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The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

Increases in power demand and consumption are very noticeable. This increase presents a number of challenges to the traditional grid systems. Thus, there is the need to come up with a new solution that copes with the stringent demand on energy and provides better power quality, which gives a better experience to the end users. This is how the concept of smart grids (SG) came to light. SGs have been introduced to better monitor and control the power produced and consumed. In addition to this, SGs help with reducing the electricity bill through the integration of renewable energy sources. The underlying smartness of the SGs resides in the flow of information in addition to the flow of energy. Information/data flowing implies the use of smart sensors and smart meters that sense and send data about the power produced and consumed, and the data about the environment where they are deployed. This makes SGs a direct application of IoT. In this paper, we are implementing an edge platform that is based on single-board computers (SBCs) to process data stemming from SG. The use of SBCs is driven by the energy efficiency and cost effectiveness concepts that the SG is trying to apply. The platform in question is tested against a distributed job that averages random numbers using Hadoop’s MapReduce programming model. The SBC that we are using in this implementation is the NVIDIA Jetson Developer Kit. The results of this work show that a cluster of SBCs is low-cost, easy to maintain, and simple to deploy, which makes it a great candidate for providing edge computing. Although it revealed a performance that beat the one of the remote cloud servers, it could not outperform the single-computer edge platform.

The major objectives of this work are: 1) to develop new efficient optimization algorithm to solve NP-hard problems, 2) to show the potential of integrating renewable energy technologies for Laayoune region- Morocco taken as case study. In this work, new parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids. The studied micro-grid is composed of four different renewable energy technologies and energy storage system. The objectives of both optimization problems are to satisfy typical load demand, to minimize energy production cost, to maximize renewable energy integration, to avoid energy losses and overload. P-GA-PSO performances are evaluated and compared with the ordinary optimization methods based on: 1) a set of benchmark functions, 2) various scenarios used to solve the proposed micro-grid optimization problems. Obtained results demonstrated that P-GA-PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality. Moreover, the proposed micro-grid is very promising in terms of load demand satisfaction, cost and pollutant emissions reduction. Indeed, the obtained energy cost does not exceed 0.17 US$/kWh, which is close to fossil fuel energy cost, and fossil fuel replacement rate exceeds 50% during all periods.

Micro-grid systems have been recently emerged for efficient integration and management of renewable energy sources, buildings’ equipment (e.g. ventilation; lighting; heating, ventilation, and air conditioning), and energy storage devices. The intermittent nature of the produced electricity from renewable energy sources constitutes, however, the main challenge for their seamless integration into buildings. Recent studies stated that storage devices, despite their actual high cost, could be used to tackle this issue by balancing between the variability of renewable energy sources production and the unpredictable building’s occupancy. However, managing the power flows in micro-grid systems is highly required in order to minimize the consumption from the electric grid while ensuring the quality and the reliability of electrical services. In this article, a micro-grid system platform is introduced for efficient integration and management of renewable energy sources and storage devices. The platform’s architecture is composed of three main components: (a) the power generation components that integrate the renewable energy sources together with the storage devices and the traditional electrical grid, (b) an advanced metering component for data gathering and analysis, and (c) control services for power flow management. The platform was developed and deployed in a real-setting scenario. Simulations and experimentations have been performed and results show the usefulness of the proposed platform for efficient management of the deployed micro-grid system.

In this work, a new hybrid cuckoo search and genetic algorithm optimization method using a novel adaptive penalty function was proposed to solve the economic dispatch (ED) problem in smart grid. Please check and confirm the edit made in article title. This method was also paralyzed in order to solve the problem within specific time suitable to solve Energy management Problems. Three improvements are achieved through this combination. First, parallelism allows further reduction of the execution time. Second, the hybridization of both cuckoo search and genetic algorithm methods allows better diversification and exploration of search space which increases the solution quality. Third, the new adaptive penalty function was developed to discard infeasible solutions and to choose near-optimal ones within a short time. The efficiency of the developed algorithm is proven theoretically and experimentally. Three scenarios are considered to prove experimentally the out-performance of the developed method: (1) the proposed method is compared with Cuckoo Search and Genetic Algorithm methods using a set of benchmark functions. (2) A comparative study is carried out by applying the method to the ED continuous problem optimization case study. (3) The method is compared with Cuckoo search to solve discrete demand side management problem, considering each consumer as an independent parameter. The performance evaluation was conducted using Matlab data parallelism library.

The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).

