Energy Management Techniques Research Articles

Energy-Efficient Resources Management in Container- Based Clouds

Cloud enables access to a shared pool of virtual resources through Internet and its adoption rate is increasing because of its high availability, scalability and cost effectiveness. However, cloud data centers are one of the fastest-growing energy consumers and half of their energy consumption is wasted mostly because of inefficient allocation of the server’s resources. Therefore, this thesis focuses on software level energy management techniques that are applicable to containerized cloud environments. Containerized clouds are studied as containers are increasingly gaining popularity. And containers are going to be major deployment model in cloud environments.

Optimum Energy Control of a Robotic Electric Vehicle at Time with Improved Control Assignment Approaches

Vehicles have made extraordinary commitment to the development of current culture by fulfilling the requirements for more prominent portability in everyday life. The development of Internal Combustion Engine has contributed a ton to the car area. In any case, a lot of harmful discharges as carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), unburned hydrocarbons (HCs, etc have been messing contamination up, a dangerous atmospheric devation, and annihilation of the ozone layer. As the current pattern proposes, this method of transport is probably going to supplant inside burning motor (ICE) vehicles soon. Every one of the primary EV segments has various innovations that are at present being used or can get conspicuous later on. Improved control assignment approach strategies are fit for securing ideal force taking care of, obliging framework errors, and fitting ongoing applications can fundamentally improve the powertrain productivity at various working conditions. Rule-based techniques are just organized and effectively implementable continuously; however, a restricted optimality in force dealing with choices can be achieved. Enhancement based strategies are more fit for achieving this optimality at the cost of expanded computational burden. Over the most recent couple of years, these improvement based strategies have been being worked on to suit continuous application utilizing more prescient, recognitive and man-made reasoning apparatuses. This paper presents a conversation about these new patterns continuously improved control assignment approach. Consequently HEVs give better fuel economy contrasted with ICE based vehicles/regular vehicle. Energy management techniques are the calculations that choose the force split among motor constantly to improve the fuel economy and advance the presentation of HEVs.

CARBON FOOTPRINT EVALUATION AND ENVIRONMENTAL SUSTAINABILITY IMPROVEMENT THROUGH CAPACITY OPTIMIZATION

The burning of fossil fuel for power generation emits Greenhouse gases into the environment. Greenhouse Gases (GHGs) emission is the principal cause of global warming. In order to regulate the emissions of these gases, the emissions need to be assessed and quantified. Carbon footprint is the evaluation of human activities that lead to GHGs emissions. The Covenant University Electricity Network during periods when utility supply fail runs on diesel powered generators located at different centers within the campus. These generators emit carbon-based compounds into the environment. Assessing the quantity of carbon dioxide which is the principal GHGs emitted per year gives 33.14-tonnes. Analysis of the load profiles in the centers showed that all the eight generators are not supposed to be running simultaneously as it is now. This study developed a new network model where all the generators were integrated into a DC microgrid that ensured resource sharing. The model was simulated using energy management and optimization techniques resulting in reduced micro-generators engagement, Green House Gases emission and fuel consumption. Consequently, carbon dioxide emitted per year dropped to 18.44-tonnes from 33.14-tonnes. The developed model improved the carbon footprint of the campus by as much as 44.3%.

REALISM: Reliability-aware energy management in multi-level mixed-criticality systems with service level degradation

