Energy Management Techniques Research Articles

Motivational Psychology Driven AC Management Scheme: A Responsive Design Approach

Global warming, and the resultant climate change, has become an urgent global issue. One potential partial solution to this problem and the focus of this paper is to design energy management mechanisms for buildings that encourage users’ acceptance of the technology for increased environmental sustainability. Particularly, this paper focuses on the design of an energy management technique for the air-conditioning (AC) systems in residential buildings that considers users’ views on how they use such technology. Note that while a large number of energy management mechanisms are available in the literature for ACs, most of these studies, however, do not consider how well the users may accept their use in the building. To address this issue, this paper first synthesizes some motivational psychology literature to understand users’ attitudes toward adopting such management techniques for the ACs within the building. Then, the obtained insights from various motivational models are incorporated into the design of an energy management scheme that encourages consumers to accept the technology that reduces electricity consumption, the cost of electricity, peak power from the grid; and the generation of CO2 in the residential building. Finally, some experimental results are provided to illustrate how the designed energy management mechanism validates the motivational psychology models in terms of providing various benefits to the users, and thus shows the potential of being accepted by them.

Multi-constrained ascent guidance for solid propellant launch vehicles

Solid rocket motors expect to remove the thrust termination mechanism to increase the strength of structure and reduce the cost, which induce new difficulties and challenges to ascent guidance. This paper presents an ascent guidance algorithm for small solid launch vehicles (SSLVs) which shut off by fuel exhaustion. A pointing algorithm is tailored for the baseline guidance algorithm of SSLVs with constraints of the velocity vectors and the position vectors. Subsequently, an energy management technique is developed for dissipating the extra energy that the solid rockets have when shutting off by fuel exhaustion. The energy management technique primarily allocates the energy to different sub-stages, while an attitude control energy management method is applied to the dissipation of the excess energy at one stage. Finally, the proposed guidance algorithm is verified by Monte Carlo simulations in which the dispersions of vehicle mass, operation temperature of motors and aerodynamic coefficient as well as random wind shear are considered. The testing results demonstrate the capability, strong robustness and excellent performance of the proposed guidance algorithm.

Prolonging the Lifetime of Wireless Sensor Networks: A Review of Current Techniques

There has been an increase in research interest in wireless sensor networks (WSNs) as a result of the potential for their widespread use in many different areas like home automation, security, environmental monitoring, and many more. Despite the successes gained, the widespread adoption of WSNs particularly in remote and inaccessible places where their use is most beneficial is hampered by the major challenge of limited energy, being in most instances battery powered. To prolong the lifetime for these energy hungry sensor nodes, energy management schemes have been proposed in the literature to keep the sensor nodes alive making the network more operational and efficient. Currently, emphasis has been placed on energy harvesting, energy transfer, and energy conservation methods as the primary means of maintaining the network lifetime. These energy management techniques are designed to balance the energy in the overall network. The current review presents the state of the art in the energy management schemes, the remaining challenges, and the open issues for future research work.

Combined vibration and RF harvester technique for energy management in sensor device

The main objective of this research work is to increase the energy efficiency in the wireless sensor network through the energy harvesting techniques. The proposed combined vibration and RF harvester (CVRH) technique can increase the energy efficiency and reduce the energy consumption. In WSN, sensor node has high energy consumption due to the process of sensing and transferring. From the physical environmental condition, vibration and RF energy will be collected from their respective ambient source. Vibration and RF energy are in the form of an electromagnetic wave and that will be converted into DC by using the bridge wave rectifier. And that DC current will be the supply of the battery of sensor node. During the sensing process, battery will be utilised by the sensor node and in the transmission process, the battery will be charged from the energy harvester. In experimental results, results have been analysed by using the parameters such as energy consumption, energy efficiency, and average power and drop rate.

Combined vibration and RF harvester technique for energy management in sensor device

The main objective of this research work is to increase the energy efficiency in the wireless sensor network through the energy harvesting techniques. The proposed combined vibration and RF harvester (CVRH) technique can increase the energy efficiency and reduce the energy consumption. In WSN, sensor node has high energy consumption due to the process of sensing and transferring. From the physical environmental condition, vibration and RF energy will be collected from their respective ambient source. Vibration and RF energy are in the form of an electromagnetic wave and that will be converted into DC by using the bridge wave rectifier. And that DC current will be the supply of the battery of sensor node. During the sensing process, battery will be utilised by the sensor node and in the transmission process, the battery will be charged from the energy harvester. In experimental results, results have been analysed by using the parameters such as energy consumption, energy efficiency, and average power and drop rate.

