Proliferation Of Electric Vehicles Research Articles

A comprehensive framework for electric vehicle charging station siting along highways using weighted sum method

The proliferation of electric vehicles (EVs) has led to an increased demand for strategically located EV charging stations (EVCSs) to ensure a balanced and accessible charging network. The siting of EVCS involves a multifaceted process that includes technological, economic, social, geographical, and environmental factors. An EVCS network should deliver high-quality service, alleviate drivers' range anxiety, and be compatible with renewable energy system integration, while also accounting for technical infrastructure and future expansion expectations. Resting areas along highways, equipped with amenities and grid connections, emerge as potential sites for EVCS installation. Accordingly, this study presents an EVCS siting framework along highways, integrating expert opinions from diverse disciplines. The proposed method employs expert opinions to weigh site selection criteria and then uses a clustering-based approach to identify suitable locations for EVCS siting, utilizing the weighted sum method. According to the experts, the most important criterion is determined as the service level of the candidate point with a weight of 0.375, followed by traffic density (0.218), and proximity to connection roads (0.215). The method is tested both on a test highway and the Edirne-Ankara highway, which is the most heavily used in Türkiye. The results demonstrate that the test highway identifies 13 out of 18 optimal locations using the clustering strategy. Similarly, the siting method identifies 18 out of 31 optimal EVCS locations along the Edirne-Ankara highway ensuring the distance constraints. This approach is scalable and adaptable for application on highways in various countries where EVCS infrastructure is still developing.

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids

The proliferation of electric vehicles (EVs) and the advancement of Vehicle-to-Everything (V2X) energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This paper proposes a novel Federated Reinforcement Learning (FRL) system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of Proof of State of Charge (PoSOC) as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system.

Can you lower the thermostat? Perceptions of demand response programs in a sample from Quebec

The current push towards electrification in various sectors as a decarbonization strategy presents significant challenges for utility providers due to unintended consequences related to higher peak demand. To ensure grid reliability, utilities resort to importing costly electricity or using fossil fuels, which counteracts these decarbonization efforts. To address this issue, they introduced demand-side management strategies, known as demand-response (DR) programs, to engage customers in reducing peak demand. While commercial and industrial customers have greater load flexibility, the significance of residential buildings in managing peak demand is growing due to electrification of heating systems and the proliferation of electric vehicles (EV). However, the development of residential DR programs lags behind their potential, facing challenges such as limited customer knowledge, price elasticity, and infrastructure costs. To increase residential DR program adoption, understanding occupants' perceptions and behaviors is vital. This paper presents the results of a survey conducted in Quebec to identify participation rates, incentives, approaches, and contextual factors influencing enrolment in DR programs and participation in peak demand events. It was found that there is significant potential for expanding these programs, especially by identifying key demographic sectors that may be more likely to participate compared to the current participant breakdown. The research also uncovered contextual factors and demographic variables that statistically influenced occupant behavior during peak demand events and their impact on energy-saving actions. Notably, more than 80 % of respondents believed that having smart thermostats would facilitate their participation in DR programs. These results offer valuable insights for further development and enhancement of DR programs in the province.

Research of electric vehicle battery packs and charging stations based on an active three-phase rectifier

