![Examples of Big Data sources [1]](https://www.researchgate.net/profile/Jerzy-Zielinski/publication/324230350/figure/fig1/AS:636311799087104@1528719963622/Examples-of-Big-Data-sources-1_Q320.jpg)
![Paths to Cloud Computing [1]](https://www.researchgate.net/profile/Jerzy-Zielinski/publication/324230350/figure/fig2/AS:636311799074817@1528719963661/Paths-to-Cloud-Computing-1_Q320.jpg)
Content uploaded by Jerzy Stanislaw Zielinski
Author content
All content in this area was uploaded by Jerzy Stanislaw Zielinski on Jun 11, 2018
Content may be subject to copyright.
A preview of the PDF is not available
Does Smart Grid need new Informatics Tools?
Abstract and Figures
The paper begins with a short presentation of Smart Grid (SG) being the starting element of a chain of developing the idea of “smartness” not only in power but also in all industry branches implying growth of data generation (Big Data problem). Parallel to the smart-and the big data problems, new informatics tools, such as Cloud Computing (CC) and Internet of Things (IoT) are developed. The main part of the paper describes specifics of these new tools, (e.g. Industrial Internet of Things, dew- and fog CC) and their collaboration in terms of solving the big data problem. Final remarks present the Author’s view on further development of these problems.
Figures - uploaded by Jerzy Stanislaw Zielinski
Author content
All figure content in this area was uploaded by Jerzy Stanislaw Zielinski
Content may be subject to copyright.
ResearchGate has not been able to resolve any citations for this publication.
The deep interdependence between electrical and gas systems entails a potential threat to the security (or reliability) of both systems. It is imperative to investigate the impacts of massive uncertainties on the overall secure and economical operation of both systems. In this paper, a probabilistic energy flow framework of integrated electrical and gas systems is initially proposed considering correlated varying energy demands and wind power. Three aspects of couplings between electrical and gas systems are considered: Gas-fired generators, electric-driven compressors, and energy hubs integrated with power to gas (P2G) units. Furthermore, a multilinear method is specially designed to produce a deterministic energy flow solution for each sample generated by Monte Carlo simulation (MCS). Finally, test results have verified that the proposed multilinear MCS method prevails over the nonlinear MCS. In addition, P2G effectively benefits the operation of both electrical and gas networks.
This paper presents a new approach for determining the day-ahead bidding strategies of a large-scale hybrid electric energy company. The company has both energy generation and energy retailing businesses in a competitive electricity market. Demand response programs are also considered in the retail side of the company in order to hedge the risk of participation in wholesale market. This paper introduces a max-min bilevel mathematical programming with equilibrium constraint model for offering a strategy that manages the risk of uncertain forecasted rivals' bids by robust optimization. The max-min bilevel model is converted to its equivalent single-level optimization using Karush-Kuhn-Tucker optimality conditions. The duality theory is utilized to find the equivalent ordinary maximization model of the max-min problem. Strong duality theory and big M method are also used to linearize the final model of offering strategy. Applicability of the proposed approach is shown by implementing it on the IEEE 118-bus test system.
The paper starts from description of series blackouts which implied to take into account necessity of application smart on transmission as well in distribution level where dispersed renewable energy resources (RES) are injected. Definitions of microgrids, smart grids and their characteristics are presented. New idea of the so called transactive energy being developed in New York region is presented with proposition of Internet of Everything application.
Definitions of Smart Grid (SG) and microgrid open the paper. When resiliency is compared to self-healing, one can see that resiliency contains functions of self-healing as well as additional services. Nanogeneration and integration of electricity and gas systems are considered in the section entitled Miscellaneous. In the conclusion, the author emphasises the need of taking the microgrids with direct current into account in terms of development of their program.
A former definition states that a brain-computer Interface provides a direct communication channel to the brain without the need for muscles and nerves. With the emergence of wearable and wireless brain-computer interfaces, these systems have evolved to become part of wireless body area networks, offering people-centric applications such as cognitive workload assessment and detection of selective attention. Currently, wireless body area networks are mostly integrated by low-cost devices that, because of their limited hardware resources, cannot generate secure random numbers for encryption. This is a critical issue in the context of new Internet of Things device communication and its security. Such devices require securing their communication, mostly by means of the automatic renewal of the cryptographic keys. In the domain of the people-centric Internet of Things, we propose to use wireless brain-computer interfaces as a secure source of entropy, based on neuro- activity, capable to generate secure keys that outperforms other generation methods. In our approach, current wireless brain-computer interface technology is an attractive option to offer novel services emerged from novel necessities in the context of the people-centric Internet of Things. Our proposal is an implementation of the human-in-the-loop paradigm, in which devices and humans indistinctly request and offer services to each other for mutual benefit.
Hierarchical control/protection applications in smart microgrids require knowledge of real-time status of distributed generation (DG) systems. Lack or failure of communications with the microgrid central controller (MGCC) can significantly undermine performance of such applications since the MGCC cannot determine the number of operational energy sources. To overcome these challenges, the MGCC needs a secondary mechanism in order to track presence or absence of DG systems. This paper proposes a new monitoring approach that empowers the MGCC to estimate the number of operational DG systems and thus determine the total generation capacity of the microgrid. A parameter estimator is developed to extract an autoregressive model for the synchrophasors of current symmetrical components (CSC) of the main point of common coupling (PCC). The extracted model is used by an adaptive algorithm which identifies abrupt changes in DG by evaluating the norm of forward prediction error. The proposed approach uses real-time synchrophasor data to dynamically update the criterion for event detection and is very robust against abrupt load changes. The performance is verified using extensive simulations of the IEEE 13-Bus benchmark with four photovoltaic (PV) units.
The battery energy storage systems (BESSs) have been increasingly installed in the power system, especially with the growing penetration rate of the renewable energy sources. However, it is difficult for BESSs to be profitable due to high capital costs. In order to boost the economic value of BESSs, this paper proposes a hierarchical energy management system (HiEMS) to aggregate multiple BESSs, and to achieve multi-market business operations. The proposed HiEMS optimizes the multi-market bids considering a realistic BESS performance model, and coordinates the BESSs and manages their state of charge (SOC) values, according to their price penalties based on dynamically generated annualized cost. By taking part in the energy market and regulation market at the same time, the cost-performance index (CPI) of the BESS aggregation is greatly improved. The impact of photovoltaic generation (PV) on system performance and CPI is also studied.