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Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. Studies suggest that fast-charging facilities are crucial for the EV market. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers station charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each EV fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity (public or private) and that the demand for these stations are interrelated (through the station choice model). To manage the system of stations, this study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The computational results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations.

The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie's definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions.

The energy communities have the potential to accelerate energy transition and empower consumers, thereby, promoting collaborative social transformation. The local energy communities can support the power grid by offering a variety of flexibility services through demand response, load shifting and energy storage. However, existing electricity markets, tariffs and regulations often hindering effective and sustainable solutions. This paper provides a comprehensive review about designing flexibility provisions for local energy communities in the context of existing and emerging flexible electricity markets. Further, it also discusses the need for social arrangements, technical designs, and their impact on energy communities. Based on reviewed literature findings and from the research projects at Department of Energy, Aalborg University as a part of the SERENE and SUSTENANCE EU Horizon 2020 projects involving sustainable energy communities, future directions will be highlighted.

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple modes, which includes P2P ridesharing, transit, city bike-sharing and walking, within the network. This study includes a case study of the operation of the multimodal system that includes P2P ridesharing participants (both drivers and riders), the Los Angeles Metro Red line subway rail, and the Los Angeles downtown bike-share system. The study conducts a simulation, enhanced by an optimization layer, of providing travel alternatives to passengers during morning peak hours. The results indicate that a multi-modal network expands the coverage of public transit, and that ride- and bike-sharing could be effective transit feeders when properly designed and integrated into the transit system.

Template-assisted growth of microporous materials has been widely researched for applications in the fields of nano-medicine, catalysis, energy conversion, and storage. Here we demonstrate a template-free method of growth of microporous In2S3 at room temperature using the minimal number of reagents and water as the solvent. X-ray diffraction and HRTEM analysis show that the synthesized In2S3 has a cubic structure with preferential growth along the (222) plane. Nitrogen physisorption analysis shows that the synthesized In2S3 power has a bimodal pore size distribution with small pores of size ~0.8 nm and larger pores of size ~2.5 nm. The SEM analysis evidenced concave surfaces for the grown In2S3 crystallite. An optical bandgap energy of 2.11 eV was obtained from the UV-VIS absorption spectrum. The grown In2S3 exhibits the photo-catalytic ability to degrade organic pollutants Rhodamine B and Sulforhodamine B dye molecules in water by ~92% and ~94% respectively, under direct sunlight. The reusability of the grown In2S3 for the degradation of these organic pollutants is demonstrated and its efficiency was found to degrade exponentially with recycling. The results demonstrate that the grown In2S3 is an efficient agent for water purification under direct sunlight. Highlights: Achieved the synthesis of In2S3 by use of a minimal number of aqueous reagents whose toxicity is insignificant and has the least impact on the environment. The growth process achieves atom economy by reducing the production of non-usable by-products. Photocatalytic degradation of the organic pollutant SRB dye under direct sunlight. The reusability of the In2S3 photocatalyst for the degradation of the SRB dye is demonstrated. [ABSTRACT FROM AUTHOR]