Intelligent energy buildings based on RES and Nanotechnology

The paper presents the design features, the energy modelling and optical performance details of two pilot Intelligent Energy Buildings, (IEB). Both are evolution of the Zero Energy Building (ZEB) concept. RES innovations backed up by signal processing, simulation models and ICT tools were embedded into the building structures in order to implement a new predictive energy management concept. In addition, nano-coatings, produced by TiO2 and ITO nano-particles, were deposited on the IEB structural elements and especially on the window panes and the PV glass covers. They exhibited promising SSP values which lowered the cooling loads and increased the PV modules yield. Both pilot IEB units were equipped with an on-line dynamic hourly solar radiation prediction model, implemented by sensors and the related software to manage effectively the energy source, the loads and the storage or the backup system. The IEB energy sources covered the thermal loads via a south façade embedded in the wall and a solar roof which consists of a specially designed solar collector type, while a PV generator is part of the solar roof, like a compact BIPV in hybrid configuration to a small wind turbine

An Intelligent Fuse-box for use with Renewable Energy Sources integrated within a Domestic Environment

This paper outlines a proposal for an intelligent fuse-box that can replace existing fuse-boxes in a domestic context such that a number of renewable energy sources can easily be integrated into the domestic power supply network, without the necessity for complex islanding and network protection. The approach allows intelligent control of both the generation of power and its supply to single or groups of electrical appliances. Energy storage can be implemented in such a scheme to even out the power supplied and simplify the control scheme required, and environmental monitoring and load analysis can help in automatically controlling the supply and demand profiles for optimum electrical and economic efficiency. Simulations of typical scenarios are carried out to illustrate the concept in operation

Modeling and Control for Smart Grid Integration of Solar/Wind Energy Conversion System

Performance optimization, system reliability and operational efficiency are key characteristics of smart grid systems. In this paper a novel model of smart grid-connected PV/WT hybrid system is developed. It comprises photovoltaic array, wind turbine, asynchronous (induction) generator, controller and converters. The model is implemented using MATLAB/SIMULINK software package. Perturb and observe (P&O) algorithm is used for maximizing the generated power based on maximum power point tracker (MPPT) implementation. The dynamic behavior of the proposed model is examined under different operating conditions. Solar irradiance, temperature and wind speed data is gathered from a grid connected, 28.8kW solar power system located in central Manchester. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for smart grid performance optimization

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Real Time Net Zero Energy Building Energy Manager with Heterogeneous Wireless Ad hoc Network Adaptable To IoT Architectures

Significant energy consumption by buildings from utility grid has made researchers revisit existing Building Energy Management Systems (BEMS). Most of the developing countries have taken a green initiative of Net Zero Energy Buildings (NZEB) to reduce carbon foot print and fast depletion of conventional energy sources. Though the integration of solar and wind based systems to grid is successful in recent years, residential building energy management systems with renewable energy sources is still an evolving research area. Monitoring, control and actuation systems should be tightly coupled with the help of any to any device communication namely Internet of Things (IoT) to realize an efficient NZEB. In this paper a real time NZEB is proposed and developed with bi-directional wireless sensor and actuation system. Proposed NZEB central server collects and maintains a database of on site solar generation, battery state of charge and load power consumption data of a building with help of IEEE 802.15.4 and IEEE 802.11 wireless networks. Proposed system was deployed as a test bed with sensing, control, actuation and server modules and connecting them with a bi-directional wireless network architecture similar to IoT. Data observed at experimental test bed confirm that developed system can estimate on site solar power generation, state of charge on battery bank and load power consumption with negligible error. A simulation study with experimental data collected at NZEB test bed shows that NZEB can optimally schedule loads between local generation and utility grid thereby minimizing peak demand on the grid

Robot graphic simulation testbed

The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts

Market and Economic Modelling of the Intelligent Grid: Interim Report 2011

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy