During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overvi... [more]

This paper investigates the possibility of converting vibrations to electricity. A numerical and an experimental study of a magnetic levitation harvester are proposed. The system can be highly efficient when the electrical parameters are correctly tuned. Mechanical and electrical interaction of the harvester is described by an electromechanical coupling. Fixed value, linear and nonlinear electromechanical coupling models are presented and compared. It has been shown that the nonlinear electromechanical coupling model is more suitable for higher oscillations of the magnet. The obtained results show that nonlinear resonance and recovered energy can be controlled by the simple configuration of the magnet coil position. The recovered energy from the top branch is significantly higher, but this solution is much harder to obtain.

Microgrids have recently emerged as the building block of a smart grid combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions [...]

Over half of California’s forestland is managed by the US Forest Service, and the agency has identified a need to scale up forest restoration treatments in the state to one million acres per year by 2025. However, the high costs of mechanical fuel reduction and lack of markets for biomass pose significant barriers to accomplishing this target. The objectives of this case study were: (1) to identify costs of forest restoration treatments on federally-managed land in the Northern Sierra under a variety of harvesting scenarios and haul distances to biomass facilities, and (2) to understand what market prices for biomass must be offered to support such efforts. We modeled silvicultural prescription and harvesting options, machine productivity and costs, and transportation costs to assess economic thresholds. Biomass harvest, chip, and haul costs ranged from $55/bone dry ton to $118/bone dry ton, depending on the harvesting system scenario and distance from the biomass disposal site. Result... [more]

The Egyptian irrigation system depends mainly on canals that take water from the River Nile; nevertheless, the arid climate that dominates most of the country influences the high rate of water losses, mainly through evaporation. Thus, the main objective of this study is to develop a practical approach that helps to accommodate solar photovoltaic (PV) panels over irrigation canals to reduce the water evaporation rate. Meanwhile, a solar PV panel can contribute effectively and economically to an on-grid system by generating a considerable amount of electricity. A hybrid system includes a solar PV panel and a diesel generator. Several factors such as the levelized cost of energy (LCOE), total net present cost, loss of power supply probability, and greenhouse gas emissions should be considered while developing a technoeconomically feasible grid-connected renewable integrated system. A mathematical formulation for the water loss was introduced and the evaporation loss was monthly estimated.... [more]

The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers’ consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the development of accurate algorithms. In such a context, this paper investigates the use of a state-of-art deep learning architecture in order to perform precise load demand forecasting 24-h-ahead in the whole country of France using RTE data. To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms. WaveNet uses dilated causal convolutions and skip-connection to utilise long-term information. This kind of novel ML architecture presents different advantages regarding other statistical algorithms. On the one hand, the proposed deep learnin... [more]

Resource use in point-to-point and point-to-multipoint communication emerges with the tremendous growth in wireless communication technologies. One of the technologies is wireless power transfer which may be used to provide sufficient resources for energy-constrained networks. With the implication of cooperative communication in 5G/B5G and the Internet of Things (IoT), simultaneous wireless information and power transfer (SWIPT)-assisted energy efficiency and appropriate resource use become challenging tasks. In this paper, multiple IoT-enabled devices are deployed to cooperate with the source node through intermediate/relay nodes powered by radio-frequency (RF) energy. The relay forwards the desired information generated by the source node to the IoT devices with the fusion of decode/amplify processes and charges itself at the same time through energy harvesting technology. In this regard, a problem with throughput, energy efficiency, and joint throughput with user admission maximizat... [more]

Drinking water distribution networks (DWDNs) have a huge potential for cold thermal energy recovery (TED). TED can provide cooling for buildings and spaces with high cooling requirements as an alternative for traditional cooling, reduce usage of electricity or fossil fuel, and thus TED helps reduce greenhouse gas (GHG) emissions. There is no research on the environmental assessment of TED systems, and no standards are available for the maximum temperature limit (Tmax) after recovery of cold. During cold recovery, the water temperature increases, and water at the customer’s tap may be warmer as a result. Previous research showed that increasing Tmax up to 30 °C is safe in terms of microbiological risks. The present research was carried out to determine what raising Tmax would entail in terms of energy savings, GHG emission reduction and water temperature dynamics during transport. For this purpose, a full-scale TED system in Amsterdam was used as a benchmark, where Tmax is currently set... [more]

Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased... [more]

Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of... [more]

A beam containing a piezoelectric layer or layers is used for piezoelectric harvesting from various processes. The structure of the beam is made by gluing the piezoelectric material on one side (unimorph) or both sides (bimorph) of a carrying substrate. Two piezoelectric layers, glued on both sides of the substrate, may be electrically parallel or series connected. This paper presents an experimental analysis of the impact of parallel and series connections of two Macro Fiber Composite (MFC) MFC patches in a bimorph on the charging of a capacitor. In experiments, the effective charging process of the capacitor was obtained both for parallel and series connection of two MFC patches. The bimorph with a parallel connection generated a larger capacitor charging power than the bimorph with a series connection in the range of voltage across the capacitor from 1 to 18 V. However, the bimorph with a series connection was more effective than a parallel connection for voltage across the charged... [more]

Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation−Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.000... [more]

The interconnection of wind power plants (WPPs) with distribution networks has posed many challenges concerned with voltage stability at the point of common coupling (PCC). In a distribution network connected WPP, the short-circuit ratio (SCR) and impedance angle ratio seen at PCC (X/RPCC) are the most important parameters, which affect the PCC voltage (VPCC) stability. Hence, design engineers need to conduct the WPP siting and sizing assessment considering the SCR and X/RPCC seen at each potential PCC site to ensure that the voltage stability requirements defined by grid codes are provided. In various literature works, optimal siting and sizing of distributed generation in distribution networks (DG) has been carried out using analytical, numerical, and heuristics approaches. The majority of these methods require performing computational tasks or simulate the whole distribution network, which is complex and time-consuming. In addition, other works proposed to simplify the WPP siting an... [more]

