumption method, the elasticity coefficient method, and the curve fitting method, combined with the electricity development trend of a certain city, the final prediction results of electricity demand are determined.

[0079] Table 2-8: Electricity demand forecast for a city by county from 2020 to 2035 Unit: 10,000 kW, %

[0080]

[0081] Distributed power supply situation. As of October 2023, the installed capacity of distributed users in a certain city is 85.76MVA, of which 10.22MVA is connected to the grid at 10kV AC, 4.83MVA is connected to the grid at 220V AC, and 70.70MVA is connected to the grid at 380V AC. See the table below.

[0082] Table 2-9 Installed capacity of distributed users in a city

[0083]

[0084]

[0085] Table 2-10 The available capacity of distributed photovoltaic power generation in a certain city's distribution network in a county

[0086]

[0087] Example 3: Analysis of factors limiting the absorption capacity of distributed photovoltaic access to distribution networks

[0088] The research sub-items of this embodiment are as follows: (2) Based on (1), the constraint factors of distributed photovoltaic access are further analyzed in combination with the interactive characteristics of the distribution network and distributed photovoltaic access. Including: studying the load classification, power consumption curve, peak load regulation capacity, controllable load status and other characteristics of distributed photovoltaic access to the distribution network; combining the grid structure, load distribution, load characteristics of the distribution network and the output characteristics of distributed photovoltaics, considering the voltage deviation, load level, line loss rate, short-circuit capacity and other limiting factors to study the distributed photovoltaic access and absorption capacity. Impact on voltage: After the photovoltaic power generation system is connected to the distribution network, it will affect the voltage quality of the power grid. It is necessary to analyze the parameters such as the injected power, current and power factor of the photovoltaic system to determine whether the access of the photovoltaic system will cause voltage fluctuations, voltage deviations or voltage imbalances. Impact on line loss rate: Due to the power volatility of photovoltaic power generation, the distribution network needs to be able to adjust the voltage in time to maintain stable operation. If the voltage regulation capacity of the distribution network is insufficient, it may cause excessive voltage fluctuations, thereby increasing the loss of the line. Impact on line capacity: The access of photovoltaic power generation will increase the load of the distribution network. If the line capacity of the distribution network is not sufficient to carry the power of photovoltaic power generation, it may cause line overload, thereby increasing line losses.

[0089] In this research sub-item, a specific data survey and analysis work is carried out by taking a certain city's power grid as an example. The details are as follows.

[0090] 3.1 Analysis of distributed power characteristics. By analyzing the output characteristics of the photovoltaic system under different typical days, and conducting correlation analysis between various environmental factors and the output of the photovoltaic system and calculating the efficiency of the photovoltaic system, the influence of environmental factors on the output of the photovoltaic system is obtained. Among them, light intensity has the greatest impact, followed by the temperature of the solar panel, and air humidity has an impact on photovoltaic output, but it can be ignored. Based on the factors affecting light intensity, the impact of the output level of photovoltaic power and light intensity is analyzed with a focus on seasons and different weather conditions.

[0091] See attached Figure 2 , about the influence of seasonal types. Affected by the rotation and revolution of the earth, the sun's direct point moves back and forth between the Tropic of Cancer and the Tropic of Capricorn, showing a periodic change in years, resulting in the four seasons of spring, summer, autumn and winter. At a location with fixed longitude and latitude, the sun's direct point changes over time, and the solar altitude angle and the distance between the sun and the earth also change accordingly, making the solar irradiance different every month. In order to further explore the changing rules of solar irradiance in different seasons, a curve of solar irradiance changes in four seasons is drawn.

[0092] From the attached Figure 2It can be seen that the temperature in spring is generally on the rise and the solar altitude angle is getting larger. The solar irradiance is generally on the rise, and the daily peak value is mainly concentrated in 1100-1200W / m2. The solar irradiance changes more dramatically in summer. The reason is that most parts of my country are located in the subtropical monsoon climate. In summer, it is mostly hot and rainy. The daily peak level is high, but the difference between sunny and rainy days is large. The average peak range is mainly concentrated in 1300-1500W / m2. In autumn, the solar radiation altitude angle becomes smaller, the temperature gradually decreases, and the solar irradiance illumination shows an overall downward trend. The daily peak value is mainly concentrated in 900-1000W / m2. Winter is low temperature and dry, and the irradiance changes are generally stable and the average peak value is low. The daily peak value is mainly concentrated in 700-800W / m2. See the table below.

[0093] Table 3-1 Daily peak range of solar irradiance in four seasons

[0094] season Climate conditions Solar irradiance Daily peak range spring The overall temperature is on the rise The overall upward trend 1100-1200W / m2 summer Hot and rainy More severe, greatly affected by weather conditions 1300-1500W / m2 autumn The temperature gradually decreases The overall downward trend 900-1000W / m2 winter Low temperature drying The overall situation is relatively stable and the average peak is low 700-800W / m2

[0095] The analysis of the seasonal variation of solar irradiance shows that the overall variation of solar irradiance in different seasons shows great differences. At the same time, the variation of various meteorological factors in different seasons and the degree of their influence on solar irradiance are also significantly different.