Problem. The proliferation of electric vehicles has underscored the need for the development of energy-efficient charging stations and systems. These systems must deliver high efficiency, maintain a power factor close to unity, adhere to electromagnetic compatibility standards, emit low levels of higher harmonics, and enable fast charging capabilities. Current electric vehicle charging devices encounter issues such as significant power losses, high harmonic emissions to the power grid, and challenges in implementing fast charging modes. Consequently, enhancing the energy efficiency of charging stations – by reducing power losses, minimizing harmonic emissions, ensuring a high power factor, and supporting modes for regulated current and voltage sources for fast charging – is a pressing concern. Goal. The aim of this work is to perform a comparative analysis of different types of batteries for electric vehicles, exemplified by TESLA cars, presenting comparative characteristics of lithium-ion, lithium-iron-phosphate, and lithium-titanate batteries. Methodology. The study showcases an evaluation of battery units for electric vehicles and outlines a comparative assessment of lithium-ion, lithium-iron-phosphate, and lithium-titanate batteries. The research focuses on enhancing the energy efficiency of electric vehicle charging station systems through the use of active rectifiers, with modes for active power factor correction and optimal configuration. Results. An examination of the quality of converters for electric vehicle charging stations was conducted, proposing schemes for a three-phase active voltage source rectifier and an active current source rectifier. Power circuitry and space-vector switching states for power transistors are introduced. Analysis concludes that an active current rectifier may be a more promising topology. Originality. A model of a charging station based on an active current source rectifier was developed using Matlab/Simulink. Transient processes of the converter's operation and the charging of a 60 kWh battery pack were examined. It was found that the active rectifier facilitates regulation of the battery charging current, achieves a power factor close to unity, and maintains a low level of higher harmonic emissions with a harmonic distortion factor of 2.52%. Practical value. The advancement of electric vehicles necessitates the ongoing development and enhancement of the energy indicators for electric vehicle batteries and the converters of charging stations, especially those that enable fast charging. Active progress is being made in each of these areas.

Ni-Ion-Chelating Strategy for Mitigating the Deterioration of Li-Ion Batteries with Nickel-Rich Cathodes

The proliferation of electric vehicles has increased exponentially over the past few years, and the ban on petrol and diesel vehicles in European nations has caused automakers to switch their primary focus toward electric vehicles. Engines, which are the most critical components of automobiles, have been replaced by motors with batteries; specifically, batteries containing Ni-rich cathodes are essential in ensuring high performances, e.g., in extending the mileage of electric vehicles. However, high-valence Ni4+, which is formed in a highly charged state in such batteries, is prone to reduction to Ni3+ and Ni2+, resulting in oxygen loss and cation mixing. In addition, residual Li species, such as LiOH and Li2CO3, induce parasitic reactions in electrolytes. Furthermore, Ni2+ dissolved from Ni-rich cathodes by acidic compounds, such as HF formed by LiPF6 hydrolysis in the electrolyte, induces structural deterioration by forming an inactive rock-salt phase and the loss of the Li storage sites of the cathode. Further, transition metals electrodeposited at the anode surface via the dissolution–migration–deposition of transition metal ions (TM-DMD) hinder the intercalation of Li+ within the anode structure, catalyze undesirable electrolyte decomposition reactions, and act as solid–electrolyte interphase (SEI) components. The electrodeposited transition metals also increase the probability of the formation of dendritic Li, which threatens battery safety. Thus far, surface coating of the cathode and forming a protective film on the cathode using functional electrolyte additives have been proposed as viable solutions to minimize transition-metal-ion dissolution from LiNi0.85Co0.1Mn0.05O2 (NCM85) cathodes. However, Ni2+, which is similar to Li+ in size, can easily penetrate the surface coating layers and cathode protective films because they should guarantee facile Li+ transport while blocking electron transfer to prevent electrolyte decomposition. Electrolyte additives that suppress HF generation or scavenge HF do not completely remove HF, and thus, the cells exhibit Ni-dissolution- and deposition-related problems.Chelation of the dissolved transition metal ions may prevent electrodeposition on the surface of the anode. However, transition-metal chelating agents can hardly be applied as electrolyte additives because they decompose electrochemically at the electrodes, resulting in shortened battery lifespans. Studies regarding chemically active separators with insoluble bipyridine (C-N) ligands and gel polymer electrolytes based on polymer matrices containing pyrrolidone (C-N-C=O) moieties as chelating functional groups have been conducted in efforts to avoid undesirable decomposition of the chelating agents at electrodes. Nevertheless, the incorporation of a chelating agent into the electrolyte without additional processing is clearly a more efficient method of capturing Ni ions from scalability and techno-economic standpoints. Further, the microquantity of chelating agent as an electrolyte additive does not cause significant changes in the rheological, chemical, or electrochemical properties of the electrolyte, which may increase the cell impedance.With the aim of enhancing cell performance, we report the use of a tricoordinate phosphorous compound, 1,2-bis(diphenylphosphino)ethane (DPPE), to provide effective donor ligands that are capable of forming complexes with Ni2+ dissolved in electrolytes, thereby preventing the electrodeposition of Ni2+ on the anode surface. Further, DPPE as a Lewis base additive can deactivate Lewis acidic PF5, which can generate corrosive HF, mitigate the damage of the SEI and cathode electrolyte interface (CEI), and alleviate PF5-driven electrolyte solvent decomposition.DPPE, as an electrolyte additive, imparted a remarkable cycling stability on a Li-ion battery (LIB)composed of an NCM85 cathode and a graphite anode. DPPE chelated Ni2+, which may occur in the electrolyte, and blocked the generation of undesirable species, which cause Ni2+ dissolution from the NCM85 cathode via the destabilization of PF5, which leads to HF generation. With the optimized binding force between Ni2+ and DPPE, dissolved Ni2+ could be effectively trapped, reducing the overpotential of lithiation of graphite caused by electrodeposited Ni. Severe structural deterioration of the NCM85 cathode, including microcracking and phase transition to the rock-salt phase, was significantly suppressed using DPPE. The results of this study will contribute to significant advances in the development of electrolyte additives, which may selectively trap transition metal ions dissolved in the electrolyte and eliminate the detrimental substances causing transition metal dissolution, thus realizing high-energy-density LIBs.

