Resilience Assessment of Hydrogen Integrated Energy System for Airport Electrification

In recent years, the idea of green aviation and environmental protection has received increasing attention from the aviation industry. Hydrogen energy has an important role in the transition to low-carbon energy systems. To address that, this article conducts the technoeconomic analysis for the hydrogen energy system, photovoltaic energy, battery storage system, electric auxiliary power unit (APU) of aircraft, and electric vehicles (EVs) into the electrified airport energy system. Specifically, the model quantifies aircraft electrical load based on passenger' travel behavior, establishes a corresponding APU load characteristic model, and establishes an EV charging load profile based on the flight schedule and sequencing algorithm. A mixed-integer linear programming optimization method based on life cycle theory was proposed to minimize the total costs of hydrogen-integrated energy systems for airports (HIES). However, the resilience advantages of hydrogen energy concerning power failure are little explored in existing academic research. Thus, a resilience assessment method and improvement measure were proposed for HIES. Case studies have been conducted under different optimal hydrogen energy integration configurations and disaster times with resilience assessment by considering periods when the power supply capacity of the grid is insufficient. The results show the effectiveness of the proposed method.</p

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the &lsquo Electric Vehicles&ndash Power Grid&ndash Traffic Network&rsquo fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners&rsquo charging behavior. Document type: Articl

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

Automated and electrified ride-hailing fleet: opportunities and management optimisation

This thesis explores key aspects and problems of technological innovations in the context of ride-hailing systems, shedding light on their profound implications for the industry. Chapter 2 introduces a centralised matching approach that integrates the EV charge scheduling problem into the optimisation framework of ride-hailing systems. The objective represents three-fold benefits: direct financial gains, service quality and system efficiency, and fleet profitability. Moreover, the chapter addresses the practical scenario where human drivers may reject charging assignments lacking personal incentives, leading to a driver compliance behavioural model and a corresponding incentivisation scheme. Chapter 3 introduces a macroscopic model underpinning demand-supply dynamics within mixed-fleet ride-hailing markets. Employing a model predictive control (MPC) framework, it optimises control variables to maximise operators' profits through dynamic trip fares for AVs and HVs, and the active AV fleet size. The study accounts for human driver work patterns and different exit behaviours. Leveraging historical data and real-time inputs, a comprehensive simulation testbed substantiates the efficacy of the proposed strategy in maximising operator profits while mitigating trip cancellations. Chapter 4 introduces a decentralised cooperative cruising approach for a-taxi fleet as an essential contingency plan during complete communication breakdowns. It quantifies road centralities using PageRank, serving as a measure for long-term passenger encounter likelihoods. This metric informs both cruising route planning and network partitioning for effective destination selection. Comparative analyses against benchmark strategies reveal significant enhancements in service performance across various fleet sizes. The research contributes comprehensive methodologies and insights, paving the way for more efficient, sustainable, and adaptable transportation systems

A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock

The construction industry has witnessed an increase in the use of digital tools and smart solutions, particularly in the realm of building energy automation. While realising the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, which are considered critical urban development units. To support the interplay of smart solutions between buildings and neighbourhoods, this study aimed to collect and review all the smart solutions presented in existing scientific articles, the technical literature, and realised European projects. These solutions were classified into two main sections, buildings and neighbourhoods, which were investigated through five domains: building-energy-related uses, renewable energy sources, water, waste, and open space management. The quantitative outcomes demonstrated the potential benefits of implementing smart solutions in areas ranging from buildings to neighbourhoods. Moreover, this research concluded that the true enhancement of energy conservation goes beyond the building’s energy components and can be genuinely achieved by integrating intelligent neighbourhood elements owing to their strong interdependencies. Future research should assess the effectiveness of these solutions in resource conservation

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Energy optimization of a concentrated solar power plant with thermal storage

