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The global electric vehicle (EV) market is experiencing exponential growth, driven by technological advancements, environmental awareness, and government incentives. As EV adoption accelerates, it introduces opportunities and challenges for power systems worldwide due to the large battery capacity, uncertain charging behaviors of EV users, and seasonal variations. This could result in significant peak–valley differences in load in featured time slots, particularly during winter periods when EVs' heating systems use increases. This paper proposes three future charging strategies, namely the Overnight, Workplace/Other Charging Sites, and Overnight Workplace/Other Charging Sites, to reduce overall charging in peak periods. The charging strategies are based on predicted load utilizing a hybrid machine learning (ML) approach to reduce overall charging in peak periods. The hybrid ML method combines similar day selection, complete ensemble empirical mode decomposition with adaptive noise, and deep neural networks. The dataset utilized in this study was gathered from 1000 EVs across nine provinces in Canada between 2017 and 2019, encompassing charging loads for thirty-five vehicle models, and charging locations and levels. The analysis revealed that the aggregated charging power of EV fleets aligns and overlaps with the peak periods of residential buildings energy consumption. The proposed Overnight Workplace/Other Charging Sites strategy can significantly reduce the Peak-to-Average Ratio (PAR) and energy cost during the day by leveraging predictions made three days in advance. It showed that the PAR values were approximately half those on the predicted load profile (50% and 51%), while charging costs were reduced by 54% and 56% in spring and winter, respectively. The proposed strategies can be implemented using incentive programs to motivate EV owners to charge in the workplace and at home during off-peak times. [ABSTRACT FROM AUTHOR]

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combination of the similar day (SD) selection, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and deep neural networks is proposed and explored in this paper. For the SD selection, an extreme gradient boosting (XGB)-based weighted k-means method is chosen and applied to evaluate the similarity between the prediction and historical days. The CEEMDAN algorithm, which is an advanced method of empirical mode decomposition (EMD), is used to decompose original data, to acquire intrinsic mode functions (IMFs) and residuals, and to improve the noise reduction effect. Three popular deep neural networks that have been utilized for load predictions are gated recurrent units (GRUs), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The developed models were assessed on a real-life charging load dataset that was collected from 1000 EVs in nine provinces in Canada from 2017 to 2019. The obtained numerical results of six predictive combination models show that the proposed hybrid SD-CEEMDAN-BiLSTM model outperformed the single and other hybrid models with the smallest forecasting mean absolute percentage error (MAPE) of 2.63% Canada-wide. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems are subject to extensive research around the world and different designs have found their way to the market and have been commercialized. These systems usually employ multiple components, both renewable and conventional, combined in a way to increase the system's overall efficiency and resilience and to lower GHG emissions. In this paper, a hybrid renewable energy system was designed for residential use and its annual energy performance was investigated and optimized. The multi-module hybrid system consists of a Ground-Air Heat Exchanger (GAHX), Photovoltaic Thermal (PVT) panels and Air to Water Heat Pump (AWHP). The developed system's annual performance was simulated in the TRaNsient SYStem (TRNSYS) environment and optimized using the General Algebraic Modelling System (GAMS) platform. Multi-objective non-linear optimization algorithms were developed and applied to define optimal system design and performance parameters while reducing cost and GHG emissions. The results revealed that the designed system was able to satisfy building thermal heating/cooling loads throughout the year. The ground source heat exchanger contributed 21.3% and 26.3% of the energy during heating and cooling seasons, respectively. The initial design was optimized in terms of key performance parameters and module sizes. The annual simulation analysis showed that the system was able to self-generate and meet nearly 29.4% of the total HVAC electricity needs, with the rest being supplied by the grid. The annual system module performance efficiencies were 13.4% for the PVT electric and 5.5% for the PVT thermal, with an AWHP COP of 4.0. [ABSTRACT FROM AUTHOR]

Globally there are several viable sources of renewable, low-temperature heat (below 130 °C), particularly solar energy, geothermal energy, and energy generated from industrial wastes. Increased exploitation of these low-temperature options has the definite potential of reducing fossil fuel consumption with its attendant very harmful greenhouse gas emissions. Researchers have universally identified the organic Rankine cycle (ORC) as a practicable and suitable system to generate electrical power from renewable sources based on its beneficial usage of volatile organic fluids as working fluids (WFs). In recent times, researchers have also shown a preference towards deployment of zeotropic mixtures as ORC WFs because of their capacity to improve thermodynamic performance of ORC systems, a feat enabled through the greater matching of the temperature profiles of the WF and the heat source/sink. This paper demonstrates the thermodynamic, economic and sustainability feasibility, and the notable advantages of using zeotropic mixtures as WFs through a simulation study of an ORC system. The study examines first the thermodynamic performance of ORC systems using zeotropic mixtures to generate electricity powered by a low-temperature solar heat source for building applications. A thermodynamic model is developed with a solar-driven ORC system both with and excluding a regenerator. Twelve zeotropic mixtures with varying compositions are evaluated and compared to identify the best combinations of mixtures that can yield high performance and high efficiency in their system cycles. The study also examines the effects of the volume flow ratio, and evaporation and condensation temperature glides on the ORC's thermodynamic performance. Following a detailed analysis of each mixture, R245fa/propane and butane/propane are selected for parametric study to investigate the influence of operating parameters on the system's efficiency and sustainability index. For zeotropic mixtures, results disclosed that there is an optimal composition range within which binary mixtures are inclined to perform more efficiently than the component pure fluids. In addition, a substantial enhancement in cycle efficiency can be obtained by a regenerative ORC, with cycle efficiency ranging between 3.1–9.8% and 8.6–17.4% for ORC both without and with regeneration, respectively. Results also revealed that exploiting zeotropic mixtures could enlarge the limitation experienced in selecting WFs for low-temperature solar ORCs. Moreover, a detailed economic with a sensitivity analysis of the solar ORC system was performed to evaluate the cost of the electricity and other economic criteria. The outcome of this investigation should be useful in the thermo-economic feasibility assessments of solar-driven ORC systems using working fluid mixtures to find the optimum operating range for maximum performance and minimum cost. [ABSTRACT FROM AUTHOR]