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

Optimal energy management of a grid-connected multiple energy carrier micro-grid

© 2019 Elsevier Ltd This paper presents a novel modeling approach to optimize the electrical and thermal energy management of a multiple energy carrier micro-grid with the aim of minimizing the operation cost such that system constraints are satisfied. The proposed micro-grid includes a micro-turbine, a fuel cell, a rubbish burning power plant, a wind turbine generator system, a boiler, an anaerobic reactor-reformer system, an inverter, a rectifier, and some energy storage units. The model uses day-ahead forecasting (24 h) to estimate the electrical and thermal loads on a micro-grid network. A day-ahead forecast is also used to estimate electricity generation from wind turbines. Due to the uncertainty associated with day-ahead forecasts, a Monte Carlo simulation is used to estimate thermal loads, electrical loads, and wind power generation. Also, a real-time pricing demand response program is used to shift non-vital loads. The operating cost of the micro-grid is minimized through the particle swarm optimization algorithm. The simulation results demonstrate the proposed modeling framework is superior over conventional centralized optimal scheduling models widely used in the literature in terms of reducing operating cost and computational complexity. In addition, the results obtained by applying the proposed modeling framework are analyzed and validated through scenario testing

Management of renewable-based multi-energy microgrids in the presence of electric vehicles

This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot‐based electric vehicles (EVs). Both thermal and electrical features of the multi‐energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price‐based and incentive‐based demand response (DR) programs are modelled in the proposed multi‐energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi‐energy MGs.©2020 IET. This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Energy management for user’s thermal and power needs:A survey

The increasing world energy consumption, the diversity in energy sources, and the pressing environmental goals have made the energy supply–demand balance a major challenge. Additionally, as reducing energy costs is a crucial target in the short term, while sustainability is essential in the long term, the challenge is twofold and contains clashing goals. A more sustainable system and end-users’ behavior can be promoted by offering economic incentives to manage energy use, while saving on energy bills. In this paper, we survey the state-of-the-art in energy management systems for operation scheduling of distributed energy resources and satisfying end-user’s electrical and thermal demands. We address questions such as: how can the energy management problem be formulated? Which are the most common optimization methods and how to deal with forecast uncertainties? Quantitatively, what kind of improvements can be obtained? We provide a novel overview of concepts, models, techniques, and potential economic and emission savings to enhance energy management systems design