Coordinated Optimization of Emergency Response Resources in Transportation-Power Distribution Networks Under Extreme Events

The proliferation of electric vehicles (EVs) and the increasing interdependence across power distribution networks (DNs) and transportation networks (TNs) have increased the complexity and vulnerability of the two systems in extreme circumstances. As the interdependence of two infrastructures tightens over time, it is viewed as a dire necessity to strengthen the resilience of the coordinated transportation-power distribution networks (TDNs) against natural disasters. This paper constructs a coordinated optimization method of TN traffic link reversing, DN line switching, and fast charging pile management, to improve the TDN performance in the emergency response stage after disasters. A dynamic TN model and a multi-period DN model are integrated in TDN modeling to capture flow propagations and state variations among time intervals. The coordinated optimization for TDN emergency response is designed as a mixed-integer nonlinear programming (MINLP) problem with high-order objective functions and nonlinear constraints to minimize TN travel costs and DN active and reactive power shortages. An accuracy-aware adaptive piecewise linearization approach combined with Gray code-based encoding is utilized to improve the computational efficiency for solving the TDN optimization problem. Numerical simulations show that the TDN performance is enhanced by coordinating various DN and TN resources, as compared with those of separate and conventional topology controls. The proposed TDN solution method has significantly reduced the computation time for managing extreme conditions while guaranteeing the accuracy of the results as compared with those of the nonlinear model and the linearized model by uniform piecewise linearization.

A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles

The proliferation of electric vehicles (EVs) hinges upon the availability of robust and efficient charging infrastructure, notably encompassing swift and convenient solutions. Among these, dynamic wireless charging systems have garnered substantial attention for their potential to revolutionize EV charging experiences. Inductive power transfer (IPT) systems, in particular, exhibit a promising avenue, enabling seamless wireless charging through integrated pavements for EVs. This review engages in an in-depth exploration of pertinent parameters that influence the inductivity and conductivity performance of pavements, alongside the assessment of potential damage inflicted by IPT pads. Moreover, the study delves into the realm of additive materials as a strategic approach to augment conductivity and pavement performance. In essence, the review consolidates a diverse array of studies that scrutinize IPT pad materials, coil dimensions, pavement characteristics (both static and dynamic), and adhesive properties. These studies collectively illuminate the intricate dynamics of power transfer to EVs while considering potential repercussions on pavement integrity. Furthermore, the review sheds light on the efficacy of various additive materials, including metal and nanocomposite additives with an SBS base, in amplifying both conductivity and pavement performance. The culmination of these findings underscores the pivotal role of geometry optimization for IPT pads and the strategic adaptation of aggregate and bitumen characteristics to unlock enhanced performance within wireless pavements.

