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In the face of escalating climate challenges, environmental sustainability has greatly become an urgent and non-negotiable priority, necessitating revolutionary advancements in energy management to reduce carbon footprints and drive profound improvements in overall sustainability. This paper presents an advanced optimization framework, PST-CESS, for managing power-sharing among multiple tenants within the centralized energy storage system (ESS). Our thorough evaluation demonstrates that the centralized ESS facilitated by PST-CESS substantially exceeds the performance of individualized ESS systems in pivotal areas such as peak load reduction, variability mitigation, and financial profitability. Specifically, the centralized ESS model achieves up to a 44.05% reduction in annual peak load for certain tenants and reduces electricity consumption variability by up to 57.67%. From a financial perspective, the centralized ESS model delivers remarkable advantages, reaching a break-even point in just 2.48 years, compared to the 5.08 years required for individualized ESS systems, even when accounting for battery capacity loss costs, also known as battery degradation costs. These results highlight the centralized ESS approach as a more economically advantageous and efficient solution, providing superior financial returns and optimized energy management for multi-tenant buildings. The strategic benefits and compelling evidence presented in this study strongly support the widespread adoption of centralized ESS models to maximize both economic and environmental benefits, establishing a new standard for sustainable energy management.

Evaluating the energy performance of existing buildings is critical for improving the efficiency and resilience of the building stock as a whole. The importance of this information holds at different scales, both locally and at the national and international levels. A major problem arises from the difficulty in obtaining information from existing buildings; often, the only available data are the yearly consumption per unit area, typically corresponding to the energy performance certificate (EPC). This paper shows how to address concerns of practical relevance with a limited number of variables by examining an EPC national database (including the major cities of Tallinn, Pärnu, Tartu, and others) that provides only EPCs, construction/renovation year and heated area. Through a systematic statistical investigation of nearly 35 000 EPCs of educational, office, commercial and other building typologies, we i) characterise the time evolution of EPC classes, ii) evaluate the impact of incentives pre/post-renovations, and iii) create benchmarking tables that allow comparisons of a specific building with the existing stock to identify representative buildings for detailed auditing. The readiness of the Estonian building stock could thus be evaluated by linear fitting. All new and renovated buildings are estimated to achieve the zero-energy building (ZEB) status by 2050; remarkably, for some categories, this will occur already in the present decade if the identified linear trends persist. Additionally, we investigated whether the COVID-19 pandemic has affected building stock readiness by comparing pre- and post-2020 ZEB year fit estimations. Contrary to what was expected, the change in working habits affected some building types only marginally, while the national regulations played a prominent role. Detached private houses exhibited a pronounced worsening in readiness, while the educational and entertainment sectors benefited from specific energy labelling remodulations.

In mixed-criticality systems (MCSs), timely handling of I/O operations is a key for the system being successfully implemented and appropriately functioned. The I/O system for an MCS must simultaneously enable different features, including isolation/separation, timing-predictability, performance, scalability, and energy-efficiency. Moreover, such an I/O system also requires to manage I/O resource in an adaptive manner to facilitate efficient yet safe resource sharing among components of different criticality levels. Existing approaches cannot achieve all of these requirements simultaneously. This article presents a mixed-criticality I/O management framework, termed MCS-IOV. MCS-IOV is based on hardware-assisted virtualization, which provides temporal and spatial isolation and prohibits fault propagation with limited extra overhead. MCS-IOV extends a real-time I/O virtualization system, by supporting the concept of mixed criticalities and customized interfaces for schedulers, which offers good timing predictability and scalability. Finally, we introduce an energy management framework for MCS-IOV, ensuring the power-efficiency of the design. The MCS-IOV is the first systematical solution that fulfills all the requirements as a mixed-criticality I/O system. [ABSTRACT FROM AUTHOR]

Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.

The ever-growing energy demand and the CO2 emissions caused by energy production and consumption have become critical concerns worldwide and drive new energy management and consumption schemes. In this regard, energy systems that promote green energy, customer-side participation enabled by the Internet of Things (IoT) technologies, and adaptive consumption mechanisms implemented on advanced communications technologies such as the Network Function Virtualization (NFV) emerge as sustainable and de-carbonized alternatives. On these modern schemes, diverse management algorithmic solutions can be deployed to promote the interaction between generation and consumption sides and optimize the use of available energy either from renewable or non-renewable sources. However, existing literature shows that management solutions considering features such as the dynamic nature of renewable energy generation, prioritization in energy provisioning if needed, and time-shifting capabilities to adapt the workloads to energy availability present a complexity NP-Hard. This condition imposes limits on applicability to a small number of energy demands or time-shifting values. Therefore, faster and less complex adaptive energy management approaches are needed. To meet these requirements, this paper proposes three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs) that, when deployed at the NFV domain, seeks the best possible scheduling of demands that lead to efficient energy utilization. The performance of the algorithmic strategies is validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and processing of demands. Additionally, simulation results reveal that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands.

Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially. [ABSTRACT FROM AUTHOR]

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns that drive the urgent creation of new solutions for energy management. In this regard, by leveraging the massive connectivity provided by emerging 5G communications, this paper proposes a long-term sustainable Demand-Response (DR) architecture for the efficient management of available energy consumption for Internet of Things (IoT) infrastructures. The proposal uses Network Functions Virtualization (NFV) and Software Defined Networking (SDN) technologies as enablers and promotes the primary use of energy from renewable sources. Associated with architecture, this paper presents a novel consumption model conditioned on availability and in which the consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as prioritization of the energy supply and workload scheduling using time-shifting capabilities. The complexity of the proposal is analyzed in order to present an appropriate architectural framework. The energy management solution is modeled as an Integer Linear Programming (ILP) and, to verify the improvements in energy utilization, an algorithmic solution and its evaluation are presented. Finally, open research problems and application scenarios are discussed.

This research work aims to provide detailed feasibility, a techno-economic evaluation, and energy management of stand-alone hybrid photovoltaic-diesel-battery (PV/DG/B) system. The proposed system can be applied to supply a specific load that is far away from the utility grid (UG) connection, and it is located in Minya city, Egypt, as a real case study. The daily required desalinated water is 250 m3. The total brackish water demands are 350-500 m3 and 250-300 m3 of water in summer and winter seasons, respectively. Two different sizes of reverse osmosis (RO) units; RO-250 and RO-500, two energy control dispatch strategies; load following (LF) and cycle charging (CC); two sizes of DG; 5 kW and 10 kW are considered in the case study. The cost of energy, renewable fraction, environmental impact, and breakeven grid extension distance are the main criteria that have been considered to determine the optimal size of PV/DG/B to supply the load demand. HOMER® software is used to perform the simulation and optimization. For this case study, the minimum cost of energy and the minimum total present cost are 0.074 $/kWh and 207676 $, respectively. This is achieved by using a RO-500 unit and a LF dispatch control strategy. The related sizes to the best option of PV/DG/B are 120 kW PV array, 10 kW DG, 64 batteries, and 50 kW converter. A comparison with grid extension and installing stand-alone diesel generation is also carried out. The results of comparison have confirmed that the grid connection is better than all considered options using the RO-250 unit. However, for the RO-500 unit, all options of hybrid PV/DG/B are more economically feasible compared with grid connection, and the best cost-effective option is the one including LF strategy with 10 kW DG. Stand-alone diesel generator produces 119110 kg/year and 117677 kg/year of CO2 respectively for RO-250 and RO-500.

The prosumer market allows prosumers to sell their energy surplus to consumers. The prosumer should offer the amount of energy to sell and its unit price to the contracted consumers while economically operating their system. This paper presents optimal operations and business strategies to maximize the prosumer's benefit by utilizing an energy storage system and ensuring a contract with residential consumers under a progressive pricing policy, where electricity unit price increases with the amount of monthly electricity consumption. By the proposed method, a prosumer under time-of-use pricing scheme stores abundant renewable energy or utility energy at a low price and uses it during a high-price period. Moreover, the proposed optimization can determine the amount of energy and the unit price that the prosumer will offer as a contract in a way that gives consumers strong motivation for the contract; the contract can eventually alleviate consumers' electricity rates by avoiding a high-price zone. For optimization, a quadratic objective problem with quadratic constraints is formulated, and the interior-point algorithm with the Hessian function is used. This study investigates the effectiveness of the proposed method not only under the various penetration rates of renewables but in consideration of uncertainties of renewables and loads. Based on actual field data from Jeju Island of South Korea for 30 days, numerical simulations were performed, and the results indicate that the prosumer's operating costs were reduced by about 12%, simultaneously offering a smaller contract price to the consumer. The Hessian function of the Lagrangian reduced the processing time for the optimization by a maximum of 98.3%. Finally, the ensemble forecast method generating multiple statistical scenarios was tested to address the uncertainty of renewables, showing that the uncertainty had no impact on the contract price and energy.