[0096] About the influence of weather conditions. The output level of photovoltaic power source is mainly determined by the light intensity, which is greatly affected by weather conditions. For example, the light intensity varies greatly on sunny days, cloudy days, rainy and snowy days, and the output level is closely related to the time series. The maximum output is often around 13:00 when the light is strong, and the output is mostly 0 from 20:00 to 5:00 the next day. Photovoltaic power generation is very regular on sunny days, followed by cloudy days, while photovoltaic power generation on rainy days is highly volatile. On rainy days, the photovoltaic output value is the smallest. The output timing characteristic curve of photovoltaic power source is as follows: Figure 3 shown.

[0097] 3.2 About the distributed power generation output characteristic model.

[0098] Distributed power sources are usually connected to the distribution network in three ways: synchronous generators, power electronic devices, and asynchronous generators. Depending on the grid connection method, DG is equivalent to four node types in the power flow calculation: PQ, PV, PI, and PQ(V), and its power output exhibits different regulation characteristics.

[0099] 1) The grid-connected interface is a synchronous generator. For example, a split-axis micro gas turbine with power factor control can be used as a PQ node, and its power output characteristics can be expressed as

[0100]

[0101] Where: S is the rated capacity of this type of DG; f is the power factor when DG is running.

[0102] For this type of DG, we can first assume that its power factor is constant, take the rated capacity S as the capacity configuration object, and adjust its power output by controlling the power factor f during operation.

[0103] 2) The grid-connected interface is a power electronic device. For example, a single-shaft micro gas turbine connected to the grid with a current-type PWM converter can be treated as a PI node with a constant current source output. Its power output characteristics are:

[0104]

[0105] Where: U is the voltage amplitude at the grid connection point; I is the current amplitude injected into the grid by the converter; δ is the phase of the current lagging behind the voltage.

[0106] In practical applications, some current control strategies are usually adopted to enable the converter to achieve current output and amplitude regulation with unity power factor. Equation (3-2) can be simplified to

[0107]

[0108] When performing power flow calculations, the active power can be iterated according to formula (3-4).

[0109] P k+1 =U k I (3-4)

[0110] Where: U k is the grid connection point voltage of the kth iteration; P k+1 The active power injected into the grid for the k+1th iteration.

[0111] The output power P of this type of DG is determined by the grid-connected current I. I can be used as the object of capacity optimization, and its power can be adjusted by controlling the magnitude of the current I during operation.

[0112] 3) Through the synchronous generator interface, a voltage-controlled split-axis micro gas turbine or a single-axis micro gas turbine connected to the grid through a voltage-type PWM converter can be used as a PV node, and its power output characteristics can be expressed as

[0113]

[0114] Where: Pmax is the maximum value of active power, and the output power is adjustable within this range; reactive power Q is a function of the terminal voltage value V and is determined by power flow calculation.

[0115] The active power and reactive power generated by this type of DG are not coupled, and its active power needs to be determined simultaneously through optimization calculation.

[0116] For active power P and reactive power Q, during operation, its reactive power output is regulated by controlling the terminal voltage V of the machine.

[0117] 4) For the P-Q(V)-type DG connected to the grid through an induction generator interface, reactive power needs to be absorbed from the system to establish the magnetic field during grid-connected operation, and it does not have the ability to regulate voltage. Therefore, the optimal configuration of this type of DG is not discussed here for the time being.

[0118] 3.3 Regarding the assessment of the grid connection capacity of distributed power sources.

[0119] Thermal stability assessment. The principle is to ensure that the thermal stability of transformers and line equipment is not exceeded. The thermal stability assessment should calculate the reverse load rate λ based on factors such as the grid operation mode, limits of power transmission and transformation equipment, load conditions, power generation conditions, and output characteristics of distributed power sources. The reverse load rate λ should be calculated according to Equation (1). In the formula, PD is the output of the distributed power source, PL is the equivalent electricity load at the same time, that is, the load minus the output of other power sources except the distributed power source; Se is the actual operation limit of the transformer or line. The maximum value λmax of the reverse load rate λ during the assessment period should be used as the assessment index for the thermal stability assessment. λ during special periods such as legal holidays that cause grid load fluctuations within the assessment period can be ignored. The additional distributed power source capacity Pm within the assessment area should be calculated according to Equation (2): Pm = (1 - λmax) × Se × kr (2) In the formula, kr is the equipment operation margin coefficient, generally taken as 0.8.

[0120] Short-circuit current verification. The principle of short-circuit current verification is that the short-circuit current at each bus node of the system after connecting the distributed power source does not exceed the opening current limit of the corresponding circuit breaker. The verification objects should include all equipment through which the short-circuit current may flow within the assessment scope. Based on the current situation of the short-circuit current under the maximum operating mode of the system within the assessment scope and the distributed power source capacity to be verified, the short-circuit current of the system bus should be calculated according to GB / T 15544 and DL / T 5729. The short-circuit current should be verified according to formula (3). The formula is: Ixz < Im (3). In the formula, Ixz is the short-circuit current of the system bus, and Im is the allowable short-circuit current limit, which should be the minimum value of the opening current limits of all equipment connected to the bus and the corresponding circuit breakers on the outgoing lines.

