Figure - available from: Energy Informatics
This content is subject to copyright. Terms and conditions apply.
Source publication
The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of fle...
Similar publications
Although the increasing demand due to the electrification of other sectors, such as transportation and heating due to the transition towards carbon-neutral energy supply, presents challenges to grid operators, it also offers high flexibility potential in grid operation. Therefore, the distribution system operators must attempt to exploit these sect...
Citations
... It often disrupts the balance between supply and demand due to the intermittent and uncertainty of renewable energy output [6]. Prosumers, which have been observed and analyzed in multiple works such as [7,8], play a dual role but do not contribute positively to energy production planning due to their unpredictable power flow. Additionally, the presence of large and small energy storage systems that are being analyzed in different works further complicates production planning [9,10]. ...
The increasing complexity of modern power systems due to the integration of prosumers, renewable energy sources, and energy storage, has significantly complicated system organization and planning. Traditional centralized power plants are being replaced by decentralized structures, making the power flow more complex to predict. As a result, alternative methodologies for power system planning are imminent. This paper introduces a novel approach using a combination of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for forecasting system states. Here, ANN model predicts energy consumption, while the ANFIS model forecasts thermal and hydro power plant production as well as CO2 emissions. The accuracy of these models results from leveraging the collective expertise of power system planning professionals, utilizing extensive databases containing hourly data from measurements in Serbian power systems. These datasets encompass hourly production data from various energy sources, energy consumption patterns, and relevant environmental parameters (such as temperature, wind speed, and solar irradiation). To underscore the effectiveness of the proposed ANN model, predictions of power consumption from ANN are compared with predictions from ARIMA (autoregressive integrated moving average) model. The developed forecasting models are employed to predict annual and daily energy consumption, seasonal variations in thermal and hydro production, and annual CO2 emissions. The dependencies between power consumption/production and ambient parameters are visually depicted in three-dimensional representations. Model accuracy is evaluated through graphical, numerical, and error-based analyses across four distinct error metrics. By utilizing historical data and expert insights from previous production scheduling, these models enhance the precision of future production scheduling decisions. This approach minimizes human error, maximizes the utilization of human expertise, and establishes a framework for effectively planning large-scale power systems. The primary contribution of this research lies in the integration of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies. This combined approach minimizes the errors inherent in each individual methodology while leveraging their respective advantages. Specifically, the consumption prediction error achieved is 5.64%. When ANFIS is utilized with a training database based on ANN consumption prediction, the prediction error for CO2 emissions is 1.27%.
