The growing penetration of Renewable Energy Sources (RES) due to the transition to future smart grid requires a huge number of power converters that participate in the power flow. Each of these devices needs the use of a complex control and communication system, thus a platform for testing real-life scenarios is necessary. Several test techniques have been so far proposed that are subject to a trade-off between cost, test coverage, and test fidelity. This paper presents an approach for testing microgrids, by developing an emulator, with emphasis on the micro-inverter unit and the possibility of flexible configuration for different grid topologies. In contrast to other approaches, our testbed is characterized by small volume and significantly scaled-down voltages for safety purposes. The examination is concentrated specifically on the inverter behavior. The test scenarios include behaviors in case of load changes, transition between grid-tied and islanded mode, connection and removal of subsequent inverters, and prioritization of inverters.
In modern Electric Power Systems, emphasis is placed on the increasing share of electricity from renewable energy sources (PV, wind, hydro, etc.), at the expense of energy generated with the use of fossil fuels. This will lead to changes in energy supply. When there is excessive generation from RESs, there will be too much energy in the system, otherwise, there will be a shortage of energy. Therefore, smart devices should be introduced into the system, the operation of which can be initiated by the conditions of the power grid. This will allow the load profiles of the power grid to be changed and the electricity supply to be used more rationally. The article proposes an elastic energy management algorithm (EEM) in a hierarchical control system with distributed control devices for controlling domestic smart appliances (SA). In the simulation part, scenarios of the algorithm’s operation were carried out for 1000 households with the use of the distribution of activities of individual SAs. In experimental studies, simplified results for three SA types and 100 devices for each type were presented. The obtained results confirm that, thanks to the use of SAs and the appropriate algorithm for their control, it is possible to change the load profile of the power grid. The efficacious operation of SAs will be possible thanks to the change of habits of electricity users, which is briefly described in the article.
Artificial intelligence is used in many different aspects of life these days. It is also increasingly used to create intelligent embedded devices. Their main task is to demonstrate intelligent features using limited hardware resources. This paper introduces the possibility of using AI on the Edge as a system to warn of low voltage on the battery in a stand-alone metering station powered by solar panels. The paper presents theoretical knowledge about artificial intelligence on the Edge, time series and algorithms in time series forecasting. In addition, a practical approach is presented in the Approach section. It also describes the stand-alone measurement station that was used, what type of data was collected and used, and what type of the time series forecasting algorithm was used. The main advantage of the approach is the practical use of Holt’s Linear Trend method in battery voltage forecasting. Work on the project is still in progress therefore the results presented in the paper are generated during simulation.
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