Future of Transport – Rise of Electric Vehicles in India

The introduction and proliferation of electric vehicles (EVs) in India signify a transformative shift in the country's automotive landscape and its commitment to sustainable transportation. This paper explores the key drivers and dynamics underpinning the rise of EVs in India, highlighting the environmental imperatives, government initiatives, technological advancements, urban challenges, global trends, and economic implications that are shaping this transition. Furthermore, it examines the challenges and opportunities associated with the adoption of EVs, such as the development of charging infrastructure and the coexistence of traditional and electric vehicle ecosystems. The paper underscores the significance of EVs in addressing pressing issues like air pollution and climate change while fostering domestic manufacturing and job creation. As India's electric vehicle market continues to evolve, it is essential to recognize its potential to redefine mobility, reduce emissions, and contribute to a more sustainable future.

A Multi-Period Charging Service Pricing Game for Public Charging Network Operators Considering the Dynamics of Coupled Traffic-Power Systems

The proliferation of electric vehicles (EVs) couples the operations of traffic systems and power systems, necessitating the interdependent traffic-power modeling to optimize the on-road EV charging decisions. In this paper, the multi-period charging service pricing interactions between multiple charging network operators (CNOs) are discussed considering the dynamics of traffic-power systems. A multi-period user equilibrium model considering mixed vehicle types is formulated to describe the possible vehicle trip transitions between different periods in urban transportation network. For the power distribution network, a convexified multi-period AC optimal power flow model is adopted for local electricity market clearing. With the dynamics of traffic-power systems, the pricing interaction between multiple CNOs is a non-cooperative game. We analyze the existence of Nash Equilibrium with fixed-point theory, and propose an iterative method based on the best response strategy to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> -Nash Equilibrium. Numerical studies based on an interdependent power-traffic system consisting of two 18-node power distribution networks and one revised Nguyen-Dupuis transportation network demonstrate that with the proposed CNOs’ charging pricing strategy, the CNOs’ service profits can increase by 1%-3% while the PDNs’ operation costs decrease about 1%-2% for the interdependent traffic-power system.

Energy management for regional microgrids considering energy transmission of electric vehicles between microgrids

As the proliferation of electric vehicles (EVs) continues to accelerate, the inherent attributes of EVs warrant meticulous consideration in the realm of energy dispatch. In order to evaluate the ability of EVs as mobile energy storage, this paper presents an energy management framework for the microgrids' online dispatch, which accounts for the spatio-temporal energy transmission of EVs between microgrids. The energy management framework contains two iterative processes: optimizing charging price and guiding charging dispatch. To sufficiently capture the uncertainties of the renewable energy and load demand, chance-constrained optimization is utilized to determine the charging price by reasonable power allocation. To achieve a continuous and efficient control policy, a normalized advantage function-deep Q learning network (NAF-DQN) is developed for EV dispatch under V2G technology. The above two processes as a coupled optimization problem are solved alternately until convergence. Numerical cases considering energy transmission of EVs between microgrids are studied to demonstrate the superiority of the proposed dispatch framework. The simulation results indicate improved computational efficiency and higher-quality solution.

Designing a transactive electric vehicle agent with customer’s participation preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner’s willingness to trade-off between savings and amenity in form of a slider, where the EV owner’s amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer’s transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed. It reveals that the V2G does not offer significant additional benefits with current battery prices, but could be promising if battery costs decline in the future.

