Inteligentna sieć energetyczna, czyli smart grid (SG) to pojęcie, które zakorzeniło się już we współczesnej energetyce. Istnieje sporo szczegółowych definicji, jednakże generalnie chodzi o sieć elektroenergetyczną, której działanie polega na korelacji wielu różnych technik i technologii energetycznych.
W pracy przedstawiono stan rozwoju systemów pomiarowych w inteligentnych sieciach energetycznych. Przedstawiono wpływ uwarunkowań formalno-prawnych na rozwój AMI. Przeprowadzono analizę porównawczą Polski z pozostałymi krajami należącymi do Unii Europejskiej. Wskazano na możliwość wykorzystania linii niskiego i średniego napięcia do transmisji danych.
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In this paper are presented state of technology metering systems in Smart Grid. Are discussed influence of formal-legal factors on development AMI. Are made comparative analysis between Poland and the EU-26. Are pointed out possibility practical application of LV power line to transmission date.
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W artykule zostały omówione podstawowe wymagania normatywne stawiane częstotliwościowemu sterowaniu odbiorami w inteligentnych sieciach elektroenergetycznych niskiego napięcia na świecie. Przedstawiono zbiór wybranych dotychczasowych wdrożeń regulacji obciążenia opartej o częstotliwość napięcia zasilającego w sieciach trwale połączonych z systemem elektroenergetycznym. Omówione zostały duże wdrożenia, programy pilotażowe oraz propozycje implementacji.
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The normative requirements for the end user loads forming based on supply voltage frequency in Smart Grids were presented in the paper. Authors showed the recent implementations of regulation based on voltage frequency in interconnected networks. Big applications, pilot programs and implementation proposals were summarized.
The comprehensive evaluation of the smart grid is of great significance to the development of the power grid. This study mainly analyzed the coordinated planning of major networks and power distribution networks of the grid. Firstly, the coordinated planningof major networks and power distribution networks was introduced, then a comprehen-sive evaluation index system was established based on six domains, i.e., economy, safety, reliability, coordination, environmental protection, and automation. The evaluation of the indexes was realized through the expert scoring method. Finally, taking the power grid planning of Boao Town, Qionghai City, Hainan Province, China, as an example, the current scheme and planning scheme were evaluated. The results showed that the planning schemehad better performance in aspects such as economy and reliability, and its score was 15.39% higher than the current scheme, which verifies the effectiveness of the planning scheme andits feasible application in practical projects.
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Future demand for managing a huge number of individually operating small and often volatile energy resources within the smart grid is preponderantly answered by involving decentralized orchestration methods for planning and scheduling. Many planning and scheduling problems are of a multi-objective nature. For the single-objective case - e.g. predictive scheduling with the goal of jointly resembling a wanted target schedule - fully decentralized algorithms with self-organizing agents exist. We extend this paradigm towards fully decentralized agent-based multi-objective scheduling for energy resources e.g. in virtual power plants for which special local constraint-handling techniques are needed. We integrate algorithmic elements from the well-known S-metric selection evolutionary multi-objective algorithm into a gossiping-based combinatorial optimization heuristic that works with agents for the single-objective case and derive a number of challenges that have to be solved for fully decentralized multi-objective optimization. We present a first solution approach based on the combinatorial optimization heuristics for agents and demonstrate viability and applicability in several simulation scenarios.
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Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).
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