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To address the two urgent issues nowadays of protecting the environment and achieving energy sustainability, it is of strategic importance on a global scale to replace oil-dependent vehicles with electric drive vehicles (EDVs). Numerical simulation and optimization are essential to simulate the actual hardware and minimize the development procedure and cost. Accurate and efficient modeling, control, and optimization technologies have been the indispensable tools. EDVs have been continuously improved owing to the advancement of systematic control and power management, energy systems, batteries, and pack technologies, as well as economic and policy incentives, public awareness of energy sustainability/affordability, and environmental concerns. The primary goal of this special section is to provide timely solutions to technological and economic challenges in modeling, simulation, control, and optimization of EDVs. The accepted papers cover a range of different aspects of modeling, control, and optimization technologies for electric vehicles. A brief summary of them is given as follows. Most of papers are used to investigate the control and power management for EDVs. C. Lin and X. Cheng investigated a traction control strategy with an efficiency model in a distributed driving electric vehicle. S. Zhang et al. studied the optimal control strategy for a dual-motor coupling propulsion electric bus. The dynamic programming is applied to find the optimal control strategy including upshift threshold, downshift threshold, and power split ratio between the main motor and auxiliary motor. To improve the regenerative braking performance, J. Peng et al. provided a hierarchical control strategy for the cooperative braking system of an electric vehicle with separated driven axles. P. Lebeau et al. explored the possible integration of electric vehicles in urban logistics operations. A fleet size and mix vehicle routing problem with time windows for electric vehicles has been developed. In particular, an energy consumption model was integrated in order to consider variable range of electric vehicles. J.-J. Hwang et al. presented a novel charging strategy for fuel cell/battery electric vehicles. In comparison to the conventional switch control, a fuzzy control approach was employed to enhance the battery's state of charge (SOC). In terms of the batteries and grouping technology, S. Barcellona et al. proposed a simple method to investigate the effect of the duty cycle on the batteries lifetime through tests performed on different cells for different kinds of cycle. In this way, a generic complex cycle could be seen as a composition of elemental cycles by means of Rainflow procedures. Consequently, the ageing due to any cycle could be estimated starting from the knowledge of simpler cycles from their research results. For the research field of energy systems and networks, L. Niu and D. Zhang analyzed the actual operational data of electric taxis in Shenzhen, China. An electric taxis charging guidance model was established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation results showed that electric taxis could be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. In summary, the included seven papers cover the topics of modeling, control, and optimization technologies in EDVs with the new research achievements; we hope and are sure that the readers of this special issue could find valuable references to their research on EDVs.

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

The main purpose for developing biofuel is to reduce GHG (greenhouse gas) emissions, but the comprehensive environmental impact of such fuels is not clear. Life cycle analysis (LCA), as a complete comprehensive analysis method, has been widely used in bioenergy assessment studies. Great efforts have been directed toward establishing an efficient method for comprehensively estimating the greenhouse gas (GHG) emission reduction potential from the large-scale cultivation of energy plants by combining LCA with ecosystem/biogeochemical process models. LCA presents a general framework for evaluating the energy consumption and GHG emission from energy crop planting, yield acquisition, production, product use, and postprocessing. Meanwhile, ecosystem/biogeochemical process models are adopted to simulate the fluxes and storage of energy, water, carbon, and nitrogen in the soil-plant (energy crops) soil continuum. Although clear progress has been made in recent years, some problems still exist in current studies and should be addressed. This paper reviews the state-of-the-art method for estimating GHG emission reduction through developing energy crops and introduces in detail a new approach for assessing GHG emission reduction by combining LCA with biogeochemical process models. The main achievements of this study along with the problems in current studies are described and discussed.

Energy is an indispensable factor for the economic growth and development of a country. Energy consumption is rapidly increasing worldwide. To fulfill this energy demand, alternative energy sources and efficient utilization are being explored. Various sources of renewable energy and their efficient utilization are comprehensively reviewed and presented in this paper. Also the trend in research and development for the technological advancement of energy utilization and smart grid system for future energy security is presented. Results show that renewable energy resources are becoming more prevalent as more electricity generation becomes necessary and could provide half of the total energy demands by 2050. To satisfy the future energy demand, the smart grid system can be used as an efficient system for energy security. The smart grid also delivers significant environmental benefits by conservation and renewable generation integration.

