Combination of the information technology and the power engineering is the feature of next-generation grid. Depending on bidirectional communications, demand side management (DSM) aims at optimizing the electricity usage pattern of customers to improve energy efficiency and alleviate environmental impact. In this study, a DSM optimization algorithm is designed, which can perform load shifting on a household level based on the Time-of-Use strategy. Several flexible appliances, plug-in hybrid electric vehicle (EV) charging and rooftop photovoltaic (PV), are considered. Results show that the daily electricity cost has declined by 19% after the optimization. A 12% reduction of the domestic carbon emission is also achieved from the variation of grid carbon intensity and energy provided by rooftop PV. It is validated that with the growing penetration rate of EVs and renewable energy generation, smart scheduling of household load can greatly benefit grid stability and energy efficiency.
Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital twins-based system is designed to visualise information from a city's street lighting system. Data is obtained in two ways: from measured parameters of a miniature model street light in realtime, and from real Durham street lighting. Machine learning is used to maximise the efficiency of purchasing electricity from the grid, and to forecast appropriate adaptive street light brightness levels based on city's traffic flow and solar irradiance. An application designed in Unity Pro is deployed on a Microsoft HoloLens 2, and it allows the user to view the processed data and control the model street light. It was found that the application performed as desired, displaying information such as voltage, current, carbon emission, electricity price, battery state of charge and LED mode, while enabling control over the model street light. Moreover, the Deep Q-Network machine learning algorithm successfully scheduled to buy electricity at times of low price and low carbon intensity, while the Long Short-Term Memory algorithm accurately forecasted traffic flow with mean Root-Mean-Square Error and Mean Absolute Percentage Error values of 12.0% and 20.0% respectively.
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Microgrid is playing an increasingly important role in making the utility grid more intelligent and efficient, since it can make better use of the renewable energy resources to simultaneously relieve the grid supply pressure and reduce carbon emissions. Innovations in electric technologies, information and communication technologies can facilitate better management of the power transmission and distribution in the microgrid. This paper proposes an optimization strategy, which considers distributed generations, photovoltaics and wind turbines, based on particle swarm optimization for the management of the microgrid. Simulation results demonstrate that with the optimal generation resources management and the effective use of demand side management in the microgrid, the proposed strategy can reduce electricity costs by 29.283% and 32.158% on weekdays and weekends, respectively.
The production of electric vehicle battery packs with ever-increasing energy densities has accelerated the electrification of the world's automotive industry. With increased attention on the electric vehicle markets, it is vital to increase the safety of these vehicles which now hold higher hazardous potential. This paper aims to explore the field of pack-level thermal runaway mechanisms and evaluate potential mitigation strategies. Most available literature concentrates on the micromanagement of thermal runaway whereas this paper takes a more holistic approach. Thermal simulations for analysing thermal runaway of modules in differing locations are run to characterise the behaviour of a thermal runaway event at pack-level. Results suggest that the propagation of thermal runaway is consistently severe in a cooling plate cooled battery pack as the cooling plate acts as a channel for high temperatures. Additionally, thermal insulation added to contain the rapid increase in temperature unfortunately results in wider spread higher temperatures.
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