[0121] Voltage deviation verification. The voltage deviation verification should be based on the principle of reactive power local balance and grid voltage not exceeding the limit after the distributed power source is connected. The verification objects should include the 10kV-220kV voltage level busbar of the 35kV-220kV substation. The maximum positive voltage deviation and negative voltage deviation of the assessment area should be calculated based on the highest and lowest operating voltages of the grid during the assessment period, combined with the voltage limit values ​​given in GB / T12325, and expressed as ΔUH and ΔUL respectively. According to the capacity of the distributed power source to be verified and the requirements of GB / T33593, the maximum positive and negative voltage deviations of the area caused by the connection of the new distributed power source should be calculated according to formula (4), expressed as δUH and δUL respectively. In the formula, Qmax is the maximum reactive positive and negative values ​​calculated based on the required values ​​of the power factor of the grid connection point of different types of distributed power sources in GB / T33593, UN is the rated voltage of the bus in the area, RL and XL are the resistance and reactance components of the grid impedance, and the grid resistance component can be ignored in the high-voltage grid. The voltage deviation should be checked according to formula (5). ΔU H >δU H And ΔU L <δU L (5).

[0122] Harmonic verification. The principle of harmonic verification is that the harmonic current value and interharmonic voltage content of the nodes where the distributed power sources are connected to the grid do not exceed the limit. The verification objects should include all nodes that may be affected by the harmonic current and interharmonic voltage provided by the distributed power sources. Harmonic current should be verified according to formula (6). Ixz,h>Ih(6). In the formula, Ixz,h is the hth harmonic current value, and Ih is the hth harmonic current limit specified in GB / T14549. The interharmonic voltage content of each node should not exceed the limit specified in GB / T24337.

[0123] Grid carrying capacity verification. The grid carrying capacity assessment level should be determined based on the calculation and analysis results, in different areas and layers. The assessment levels can be divided into green, yellow and red from low to high. When determining the assessment level, the local should be subordinate to the overall. When the assessment level of the next-level grid is lower than that of the previous grid, the assessment level should be based on the previous grid. If the short-circuit current, voltage deviation or harmonic verification in the assessment area fails, the corresponding assessment level should be red. If the assessment area is reversely fed to the 220kV and above grid due to distributed power sources, the assessment level of the area should be red. The assessment level division should comply with the provisions of the following table.

[0124] Evaluation level table

[0125]

[0126] Example 4: Evaluation of Distributed Photovoltaic Absorption Capacity

[0127] The research sub-items of this embodiment are as follows: (3) By analyzing the correlation between distributed photovoltaic access and the distribution network voltage index, line loss index, protection index and other grid operation states, a specific calculation model of the index system constraining distributed photovoltaic access is proposed; combined with the tree-enhanced naive Bayes algorithm (TAN algorithm), the correlation and independence of various grid indicators are comprehensively considered to construct a comprehensive evaluation method for distributed photovoltaic access to the distribution network, and a scientific and universal evaluation is conducted on the strategies for the distributed photovoltaic absorption capacity in the distribution network, and a distributed photovoltaic absorption strategy is proposed that is beneficial to the friendly interaction between photovoltaic power grids and improves the absorption capacity.

[0128] 4.1 Evaluation of medium voltage distributed generation access to distribution network.

[0129] The purpose of the distributed power generation consumption assessment business is to comprehensively assess the consumption capacity of distributed power generation after it is connected to the power system, taking into account factors such as power output, grid acceptance capacity, economic benefits, technical feasibility, and environmental and social benefits, to provide a basis for the formulation of distributed power generation access policies and development plans, and to provide guidance for the dispatching and operation of the power system, so as to achieve the coordinated development of distributed power generation and the power system. Based on the regional distribution network equipment and operating conditions, this project calculates the distribution line flow distribution of distributed power generation of different categories and installed capacities under different grid connection points, searches for the optimal access location and maximum access capacity of distributed power generation that meet the constraints of voltage, load rate, and qualified power quality, optimizes the operation mode of the distribution network, and further improves the consumption capacity of distributed power generation. By comparing with the line loss rate before the distributed power generation is connected, the degree of influence of distributed power generation on the system energy loss is understood, thereby providing data support for system optimization, and finally fully understanding the impact after the access of distributed power generation, in order to optimize the performance and efficiency of the power system. The 10kV distributed power generation consumption assessment optimization business follows the process sequence, and the node power process is as follows Figure 4 shown.

[0130] 4.2 Evaluation of low voltage distributed generation access to distribution network

[0131] The distributed generation capacity assessment business aims to comprehensively assess the capacity of distributed generation after it is connected to the power system, taking into account factors such as power output, grid acceptance capacity, economic benefits, technical feasibility, and environmental and social benefits, to provide a basis for the formulation of distributed generation access policies and development plans, and to provide guidance for the dispatching and operation of the power system, so as to achieve the coordinated development of distributed generation and the power system.

[0132] This project is based on the operating conditions of low-voltage substations. It calculates the substation power flow distribution of photovoltaic power plants of different categories and installed capacities under different grid connection points, evaluates the voltage, load rate, and power quality, and further improves the distributed power supply absorption capacity. By comparing the line loss rate before the distributed power supply is connected, the influence of the distributed power supply on the system energy loss is understood, thereby providing data support for system optimization. Finally, the impact after the distributed power supply is fully understood to optimize the performance and efficiency of the power system.

[0133] The low-voltage photovoltaic access distribution network absorption capacity assessment business follows the process sequence, and the node process is as follows Figure 5 shown.