The aim of this paper is to present a new bidirectional DC-DC linearized converter model for use in power demand and recovery units mainly used in Lightweight Electric Vehicle applications. The model significantly reduces the simulation time of the experiments performed, with up to a 4450-fold decrease in simulation times with respect to the original switched DC-DC topology. The study begins with a literature review of available switched converters, after which the presented topology is selected. The object-oriented modeling language Modelica® is used to implement the converter in the Dymola® modeling environment. Components and base classes from the Modelica Standard Library and VehicleInterfaces library are mainly used for better interoperability. Because of the intensive use of converters in the whole vehicle and the time consumed by the converter simulations due to high frequency commutation, a linearized DC-DC converter model is proposed. Comparison tests are performed between the reference switched models and the proposed linearized models in Dymola® tool to validate the linearized model behaviour. Nearly identical responses are obtained for both models, while simulation times are reduced as much as 1/4450 for the linearized converter. Furthermore, validation tests are carried out between the proposed linearized model in Dymola® and a reference switched model in LTspice® specific purpose simulation package for switched electronic circuits. Excellent agreement in the responses of both models is observed.

Traditionally, the energy management objectives of a fuel cell–battery hybrid electric vehicle (FCBHEV) powertrain system is achieved by using the centralized control strategy, which suffers from poor flexibility, scalability, and reliability. What is worse, the system stability can hardly be guaranteed because of the serious interactions between the fuel cell (FC) as well as the battery converters and motor drive loads. In this article, a decentralized energy management strategy (EMS) based on a composite control scheme that combines mixed droop control and disturbance-observer-based control is proposed to achieve the dynamic load power allocation, extend the service life, improve the energy efficiency, enhance the robustness (i.e., realize the disturbance and uncertainty rejection), and guarantee the global stability of the FCBHEV powertrain system in a decentralized way simultaneously. First, the architecture of an FCBHEV powertrain system is described in detail. Then, analyses on the design and operating principle of the proposed EMS are deeply studied. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 50 kW FCBHEV powertrain system is established using the Typhoon HIL 602 device. The HIL simulation results verified the correctness of the theoretical analysis and effectiveness of the proposed EMS. [ABSTRACT FROM AUTHOR]

Self-sustainability and intelligence will be critical features of the next generation of Internet of Things devices. Power management circuits need to be energy-efficient in multiple power modes, from nW sleep to mW active, and to support energy harvesting (EH) from multiple environmental sources. This article presents an adaptive and firmware-configurable power supply unit that enables intelligent devices to operate in the sub μW range while achieving self-sustainability through EH. The microcontroller-based design allows efficient conversion from batteries, dc, ac environmental sources, and nA quiescent currents. Source and load power points are decoupled with multiple dc–dc converters aiming to supply loads with adaptive voltage scaling and achieving high reliability. Experimental results show the flexibility and efficiency of this approach: the proposed power supply unit achieves a quiescent current of 54 nA and a maximum peak load current of 300 mA, delivered with an end-to-end power efficiency above 85%. [ABSTRACT FROM AUTHOR]

An optimized energy management strategy (EMS) based on a recent coyote optimization algorithm (COA) applied to a hybrid electric power system is proposed in this paper. The proposed hybrid system comprises fuel cell (FC), battery storage bank (BSB) and supercapacitors (SCs). The FC has been selected to be the chief power source to meet the load demand at steady state. Whereas BSB is used as the chief energy buffer and to help the FC during deficit periods and SCs are employed to meet the transient maximum power. The performance of the hybrid electric power system mostly depends on how to distribute the demanded load through different kinds of power sources. Therefore, optimized EMS is highly required to do this job. The key objective of the proposed EMS is to reduce hydrogen consumption by the hybrid system and increase the durability of power sources. To investigate the superiority and validity of COA, a comparison with other approaches is carried based on minimum hydrogen consumption and high energy efficiency. Such methods include external energy maximization strategy (EEMS), particle swarm optimizer (PSO), genetic algorithm (GA), grey wolf optimizer (GWO), grasshopper optimization algorithm (GOA), multi-verse optimizer (MVO), salp swarm algorithm (SSA) and sunflower optimization (SFO). The obtained results confirmed the superiority of the proposed COA. Using COA reduced hydrogen consumption by 38.8% compared to the EEMS method. Based on the minimum hydrogen consumption, the strategies are ranked from the best as following; COA, GWO, SSA, GOA, MVO, GA, PSO, and EEMS.