Energy-Efficient Fault-Tolerant Mapping and Scheduling on Heterogeneous Multiprocessor Real-Time Systems

Energy saving and system reliability are two crucial issues for designing modern multiprocessor systems. There has been reliability-aware power management with dynamic voltage–frequency scaling (DVFS) schemes in recent studies. However, they are limited to optimization under the impact of DVFS on energy and reliability and have not considered reducing the non-negligible leakage energy consumption. In this paper, we focus on co-management of system reliability and total energy for applications with precedence constrained tasks on heterogeneous multiprocessor real-time systems. We first investigate the impact of energy management techniques on both reliability and energy of the systems using task recovery for fault tolerance and then propose an Energy-efficient Fault-tolerant Scheduling (EFS) scheme integrated with power mode management, which can mitigate the negative impact of DVFS on system reliability. To obtain the optimal energy-efficient reliability-guaranteed scheduling for pre-mapped applications on systems considering various realistic issues, we build mixed integer linear programing formulations with the proposed EFS scheme. To address mapping and scheduling for energy-efficiency and fault-tolerance, we finally develop a framework implemented by a List-based Binary Particle Swarm Optimization algorithm. The extensive comparative evaluations for synthetic and realistic benchmarks show that our approaches outperform several related studies in terms of energy consumption and system reliability.

Robust thermal and electrical management of smart home using information gap decision theory

After restructuring in the electricity industry, some concepts such as smart home and renewable energy sources improved extensively. This paper proposes information gap decision theory (IGDT) technique for robust energy management of smart home in summer season in the presence of market price fluctuation. Therefore, the proposed model is practical in a realistic model. The IGDT method contains the robustness and opportunity functions. The harmful aspect of price uncertainty is modeled by robustness function and the beneficial aspect of price uncertainty is modeled by opportunity function. The proposed IGDT-based performance optimization problem of low-energy smart home is formulated as mixed-integer non-linear programming (MINLP) and solved by General Algebraic Modeling System (GAMS) optimization software. Two scenarios as normal and smart scenarios are used to investigate the proposed model.

Novel Energy Management Technique for Hybrid Electric Vehicle via Interconnection and Damping Assignment Passivity Based Control

Abstract The energy management of Hybrid Electric Vehicles (HEV) has witnessed significant academic and industrial attention in recent years. Indeed, the use of different power sources in HEV requires both smart and efficient energy management scheme to split and manage power among them. The energy management strategy should enable continuous supply load balance. In HEVs, the energy management procedure should consider the constraints of load and the different available sources. The fundamental contribution of this paper is the energy management in the HEV in presence of faults in the fuel cell (FC) level while considering battery state of charge constraints. For the flexibility and durability of the proposed energy management scheme, the system mathematical modeling using Port-Controlled Hamiltonian (PCH) approach is developed. Therefore, the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) is used for a smartly energy management. According to the simulation results, the IDA-PBC is an adequate nonlinear control method that guarantees the stability of the system.

Priority-based Energy Management Technique for Integration of Solar PV, Battery, and Fuel Cell Systems in an Autonomous DC Microgrid

Recent times have witnessed a significant positive trend in adoption of renewable energy sources and DC-based loads, which questions the efficiency and reliability of existing structure of electrical power system. DC microgrids are identified as potential solutions for addressing India's and other developing nations' rural electrification. This paper presents an energy management technique for isolated DC microgrids for academic readers, as well as provides an introduction to not-familiarized readers for understanding the common design challenges for implementing off-grid power systems. The principle of the proposed technique is based on law of energy conservation defined by the energy balance equation and is implemented by synthesizing the instantaneous current reference and hysteresis control with source priority, which was validated by rigors simulation studies in Matlab/Simulink. Furthermore, in order to demonstrate the modularity and scalability of the algorithm, it was tested on a prototype hardware test setup at the Solar Energy Research Centre, VIT University.

Fast Power and Energy Management for Future Many-Core Systems

Future servers will incorporate many active low-power modes for each core and for the main memory subsystem. Though these modes provide flexibility for power and/or energy management via Dynamic Voltage and Frequency Scaling (DVFS), prior work has shown that they must be managed in a coordinated manner. This requirement creates a combinatorial space of possible power mode configurations. As a result, it becomes increasingly challenging to quickly select the configuration that optimizes for both performance and power/energy efficiency. In this article, we propose a novel queuing model for working with the abundant active low-power modes in many-core systems. Based on the queuing model, we derive two fast algorithms that optimize for performance and efficiency using both CPU and memory DVFS. Our first algorithm, called FastCap, maximizes the performance of applications under a full-system power cap, while promoting fairness across applications. Our second algorithm, called FastEnergy, maximizes the full-system energy savings under predefined application performance loss bounds. Both FastCap and FastEnergy operate online and efficiently, using a small set of performance counters as input. To evaluate them, we simulate both algorithms for a many-core server running different types of workloads. Our results show that FastCap achieves better application performance and fairness than prior power capping techniques for the same power budget, whereas FastEnergy conserves more energy than prior energy management techniques for the same performance constraint. FastCap and FastEnergy together demonstrate the applicability of the queuing model for managing the abundant active low-power modes in many-core systems.

