Topic
Smart grid
About: Smart grid is a research topic. Over the lifetime, 37536 publications have been published within this topic receiving 627844 citations. The topic is also known as: intelligent grid.
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TL;DR: A mean field game formulation is provided for this competition, and the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium is introduced, referred here to as the mean field equilibrium.
Abstract: In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.
152 citations
13 Apr 2010
TL;DR: It is discussed how getting consumers "into the loop" can achieve energy savings on top of the efficiency gains resulting from automated systems, and a prototype application is described aimed at inducing behavioral change by providing direct feedback on household electricity consumption.
Abstract: Information and communication technology (ICT) consumes energy, but is also an important means of conserving energy. Conventionally, it has done so by optimizing the performance of energy-using systems and processes in industry and commerce. In the near future, ICT will also play a critical role in supporting the necessary paradigm shifts within the energy sector towards more sustainable electricity generation. However, with the advent of "smart" technology from the field of ubiquitous computing, further ways of reducing growing levels of domestic energy consumption are now emerging. With this in mind, we discuss how getting consumers "into the loop" can achieve energy savings on top of the efficiency gains resulting from automated systems, and we describe a prototype application aimed at inducing behavioral change by providing direct feedback on household electricity consumption.
152 citations
TL;DR: In this article, the authors investigated an experimental smart home with various renewable energy sources and storage systems in terms of several aspects such as in-home energy management, appliances control and power flow.
152 citations
TL;DR: This paper addresses the attack modeling using vulnerability of information, communication and electric grid network using graph theory based approach and shows the possible impact on smart grid caused by integrated cyber-physical attack.
Abstract: This paper addresses the attack modeling using vulnerability of information, communication and electric grid network. Vulnerability of electric grid with incomplete information has been analyzed using graph theory based approach. Vulnerability of information and communication (cyber) network has been modeled utilizing concepts of discovery, access, feasibility, communication speed and detection threat. Common attack vector based on vulnerability of cyber and physical system have been utilized to operate breakers associated with generating resources to model aurora-like event. Real time simulations for modified IEEE 14 bus test case system and graph theory analysis for IEEE 118 bus system have been presented. Test case results show the possible impact on smart grid caused by integrated cyber-physical attack.
152 citations
TL;DR: The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.
Abstract: With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability, and quality of smart grids have become increasingly important. The renewable energy output forecasting applications have also been developing rapidly in recent years, and such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, and the model is able to generate a 24-h probabilistic and deterministic forecasting of PV power output based on meteorological information, such as temperature, solar radiation, and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. The results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.
152 citations