Data integration from heterogeneous data sources in low-voltage (LV) power distribution grids will be valuable to distribution system operators (DSOs). The power measurements from customer premises need to be processed with other data such as grid topology, line parameters etc., to deploy smart grid applications (SGA) such as real-time grid monitoring and voltage regulation. The most challenging task for DSOs is to collect and integrate data from several sources as several entities are involved in the data management and access to databases are restricted. This paper presents an op E n common information ${M}$ odel (CIM) BA sed sma ${R}$ t grid application framewor ${K}$ ( EMBARK ) to address the above-mentioned challenge. EMBARK is developed to be an efficient, modular and scalable architecture for extracting relevant grid related information from various asset management databases. A novel data management functionality is a part of EMBARK that enables data-driven update of settings and parameters of the algorithms behind smart grid applications. The proposed approach is demonstrated and numerically validated using grid data from a medium-sized distribution grid operator in Denmark. The architecture developed and presented in this paper can support all the phases from planning to the actual smart grid operation, i.e., automatically building the models to perform load flows, grid impact studies, planning, asset management etc.

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes.

Remote access to dynamically changing information elements is a required functionality for various network services, including routing and instances of context-sensitive networking. Three fundamentally different strategies for such access are investigated in this paper: (1) a reactive approach initiated by the requesting entity, and two versions of proactive approaches in which the entity that contains the information element actively propagates its changes to potential requesters, either (2) periodically or triggered by changes of the information element (3). This paper first develops a set of analytic models to compute different performance metrics for these approaches, with special focus on the so-called mismatch probability. The results of the analytic models allow for design decisions on which strategy to implement for specific input parameters (change rate of the information element, network delay characterization) and specific requirements on mismatch probability, traffic overhead, and access delay. Finally, the analysis is applied to the use-case of context-sensitive service discovery.

The increased penetration of Renewable Energy Generation (ReGen) plants in future power systems poses several challenges to the stability of the entire system. In future green energy rich power systems, the responsibility for providing ancillary services will be shifted from conventional power plants towards ReGen plants, such as wind and photovoltaic power plants. Frequency control support from the Wind Power Plants (WPPs) is one of the crucial ancillary services in order to preserve operational stability in case of grid disturbances. Among other requirements, the ability to provide fast frequency control support from ReGen plants will highly depend on the underlying communication infrastructure that allows an exchange of information between different ReGen plants and the control centers. This paper, therefore, focuses on the evaluation of the impact of communication and the related aspects to provide online frequency control support from ReGen (with special focus on WPP). The performance evaluation is based on an aggregated WPP model that is integrated into a generic power system model. This generic power system model is specifically designed to assess the ancillary services in a relatively simple yet relevant environment. Several case studies with different wind speeds at a particular wind-power penetration level and communication scenarios are considered to evaluate the performance of power system frequency response. The article provides the Transmission System Operator (TSO) and other communication engineers insights into the importance and various aspects of communication infrastructure for general service coordination between WPPs and specifically primary frequency control coordination from WPPs in future power systems.