Mixed-criticality embedded systems, as the next-generation of safety-critical systems, are increasingly employed in the industry due to consolidating functionalities with varying criticality levels onto the same computing platform. Technology scaling, battery-supplied design, and heavy computation in mixed-criticality systems necessitate employing energy management techniques. Due to the degrading effects of these techniques on the system's reliability, minimizing energy consumption and meeting tasks’ timing constraints without sacrificing the reliability requirement is a vital challenge in designing mixed-criticality systems. In this paper, we propose REALISM; a novel Reliability- and Energy-Aware task mapping and scheduling approach in multi-core mixed-criticality systems with three levels of criticality. To guarantee the desired QoS of tasks from lower levels of criticality, our approach exploits the degraded WCET for these imprecise mixed-criticality tasks in critical modes. In design time, tasks are mapped to cores based on dynamic switching between different system utilizations in different modes. In each core, task instances are scheduled according to their virtual deadlines, which are calculated based on the system's overrun tolerance limit (TL) and the desired QoS, by time reservation. Then our heuristic DVFS techniques are applied in order to reduce system energy consumption. Our simulation results show that the REALISM provides on average ~41.5% (up to 52.7%) energy savings compared to the approach without energy management with 100% QoS degradation in lower levels of criticality. Also, with guaranteeing the system's desired QoS, REALISM provides on average ~38% (up to 46.2%) energy savings compared to the approach without energy management.

Energy management techniques and topologies suitable for hybrid energy storage system powered electric vehicles: An overview

Energy management system (EMS) in an electric vehicle (EV) is the system involved for smooth energy transfer from power drive to the wheels of a vehicle. During acceleration and deceleration periods, batteries in EV undergo high peak power consumption. Therefore, battery lifecycle degrades and subsequently reduces the drive range of an EV. Thus, hybridization of different energy resources becomes essential and seen as one of the alternative solutions for afore said issues. Further, hybridization along with efficient EM strategies helps to: (i) optimally utilize the energy storage systems during discharging and charging, (ii) improve the performance which in turn improves efficiency, and (iii) extend the drive range (iv) reduce the battery size. Though, many articles have been reported so far in literature for hybrid energy storage system (HESS) related to EM techniques; comprehensive review on: the configurations related to HESS, various EM strategies used in EV, performance evaluation of EM strategies for HESS configurations is not yet published. Therefore, this paper intends to provide a comparative assessment on different types of HESS topologies, types of EM techniques. The performance indices based on battery peak current reduction; amount of power stored back during regenerative braking has been compared and discussed. Further, a comparative analysis has been made on the system design and voltage variations. Hence, this review may be a nonstop solution for researchers and engineers working in the field of EVs.

A review on braking control and optimization techniques for electric vehicle

In this modern world, due to limited fossil fuels, increasing cost, and environmental concerns a clean and sustainable transportation system in the form of electric vehicles (EV) is now becoming popular. On the other hand, electric vehicles have an advantage such as zero-emission, less maintenance, comfortable ride, less noise, etc. An electric vehicle is driven by an electric motor so considering driving patterns and road traffic conditions the vehicle often requires braking operation. The most common method is to employ mechanical brakes to reduce the speed of EV. However, mechanical braking mechanism results in friction which in turn, into heating losses and reduces the efficiency of EV. Therefore, regenerative braking system (RBS) and energy management system are employed in addition to mechanical braking for increasing the braking efficiency of the EV system. It is reported in many literatures about different regenerative braking control techniques for EV systems. This paper presents a review of regenerative braking and braking energy management techniques by considering different driving situations and road conditions.

Development of a Smart Energy Monitoring System

The preservation of energy and the fight against global climate change are both benefited by computationally intelligent energy forecasting techniques for effective energy management at the consumer/producer side. Worldwide energy production and consumption are quite high, necessitating intelligent approaches with practicable implementation potential for effective energy management. The development of smart energy monitoring systems has become an increasingly important area of research in recent years. With the growing concern over climate change and the need to reduce greenhouse gas emissions, it is essential to find new ways to conserve energy and promote sustainable practices. One way to achieve this goal is by using smart energy monitoring systems that enable users to monitor and control their energy consumption. In this article, we will discuss the development of a smart energy monitoring system, its components, benefits, and challenges.

Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Having neither precise definition nor a commonly accepted scope, the term “MicroGrid” tends to be used differently across researchers and practitioners alike. The management of energy usage within a microgrid is one of the topics that was handled from numerous perspectives. This study presents systematic literature review (SLR) of research on architectures and energy management techniques for microgrids, providing an aggregated up-to-date catalogue of solutions suggested by the scientific community. The SLR incorporated 45 papers selected according to inclusion/exclusion criteria and defined a priori. The selection process was based on an automated search and covered three known digital libraries. The extraction process covers three main questions. (i) The architectures of microgrids including their components, their bus configuration, and the adopted utility grid policy. (ii) The employed methods to ensure an optimal usage of energy under uncertainty. (iii) The confronted challenges and constraints of the suggested strategies. The findings of this SLR indicate a great diversity of methods and a rich background. Finally, the SLR suggests that future research should take into account the uncertainty aspect relating to energy management rather than the direct use of historical data as it is commonly done in most research papers. A sensitivity analysis should be provided in the latter case.

An intelligent energy management and traffic predictive model for autonomous vehicle systems

In recent times, the utilization of autonomous vehicles (AVs) has been significantly increased over the globe. It is because of the tremendous rise in familiarity and the usage of artificial intelligence approaches in distinct application areas. Though AVs offer several benefits like congestion control, accident prevention, and so on, energy management and traffic flow prediction (TFP) remain a challenging issue. This paper concentrates on the design of intelligent energy management and TFP (IEMTFP) technique for AVs using multi-objective reinforced whale optimization algorithm (RWOA) and deep learning (DL). The proposed model involves an energy management module using fuzzy logic system to reach the specified engine torque with respect to different measures. For optimal tuning of the variables involved in the fuzzy logic membership functions (MFs), RWOA is employed to further reduce the energy utilization. Besides, the proposed model uses a DL-based bidirectional long short-term memory (Bi-LSTM) technique to perform TFP. For validating the efficacy of the IEMTFP technique, an extensive experimental validation is carried out. The resultant values ensured the goodness of the IEMTFP model in terms of energy management and TFP.

Computer‐assisted demand‐side energy management in residential smart grid employing novel pooling deep learning algorithm

Demand-side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer-assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling-based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real-time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short-term load forecasting and management over other traditional methods. Highlights In this research work: Novel pooling-based deep neural network is applied for residential energy management in a smart grid. The copula fusion theory is adopted to improve the accuracy of load management in a smart grid. Day-ahead and week-ahead prediction load on Tamil Nadu Electricity Board dataset in summer and winter season was used to validate the performance of the proposed method with other data-driven methods.

A Systematic Review of Algorithms in People Images Detection Based on Artificial Vision Techniques for Energy Management in Air Conditioners

Over the years, the development of artificial intelligence has influenced the fact that the algorithms applied in video devices are renewed every day in their object detection area, such as pattern recognition for detecting an object, image, person. This research aims to identify the algorithms for detecting people's image through artificial vision, and its application focused on energy management in air conditioners. The following research questions were established: Q1: How many studies refer to algorithms based on detecting a person's image? Q2: How many studies refer to energy management in air conditioners based on artificial vision? Q3: Are there artificial vision techniques for the automatic turning on and off a device? Q4: What types of algorithms in detecting a person's image based on artificial vision exist today? The search for information was based on research criteria related to the topic developed in four virtual libraries. This research criterion includes 213 relevant studies, including 46 that were of interest for developing the research. This research's results determine that the viola-jones algorithm shows greater effectiveness with 91.5% in relation to the other algorithms, followed by the HOG algorithm with 90.29% effectiveness. Considering the different efficiency parameters established in this study, we can conclude that this algorithm can be used in various applications, security, energy management, video surveillance and environment.

A probabilistic hot water usage model and simulator for use in residential energy management

Water heating contributes up to 40% of a household’s total electricity usage and places a substantial burden on the electricity grid due to high power ratings and users’ largely simultaneous hot water usage. The main determinants of its electricity draw are physical properties such as set temperature, insulation, and plumbing configuration; environmental conditions such as ambient temperature and inlet temperature; and the hot water usage profiles. These profiles include the usage volumes, the times of usage and the outlet temperatures. The efficacy of energy management techniques that model water heaters and the accuracy of their simulation results therefore rely on representative hot water usage profiles. Existing models for household hot water usage neglect differences between users, and temporal variations such as the season and the day of the week, and are not fully autonomous. We propose a probabilistic data-driven model for modelling individualised hot water profiles and an accompanying hot water usage simulator that includes all these factors. We gathered data from 77 residential households over a period of one year to train and evaluate the model for all four seasons. The results show that the simulated hot water usage profiles match the statistical properties of the measured data. Moreover, the individual hot water usage modelling and the resulting aggregated energy load on the grid closely match the measured data, improving on the existing hot water usage by halving the modelling error.

