Plug-in electric vehicles (PEVs) present environmental and energy security advantages versus conventional gasoline vehicles. In the near future, the number of plug-in electric vehicles will likely ...grow significantly in the world. Despite the aforementioned advantages, the connection of PEV to the power grid poses a series of new challenges for electric utilities. This paper proposes a comprehensive approach for evaluating the impact of different levels of PEV penetration on distribution network investment and incremental energy losses. The proposed approach is based on the use of a large-scale distribution planning model which is used to analyze two real distribution areas. Obtained results show that depending on the charging strategies, investment costs can increase up to 15% of total actual distribution network investment costs, and energy losses can increase up to 40% in off-peak hours for a scenario with 60% of total vehicles being PEV.
Under the increasing penetration of distributed energy resources and new smart network technologies, distribution utilities face new challenges and opportunities to ensure reliable operations, manage ...service quality, and reduce operational and investment costs. Simultaneously, the research community is developing algorithms for advanced controls and distribution automation that can help to address some of these challenges. However, there is a shortage of realistic test systems that are publically available for development, testing, and evaluation of such new algorithms. Concerns around revealing critical infrastructure details and customer privacy have severely limited the number of actual networks published and that are available for testing. In recent decades, several distribution test feeders and US-featured representative networks have been published, but the scale, complexity, and control data vary widely. This paper presents a first-of-a-kind structured literature review of published distribution test networks with a special emphasis on classifying their main characteristics and identifying the types of studies for which they have been used. This both aids researchers in choosing suitable test networks for their needs and highlights the opportunities and directions for further test system development. In particular, we highlight the need for building large-scale synthetic networks to overcome the identified drawbacks of current distribution test feeders.
The steady decline in the prices of distributed energy resources (DERs), such as distributed renewable generation and storage systems, together with more sophisticated monitoring and control ...strategies allow power distribution companies to enhance the performance of the distribution network, for instance improving voltage control, congestion management, or reliability. The latter will be the subject of this paper. This paper addresses the improvement of continuity of supply in radial distribution grids in rural areas, where traditional reinforcements cannot be carried out because they are located in secluded areas or in naturally protected zones, where the permits to build new lines are difficult to obtain. When a contingency occurs in such a feeder, protection systems isolate it, and all downstream users suffer an interruption until the service is restored. This paper proposes a novel methodology to determine the optimal location and size of micro-grid systems (MGs) used to reduce non-served energy, considering reliability and investment costs. The proposed model additionally determines the most suitable combination of DER technologies. The resulting set of MGs would be used to supply consumers located in the isolated area while the upstream fault is being repaired. The proposed methodology is validated through its application to a case study of an actual rural feeder which suffers from reliability issues due to the difficulties in obtaining the necessary permissions to undertake conventional grid reinforcements.
In the context of incentive regulation applied to electricity distribution companies, reference network models (RNMs) can be a valuable tool to estimate their efficient costs. These models have to ...plan large-scale electricity distribution areas with different voltage levels. This paper describes the planning algorithms proposed to optimize the location, size, and supply area of the medium-voltage/low-voltage transformer substations in an RNM that has to plan a network from scratch (Greenfield planning). The presented methodology aims to divide the entire planning area into small zones with a nonpredetermined area so that each one can be optimized separately. Then, a heuristic process based on k-means algorithm seeks to find the set of clusters that minimize the cost function, considering the cost of the transformer substations, as well as the LV and MV network costs. The presented case studies show the benefits of applying the proposed methodology.
This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have ...important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the end-user load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.
This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset ...management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PDF
•Natural disasters and deliberate attacks have highlighted the importance of a resilient distribution power system.•Reinforcing the distribution power grid to improve resilience is very costly, and ...investment decisions must be justified.•The proposed methodology allows maximizing the resilience while minimizing the investment incurred.•The investments considered in this model are the installation of Remotely Controlled Switches, Distributed Energy Resources (PVs and batteries), and the undergrounding of overhead lines.•The proposed methodology can be applied to large-scale networks.
In recent years, natural disasters such as hurricanes Katrina and Sandy, or deliberate attacks on the power system, have highlighted the importance of a resilient power distribution system that can maximize the energy supply even in the most stressful situations. However, reinforcing the distribution power grid is very costly, and investment decisions must be adequately justified.
This paper proposes a single-stage multi-criteria optimization model to maximize the resilience of a distribution system through a series of investments while minimizing the total cost incurred. The assets to be invested in under this model are the installation of Remotely Controlled Switches (RCSs), Distributed Energy Resources (DERs) such as storage and photovoltaic units, and the undergrounding of overhead lines. The optimization method is based on a customized genetic algorithm that can be successfully applied to solve large-scale networks. To exemplify the application of the proposed optimization method, an actual distribution network is simulated under extreme weather conditions. The results obtained show how the different type of investments are prioritized and the importance of including managerial and logistic training in distribution companies to deal with extreme weather events.
A Reference Network Model (RNM) is a large-scale distribution planning tool that can help regulators to estimate efficient costs in the context of incentive regulation applied to distribution ...companies. This paper presents the main features of an RNM developed for planning distribution networks from scratch, greenfield planning, or incrementally from an existing grid. Two properties of the model are highlighted: the simultaneous planning of high-, medium-, and low-voltage networks by using simultaneity factors; and the layout of cables in urban areas, taking into consideration the street map, which is automatically generated by the model. A case study evaluates the impact of these features on the results.
Asset Management is one of the foremost vital chapters within the power system's operation and, in general, within energy systems. Electric utilities are a capital-intensive industry with assets such ...as power transformers, power lines, and switch gears spread across a large geographic area. This paper examines the business drivers, challenges, and innovations for maximizing power network reliability through Asset Management (AM). It presents the main features of an open-source software platform that can be used to evaluate indicators that guide the process of making decisions. This tool is being developed inside a European research project named ATTEST. The machine learning algorithms implemented in the tool for AM and described in the paper can assess indicators for evaluating asset health and prioritize preventive and proactive maintenance strategies. The article describes the tool's outcomes, including an overall health score and risk ranking.
This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have ...important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the end-user load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.
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