[0134] 4.3 Optimization of photovoltaic access to distribution network

[0135] Optimization of photovoltaic access to distribution network refers to improving the efficiency and stability of photovoltaic power access to distribution network through a series of technical means and management measures, while ensuring the safe operation of distribution network. Reasonable planning of photovoltaic power access location and capacity: When planning the access of photovoltaic power, the access location and capacity of photovoltaic power should be reasonably selected according to the actual situation of distribution network and load demand, so as to avoid problems such as overload or voltage over-limit. Regarding the distributed access analysis of substations for multi-scenario applications, custom settings: distributed installed capacity. Operation constraints: It is necessary to maintain the distributed output curve, and obtain the distributed access power according to different output curves. It is assumed that only active power P is generated, and reactive power Q is not considered. Constraint settings: Custom settings do not allow access locations. The voltage per unit value is normalized by norm[i]=V[i] / 0.95 using the line loss sensitivity coefficient LSF. For branches with normalized voltage per unit value less than 1.01, they are regarded as candidate branches that DG can access. LSF determines the order of line compensation DG, and the normalized value of voltage per unit value determines whether the branch needs DG compensation.

[0136] 4.4 Research on Distributed Photovoltaic Accommodation Strategy

[0137] Considering the load characteristics of distributed photovoltaic access to the distribution network, the output characteristics and installed capacity of distributed photovoltaics, this paper studies the distributed photovoltaic accommodation strategy based on distribution network grid reconstruction, distribution network operation optimization, photovoltaic storage coordinated control, flexible direct current and other technologies to maximize the cleanliness and sustainability of renewable energy.

[0138] 4.4.1 Distribution Network Reconfiguration

[0139] 4.4.1.1 Theoretical basis

[0140] 1) In graph theory, the concept of a tree can be understood as a connected graph without loops. The necessary and sufficient conditions for the topological structure of a radial distribution network with nb nodes to be a tree are: first, there must be nb-1 branches. Second, it must be a connected graph. 1) In the case of a 10kV distribution network with only one substation supplying power, if the number of branches = the number of nodes - 1 is satisfied, and since all load nodes (assuming that all nodes are load nodes) require power supply, there must be a path from the substation to any load node, so it is a connected graph, then the topological structure of the distribution network is a tree.

[0141] 2) In the actual distribution network, there are multiple substations. Assuming there are nhs substation nodes, and the radial distribution network connected to a substation node is a tree, then for the i-th tree graph, the number of branches = the number of nodes (ki) - 1, for nhs trees, the total branch tree At the same time, every tree is a connected graph.

[0142] If the distribution network meets And the distribution network topology is a connected graph, then it is necessary to prove that the distribution network must have nhs trees, and each tree has only one substation node.

[0143] Suppose there are s trees and s = n bs +β>n bs , β is any integer greater than 0, then there must be at least one node in the graph without a substation node, and its load has no power supply, so it must be s≤n bs If s = n bs -β≤n bs , then the number of nodes ki and branches of the i-th graph is equal to ki-1, which satisfies the following formula requirements:

[0144]

[0145] This is consistent with the aforementioned Contradiction. In summary, s = n bs . Suppose the i-th graph removes the i branches to form a tree, then:

[0146]

[0147] because but Then ci = 0. This proves that the nhs graph is nhs trees.

[0148] 3) There are a large number of unloaded T-junctions in the distribution network. T-junctions with the function of transferring power are connected to at least two branches, and independent T-nodes are not connected to any branches.

[0149] 4) Distributed power sources can be considered as loads or power sources, and can operate independently or in parallel. When considered as a load, it can be considered as a branch, forming a tree with a substation and other load nodes; when considered as a power source, it can be considered as a substation, operating independently with other load nodes to form a tree. This paper regards distributed power sources as negative load nodes and operates in parallel.

[0150] 4.4.1.2 Mathematical model

[0151] Distributed generation is integrated into the distribution network for network optimization and reconstruction, and its objective function is to minimize the network loss of the distribution network.

[0152]

[0153] Where, v is the power loss of the distribution network, unit is kw; V i ,V j is the node voltage, in kV; g ij is the susceptance, unit S; θ ij is the phase angle difference between node i and node j; x ij is a binary discrete variable introduced in this paper, which indicates the branch state. If x..=1J, the branch from node i to node j is a connected branch, and x ij =0 is a time-disconnected branch; Ω is the branch set.

[0154] The constraints are:

[0155] 1) Power balance equation, voltage and current constraint equations, and power constraint equations.

[0156] 2) T-node constraint equation

[0157]

[0158] In the formula, Ω bp is a set of T nodes; yi is the type of T node. T nodes generally appear in two states in the distribution network, one is a transfer node, which is generally connected to at least two branches, and the other is not connected to the power grid and is an independent terminal node. If node j is an independent terminal node with no branch connected to it, then y}>1, then the T node is connected to at least two branches and is a transfer node.

[0159] Constraint equations for radial operation of distribution network

[0160]

[0161] Where n b is the number of nodes; n bs is the number of substation nodes; n dgNumber of distributed generation nodes. Formula (6) indicates that there are multiple substations, and formula (7) indicates that there is only one substation node.

