Dataset Information

Mind the goal: Trade-offs between flexibility goals for controlled electric vehicle charging strategies.

ABSTRACT: Electrification is one of the main decarbonization strategies for transportation. While uncontrolled electric vehicle (EV) charging can challenge the electricity system, controlled EV charging can offer flexibility. Using an agent-based model, we simulate combinations of two elements of EV charging, plug-in behaviors and controlled-charging processes, and measure flexibility goals with four metrics: total load shift, increase in midday load, peak reduction, and flatness of the load curve. We reveal trade-offs between these flexibility goals, which indicate that the most beneficial combinations are specific to spatial areas and their flexibility goals. Furthermore, we find that controlled-charging processes show higher impact on the flexibility metrics than plug-in behaviors, particularly with high EV and charging-station diffusion, but less so in rural areas. Incentivizing beneficial combinations can increase the flexibility potential of EV charging and potentially avoid grid reinforcements.

SUBMITTER: Gschwendtner C

PROVIDER: S-EPMC9947308 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mind the goal: Trade-offs between flexibility goals for controlled electric vehicle charging strategies.

Gschwendtner Christine C Knoeri Christof C Stephan Annegret A

iScience 20230109 2

Electrification is one of the main decarbonization strategies for transportation. While uncontrolled electric vehicle (EV) charging can challenge the electricity system, controlled EV charging can offer flexibility. Using an agent-based model, we simulate combinations of two elements of EV charging, plug-in behaviors and controlled-charging processes, and measure flexibility goals with four metrics: total load shift, increase in midday load, peak reduction, and flatness of the load curve. We rev ...[more]

PMID: 36844453

Similar Datasets

Project description:Palm oil (PO) is an important source of livelihood, but unsustainable practices and widespread consumption may threaten human and planetary health. We reviewed 234 articles and summarized evidence on the impact of PO on health, social and economic aspects, environment, and biodiversity in the Malaysian context, and discuss mitigation strategies based on the sustainable development goals (SDGs). The evidence on health impact of PO is equivocal, with knowledge gaps on whether moderate consumption elevates risk for chronic diseases, but the benefits of phytonutrients (SDG2) and sensory characteristics of PO seem offset by its high proportion of saturated fat (SDG3). While PO contributes to economic growth (SDG9, 12), poverty alleviation (SDG1, 8, 10), enhanced food security (SDG2), alternative energy (SDG9), and long-term employment opportunities (SDG1), human rights issues and inequities attributed to PO production persist (SDG8). Environmental impacts arise through large-scale expansion of monoculture plantations associated with increased greenhouse gas emissions (SDG13), especially from converted carbon-rich peat lands, which can cause forest fires and annual trans-boundary haze; changes in microclimate properties and soil nutrient content (SDG6, 13); increased sedimentation and change of hydrological properties of streams near slopes (SDG6); and increased human wildlife conflicts, increase of invasive species occurrence, and reduced biodiversity (SDG14, 15). Practices such as biological pest control, circular waste management, multi-cropping and certification may mitigate negative impacts on environmental SDGs, without hampering progress of socioeconomic SDGs. While strategies focusing on improving practices within and surrounding plantations offer co-benefits for socioeconomic, environment and biodiversity-related SDGs, several challenges in achieving scalable solutions must be addressed to ensure holistic sustainability of PO in Malaysia for various stakeholders.Supplementary informationThe online version contains supplementary material available at 10.1007/s11625-021-01052-4.

Project description:Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Dataset Information

Mind the goal: Trade-offs between flexibility goals for controlled electric vehicle charging strategies.

Publications

Mind the goal: Trade-offs between flexibility goals for controlled electric vehicle charging strategies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets