Implementation of Efficient B2G and V2G in Practical Cases

In this paper, building to grid(B2G) and vehicle to grid(V2G) have been defined with clear and practical understanding. Both of them are new generation technologies which are the essential part of smart city living and crowd energy clustering. Firstly, an in-detailed overview has been provided with an introduction to B2G and V2G followed by a historical overview and theoretical analysis in respect to smart city planning. Next, a review is conducted on current and previous smart living research, which deals with B2G and V2G. Efficient B2G and V2G implementations in practical cases then have been discussed. Lastly, both of these technical prospects have been analyzed in crowd energy diagram.

Research and Implementation of the Practical Texture Synthesis Algorithms

How to generate pictures real and esthetic objects is an important subject of computer graphics. The techniques of mapping textures onto the surfaces of an object in the 3D space are efficient ap- proaches for the purpose.We developed and implemented algorithms for generating objects with appear ances stone,wood grain,ice lattice,brick,doors and windows on Apollo workstations. All the algorithms have been incorporated into the 3D geometry modelling system(GEMS)developed by the CAD Center of Tsinghua University.This paper emphasizes the wood grain and the ice lattice algorithms.

P2P energy trading via public power networks:Practical challenges,emerging solutions,and the way forward

Peer-to-peer(P2P)energy trading is an emerging energy supply paradigm where customers with distributed energy resources(DERs)are allowed to directly trade and share electricity with each other.P2P energy trading can facilitate local power and energy balance,thus being a potential way to manage the rapidly increasing number of DERs in net zero transition.It is of great importance to explore P2P energy trading via public power networks,to which most DERs are connected.Despite the extensive research on P2P energy trading,there has been little large-scale commercial deployment in practice across the world.In this paper,the practical challenges of conducting P2P energy trading via public power networks are identified and presented,based on the analysis of a practical Local Virtual Private Networks(LVPNs)case in North Wales,UK.The ongoing efforts and emerging solutions to tackling the challenges are then summarized and critically reviewed.Finally,the way forward for facilitating P2P energy trading via public power networks is proposed.

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.