This paper describes an overview of the optimal energy coordination/management approaches for microgrids. The article presents the smart grid environment in conjunction with their technologies into the applications of a microgrid when the energy coordination aims to create power flow stability between the generation and consumption of the electricity. This energy equilibrium is made regardless of a power grid complexity that can contain diverse load demands and distributed energy resources (DERs), including renewable energy system (RES), energy storage system (ESS), electric vehicle (EV), etc. A microgrid often contains an energy mix system that requires three control levels, namely primary, secondary and tertiary, to optimize the energy cost and behavior of the system operation and exploitation. Based on several DERs, a microgrid can operate in island mode or be connected to the main grid. The energy coordination for both features is to deal with the energy resources uncertainty, the climate impact, to reduce atmospheric pollution deriving from the conventional power grid, and the energy demand growth. Through the smart grid technology, the optimization approaches of this coordination have brought several improvements into the electrical system. Thus, an overview of an intelligent energy management system for microgrid applications is intensively detailed to structure the implementation strategies which aim to coordinate the energy flow of an electrical system optimally.

Recent improvements in shipboard systems focus on the use of new technological systems and the adoption of innovative operating strategies for both increasing energy efficiency and reduce gas emissions. In such a context, this article studies a ship power system, where an energy storage system and a tailored energy management system are applied. It focuses on a detailed model of the on-board power system and points out a scenario of analysis larger than those usually adopted in the literature. The model is carried out with reference to DC, AC and hybrid AC/DC power grid architectures, by means of a power flow approach. The strategy of the energy management system optimally controls the on-board power system integrated with a battery storage device. The strategy aims to minimizes fuel consumption of the prime movers, while satisfying time-varying load demand under different operating scenarios. The procedure, tested on actual operative conditions of a supply vessel, shows feasibility and effectiveness of the power flow approach, giving evidence to the reduction of fuel consumption, while the correct operation of the power system is guaranteed. [ABSTRACT FROM AUTHOR]

With extraordinary growth in the Internet of Things (IoT), the amount of data exchanged between IoT devices is growing at an unprecedented scale. Most of the IoT devices are low-resource devices handling sensitive and confidential data. Conventional encryption methods are inappropriate for lowresource devices. Lightweight block ciphers are used to encrypt data on such devices, as it balances security requirements and energy consumption. The objective of this paper is to explore opportunities to improve performance and optimize energy consumption for cipher designs targeted for low-resource IoT devices. This paper also presents an energy management algorithm to improve IoT survivability against Denial-ofservice attacks in the form of battery exhaustion. We developed a simple and effective model for lightweight cipher performance metrics. Model results were compared and validated with published application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) designs. Using the model, we explored opportunities for performance enhancement in future cipher designs. Our analysis indicates that the optimum energy is achieved when block size is between 48-bit and 96-bit. Also, increasing size of overhead logic from one round to two rounds increases encryption energy-per-bit by 3.4%. Further, the optimum energy is attained when the number of algorithm rounds is 16 or less. Optimum throughput is achieved by implementations with large block sizes and large number of implemented rounds. Next, we present a novel algorithm to manage cipher energy consumption. The algorithm allows low-resource IoT devices to encrypt critical messages during low-energy mode while balancing throughput, energy per bit, and device activity.

This article presents optimal strategies in the home energy management system (HEMS) integrating solar power, energy storage, and vehicle-to-grid (V2G) capability for predetermined scenarios. The proposed system aims to address the demand response schemes, both real-time pricing and emergency load curtailment, V2G mode of operation. In the interruption event, the HEMS operates in the vehicle-to-home mode to ensure supply continuity. The control algorithms are experimentally implemented on the created hardware platform of EV and home batteries, solar power, and household loads with preset priorities. Both ac and dc charging technologies for EVs are considered. The HEMS simultaneously optimizes the scheduling of charging and discharging of EVs and home battery. Results on operation under predetermined scenarios have confirmed the validity of the proposed HEMS. [ABSTRACT FROM AUTHOR]

Taking advantage of the low-cost photovoltaic (PV) energy requires overcoming several process challenges. The Triple intercell bar technology, with current source topology, is a synergic solution. This bar overcomes faulty or open contacts and surpasses the anomaly of metallurgical short circuits. In addition, an extra 10% in sunlight PV energy is possible, thanks to lower current densities dispersions. This requires modulating the day–night process current of the copper cathodes electrowinning facility. The technology ensures bypass of defective contacts and prevention or attenuation of short circuits. Moreover, the technology extends the lifetime of the electrodes and improves the energy efficiency. A concentrated parameter model, using industrial data projects the results. [ABSTRACT FROM AUTHOR]