Cognitive radio is expected to be implemented in smart grids since it presents high reliability, high accuracy and low transmission time by utilizing licensed bands opportunistically. Shadowing environment affects the performance of channel availability detection of local spectrum sensing since it occurs occasionally. Therefore, the cooperative spectrum sensing is encouraged to be used for addressing shadowing issues. The principle cooperative spectrum sensing techniques suffer from unreliable local information from secondary users (SUs) who are encountered by the shadowing effect. Then, several alternative methods, adaptive majority rule and improved weight algorithm (IMA) is proposed by taking the SUs reliability into account. However, the unreliable SUs are still considered according to the algorithm. Therefore, in this paper, we propose an adaptive multi-criteria thresholding (AMT) to determine the channel availability according to the SUs reliability. The main contribution of AMT... [more]

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our re... [more]

In the framework of the empir projects myrails and windefcy, metas developed a primary standard for electrical power using commercial off-the-shelf components. The only custom part is the software that controls the sampling system and determines the amplitude and phase of the different frequency components of voltage and current. The system operates from dc up to 9 kHz, even with distorted signals. The basic system is limited to 700 V and 21 A. Its power uncertainty is 15 μW/VA at power frequencies and increases to 1.8 mW/VA at 9 kHz. With the extension up to 1000 V and 360 A, the system reaches power uncertainties of 20 μW/VA at power frequencies, increasing to 510 μW/VA at 9 kHz. For higher voltages or higher currents, the same principle is used. However, the uncertainties are dominated by the stability of the sources. The voltage and current channels can also be used independently to calibrate and test power quality instruments. Thanks to a time-stamping system, the system can also... [more]

With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be the primary premise for the optimal operation of integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network forecasting model. First, Copula correlation analysis is used to conduct correlation analysis on multiple loads and various influencing factors. The influencing factors that have a great correlation with multiple loads were screened out as the input feature set of the model to eliminate the influence of interfering factors. Then, a deep bidirectional long and short-term memory neural network was constructed. Combined with the input feature set screened by the Copula correlation analysis method, the useful information contained in the historical data was more comprehensively learned from the forward and backward directions for training and forecasting. Through the actual calculation examp... [more]

Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations.

A microgrid is an independent power system that can be connected to the grid or operated in an islanded mode. This single grid entity is widely used for furthering access to energy and ensuring reliable energy supply. However, if islanded, microgrids do not benefit from the high inertia of the main grid and can be subject to high variations in terms of voltage and frequency, which challenge their stability. In addition, operability and interoperability requirements, standards as well as directives have addressed main concerns regarding a microgrid’s reliability, use of distributed local resources and cybersecurity. Nevertheless, microgrid systems are quickly evolving through digitalization and have a large range of applications. Thus, a consensus over their testing must be further developed with the current technological development. Here, we describe existing technical requirements and assessment criteria for a microgrid’s main functionalities to foster harmonization of functionality-... [more]

Protecting inverter-interfaced microgrids is challenging as conventional time-overcurrent protection becomes unusable due to the lack of fault current. There is a great need for novel protective relaying methods that enable the application of protection coordination on microgrids, thereby allowing for microgrids with larger areas and numbers of loads while not compromising reliable power delivery. Tools for modeling and analyzing such microgrids under fault conditions are necessary in order to help design such protective relaying and operate microgrids in a configuration that can be protected, though there is currently a lack of tools applicable to inverter-interfaced microgrids. This paper introduces the concept of applying an optimization problem formulation to the topic of inverter-interfaced microgrid fault modeling, and discusses how it can be employed both for simulating short-circuits and as a set of constraints for optimal microgrid operation to ensure protective device coordin... [more]

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artifici... [more]

Medium voltage DC (MVDC) networks are attracting more attention amid increased renewables penetration. The reliability of these DC systems is critical, especially following grid contingencies to maintain critical loads supply and provide ancillary services, such as black-start. This paper proposes an innovative energy management system (EMS) to maintain reliable MVDC network operation under prolonged AC grid contingencies. Similar EMS designs in literature tend to focus on limited operating modes and fall short of covering comprehensive elongated blackout considerations. The proposed EMS in this paper aims to preserve the distribution network functionality of the impacted MVDC system through maintaining a constant DC bus voltage, maximizing critical load supply duration, and maintaining the MVDC system black-start readiness. These objectives are achieved through controlling generation units between Maximum Power Point Tracking (MPPT) and Voltage Regulation (VR) modes, and implementing... [more]

This article studies the strategic access of single-server retrial queue with two types of customers, where priority is given according to their category. On the basis of this concept, a cognitive-radio network was developed as retrial queue with energy harvesting. Cognitive radio allows for a secondary user to opportunistically access the idle spectrum of a primary user (PU). Upon arrival of a primary user, the service given to the secondary user by the cognitive radio is interrupted, and the PU band is available for the primary user. After completion of service for the primary user, the PU band is again available to secondary users. Performance metrics are derived to study the equilibrium strategies of secondary users. A Stackelberg game was formulated and Nash equilibrium was derived for the noncooperative strategy of the secondary user. Game-theory concepts are incorporated with queuing theory ideas to obtain the net benefit for the noncooperative strategy and social benefit for co... [more]

Here, we overview the Energies journal special issue that is dedicated to the topic of “Energy Efficiency in Smart Homes and Smart Grids” (https://www [...]

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showin... [more]