Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters.•From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised.•Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.•Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.

Virtual power plant for energy management: Science mapping approach

A bibliometric analysis was conducted to examine the trends and developments in the field of Virtual Power Plants (VPPs) from 2000 to June 2022. The selection and identification of data involved a systematic search resulting in 1245 articles for bibliometric analysis after applying the inclusion and exclusion criteria. Strategic diagrams, overlay graphs, and evolution maps were used to analyze the trends and themes in different periods. The analysis reveals the emergence and evolution of various themes and their interconnections. In the early periods, the focus was on energy market issues, distributed generation, and the control of Distributed Energy Resources. Themes such as microgrids, renewable energy, electric vehicles, and economic analysis have gained prominence over time. The present study also identified the introduction of new concepts such as prosumers, collaborative networks, and dynamic power plants in later periods. The performance analysis for the last period (2022) highlighted the centrality and density of themes such as power plants, renewable power plants, battery energy storage systems, and robust optimization. These themes are considered both fundamental and transverse in the research field. This study discusses the importance of VPPs and battery energy storage systems in addressing grid intermittency issues and providing auxiliary market services. The analysis also emphasized the management of the demand side and the integration of electric vehicles and Building Energy Management Systems in VPPs. Therefore, future directions for VPP research include innovative structures and topologies, big-data analysis, and diversified optimization techniques. This study provides insights into the evolution and current state of research in the field of VPPs and identifies areas for further exploration and development.

What is the role of distributed energy resources under scenarios of greenhouse gas reductions? A specific focus on combined heat and power systems in the industrial and commercial sectors

Combined heat and power (CHP) is promoted as an economical, energy-efficient option for reducing air emissions, mitigating carbon emissions and reducing reliance on grid electricity. However, its potential benefits have only been analyzed within the context of the current energy system. To fully examine the viability of CHP as a clean-technology alternative, its growth must be analyzed considering how the energy sector may transform under the influence of various technological and policy drivers that are specifically geared toward limiting greenhouse gas (GHG) emissions. Scenarios were developed through a bottom-up technology model of the U.S. energy system to determine the impacts on CHP development and both system-wide and sectoral GHG and air pollutant emissions. Various scenarios were considered, from CO₂ emissions reductions in the electric generating units (EGU) sector to GHG reductions across the whole energy system while considering levels of CHP investment. The largest CHP investments were observed in scenarios that limited CO₂ emission from the EGU sector alone. The investments were scaled back in the scenarios that incorporated energy system level GHG reductions. The energy system level reduction scenarios yielded rapid transformation of the EGU sector towards zeroemissions technologies as reliance on electricity increases with the electrification of the many end-use sectors such as buildings, transportation and industrial sectors, reducing investment in CHP. The prime mover and fuel choice heavily influenced the air pollutant emissions resulting in trade-offs among pollutants including GHG emissions. The results suggest that CHP could play a role in a future low-carbon energy system, but that role diminishes as carbon reduction targets increase.