The resent infrastructure of the buildings is becoming socio-technical systems that integrate different heterogeneous entities (e.g., sensors, actuators, lighting, HVAC (Heating, Ventilation, and Air Conditioning), occupants, renewable energy, storage systems), which could interact dynamically and in a collective manner to balance between energy efficiency, occupants’ comfort, sustainability, and adaptability. This new infrastructure is known as the “Micro-grid” (MG) system concept. However, the main challenge for this infrastructure is real-time monitoring and data processing, which requires the use of new information and communication technologies. In addition, incorporating mechanisms and techniques are required in order to have buildings more energy-efficient while ensuring occupants’ comfort by allowing entities interaction for suitable actions (e.g., turning On/Off HVAC and lighting, balancing the fluctuation between power production and consumption). In this work, a new holistic architecture of smart buildings is presented by improving the main layers of MG systems. This architecture is proposed in order to integrate all buildings’ aspects with the main trade-off is to efficiently manage the building while maintaining a suitable occupants’ comfort. In fact, an MG system is structured into three layers following the proposed holistic architecture. More precisely, we shed more light on the MG system’s layer by integrating recent IoT/Big-Data technologies for data gathering, processing, and control. A set of sensors are installed for power measurement in the MG system while the measured data is gathered and transmitted to an IoT/Big-Data platform for analysis, processing, and storing. The main aim is to store a historical of data (e.g., power production/consumption, weather conditions) from the installed MG system with open-access tools.

Recent studies showed that energy consumption in buildings could be efficiently reduced by including recent IoT (Internet of Things) and Big-Data technologies into microgrid systems. In fact, three major aspects could be further considered for reducing energy consumption while maintaining a suitable occupants’ comfort, (i) integrating renewable energy sources and storage devices, (ii) integrating programmable and less-energy-consuming equipment, and (iii) deploying innovative information and communication technologies. These aspects might contribute substantially to the improvement of winning and saving energy toward smart and energy-efficient buildings. In this chapter, a microgrid system infrastructure is developed together with a platform for data gathering, monitoring, and processing. We put more emphasis on microgrid systems as crucial infrastructures for leveraging energy-efficient and smart buildings by developing and deploying a holistic IoT/Big-Data platform in which sensing and actuation tasks are performed according to the actual contextual changes. Scenarios are presented in order to show the usefulness of this holistic platform for monitoring, data processing, and control in energy-efficient buildings.

The accurate position of nodes in Wireless Sensor Networks (WSNs) is considered a critical problem in the majority of the Internet of Things (IoT) applications. Recently a large number of contribution in localization have been recommended to determine the location of nodes. However, a number determinated of these techniques have been presented to precisely determine the nodes locations in the IoT. In this work, we discuss the new three localization techniques, named Centroid + 4A, ICentroid, and ICentroid + 4A respectively, based on the Centroid localization technique and a new weighted formula to estimate the target nodes’ positions. The OMNeT++ network simulator was used to figure out and the performance of the discussed solutions in comparison with the Centroid localization technique. The examined results reveal that a significant improvement in the localization precision of the discussed contributions in Wireless Sensor Networks.

The intermittent nature of renewable energy sources has attracted much interest for integrating storage devices towards the deployment of micro-grid systems. In fact, these devices could be deployed into the system by decoupling energy production from consumption mainly by storing the extra produced energy from RES during the day for eventual usage at night. However, for standalone systems, computing the optimum size of energy production systems and storage devices is required for continuous electricity supply. In this paper, a micro-grid system is modeled, sized, and experimented according to a given consumption profile using a deployed prototype at our EEBLab.

Information broadcasting in wireless network is a necessary building block for cooperative operations. However, the broadcasting causes increases the routing overhead. This paper brings together an array of tools of our adaptive protocol for information broadcasting in MANETs. The proposed protocol in this paper named WAODV (WAIT-AODV). This new adaptive routing discovery protocol for MANETs, lets in nodes to pick out a fantastic motion: both to retransmit receiving request route request (RREQ) messages, to discard, or to wait earlier than making any decision, which dynamically confgures the routing discovery feature to decide a gorgeous motion through the usage of neighbors’ knowledge. Simulations have been conducted to show the effectiveness of the using of techniques adaptive protocol for information broadcasting RREQ packet when integrated into ad hoc on-demand distance vector (AODV) routing protocols for MANET (which is based on simple flooding).

Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.

