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Capital cost is a practical limitation in distributed energy resources (DER) system sizing and the optimal size may lead to high capital investment. This calls for the development of a framework that provides alternate system sizing with the ability to address capital cost constraints. A framework for sizing microgrid DER considering capital cost constraints and inclusion of manufacturer catalog ratings is introduced in this article. This framework uses a system-wide, composite, cost metric that combines capital costs and operating costs per month. This per-month optimization results are transformed to evaluate the feasible DER ratings and annual savings possible with imposed capital and operating cost constraints. The formulated framework is demonstrated using two microgrid case studies in peak shaving applications at industrial facilities located in the U.K. and USA. This article also introduces development of hardware-in-loop (HIL) system as a tool to validate microgrid sizing results demonstrating that real-time operation can provide the savings projected by the optimized assets. The HIL simulation, with a microgrid controller and the plant sized per the optimization verifies the annual savings. This HIL confirmed that the sizing did not result in violations of state of charge limits of battery energy storage. Traditional optimal sizing approaches do not verify for violations and can result in suboptimal or oversized designs. [ABSTRACT FROM AUTHOR]

Power-hardware-in-the-loop (PHIL) is a vital technique for realistic testing of prototype systems. While the application of power electronics-based amplifiers to enable PHIL capability has been widely reported, the use of motor-generator (MG) sets as the PHIL interfaces has not been fully investigated. This article presents the realization of the first MW-scale PHIL setup using an MG set as the power amplifier, which offers a promising solution for testing novel systems for the integration of distributed energy resources. Uniquely, this article presents a methodology that introduces augmented frequency and phase control loops that can be integrated to commercially-available MG set's existing frequency controller for precise frequency and phase tracking. Internal model control is used for the controllers design and tuning. The developed control algorithm is tested in a MW-scale MG set that couples a Great Britain (GB) transmission network model simulated in a real time simulator to an 11 kV distribution network. Experimental results are presented, which demonstrate that the proposed control methodology is highly effective in maintaining the synchronization between the simulated and physical systems, thereby capable of enabling the MG set as a PHIL interface. [ABSTRACT FROM AUTHOR]

Autonomous vehicles technology is an emerging area and has attracted lots of recognition in recent times. Accidents-free driving has always been the focal point of autonomous vehicles. Autonomous vehicles have the potential to eliminate human errors while driving, which has been argued as the predominant cause of traffic accidents. In autonomous vehicles technologies, a variety of efforts have been made to eliminate human drivers. The full elimination of humans is not possible at this moment, but some of the tasks can be automated to facilitate the drivers. In this paper, we investigate the leading causes of accidents based on UK vehicle safety data of 2017-2018. We analyze the data and investigate the leading factors which cause traffic crashes. Based on the leading features in the dataset, we then run different prediction algorithms to predict the severity of accidents under a given input feature set. The accuracy of the model with Decision Tree classifier, Random Forest, and Logistic Regression are compared, and it has been found that Random Forest performs best among others with 95% accuracy. The trained random forest model is deployed on the Internet of Things server based on Arduino, and a lightweight application is developed to get the vital data from the driver. The data is applied to the trained model to predict the risk index of driving. This application is lightweight but yet provide a significant contribution in terms of safety in autonomous vehicles. [ABSTRACT FROM AUTHOR]

Autonomous vehicles technology is an emerging area and has attracted lots of recognition in recent times. Accidents-free driving has always been the focal point of autonomous vehicles. Autonomous vehicles have the potential to eliminate human errors while driving, which has been argued as the predominant cause of traffic accidents. In autonomous vehicles technologies, a variety of efforts have been made to eliminate human drivers. The full elimination of humans is not possible at this moment, but some of the tasks can be automated to facilitate the drivers. In this paper, we investigate the leading causes of accidents based on UK vehicle safety data of 2017-2018. We analyze the data and investigate the leading factors which cause traffic crashes. Based on the leading features in the dataset, we then run different prediction algorithms to predict the severity of accidents under a given input feature set. The accuracy of the model with Decision Tree classifier, Random Forest, and Logistic Regression are compared, and it has been found that Random Forest performs best among others with 95% accuracy. The trained random forest model is deployed on the Internet of Things server based on Arduino, and a lightweight application is developed to get the vital data from the driver. The data is applied to the trained model to predict the risk index of driving. This application is lightweight but yet provide a significant contribution in terms of safety in autonomous vehicles. [ABSTRACT FROM AUTHOR]

The rise of distributed energy resources (DERs) can enhance the efficiency of system operations by providing flexibility services to the different agents involved, but they also pose a major resource allocation problem. This study considers three different agents procuring DER services: distribution system operators (DSOs) for local congestion management, transmission system operators (TSOs) for system-wide reserve deployment, and retailers for hedging against network usage tariffs based upon peak-load pricing. A variety of market mechanisms are identified to co-ordinate these needs, and three schemes are developed in detail. These are separate markets for each agent, co-ordinated Shapley value allocations for TSO and DSO, and a co-ordinated mechanism including retailers. These designs are evaluated on a realistic distribution network in Britain for two operational days. The results show a more efficient dispatch from the TSO–DSO co-ordinated procurement over independent sequential procurements. However, the inclusion of retailers in the joint dispatch is surprisingly less attractive due to the lack of improvement in social welfare and the undesirable impacts on the DSO. [ABSTRACT FROM AUTHOR]