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This work is the first part of a study that aims to analyze the technical and economic implications of the penetration of wind power in the Colombian Interconnected system (SIN). Using the software DigSilent™, studies of steady state, contingencies and short-circuit were conducted in order to identify problems of stress, overload, voltage profiles, transmission bottlenecks and the most neuralgic elements for the operation of the proposed wind parks. Additional electrical studies, such as stability, power quality, voltage ride through capability, and the development of some indicators associated with the penetration of wind power in Colombia, will be addressed on a second part of this work.
The control of mechanical power in wind turbogenerators involves the participation of many subsystems. One of them is related to pitch mechanism, which usually employs conventional PI strategies. This paper presents an alternative for estimating the parameters of this PI controller, using Fuzzy Logic (FL). Matlab-Simulink™ software was used in order to verify the performance of the FL-based parameter estimator in some general cases.
Due to an increasing share of renewable energy sources the balancing of energy production and consumption is getting a lot of interest considering future smart grids. In this context, many investigations on demand-response programs are being conducted to achieve flexibility from different energy storages and loads. As space heating is an important schedulable load for flexibility simulations, there are different modelling approaches due to its interdisciplinary nature. Models can be built from the civil engineering or electrical engineering point of view, depending on the computational expense and accuracy level. Scheduling optimizations need a lot of simulations, preferably with computationally light models. Thus, this work will use a computationally light neural network load prediction model for space heating which is based on a detailed civil engineering model. Simulations with different scheduling times were conducted to see the long- and short-term effects of the demand response action. Results show, that applying the same demand response action at different times results in different behaviors of the system resp. energy consumption, which requires further studies for developing optimized scheduling methods.
The widespread implementation of smart meters (SM) and the deployment of the advanced metering infrastructure (AMI) provide large amounts of fine-grained data on prosumers. Machine learning (ML) algorithms are used in different techniques, e.g. non-intrusive load monitoring (NILM), to extract useful information from collected data. However, the use of ML algorithms to gain insight on prosumer behavior and characteristics raises not only numerous technical but also legal concerns. This paper maps electricity prosumer concerns towards the AMI and its ML based analytical tools in terms of data protection, privacy and cybersecurity and conducts a legal analysis of the identified prosumer concerns within the context of the EU regulatory frameworks. By mapping the concerns referred to in the technical literature, the main aim of the paper is to provide a legal perspective on those concerns. The output of this paper is a visual tool in form of a table, meant to guide prosumers, utility, technology and energy service providers. It shows the areas that need increased attention when dealing with specific prosumer concerns as identified in the technical literature.
Balancing the energy production and consumption is a huge challenge for future smart grids. In this context, many demand-side management programs are being developed to achieve flexibility from different loads like space heating. As space heating models for flexibility simulations are an interdisciplinary field of work, complex civil engineering thermal models need to be combined with complex electrical engineering control simulations in different software frameworks. Traditionally used methods have shortcomings in one of those two domains as the publications that provide complex control strategies for demand response are lacking complex thermal models and vice versa. Co-simulations overcome this problem but are computationally expensive and have compatibility limitations. Thus, the aim of this work is to develop a methodology for designing space heating/cooling models, intended for positive energy district- or smart city simulations, which provide high accuracy at low computational expense. This could be achieved by synthesizing neural network object models from IDA-ICE civil engineering models in Matlab. These machine learning models showed improvements of more than 30% in different error metrics and a simulation time reduction of more than 80% compared to other methods, making them suitable for use in microgrid simulations, including flexibility analyses.
Bei dem Verbundvorhaben IREN2 (Zukunftsfähige Netze für die Integration Regenerativer Energiesysteme), das im Rahmen der Förderinitiative "zukunftsfähige Netze" durchgeführt wurde, lag der Fokus auf der anwendungsorientierten Forschung und Entwicklung auf dem Gebiet "Intelligenter Verteilnetze". Es wurden Verfahren und Konzepte erarbeitet, wie Verteilnetze mit hohem Anteil an regenerativer Energieerzeugung als inselfähige Microgrids stabil und zuverlässig betrieben werden können.
Microgrids in island mode with high penetration of renewable energy sources in combination with gensets and battery storage systems need a control system for voltage and frequency. In this study the main goal is maximization of the energy feed-in by renewable sources. Therefore it is necessary to keep the State of Energy for the Battery Storage System in a range that the excess energy can be absorbed and used in a later period of the day. In this paper an approach for State of Charge scheduling based on load and generation prediction is described.