Cooperative communication (CC) is used in topology control as it can reduce the transmission power and expand the transmission range. However, all previous research on topology control under the CC model focused on maintaining network connectivity and minimizing the total energy consumption, which would lead to low network capacity, transmission interruption, or even network paralysis. Meanwhile, without considering the balance of energy consumption in the network, it would reduce the network lifetime and greatly affect the network performance. This paper tries to solve the above problems existing in the research on topology control under the CC model by proposing a power assignment (DCCPA) algorithm based on dynamic cooperative clustering in cooperative ad hoc networks. The new algorithm clusters the network to maximize network capacity and makes the clusters communicate with each other by CC. To reduce the number of redundant links between clusters, we design a static clustering method by using Kruskal algorithm. To maximize the network lifetime, we also propose a cluster head rotating method which can reach a good tradeoff between residual energy and distance for the cluster head reselection. Experimental results show that DCCPA can improve 80% network capacity with Cooperative Bridges algorithm; meanwhile, it can improve 20% network lifetime.

Increasing in energy consumption, particularly with the ever-increasing growth and development of urban systems, has become a major concern in most countries. In this paper, the authors propose a cost-effective ZigBee-based building energy monitoring and control system (ZBEMCS), which is composed of a gateway, a base station, and sensors. Specifically, a new hardware platform for power sensor nodes is developed to perform both local/remote power parameter measurement and power on/off switching for electric appliances. The experimental results show that the ZBEMCS can easily monitor energy usage with a high level of accuracy. Two typical applications of ZBEMCS such as subentry metering and household metering of building energy are presented. The former includes lighting socket electricity, HVAC electricity, power electricity and special electricity. The latter includes household metering according to the campus’s main function zone and each college or department. Therefore, this system can be used for energy consumption monitoring, long-term energy conservation planning, and the development of automated energy conservation for building applications.

Recently, a novel biomass pulverization technology was proposed by our group. In this paper, further detailed studies of this technology were carried out. The effects of feed moisture and crusher operational parameters (rotor speed and blades gap) on product particle size distribution and energy consumption were investigated. The results showed that higher rotor speed and smaller blades gap could improve the hit probability between blades and materials and enhance the impacting and grinding effects to generate finer products, however, resulting in the increase of energy consumption. Under dry conditions finer particles were much more easily achieved, and there was a tendency for the specific energy to increase with increasing feed moisture. Therefore, it is necessary for the raw biomass material to be dried before pulverization.

The book consists of chapters based on selected papers of international conference Power, Control and Optimization 2012, held in Las Vegas, USA. Readers can find interesting chapters discussing various topics from the field of power control, its distribution and related fields. Book discusses topics like energy consumption impacted by climate, mathematical modeling of the influence of thermal power plant on the aquatic environment, investigation of cost reduction in residential electricity bill using electric vehicle at peak times or allocation and size evaluation of distributed generation using ANN model and others. Chapter authors are to the best of our knowledge the originators or closely related to the originators of presented ideas and its applications. Hence, this book certainly is one of the few books discussing the benefit from intersection of those modern and fruitful scientific fields of research with very tight and deep impact on real life and industry.This book is devoted to the studies of common and related subjects in intensive research fields of power technologies. For these reasons, we believe that this book will be useful for scientists and engineers working in the above-mentioned fields of research and applications.

The energy utilization of sensor nodes in large scale wireless sensor network points out the crucial need for scalable and energy efficient clustering protocols. Since sensor nodes usually operate on batteries, the maximum utility of network is greatly dependent on ideal usage of energy leftover in these sensor nodes. In this paper, we propose an Energy Efficient Cluster Based Scheduling Scheme for wireless sensor networks that balances the sensor network lifetime and energy efficiency. In the first phase of our proposed scheme, cluster topology is discovered and cluster head is chosen based on remaining energy level. The cluster head monitors the network energy threshold value to identify the energy drain rate of all its cluster members. In the second phase, scheduling algorithm is presented to allocate time slots to cluster member data packets. Here congestion occurrence is totally avoided. In the third phase, energy consumption model is proposed to maintain maximum residual energy level across the network. Moreover, we also propose a new packet format which is given to all cluster member nodes. The simulation results prove that the proposed scheme greatly contributes to maximum network lifetime, high energy, reduced overhead, and maximum delivery ratio.