A comparative techno-economic assessment of bidirectional heavy duty and light duty plug-in electric vehicles operation: A case study

The proliferation of electric vehicles (EVs) all around the world offers both challenges and opportunities to build a sustainable city and transportation system. Bidirectional charging capabilities at workplace charging facilities (e.g., as part of a microgrid) have made the overall economic optimization more attractive on one hand, but also more complex on the other hand. This paper investigates the cost optimization problem for bidirectional charging at a workplace microgrid connected to two different buildings to determine the optimal framework for a combination of both heavy-duty and light-duty electric vehicles (HDEV and LDEV). A deep learning-based model has been developed to forecast the 15-minute solar generation and building power consumption. Real-time travel profile data has been used to represent the temporal uncertainty of electric vehicle charging. The cost optimization problem is formulated as a Mixed Integer Programming (MIP) model which also addresses battery life degradation. Furthermore, a comprehensive economic analysis has been carried out to analyze the payback period, peak reduction, and cost savings for two different buildings at the same workplace with both on-board and off-board charger configurations. It has been found that HDEV is a better cost-effective solution in comparison to LDEV in terms of energy cost reduction and payback periods. Net metering capability leads to higher energy savings and peak reductions in most cases.

Security-Constrained Optimal Traffic-Power Flow With Adaptive Convex Relaxation and Contingency Filtering

The ongoing transportation electrification [e.g., the proliferation of electric vehicles (EVs)] strengthens the interdependence between power and transportation networks and brings tremendous operational challenges to the two critical infrastructure systems. This article proposes an N–1 security-constrained optimal traffic-power flow (SCOTPF) model that includes bilateral contingencies (closure of traffic link and outage of charging station) to coordinate the two networks toward N–1 secure and reliable. The partial user equilibrium (PUE) criterion is adopted to capture the driver’s rerouting behavior in response to the contingency. To facilitate the model tractability, we exploit an adaptive piecewise convex relaxation method based on the convex hull and McCormick envelope to derive a mixed-integer second-order cone programming (MISOCP) model and design a binding contingency identification algorithm to screen redundant contingencies. Finally, the optimal preventive operation strategy is efficiently obtained to reallocate the precontingency traffic-power flow and hence ensures the N–1 reliability and security of the coupled networks. Numerical results on two test systems validate both solution quality and computational efficiency of the SCOTPF. Meanwhile, interdependence between the two networks in contingency scenarios is thoroughly analyzed.

Pricing for Electric Vehicle Charging Stations Based on the Responsiveness of Demand

Electric vehicles (EVs) have the promising potential to be effective in mitigating greenhouse gas emissions in the transportation sector. Hence, the penetration of EVs in some countries experiences steady growth. However, the proliferation of EVs can cause negative impacts on the power distribution network (PDN) and the transportation network (TN). Thus, in this paper, a dynamic pricing strategy for electric vehicle charging stations (EVCSs) is proposed to facilitate EVCSs to earn higher profits while alleviating the potential negative impacts on both PDN and TN. First, a pricing strategy, considering the competition effect established based on the TN, is formulated to facilitate the EVCS to attract the defined competitive charging demand. Second, a two-step approach is proposed to mathematically formulate the responsiveness of demand towards the charging price. Third, EV user behaviors are incorporated based on both an admission control scheme and a queueing model to further adjust the charging demand. We have conducted simulations to verify the effectiveness of the pricing strategy in a PDN and TN coupled system, which contains approximately 2000 EVs. Results show that the proposed pricing strategy can facilitate the EVCS to gain 14.2% more net charging profit on average compared with the other three cases. Moreover, the operational stability of the EVCS can be ensured because the proposed pricing strategy can result in the least queueing length volatility with moderate profit variance compared with the other three cases. Furthermore, the proposed pricing strategy can achieve spatial load shifting by incentivizing EVs to alter their station-selection decisions to avoid possible power congestion in the electricity network.