The integration of Combined Heat and Power (CHP) systems, especially fuel cell systems, into buildings have attracted a lot of interest these last years. It is due to their higher efficiency, cleanliness and their capability for producing both valuable thermal energy and electricity, from a single source of fuel, such as natural gas or hydrogen. Their high efficiency, which can reach 85%–90%, leads to a decrease in both greenhouse gas emissions and costs when compared to the conventional methods of generating heat and electricity separately. The aim of this work is towards efficient integration of a Proton Exchange Membrane (PEM) fuel cell into buildings in order to supply it with both electricity and heat. Experiments on a system composed of fuel cell, converter, and hydrogen tank have been carried out and compared to the simulations conducted using Matlab/Simulink where the system’s model has been developed. Simulation and experimental results are compared in order to show the accuracy of the models that have been developed and the efficiency of the fuel cell when operating as a CHP system to supply the building with both electricity and heat. Keywords: CHP system, Fuel cell, Hydrogen, Matlab/Simulink softwares

Green Computing has been the trend among computer scientists for its eco-friendliness. It serves as a great solution to be integrated with Smart Grids (SG). Data stemming from SGs falls under the realm of Big Data as it is voluminous, various, and has a great velocity. Hence, these data need processing and storage. For this, High-Performance Computing, through clustering a set of computers, proves necessary. Nowadays, with the hardware advances that the world is witnessing, the Raspberry Pi (RP) creates a number of opportunities to deploy cost-effective and energy-efficient clusters, which respect the concepts of Green Computing. In this paper, we are presenting the work done within a USAID sponsored project which aims at developing a SG testbed at Al Akhawayn University in Ifrane, Morocco. We are presenting the deployment of a 5-node cluster based on RPs. The cluster has Hadoop installed and runs the TestDFSIO and Terasort benchmarks for the performance analysis in addition to an energy efficiency analysis.

New sources of fuel have been extensively searched in order to replace fossil products. Among these sources, hydrogen has attracted much interest due to its cleanliness, availability, and efficiency. It can be produced from numerous resources, such as water electrolysis, natural gas reforming. For instance, it is widely used by fuel cells in vehicles and buildings in order to produce both valuable electricity and thermal energy. The electrolysis of water for producing hydrogen using renewable energy sources, especially solar photovoltaic systems, is one of the appropriate solutions to produce hydrogen. This paper focuses on the performance analysis of a system consisting of a grid connected photovoltaic panels, an electrolyzer for producing hydrogen, and a proton exchange membrane fuel cell operating as a cogeneration system for supplying the building with both electricity and thermal energy. The particularity of this system is that the production of the electricity using photovoltaic panels and the production of hydrogen using electrolyzer can be performed in different sites and are connected through the traditional electricity grid. As a result, the overall efficiency of the studied system reaches 60%, which shows its effectiveness when supplying the building with both electricity and thermal energy.

Mobile Ad Hoc Networks are wireless networks, of which the installation does not require any existing infrastructure, this property has induced the philosophy of “all distributed” and distributed control. The departure and arrival of a node is autonomous and does not interfere with the continuity of services. These advantages allow the deployability of these networks in situations where the use of wired networks is expensive, difficult or even impossible. However, with these advantages, some security concerns are emerging. Indeed, with a shared communication medium and in the absence of a central entity or a fixed infrastructure, traditional security solutions are not adapted, it is now difficult to manage encryption keys, certificate distribution, and trust management between nodes. As a node does not necessarily have knowledge about others when the network is growing, trust a priori may not exist. We are interested in this paper to study security problems in Manets in general, and more particularly those related to trust management. This paper detail, the principle, the constraints, and the challenges of deploying suitable trust models, to deal with Manets constraints. An example of a light trust model for enforcing security in OLSR routing protocol used for Manets, is presented and evaluated.

Balancing production and demand of energy is the main challenge to integrate renewable energy sources (RES) in micro-grid (MG) systems. However, the variability and the uncertainty nature of the production and the consumption make the system more difficult to control. In fact, weather conditions influence on the production of renewable energy sources while occupancy influences on the power consumption. Therefore, the development of accurate short term forecasts are needed for a seamless integration of RES (e.g. photovoltaic system, wind turbine) together with the traditional electrical grid in MG systems. This paper presents a forecasting model for predicting the power production and consumption in MG systems together with the battery state of charge (SoC). A control strategy is then implemented to balance the Demand/Response by taking into account the forecasted and real-time values. Based on the data collected from a real MG system, simulation results are presented to show the effectiveness of power forecasting for MG control.