[0162] Distributed power generation constraint equations:

[0163]

[0164] Due to the existence of distributed power sources, it is possible that distributed power sources independently supply power to several load nodes, and the distribution network is no longer a connected graph, and does not meet the conditions for radial operation. Therefore, it is assumed that the distributed power source is a negative load ki that must be supplied by the substation, kij is the virtual power flow on the branch, and Ωdg is a set of distributed power source nodes;

[0165] 4.4.1.3 Algorithm. Combining modern interior point algorithm and branch and bound method, it is possible to accurately solve 0-1 mixed integer programming problems with both discrete variables and continuous variables. The branch and bound method can transform the integer programming of discrete variables into a programming problem containing only continuous variables, while the interior point algorithm can solve the programming problem containing only continuous variables. Since the branch states of the distribution network reconstruction are represented by 0-1 variables, when the branch and bound method is used to process the mixed 0-1 integer programming, the original problem is transformed into a series of continuous variable programming problems for solution. First, the original mixed 0-1 integer programming problem is relaxed, and the discrete variables are relaxed into continuous variables between [0,1]. The primal-dual interior point method is used to solve it, and a non-integer solution to the relaxed problem is obtained. Then, starting from the optimal value of the relaxed problem corresponding to the original problem, a variable that does not meet the integer requirement is branched through the variable dichotomy method, and the variable to be branched is decomposed into 0 and 1, thereby obtaining the corresponding two sub-problems. This is the first branch. Then, the primal-dual interior point method is used to solve the relaxed subproblems corresponding to two or more subproblems and find the optimal value of the relaxed subproblems. As the branching proceeds, the subproblems of the original problem become larger and larger. In order to speed up the calculation and reduce the amount of calculation, the algorithm makes a pruning judgment on all subproblems, prunes the subproblems that meet the pruning criteria, and branches the subproblems that do not meet the criteria until all branch subproblems have been processed and the optimal solution to the problem is obtained. The specific algorithm is as follows:

[0166] Branch and Bound Model:

[0167] P:minv(y,x)

[0168] st h(y,x)=0

[0169] q(y,x)≤0

[0170]

[0171] Where v(y,x) is the objective function, h(y,x) and q(y,x)≤0 are the equality and inequality constraints, respectively.

[0172] After making the discrete variables continuous, the model is obtained as follows. This model is the standard mathematical model for solving general nonlinear programming with two-bound constraints using the primal-dual interior point method.

[0173] P0:minv(y,x)

[0174] st h(y,x)=0

[0175] q(y,x)≤0

[0176] 0≤x≤1

[0177] Solve P0 using the primal-dual interior point method to obtain the optimal solution y0* and x0* and the lower bound L0 = v(y0*, x0*). ij Satisfies the following conditions, x ij =min{|x ij *-0.5|}x ij *∈x0* can be used as a branch variable. Setting the branch variables to 0 and 1, we get two relaxed subproblems

[0178] P1:minv(y,x ij )

[0179] st h(y,x ij )=0

[0180] q(y,x ij )≤0

[0181] 0≤x≤1

[0182] x ij =0

[0183] P2:minv(y,x ij )

[0184] st h(y,x ij )=0

[0185] q(y,x ij )≤0

[0186] 0≤x≤1

[0187] x ij =1

[0188] Solve P1 and P2 using the primal-dual interior point method to obtain two optimal solutions and the lower bound value. If the optimal solution is an integer solution and the lower bound value is less than the initially set upper bound value, then take the objective function value corresponding to this integer solution as the upper bound U. Otherwise, select the subproblem with the smaller lower bound value among P1 and P2 for branching. Iterate the above process until the lower bound value of the branched subproblem is greater than or equal to the upper bound U, at which point the iteration ends.

[0189] 4.4.1.4 Algorithm Flow

[0190] Based on the mathematical model for network loss optimization, the algorithm flow is summarized as follows in this chapter:

[0191] Step 0 Initialization: Set the upper bound v of the original problem, set the initial value of the iteration count k = 0, and the maximum iteration count kmax. The queue RP for branching and the subproblems IP that have obtained integer solutions.

[0192] Step 1 Relax the original problem: Relax the 0-1 discrete variables such as xij into continuous variables in the interval [0, 1], and use the primal-dual interior point algorithm to solve the corresponding relaxed problem of the original problem. Determine whether the original relaxed problem is feasible. If there is no feasible solution, then the original problem has no solution. If an optimal solution that meets the integer requirements is found, it is the optimal solution of the original problem. If an optimal solution that does not meet the integer requirements is found, then add it to the queue RP of subproblems to be branched, and set the objective value of the original relaxed problem as the lower bound v of the original problem.

[0193] While k < kmax, execute Step 2 in sequence if the condition is met, otherwise end.

[0194] Step 2 Determine whether the queue RP for branching is empty. If not, continue; otherwise, jump out of the main loop and output the optimal solution of the original problem from the integer feasible solutions.

[0195] Step 3 Count the number of problems in the queue RP for branching, and set up the RP queue and IP queue to store the newly relaxed subproblems that have not reached integer solutions and the newly relaxed subproblems that have reached integer solutions after branching, respectively.

[0196] Step 4 Enter the sub-loop, branch all the subproblems to be branched, select a relaxed problem from the queue RP for branching, divide the variable to be branched into two cases of 0 and 1, and use the primal-dual interior point algorithm to solve them respectively. The following three conditions can be used to detect the branching nodes in order to delete infeasible branches in a timely manner, reduce the workload, speed up the calculation, and avoid converging to a local optimal solution.

[0197] If this branched problem is infeasible, for example, it does not meet the security constraints, then this branched problem is infeasible;

[0198] b) If the optimal solution vfound of the objective value sought, has vfound > v* + ε;

[0199] c) If the optimal solution is less than v*, then v*=vfound, and turn it to condition b.