An Energy-Aware Runtime Management of Multi-Core Sensory Swarms.

In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configuration adaptively to satisfy the performance goal. As today’s sensory swarms are usually implemented using multi-core processors with adjustable clock frequency, we propose to monitor the CPU workload periodically and adjust the task-to-core allocation or clock frequency in an energy-efficient way in response to the workload variations. In doing so, we present an online heuristic that determines the most energy-efficient adjustment that satisfies the performance requirement. The proposed method is based on a simple yet effective energy model that is built upon performance prediction using IPC (instructions per cycle) measured online and power equation derived empirically. The use of IPC accounts for memory intensities of a given workload, enabling the accurate prediction of execution time. Hence, the model allows us to rapidly and accurately estimate the effect of the two control knobs, clock frequency adjustment and core allocation. The experiments show that the proposed technique delivers considerable energy saving of up to 45%compared to the state-of-the-art multi-core energy management technique.

Energy management in DC microgrid with energy storage and model predictive controlled AC–DC converter

Renewable energy-based direct current microgrids are becoming popular due to their higher energy efficiency than AC microgrids. Energy storage system (ESS) helps to stabilise the system against the instability caused by stochastic nature of the renewable sources as well as demand variation within a microgrid. This work proposes effective energy management and control techniques for a photovoltaic-based DC microgrid. In order to regulate power exchange with the main grid, model predictive controller (MPC) is proposed to control bidirectional voltage source converter. The performance of MPC is compared with classical PI controller. Frequency approach control of hybrid ESS comprising of battery and supercapacitor is used to suppress both low- and high-frequency power variation in the microgrid by exchanging power with DC bus. To confirm the efficacy of proposed control strategy, real-time simulations of the system are carried out considering both grid-connected and islanded modes of the microgrid operation. The results show the effectiveness of the proposed control strategy for energy management in the microgrid. Further, real-time controller hardware-in-the-loop experiment validates the simulation results.

Energy Management in Wireless Sensor Networks-A Review

Wireless Sensor Networks in recent times have been intensively studied owing to their ubiquitous nature, small size and limited resources. Decreasing size along with smart technological advances have enabled them to be applied extensively ranging from ecological monitoring, target tracking to the vast fields of defence and security. Wireless Sensor Networks comprise of small sensor nodes which are networked in such a way that they are capable of sensing, processing and communicating information for human welfare. As the sensor nodes are primarily battery driven and usually operative in inaccessible areas therefore manual replacement of batteries for such kind of small devices is practically impossible. Therefore Wireless Sensor Networks face acute energy shortage which constrains their network lifetime. Lifetime Enhancement, judicious and efficient energy utilization are therefore major areas where research is going on. In this paper a comprehensive and holistic view of energy management and optimization techniques have been discussed. The goal of this paper is to analyse different approaches to energy conservation prevalent at the present time which include duty cycle, data driven and mobility based approaches. We have concluded the paper by suggesting some future research strategies for energy optimization and enhancing the life time of the network.

Demand Side Management Based Optimal Energy Management Technique for Smart Grid

Smart grid (SG) is explained as an intensification of 20th century grid. It delivers electricity from the production side to the consumer side using modern digital technology to reduce the customer’s energy consumption and save energy. Demand side management (DSM) has great importance in smart grids, since it ensures demand balance in real time. Along with improvement in cost reduction, we have worked on rebound peak problem. This research seeks an optimal energy management technique for minimizing electricity cost of consumers along with cut down in rebound peak. An optimal algorithm is designed for reducing cost and rebound peak while fulfilling the demands of consumers. Moreover, we worked on more efficient constraints to attain more effectual results. Optimization techniques are used to find productive solutions. Suboptimal solution is proposed to reduce computational complexity. The effectiveness of the proposed solution is demonstrated via simulation and noticeable results.

