A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

Estimation Of Idle Time Using Machine Learning Models For Vehicle-To-Grid (V2G) Integration And Services

As the Electric Vehicles (EVs) market continues to expand, ensuring the access to charging stations remains a significant concern. This work focuses on addressing multiple challenges related to EV charging behavior and Vehicle-to-Grid (V2G) services. Firstly, it focuses on accurate minute-ahead (20 minute \& 30 minute intervals) load forecasts for an EV charging station by using four years of historical data, from 2018-2021. This data is recorded from a university campus garage charging station. Machine Learning (ML) models such as Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Random Forest (RF), and Neural Networks (NN) are employed for load forecasts in terms of Kilowatt hour (kWh) delivered from 54 charging stations. Preliminary results indicate that RF method performed better compared to other ML approaches, achieving a average Mean Absolute Error (MAE) of 7.26 on historical weekdays data. Secondly, it focuses on estimating the probability of aggregated available capacity of users for V2G connections, which could be sold back to the grid through V2G system. To achieve this, an Idle Time (IT) parameter was tracked from the time spent by the EV users at the charging station after being fully charged. ML classification methods such as Logistic Regression (LR) and Linear Support Vector Classifier (SVC) were employed to estimate the IT variable. The SVC model performed better in estimating IT variable with an accuracy of 85% over LR 81%. This work also analyzes the aggregated excess kWh available from the charging stations for V2G services, which offer benefits to both EV owners through incentives and the grid by balancing the load. ML models, including Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), Long-Short Term Memory (LSTM), and Random Forest (RF), are employed. LSTM performs better for this prediction problem with a Mean Absolute Percentage Error (MAPE) of 3.12, and RF as second best with lowest 3.59, when considering historical data on weekdays. Furthermore, this work estimated the number of users available for V2G services corresponding to 15\% and 30\% of excess kWh, by using ML classification models such as Decision Tree (DT) and K Nearest Neighbor (KNN). Among these models, DT performed better, with highest 89% and 84% accuracy respectively. This work also investigated the impact of the COVID-19 pandemic on EV users\u27 charging behavior. This study analyzes the behavior modelled as before, after, and during COVID-19, employing data visualization using K-means and hierarchical clustering methods to identify common charging pattern with connection and disconnection time of the vehicles. K-means clustering proves to be more effective in all three scenarios modeled with a high silhouette index. Furthermore, prediction of collective charging session duration is achieved using ML Models, RF and XgBoost which achieved a MAPE of 14.6% and 15.1% respectively

EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition.

As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence-based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R2 value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition

Smart and sustainable scheduling of charging events for electric buses

This paper presents a framework for the efficient management of renewable energies to charge a fleet of electric buses (eBuses). Our framework starts with the prediction of clean energy time windows, i.e., periods of time when the production of clean energy exceeds the demand of the country. Then, the optimization phase schedules charging events to reduce the use of non-clean energy to recharge eBuses while passengers are embarking or disembarking. The proposed framework is capable of overcoming the unstable and chaotic nature of wind power generation to operate the fleet without perturbing the quality of service. Our extensive empirical validation with real instances from Ireland suggests that our solutions can significantly reduce non-clean energy consumed on large data setsThis work received funding from the Sustainable Energy Authority of Ireland (SEAI) Research, Development and Demonstration (RDD) 2019 programme under the grant number 19/ RDD/51

Seasonality effect analysis and recognition of charging behaviors of electric vehicles: A data science approach

Electric vehicles (EVs) presence in the power grid can bring about pivotal concerns regarding their energy requirements. EVs charging behaviors can be affected by several aspects including socio-economics, psychological, seasonal among others. This work proposes a case study to analyze seasonal effects on charging patterns, using a public real-world based dataset that contains information from the aggregated load of the total charging stations of Boulder, Colorado. Our approach targets to forecast and recognize EVs demand considering seasonal factors. Principal component analysis (PCA) was used to provide a visual representation of the variables and their contribution and the correlation among them. Then, twelve classification models were trained and tested to discriminate among seasons the charging load of electric vehicles. Later, a benchmark stage is presented for regression as well as for classification results. For regression models, examined through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the random Forest provides better prediction than quasi-Poisson model widely. However, it was observed that for large variations in electric vehicles’ charging load, quasi-Poisson fits better than random forest. For the classification models, evaluated through Accuracy and the Area under the Curve, the Lasso and elastic-net regularized generalized linear (GLMNET) model provided the best global performance with accuracy up to 100% when evaluated on the test dataset. The results of this work offer great insights for enhancing demand response strategies that involve PEV charging regarding charging habits across seasons

Electric Vehicle - Smart Grid Integration: Load Modeling, Scheduling, and Cyber Security

The modern world has witnessed the surge of electric vehicles (EVs) driven by government policy worldwide to reduce transportation’s dependence on fossil fuels. According to (Slowik, 2019), the global EV market has grown sharply with the annual light-duty EV sales surpassing 2 million in 2018, which is about a 70% increase from 2017. The increase in EV population implies the rise in energy demand, and that introduces new challenges to the electricity sector. EV charging load demand in high penetration scenarios, which is foreseen, may lead to stability and quality issues in power grids. Generation capacity and the electricity infrastructure upgrade may be required to address those issues; however, it increases generation costs significantly. The most common EV chargers installed today deliver around 7 kW of power, which is over four times that of an average household power consumption in the US. EV charging load often shows two peaks in a day, one in the morning when people plug in the EV at the workplace and the other in the evening when people get home from work. Without proper energy management for EV charging, the vast power demand due to a large number of plugged-in EVs can stress the electric grid, degrade the electric power quality, and impact the wholesale electricity market. Although an EV battery may store energy up to 80 kWh, which requires more than 10 hours to charge at 7kW from empty, we found that most EVs need only 12 kWh per charge or 1.7 hours at 7 kW to meet daily commute requirement while they stay in the parking garage for a more extended period. This implies that EVs can have considerable time-flexibility for charging, and it is not necessary to start chargingright after plugging in, which is likely to result in the charging power add-up. A proper EV charging schedule can well allocate the charging load to prevent power peaks. Therefore, EV charging scheduling can play a significant role in mitigating the adverse effects of vast EV charging demand without upgrading the power grid capacity.To optimize the EV charging schedule while satisfies EVs’ charging demand, each EV’s stay duration and energy need are essential parameters for the optimization. Those parameters are based on predictions to minimize human intervention. Nonetheless, the uncertainty of EV user behavior poses a challenge to the prediction accuracy. Therefore, this dissertation demonstrates an ensemble machine learning-based method to model and predict the EV loads accurately, thereby improving the performance of EV charging scheduling.On the other hand, this smart EV-grid integration, which requires massive communication, including collecting, transmitting, and distributing real-time data within the network, makes it more susceptible to cyber-physical threats. Potential breaches could not only affect grid operation but also reduce consumers’ willingness to adopting EVs over conventional fuel-powered vehicles. This dissertation also presents the vulnerability analysis and risk assessment for a smart EV charging system to develop the countermeasures to secure the network. Also, while it is inevitable that the security has flaws, this dissertation provides a novel anomaly detection approach based on the invariant correlations of different measurements within the EV charging network