Energy management solutions in the Internet of Things applications: Technical analysis and new research directions

By advancement of Internet of Things (IoT) technology in smart life such as smart city, smart home, smart healthcare and smart transportation, interconnections between smart things are growing that complicate evaluation of efficiency factors on the intelligent systems. Energy consumption as one of the most challenging issues is increasing with the growing IoT devices and existing interconnections between cloud data centers, mobile applications and human activities. Managing energy efficiency and power consumption is one of the important issues in green IoT-enabled technologies. This paper presents an overview on the energy management solutions in the IoT based on Systematic Literature Review (SLR). The main goal of this SLR-based overview is to recognize significant research trends in the field of energy management and power consumption techniques which need additional consideration to highlight more efficient and effective methods in IoT. Also, a taxonomy is proposed to categorize the existing research studies on energy management solutions. A statistical and technical analysis of reviewed existing papers are provided, and evaluation factors and attributes are discussed. We observed that variety of published research papers in smart home have highest percentage to evaluate energy management in the IoT. Also, deep learning and clustering methods are must popular techniques that were applied to evaluate the energy management in IoT case studies. Finally, new challenges and forthcoming issues of the energy management and efficient power consumption methods are presented.

Linear Regression Algorithm Results for a PV Dual-Axis Tracking-Type System

A photovoltaic (PV) module converts solar energy into electrical energy. In order to increase the output power of any PV module, several factors including tilt angle, orientation angle, load profile, environmental condition, latitude of the location site, and energy management techniques should be considered. It is essential to continuously deliver the highest possible power to a load for a given day, which may be achieved by using a tracking-type system as compared to a fixed-type system. The purpose of this paper is to present the results of an algorithm that may be applied to a dual-axis system located in an elevated plateau of the interior of South Africa in order to sustain a high output power. Two identical 310W PV modules were used for a fixed-type and tracking-type system. The fixed-type system was installed at a tilt angle of Latitude minus 10° serving as a baseline to the tracking-type system. A LabView user interface was developed to record and display the voltage and current measurements from the PV modules. Results indicate that the dual-axis tracking-type system extracted more power (on average 39.32% more power) as compared to the fixed-type system. A key recommendation is to use a linear regression algorithm with a tracking-type system to enable a higher output energy yield for a given day.

A PARAMETRIC STUDY OF THE ENERGY EFFICIENCY OF EXISTING AIR-CONDITIONED BUILDINGS IN KUWAIT

ABSTRACT In Kuwait air-conditioning systems consume approximately 70% of the grid energy during the long summer months. In this paper, various practical approaches are investigated to enhance the energy efficiency and decrease the energy consumption of five existing air-conditioned (AC) buildings in Kuwait City. The process of energy management in air-conditioned buildings in Kuwait is overwhelming due to high energy consumption in the building sector. This study proposed an optimization technique for the proper energy management of installed AC systems to target energy-efficient buildings. In this study the aim is to explore the effect of different operating parameters, both theoretically and experimentally, and to contribute to the reduction of AC energy consumption. Consequently, the relationship between the thermal load in the air-conditioned buildings and the actual electrical energy consumption is determined, and remedial measures, along with different recommendations for energy saving, are presented. The actual thermal loads of each selected building were calculated and compared with the installed cooling capacity of the AC systems. From the results obtained it was concluded that, by implementing the suggested remedial measures, the predicted load in the selected buildings could be decreased significantly from the existing installed capacity of the cooling systems. Most of the remedial measures suggested for energy management lead to a reduction in power consumption and increased energy efficiency at different levels based on the specifications of the buildings considered and the AC systems installed, resulting in improved in economy, a reduced carbon footprint, and a cleaner environment.