One of the most relevant problems to solve at a planetary scale is the access to an affordable clean source of energy as CO2 equivalent emissions should be reduced significantly. Some authors aim for a zero emissions target for 2050. Renewable energies will play a leading role in this energy transition, and solar energy with storage is a promising technology exploring a renewable and worldwide available resource. Within the present thesis component development like a new thermal storage thermocline tank design or having latent heat storage capability are technological developments that have been pursued and analyzed on a system perspective basis, focusing on reducing the LCOE value of a commercial STE plant using TRNSYS software. Material research with molten salts mixtures and cement based materials has been performed at lab scale. A fully validation should occur through a 13 partners pan-European H2020 project called NEWSOL which has been developed supported on the laboratory data obtained. Moreover, incorporation of local available material, “modern slag” from an old mine of Alentejo region, was also studied. The material could be used as an aggregate incorporated into calcium aluminate cement (CAC) or as filler. This would help to solve a local environmental complex problem related to soil, air and water pollution due to heavy metals and mining activity in Mina de São Domingos, Southeast of Portugal. The integration of these results underlies a broad energy transition model, a proposal is presented in this thesis, with the aim to foster development towards a sustainable usage of resources and promote clean technologies especially in the energy sector. This model can be locally adapted depending on the pattern of existing industries. The goal is to achieve a smooth transition into a clean tech energy society in line with the target of achieving zero emissions for 2050; Optimização Energética de uma Central de Concentração Solar com Armazenamento de Energia Resumo: Um dos problemas mais relevantes a resolver a uma escala planetária é o acesso, com um custo moderado, a fontes limpas de energia considerando que as emissões equivalentes de CO2 derão ser reduzidas drasticamente. Alguns autores ambicionam mesmo um objetivo de zero emissões em 2050. As energias renováveis irão desempenhar um papel preponderante nesta transição energética, sendo que a energia solar com armazenamento é uma tecnologia promissora que aproveita um recurso renovável e disponível em boa parte do Planeta. Na presente tese foi realizado o desenvolvimento de componentes nomeadamente o design que um novo tanque do tipo termocline, ou de novos elementos recorrendo ao calor latente, desenvolvimentos tecnológicos que foram analizados de uma perspectiva de sistema, dando o enfoque na redução do custo nivelado da electricidade (LCOE) para uma planta Termosolar usando o software TRNSYS. Foi também realizada investigação em laboratório ao nível dos materiais com várias misturas de sais fundidos inclusivé em contacto directo com materiais de base cimenticia. Uma validação completa deverá ocorrer no projeto NEWSOL do programa H2020 que reúne um consórcio de 13 parceiros europeus e que foi preparado e submetido tendo por base os resultados laboratoriais obtidos. Adicionalmente, incorporação de material disponível (escória de minério) de uma mina abandonada da região do Alentejo foi outro dos aspectos estudados. Verificou-se que este material poderá ser utilizado como agregado num ligante do tipo cimento de aluminato de cálcio (CAC) ou como “filler”. Este re-aproveitamento resolveria um problema ambiental complexo derivado do elevado conteúdo de metais pesados resultantes da actividade de mineração e que actualamente provocam poluição do solo, água e ar na área da Mina de São Domingos, Sudeste de Portugal. Estes progressos deverão ser integrados num modelo de transição energética mais amplo. Na presente tese, uma proposta concreta é apresentada, com o objectivo de incentivar o desenvolvimento na direção de uma utilização sustentável dos recursos e a promoção de tecnologias limpas nomeadamente no sector da energia. Este modelo poderá ser adaptado localmente dependendo do padrão de indústrias existente. O objectivo é atingir uma transição suave para uma sociedade de energias limpas em linha com o objectivo de atingir zero emissões de CO2 equivalente em 2050

Accelerating Australia’s electric vehicle uptake: Overcoming socio-technical inertia and bridging the gaps with public policy options designed to transform road transport for a decarbonised future

To obviate significant and growing road vehicle greenhouse gas (GHG) emissions contributing to climate change, transitioning to battery electric vehicles (BEV) is urgently required to maximise fleet emissions reductions soonest, deploying the most suitable available technology. Many countries have implemented policies to incentivise electric vehicle (EV) uptake, which have been well studied. This thesis undertakes novel research by employing a case study of New Zealand to examine consumer responses to EV policies implemented in 2016, plus two mooted policies. Questionnaires and interviews surveyed private motorists from a demand perspective, capturing quantitative and qualitative data to assess attitudes, values, and perceptions of EVs, awareness of government policies, and to reveal those most popular. Employing a unique innovation, four motorist groups (segmented by attitude to EVs, which influences adoption rates) were compared. As additional novelty the role of communication channels, including print media, in influencing consumer behaviour was investigated. Results revealed New Zealand’s conventional motorists, in contrast with EV owners, had low policy awareness, confirming international findings. EV Positives, the next-most ‘EV ready’ segment, favoured policies designed to reduce EV purchase price and increase nationwide charger deployment. Concordant with social marketing research, governments should focus on such buyers’ preferences. Furthermore, to improve BEV readiness, disseminating updated information about EVs via multiple communication channels could shift perceptions of EVs from ‘expensive and inconvenient’ to ‘fun and economical’. Thus, two key concepts namely purchase price-parity and charging infrastructure availability, were incorporated into models specifically for Australia, where policies are limited, to investigate the feasibility of transitioning Australia’s road vehicle fleet to electromobility to achieve net-zero emissions by 2050. A national scale, integrated, macro-economic, system dynamics model (iSDG Australia) was used innovatively to project Australia’s future road transport demand, vehicle mix, energy consumption and GHG emissions. Firstly, the model applied numerous ‘adoption target’ scenarios comparing them to Business-as-Usual; secondly, various combinations of policy options were modelled to project potential outcomes and implementation costs. Based on the assumptions, results suggest emissions reductions are maximised by the fastest passenger vehicle fleet transition to BEVs, entailing declining but ongoing transformational government policy support to achieve net-zero by 2050

Powering up a slow charging market: how do government subsidies affect charging station supply?

Electric vehicle adoption is considered by policymakers to be a promising pathway for addressing climate change. However, the market for charging stations suffers from a market failure: a lack of EV sales disincentivizes charging station production, which in turn inhibits mass EV adoption. Charging station subsidies are discussed as policy levers that can stimulate charging station supply to correct this market failure. Nonetheless, there is limited research examining the extent such subsidies are successful. Using annual data on electric vehicle sales, charging station counts, and subsidy amounts from 57 California counties and a staggered difference-in-differences methodology, I find that charging station subsidies are highly effective: a 1% increase in subsidies expands the supply of stations by 2.5%. This finding suggests that governmental intervention can help correct the market failure in the charging station market