Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community

Due to the wide-spread use of photovoltaic (PV) systems, interest regarding economic benefit and energy-sharing from surplus electricity has been raised. In this study, decentralized thermal and electric convergence energy prosumers for an energy-sharing community are proposed. For the convergence energy system, a simultaneous heating and cooling heat pump (SHCHP) system integrated with a thermal network is proposed, and the energy performance and operating energy savings of the proposed system were investigated. A smart village located in the Busan Eco Delta Smart City was selected as a case study for the simulation analysis. Experimental data of the heat pump system were used to analyze the SHCHP. The analysis results showed that the proposed system could provide over 53% and 86% of the load cover and supply cover factors, respectively. The proposed system can earn economic benefits, such as energy trading from the surplus electricity of PV systems and thermal energy produced by an SHCHP, more than 30.5 times those of conventional air-source heat pump systems. These benefits mainly originate that a conventional system can trade the surplus electricity from a PV system but the proposed system can trade produced thermal energy from SHCHPs and the surplus electricity from PV systems.

Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

Predictive Analytics for Maintaining Power System Stability in Smart Energy Communities

Digitalization and decentralization of energy supply have introduced several challenges to emerging power grids known as smart grids. One of the significant challenges, on the demand side, is preserving the stability of the power systems due to locally distributed energy sources such as micro-power generation and storage units among energy prosumers at the household and community levels. In this context, energy prosumers are defined as energy consumers who also generate, store and trade energy. Accurate predictions of energy supply and electric demand of prosuemrs can address the stability issues at local levels. This study aims to develop appropriate forecasting frameworks for such environments to preserve power stability. Building on existing work on energy forecasting at low-aggregated levels, it asks: What factors influence most on consumption and generation patterns of residential customers as energy prosumers. It also investigates how the accuracy of forecasting models at the household and community levels can be improved. Based on a review of the literature on energy forecasting and per- forming empirical study on real datasets, the forecasting frameworks were developed focusing on short-term prediction horizons. These frameworks are built upon predictive analytics including data col- lection, data analysis, data preprocessing, and predictive machine learning algorithms based on statistical learning, artificial neural networks and deep learning. Analysis of experimental results demonstrated that load observa- tions from previous hours (lagged loads) along with air temperature and time variables highly affects the households’ consumption and generation behaviour. The results also indicate that the prediction accuracy of adopted machine learning techniques can be improved by feeding them with highly influential variables and appliance-level data as well as by combining multiple learning algorithms ranging from conventional to deep neural networks. Further research is needed to investigate online approaches that could strengthen the effectiveness of forecasting in time-sensitive energy environments.