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

Domestic load profiles in the residential sectors are being modified with the adoption of smart home management systems and solar generation. In addition, houses with rooftop PV behave like local generators, contributing to the growth of the penetration of PV energy. Hence, the demand for power is declining day by day. However, the increasing PV penetration causes technical challenges for the power system, such as the “duck curve”. This can be addressed through home energy management (HEM) techniques including peak shaving, load shifting with smart home devices. In this regard, electric water heaters (EWH), with high thermal mass and being ubiquitous, are attractive and low-cost energy storage systems. In this article, a case study for one of the largest rural field smart energy technology demonstrators involving business, industries, and more than 5,000 residences, located in Glasgow, KY, US, is presented. Furthermore, a HEM system, which aims to minimize the total energy usage and peak demand by regulating the heating, ventilation, and air-conditioning (HVAC) systems, water heaters, and batteries, thereby benefiting both the utility and the consumer is proposed. This work also demonstrates the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries are calculated and compared with respect to different peak reduction targets. Long term load prediction by considering different fractions of smart homes for the utility is also provided.

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems.

A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation

The present research investigations in power management fraternity are focused towards the realization of smart microgrid (MG) technologies. Due to intrinsic advantages of Direct Current (DC) system in terms of compatibility with power generation sources, modern loads and storage devices, DC MG has becoming popular over Alternate Current (AC) system. A secondary voltage and current control schemes of DC MG system are mainly based on the distributed consensus control of Multi-agent system (MAS) to balance generation and the demand. The basic concern of the cooperative control of MAS is consensus, which is to design a suitable control law such that the output of all agents can achieve synchronization. The distributed consensus algorithm requires much less computational power and information exchange in between the neighbor’s agent. Meanwhile the other controllers such as model predictive control (MPC) requires accurate dynamic models with high computational cost and it suffers from lack of flexibility. The hierarchical consensus control technique is classified into three levels according to their features, namely primary, secondary, and tertiary. MAS is a popular distributed platform to efficiently manage the secondary control level for synchronization and communication among the power converters in autonomous MGs. In this article, various primary control techniques for local voltage control, voltage restoration in the secondary control level and tertiary control for energy management techniques are discussed. With this, the key emphasis to reduce the voltage deviation and disturbances in a heterogeneous DC MG network solutions are discussed. Furthermore, to analyze the system response and the charging and discharging characteristics of the battery unit, the developed second order heterogeneous consensus controller is compared with the traditional homogeneous consensus control and droop control methods. Finally a detailed discussion on simulation case study using heterogeneous consensus control method over the traditional methods are provided using MATLAB/Simulink platform.

An overview of thermal energy storage system

As we known of humanity's massive use of thermal energy, any advances in thermal energy management techniques may have a major positive impact on society. Thermal energy storage is an important part of thermal energy management. This review covers the following features of TES: (1) A broad range of topics in the area of thermal energy storage are addressed. The role of TES in the context of various thermal energy sources is discussed, as well as how TES eliminates the need for fossil fuel combustion. The use of TES in solar power production, building thermal comfort, and other specialized applications is discussed. (2) Provides insight into several types of TES storage materials, including physical characteristics, cost, operating performance, and application appropriateness. (3) A description of the many kinds of TES systems is provided. Different kinds of criteria are used to classify TES systems. Seasonal TES systems, CSP plant TES systems, TES systems for residential solar thermal applications, heat and cold storages in building HVAC systems, and other TES systems are discussed. Thermo cline, packed bed, fluidized bed, moving bed, and other active TES systems are investigated. The use of passive TES systems in buildings, textiles, cars, and other applications is discussed. The following is a list of TES systems that operate in the cold, low, medium, and high temperature ranges. TES system design parameters, operational problems, and cost models are addressed.

Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This article focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon partial observable Markov decision process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated, respectively.