Adaptive Integrated Planning of Electricity Networks and Fast Charging Stations Under Electric Vehicle Diffusion

The proliferation of electric vehicles (EVs) is an inevitable trend to realize transportation electrification. However, the increasing charging demand of EVs also brings challenges to future smart grids. To cope with the EV diffusion, this paper presents an adaptive fast charging station (FCS) strategy that considers the integrated network planning and quality of service (QoS) guarantee. First, based on the EV diffusion model and the proposed EV-integrated traffic assignment model, the charging demands at different regions and stages are forecasted. Based on the predicted spatial and temporal EV charging demand, a multistage stochastic distribution expansion planning model is presented, where the distribution network assets and FCSs are jointly considered. To guarantee the quality of the fast-charging services under EV diffusion, a QoS assessment is conducted based on the average waiting time and the average number of dissatisfied leaves. Finally, the proposed planning model allows several alternatives at both the distribution and transmission levels. The cost of electricity network expansion planning is considered as the adaptive cost arisen from the EV diffusion and the increase of the other electrical loads. We aim to find a planning strategy that can adapt to different penetration levels of EVs. The proposed framework and methodology are verified on the IEEE 24-bus power system encompassing 6 IEEE 33-bus distribution networks. It is found out that the proposed strategy shows superiority in both QoS and total costs.

Analysis and Sizing of Charging Stations in Kota City

This paper focuses on optimization of charging station capacities and locations. The proliferation of electric vehicles is inextricably linked to the deployment of charging infrastructure. Currently, there are issues with locating and sizing when it comes to the building of electric vehicle charging stations. In order to find out the capacity of charging stations, a mathematical model was developed, which includes a site survey, EV density, electricity demand analysis, load modeling, costing analysis, EV battery charging time and power quality. The proposed approach was tested in MATLAB/Simulink environment.

Electric Vehicle Owners’ Perception of Remanufactured Batteries: An Empirical Study in China

The proliferation of electric vehicles (EVs) globally is remarkable progress in strides toward a low carbon society. However, the volume of end-of-life EV batteries will hit a critical mass in the future. Widespread adoption of EV battery remanufacturing is essential in achieving higher resource efficiency. The current study investigated Chinese EV owners’ perceptions of remanufactured EV batteries: 420 respondents in China who own and drive EVs participated in the survey. This study modeled respondents’ acceptance, purchase intention, and willingness-to-pay for remanufactured EV batteries by adapting the structural equation model (SEM). The results showed that consumers’ price consciousness and perceived benefits both directly influence their purchase intention of remanufactured batteries. Unlike previous studies, this study found that consumers’ perceived risks on remanufactured batteries do not directly influence their purchase intention. Instead, the influence of perceived risks on purchasing behavior is mediated by perceived benefits. The study also found that purchase intention affects willingness to pay and acceptance of remanufactured batteries. Drawing on our study results, this research suggests measures to promote markets for remanufactured EV batteries and provides corporate marketing options to accelerate proliferation of remanufactured batteries.

Techno-economic modelling for energy cost optimisation of households with electric vehicles and renewable sources under export limits

With the proliferation of electric vehicles (EVs), EV charging cost will become an integral part of household energy cost. This research proposes a novel household energy cost optimisation method for grid-connected homes with EV under power export constraints. It addresses the limitations of previous studies by incorporating more realistic variable EV charging characteristics, power export limits, degradation of battery energy storage (BES) and battery salvage revenue into a comprehensive techno-economic energy system model. Cost optimisation results are presented for four system configurations using relatively new time-of-use (ToU) tariff and real load and photovoltaic (PV) generation data for South Australian households. Sensitivity analysis for annual energy cost (AEC) is conducted by varying daily household load demand, PV/BES capacity, power export limits and PV/BES cost. Power flow and peak demand analyses are presented to the impacts of PV, BES and EV on household demand. Results show that PV with BES and EV is the most economical configuration for individual households, where the AEC can be reduced by up to 39.6% compared to a normal household without EV, PV and BES. The BES can effectively reduce household power and energy demands during peak periods by up to 80.4% and 89.1%, respectively.

Transformer-Based Model for Electrical Load Forecasting

Amongst energy-related CO2 emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.