[0200] If the branch layer does not satisfy the three cases a, b, and c, solve the next candidate branch. If it satisfies the requirements, delete the infeasible subproblem from the RP queue and the IP queue. Delete the relaxed subproblem that has obtained an integer solution from the RP queue. At this time, the IP queue stores the integer feasible solution information. In addition, the relaxed subproblem that does not meet the integer solution requirements is added to the pending branch queue RP.

[0201] Step 5 determines whether all branch sub-problems have been processed. If yes, exit the sub-loop; if no, continue.

[0202] Step 6 rearranges the target values ​​of the sub-problems in the IP queue from small to large.

[0203] Step 7: Adjust the upper and lower bounds of the original problem. Assign the minimum value of the sub-problem target value in the RP queue to the lower bound, and assign the minimum value of the sub-problem target value in the IP queue to the upper bound. When IP is empty, the upper bound is an arbitrarily large value. Add the valid sub-problems back to the pending branch queue RP and wait for solution.

[0204] Step 8: Use the RP queue to refresh the original RP queue with branches.

[0205] Step 9 adds IP to the integer feasible solution set IP.

[0206] Step 10: Clear RP and IP. Set k=k+1.

[0207] End The loop ends.

[0208] When solving mixed integer linear programming using branch and bound, the objective function of the child branch layer is always greater than or equal to the objective function of the parent branch layer. However, in mixed nonlinear integer programming (MINLP), the objective function of the child branch layer is not necessarily always greater than or equal to the objective function of the parent branch layer. For this reason, a safety factor ε is introduced in condition b, which has the following function: Figure 1 As shown. If there is no ε, then node l is the optimal point, and the solution falls into the local optimum. The safety factor ε ensures that the global optimal solution is found at point g. In the figure, node l is the local optimal solution, node g is the global optimal solution, and k is the branch node of the branch and bound method.

[0209] 4.4.2 Coordinated control of photovoltaic and energy storage. Timely adjust the grid construction plan and strengthen regional coordination and cooperation. The development of new energy in a certain city is rapid, and the absorption capacity of some local areas is close to saturation. The next step should be to carry out special research in relevant areas in a timely manner and comprehensively consider the regional source network planning. In order to solve the problem of regional new energy absorption, possible measures include the construction and expansion of 220kV substations and the configuration of energy storage facilities according to the installed capacity of power generation.

[0210] Example 5

[0211] This embodiment is aimed at the research sub-item: (5) constructing a new data model, taking the effective access to the grid of distributed photovoltaics and its energy consumption optimization as the data construction orientation, constructing a dedicated data model, and layering the grid operation data related to the effective access to the grid of distributed photovoltaics and / or the distributed photovoltaic grid energy consumption into multiple independent data layers; this data model is oriented to the multi-layer and multi-dimensional construction and data mapping of distributed photovoltaic access and consumption in the distribution network, and at the same time has both basic versatility (data model architecture) and depth of data processing (construction of data processing algorithm), and can develop a new model with adjustable applicability to the photovoltaic grid access data accumulated by the power grid and various specific data optimization goals.

[0212] 5.1 Development Background

[0213] At present, there are some related technologies for optimizing photovoltaic access and consumption, such as prediction models based on photovoltaic power generation and load demand, optimization algorithms based on grid topology and operating parameters, etc. However, these existing technologies often only consider the single factor of photovoltaic power generation and load demand and ignore other factors related to photovoltaic access and consumption, such as weather conditions, technical parameters of photovoltaic systems, etc., which leads to the incompleteness and lack of accuracy of the model; some models often only focus on local optimization goals and lack consideration of the global benefits of the entire power grid system, so that the local optimization of photovoltaic access and consumption may have a negative impact on the entire power grid system, typically, such as affecting indicators such as the stability and economy of the power grid. This is what we often say in the industry as "taking care of one thing and losing another." Another point is that the existing models lack compatibility with specialization and generality. Specifically, a model is targeted at a specific photovoltaic access problem or a specific access scenario, but lacks compatibility with other scenarios or problems.

[0214] Based on the above technical status, the goal of this research sub-item is to develop a new data structure and data paradigm that can simultaneously combine basic and universal (data model architecture) and data processing depth (data processing algorithm construction), and can be adjustable and applicable to the photovoltaic grid-connected data accumulated by the power grid and various specific data optimization goals.

[0215] 5.2 Specific technical development route and model framework of the new data model.

[0216] This research sub-project has developed a new hierarchical mapping data model for distributed photovoltaic access and consumption in distribution networks in collaboration with multiple scientific research forces. In terms of the main technical architecture, the data model is initialized based on the optimization of photovoltaic energy access and consumption and its global benefit extension. Based on this technical orientation, based on the collection and processing of the accumulated data of the power grid, it focuses on building a data mapping structure with multi-dimensional attributes to achieve the overall construction of the data model; wherein, the initialization of the data model is characterized by the optimization orientation of the data model construction, which is characterized as data goals or problems to be solved and their definitions in the optimization of the data model or data algorithm processing; wherein, the global benefit extension is initialized to construct the stability and energy utilization efficiency and / or its quantitative indicators for photovoltaic access and consumption; this is a relatively broad but still insufficiently comprehensive initialization global extension, which can be replaced or expanded as needed later.