Improved Home Energy Management Techniques for Smart Grids

Improved Home Energy Management Techniques for Smart Grids

Assessment of energy consumption in existing buildings

There has been general recognition within the construction industry that there is a discrepancy between the amount of energy that buildings actually use and what designers considered that they should use. This phenomenon is termed “The Performance Gap” and is normally associated with new buildings. However, existing and older buildings contribute a greater amount of operational carbon. In response to the Performance Gap, CIBSE have developed the TM54 process which is aimed at improving energy estimates at design stage. This paper considers how the TM54 process can also be used to develop energy management procedures for existing buildings. The paper describes an exercise carried out for a university workshop building in which design energy use has been compared with the actual building energy use and standard benchmarks. Moreover, a sensitivity assessment has been carried out using different scenarios based on operation hours of building/equipment, boiler efficiency and impact of climate change. The analysis of these results showed high uncertainty in estimates of energy consumption. If carbon challenges are to be met then improved energy management techniques will require a more systematic approach so that facilities managers can identify energy streams and pinpoint problems, particularly where they have assumed responsibility for existing buildings which often have a legacy of poorly metered fuel consumption.

A STUDY OF ENERGY FLOW AT AN ELECTRICAL SUBSTATION THROUGH ENERGY AUDIT

This paper deals with the study of energy flow in a 220/ 66 kV substation. This study emphasizes on energy conservation by reducing power losses. Saving energy is the primary and overwhelming motivating factor for making energy improvements. Energy conservation can increase the environmental quality and higher the savings. Energy conservation lowers the energy costs by preventing future resource depletion (renewable energy source). Utilizing the electric power in the proper manner without wastage is the most important thing to consider. The main objectives are accumulating data at various energy points, identifying energy losses and critical junctions, enabling energy conservation and cost savings. Planning the use of electrical power in the most judicious way such that the usage as well as its wastage is minimized. For this purpose a energy management technique called energy audit is adopted. Energy auditing is required to determine the energy flows through various electrical installations such as feeders, transformers, substations in an electrical network system.

ENERGY AWARE ZONE ROUTING PROTOCOL FOR MANET

Mobile Ad hoc Network (MANET) is a dynamic network consisting of a collection of wireless mobile nodes that communicate with each other without the need of centralized authority. Each node can send and receive data, and it should also forward routing information unrelated to its own use. Routing protocols in MANET establish path between source and destination based on number of hops. Establishment of shortest path alone is not sufficient to prolong the network lifetime. Energy consumption reduction methods are necessary as the nodes in MANET are restricted by battery supply. Energy is drained when the MANET nodes transmit and receive the data. For itself, energy management techniques are necessary in order to improve the performance of the routing protocol. Both proactive and reactive protocols have trade-off in them. ZRP is a hybrid protocol which overcomes the shortcomings of proactive and reactive routing protocols. ZRP divides the entire network into zones of variable size where routing inside the zone is performed using proactive routing approach and exterior to zone is performed using reactive routing approach. The performance characteristics of the ZRP protocol are established through simulations by comparing it to well-known routing protocol namely AODV.

PV-Powered CoMP-Based Green Cellular Networks with a Standby Grid Supply

This paper proposes a novel framework for PV-powered cellular networks with a standby grid supply and an essential energy management technique for achieving envisaged green networks. The proposal considers an emerging cellular network architecture employing two types of coordinated multipoint (CoMP) transmission techniques for serving the subscribers. Under the proposed framework, each base station (BS) is powered by an individual PV solar energy module having an independent storage device. BSs are also connected to the conventional grid supply for meeting additional energy demand. We also propose a dynamic inter-BS solar energy sharing policy through a transmission line for further greening the proposed network by minimizing the consumption from the grid supply. An extensive simulation-based study in the downlink of a Long-Term Evolution (LTE) cellular system is carried out for evaluating the energy efficiency performance of the proposed framework. System performance is also investigated for identifying the impact of various system parameters including storage factor, storage capacity, solar generation capacity, transmission line loss, and different CoMP techniques.

Management and control strategy of a hybrid energy source fuel cell/supercapacitor in electric vehicles

Summary This paper deals with the modeling of electric vehicles using hybrid power source: fuel cell/supercapacitor. The fuel cell is connected to the direct current (DC) Bus via a boost converter and satisfies the load average power requirements. The supercapacitor is linked to the DC-Bus through a buck-boost converter and ensures the transient power requirements. A permanent-magnet synchronous motor is used as traction motor and connected to the DC-Bus through a 3-phase inverter. We propose an energy management technique, based on the urban driving cycle (ECE-15 driving cycle) and the extraurban driving cycle, with the use of conventional proportional-integral (PI) controllers. The main goals are as follows: first, to avoid fast operation of the fuel cell current so as to ensure optimal operation of the fuel cell system, and second, the supercapacitor allows a satisfactory dynamic of the DC-Bus so as to provide the required energy for the car transients so as to meet both urban and extraurban driving cycles. The performances of the proposed strategy are evaluated through some simulations dedicated to electric vehicle applications.