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network’s lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

Energy consumption in optical access networks costs carriers substantial operational expense (OPEX) every year and is one of contributing factors for the global warming. To reduce energy consumption in the 10-gigabit Ethernet passive optical network (10G-EPON), a hybrid intracycle and cyclic sleep mechanism is proposed in this paper. Under heavy traffic load, optical network units (ONUs) can utilize short idle slots within each scheduling cycle to enter intracycle sleep without postponing data transmission. In this way, energy conservation is achieved even under heavy traffic load with quality of service (QoS) guarantee. Under light traffic load, ONUs perform long cyclic sleep for several scheduling cycles. The adoption of cyclic sleep instead of intracycle sleep under light traffic load can reduce unnecessary frequent transitions between sleep and full active work caused by using intracycle sleep. Further, the Markov chain of the proposed mechanism is established. The performances of the proposed mechanism and existing approaches are analyzed quantitatively based on the chain. For the proposed mechanism, power saving ability with QoS guarantee even under heavy traffic and better power saving performance than existing approaches are verified by the quantitative analysis. Moreover, simulations validate the above conclusions based on the chain.

In multihop data collection sensor network, nodes near the sink need to relay on remote data and, thus, have much faster energy dissipation rate and suffer from premature death. This phenomenon causes energy hole near the sink, seriously damaging the network performance. In this paper, we first compute energy consumption of each node when sink is set at any point in the network through theoretical analysis; then we propose an online distributed algorithm, which can adjust sink position based on the actual energy consumption of each node adaptively to get the actual maximum lifetime. Theoretical analysis and experimental results show that the proposed algorithms significantly improve the lifetime of wireless sensor network. It lowers the network residual energy by more than 30% when it is dead. Moreover, the cost for moving the sink is relatively smaller.

Energy hole is an inherent problem caused by heavier traffic loads of sensor nodes nearer the sink because of more frequent data transmission, which is strongly dependent on the topology induced by the sensor deployment. In this paper, we propose an autonomous sensor redeployment algorithm to balance energy consumption and mitigate energy hole for unattended mobile sensor networks. First, with the target area divided into several equal width coronas, we present a mathematical problem modeling sensor node layout as well as transmission pattern to maximize network coverage and reduce communication cost. And then, by calculating the optimal node density for each corona to avoid energy hole, a fully distributed movement algorithm is proposed, which can achieve an optimal distribution quickly only by pushing or pulling its one-hop neighbors. The simulation results demonstrate that our algorithm achieves a much smaller average moving distance and a much longer network lifetime than existing algorithms and can eliminate the energy hole problem effectively.

The course, technical progresses, and achievements of energy conservation of China steel industry (CSI) during 1980–2010 were summarized. Then, the paper adoptede-pmethod to analyze the variation law and influencing factors of energy consumptions of large- and medium-scale steel plants within different stages. It is pointed out that energy consumption per ton of crude steel has been almost one half lower in these thirty years, with 60% as direct energy conservation owing to the change of process energy consumption and 40% as indirect energy conservation attributed to the adjustment of production structure. Next, the latest research progress of some key common technologies in CSI was introduced. Also, the downtrend of energy consumption per ton of crude steel and the potential energy conservation for CSI during 2011–2025 were forecasted. Finally, it is indicated that the key topic of the next 15 years’ research on the energy conservation of CSI is the synergistic operation of material flow and energy flow. It could be achieved by the comprehensive study on energy flow network optimization, such as production, allocation, utilization, recovery, reuse, and resource, according to the energy quantity, quality, and user demand following the first and second laws of thermodynamics.