[0217] In terms of the optimization of photovoltaic energy access and consumption and its global benefit extension orientation, different concrete steps are constructed for the specific details or specific orientations of photovoltaic energy access and consumption optimization under the same technical route, thereby generating multiple groups of concrete related data model groups. For the concrete model units in each different related data model group, the construction steps include some common processes, such as: first initialize the data target and collect photovoltaic grid-connected data, and then preprocess the data. Among them, two data paths are constructed for the collection of photovoltaic grid-connected data: the first is to directly collect photovoltaic grid-connected data associated with data target initialization; the second is to collect photovoltaic grid-connected data broadly, and retrieve corresponding related data from it based on the specific scenarios of photovoltaic energy access and consumption and the current data initialization targets; the second data path is preferred. As for the collected data, it generally includes: time series data of photovoltaic power generation; time series data of distribution network load demand; weather condition data, including solar radiation, temperature and other weather condition data; technical parameters of photovoltaic systems, including capacity, location and other technical parameter data of photovoltaic systems; topological structure and operating parameters of distribution networks, etc. It is necessary to set up expanded data capacity in the model to supplement the storage of necessary new data in the future. As for data preprocessing, the existing data planning processing paradigm can generally be directly called.

[0218] 5.3 The essential data structure and algorithm of the new data model,

[0219] The data model collects data on at least photovoltaic power generation, distribution network load, grid stability and other grid operation data. The data collection includes importing relevant data from the existing grid operation database and obtaining structured or unstructured data from the newly created special data acquisition port; and integrating photovoltaic power generation related data, distribution network load related data, grid stability related data and their associated data into multiple independent data layers; further, a hierarchical mapping architecture is constructed between multiple independent data layers, and the hierarchical mapping architecture includes two data paths. The first data path is: the integrated data of each layer is retrieved from the existing basic database or the newly added special data port according to the needs of each layer, allowing the same data to be repeatedly loaded into different layers and independently called and processed for each data layer. The second data path is: the data structure and data algorithm compatibility constructed in any data layer are mapped to any other data layer, so that the general data structure and algorithm only need to be constructed in one data layer, and the other data layers only need to call the constructed data structure and algorithm framework as needed. In the second data path, generally, multiple groups of specific data models are first constructed with the photovoltaic power generation data layer as the object, and then the data layer mapping is performed for other data layers.

[0220] In the second data path, several specific data models are constructed, including secondary segmentation, side extraction and return, hierarchical prediction, and multi-layer connection. The details are as follows.

[0221] 5.3.1 Secondary segmentation: The photovoltaic power generation data layer allows for secondary segmentation, which can be divided into independent data layers with a serialized internal secondary hierarchical structure according to time, geographic information, grid access direction or other indicators.

[0222] 5.3.2 Side extraction and return. Build a data extraction and return processing layer next to the photovoltaic power generation data layer, and perform unified algorithm application and data processing for the same type of data processing in the data layer;

[0223] 5.3.3 Hierarchical prediction. Construct a photovoltaic power generation prediction data model, which specifically includes a basic prediction layer and an optimized prediction layer that introduces secondary fluctuation factors on the basic prediction layer; a. Basic prediction layer. This data layer is based on the existing time series prediction data algorithm, and further adds differential operations under the time series to make the non-stationary time series stable; set adjustable parameters for it: differential order parameters, order parameters of moving average items, and adapt to different specific photovoltaic grid access scenarios based on the adjustability of parameters; b. Optimized prediction layer. On top of the basic prediction layer, the fluctuation factors that have a consistent impact on the overall data processing paradigm of the basic prediction layer are incorporated. For the photovoltaic annual cycle fluctuation factors, the optimized prediction layer includes four sets of adjustable parameters to introduce fluctuation factors into the model and adapt to different specific photovoltaic grid access scenarios at the same time. The four sets of parameters are: annual cycle volatility autoregression parameters, annual cycle volatility differential order parameters, annual cycle volatility moving average order parameters, annual cycle volatility period and / or fluctuation amplitude;

[0224] 5.3.4 Multi-layer connection: Construct a global connection layer for all data layers.

[0225] In terms of inter-layer mapping, mapping 5.3.1 secondary segmentation data structure to other data layers besides the photovoltaic power generation data layer means that other data layers can also be secondary layered according to their internal data characteristics. Mapping 5.3.2 data extraction and return processing layer to other data layers besides the photovoltaic power generation data layer can achieve unified calling of this side data extraction and return processing layer for the data processing types that exist in each data layer, thereby avoiding the repeated distribution of the same algorithm to different data layers, resulting in low data storage and computing efficiency. When mapping the 5.3.3 photovoltaic power generation prediction data model to other data layers such as photovoltaic power generation related data, distribution network load related data, and power grid stability related data, specifically: for a, the structure of the basic prediction layer remains basically unchanged (because it directly quotes the existing time series algorithm), and only the prediction data needs to be replaced with distribution network load related data and power grid stability related data, and the difference order parameters and the order parameters of the moving average items are set to adapt to the specific scenario; for b, on the one hand, the optimization data is replaced with distribution network load related data and power grid stability related data, and on the other hand, the structure of the four groups of parameters remains unchanged, and the volatility autoregression parameters, volatility difference order parameters, volatility moving average order parameters, volatility period and / or volatility amplitude parameters are still used, but adjusted to the corresponding period interval according to the actual data environment of the target data layer. For 5.3.4, the full layer is constructed in front of itself, in which, on the one hand, the data orientations of different data layers are globally coordinated, including weighted integration of linear correlation data orientations to form global data orientations, selective coordination or compromise allocation coordination of conflicting data orientations, and verification coordination of common data orientations.

[0226] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0227] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0228] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0229] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0230] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A hierarchical mapping data model for photovoltaic consumption in distribution networks, characterized by: This data model is guided by the effective grid access of distributed photovoltaics and the optimization of their energy consumption, and the grid operation data related to the effective grid access of distributed photovoltaics and / or the distributed photovoltaic grid energy consumption are layered and constructed into multiple independent data layers.

2. A hierarchical mapping data model for photovoltaic consumption in distribution network according to claim 1, characterized in that: At least data collection is performed on photovoltaic power generation, distribution network load, grid stability and grid operation data related to the above three items. Data collection includes importing relevant data from the existing grid operation database and obtaining structured or unstructured data through a new special data acquisition port; and integrating photovoltaic power generation related data, distribution network load related data, grid stability related data and their related data into multiple independent data layers.

3. A hierarchical mapping data model for photovoltaic consumption in distribution networks according to claim 2, characterized in that: Furthermore, a layered mapping architecture is constructed between multiple independent data layers, and the layered mapping architecture includes two data paths.

4. A hierarchical mapping data model for photovoltaic consumption in distribution networks according to claim 3, characterized in that: The first data path is: the integrated data of each layer is retrieved from the existing basic database or the newly added special data port according to the needs of each level, allowing the same data to be repeatedly loaded into different layers and independently called and processed for each data layer; The second data path is: the data structure and data algorithm built in any data layer are mapped compatibly to any other data layer, so that common data structures and algorithms only need to be built in one data layer, and other data layers only need to call the built data structure and algorithm framework as needed.

5. A hierarchical mapping data model for photovoltaic consumption in distribution network according to claim 4, characterized in that: For the second data path, multiple groups of specific data models are first constructed with the photovoltaic power generation data layer as the object, and data layer mapping is performed for other data layers.

6. A hierarchical mapping data model for photovoltaic consumption in distribution networks according to claim 5, characterized in that: in, The multiple groups of specific data models may optionally include: 2-A. The photovoltaic power generation data layer allows for secondary segmentation, which can be segmented into independent data layers with a serialized internal secondary hierarchical structure according to time, geographic information, network access direction or other indicators; Mapping this secondary segmentation data structure to other data layers besides the photovoltaic power generation data layer means that the other data layers can also be secondary stratified according to their internal data characteristics.

7. A hierarchical mapping data model for photovoltaic consumption in distribution networks according to claim 5, characterized in that: in, The multiple groups of specific data models also include: 2-B. Construct a data extraction and return processing layer next to the photovoltaic power generation data layer, and perform unified algorithm application and data processing for the same type of data processing in the data layer; By mapping this data extraction and return processing layer to other data layers outside the photovoltaic power generation data layer, it is possible to uniformly call this side data extraction and return processing layer for the data processing types that are common in each data layer, thereby avoiding repeatedly distributing the same algorithm to different data layers, resulting in low data storage and computing efficiency.

8. A hierarchical mapping data model for photovoltaic consumption in distribution networks according to claim 5, characterized in that: in, The multiple groups of specific data models also include: 2-C. Constructing a photovoltaic power generation prediction data model, which specifically includes a basic prediction layer and an optimized prediction layer that introduces secondary fluctuation factors on the basic prediction layer; 2-Ca, basic prediction layer, this data layer is based on the existing time series prediction data algorithm, and further adds the difference operation under the time series to make the non-stationary time series stable; set adjustable parameters for it: difference order parameter, order parameter of moving average item, based on the adjustability of parameters to adapt to different specific photovoltaic grid access scenarios; 2-Cb, optimized prediction layer, above the basic prediction layer, incorporates the fluctuation factors that have a consistent impact on the overall data processing paradigm of the basic prediction layer. The optimized prediction layer for the annual cycle fluctuation factors of photovoltaics contains four sets of adjustable parameters to introduce the fluctuation factors into the model and adapt to different specific photovoltaic grid access scenarios. The four sets of parameters are: annual cycle volatility autoregressive parameters, annual cycle volatility difference order parameters, annual cycle volatility moving average order parameters, annual cycle volatility period and / or fluctuation amplitude; Finally, when mapping the above photovoltaic power generation prediction data model to other data layers such as photovoltaic power generation related data, distribution network load related data, and grid stability related data, specifically: For 2-Ca, the structure of the basic prediction layer remains basically unchanged (because it directly refers to the existing time series algorithm). It only needs to replace the prediction data with distribution network load-related data and grid stability-related data, and set the difference order parameters and the order parameters of the moving average term to adapt to the specific scenario. For 2-Cb, on the one hand, the optimization data is replaced by distribution network load-related data and grid stability-related data. On the other hand, the structure of the four groups of parameters is kept unchanged. Volatility autoregressive parameters, volatility difference order parameters, volatility moving average order parameters, volatility period and / or volatility amplitude parameters are still used, but they are adjusted to the corresponding period interval according to the actual data environment of the target data layer.

9. A hierarchical mapping data model for photovoltaic consumption in distribution networks according to claim 5, characterized in that: in, The multiple groups of specific data models also include: 2-D. A global connection layer for all data layers is further constructed within the second data path. On the one hand, global coordination is performed on the data orientations of different data layers, including weighted integration of linearly associated data orientations to form a global data orientation, selective coordination or compromise allocation coordination of conflicting data orientations, and verification and coordination of common data orientations.

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