An energy management information system (EMIS) combines software, hardware, and data to support people in their efforts to manage energy at the process, system, facility, and enterprise level, year after year. A distributed measurement and monitoring system (DMS) is a fundamental part of an EMIS; it is constituted by the meters distributed in the switchboards of the power system. This article suggests a method of assessment of a DMS based on an indicator called level of coverage (LOC). This indicator can evaluate the LOC of a single meter or of a whole system considering the classification in significant energy uses according to the standard ISO 50001. [ABSTRACT FROM AUTHOR]

Accurately predicting the changes in the speed has a significant impact on the quality of the energy management in hybrid vehicles. Many methods for predicting the speed have been proposed in the literature, but few fully consider vehicle dynamics to predict speed changes. To this end, a new method is introduced to predict the vehicle speed and to perform energy management for hybrid vehicles in situations where lateral dynamics plays a significant role. Based on the tire–road friction coefficient and the GPS signal, the maximum cornering speed of the vehicle, in which each tire force does not saturate, is evaluated. Then, the principle of using less friction braking and using more regenerative braking, the vehicle speed prediction controller is designed. In the end, an optimal control method with a new equivalent factor (EF) adaptive algorithm is designed to distribute the torque of the engine and the motor, as well as the shift schedule of the gearbox. A driver-in-the-loop experiment is used to prove that the vehicle installed with the proposed speed prediction controller has an average 29.1% increase in energy efficiency compared to vehicle that do not have speed prediction controller. And, the EF adaptive algorithm keeps the battery SoC at a reasonable interval. [ABSTRACT FROM AUTHOR]

Global competition and reduced profit margins often push modern manufacturing systems toward continuously operating close to their full capacity. This can cause additional stress on the components and even lead to their premature failure, which could in turn result in halt in operation, inventory buildup, potential waste, and possible need for component replacement. Sustainability has often been viewed as utilization of renewable and green energy. However, this paper puts forth an integrated framework for optimal asset and energy management in a manufacturing plant in order to promote sustainable operation. An operation strategy has been devised that minimizes the risk of component failure on the rest of the system, the operation cost, the emissions, as well as material waste due to unwanted postprocess inventory buildup. The problem formulation is nonlinear and mixed-integer in nature, with multiple objectives that at times can be contradictory. Hence, a goal programming approach is used in order to ensure that all objectives are given equal priority. [ABSTRACT FROM PUBLISHER]

To keep pace with the rapid growth of mobile traffic demands, dense deployment of small cells in millimeter wave (mmWave) bands has become a promising candidate for next-generation wireless communication systems. With a greatly increased data rate from huge bandwidth of mmWave communications, energy consumption should be mitigated for higher energy efficiency. Due to content popularity, many content-based mobile applications can be supported by the multicast service. mmWave communications exploit directional antennas to overcome high path loss, and concurrent transmissions can be enabled for better multicast service. On the other hand, device-to-device (D2D) communications in physical proximity should be exploited to improve multicast performance. In this paper, we propose an energy-efficient multicast scheduling scheme, referred to as EMS, which utilizes both D2D communications and concurrent transmissions to achieve high energy efficiency. In EMS, a D2D path planning algorithm establishes multi-hop D2D transmission paths, and a concurrent scheduling algorithm allocates the links on the D2D paths into different pairings. Then, the transmission power of links is adjusted by the power control algorithm. Furthermore, we theoretically analyze the roles of D2D communications and concurrent transmissions in reducing energy consumption. Extensive simulations under various system parameters demonstrate the superior performance of EMS in terms of energy consumption compared with the state-of-the-art schemes. Furthermore, we also investigate the choice of the interference threshold to optimize network performance. [ABSTRACT FROM PUBLISHER]

Electric motors and the drive systems approximately consume half of the global electricity consumption. In the European Union (EU), electric motors and the drive systems are estimated to account for about 70% of all industrial electricity consumption. Therefore, mandatory applications accrue from the legislation of governments or unions like the EU for customs and market surveillance or eco-design requirements. The mandatory applications and the global market size find out the necessity of efficiency measurement. There are several compliance and precompliance efficiency test methods in the literature. The compliance tests should execute reliable, accurate, and reproducible results. The purpose of this paper is to define a simple field method that will be used for measuring and determining in-service efficiency at industrial conditions. The proposed method in this paper is an inexpensive and easy method that would help to energy managers and engineers to make correct decision in replacing inefficient motors with efficient and new ones. In this paper, a novel induction motor-efficiency model function has been proposed based on the International Electrotechnical Commission 60034-2-1 standard equations, and a new field test procedure has been used in efficiency estimation. The new efficiency model function variables and constants are determined from the regression analysis, which depends on 86 tests of 437 different load points’ efficiency data. Furthermore, the proposed field test procedure, equations, and compliance test methods are tested on a sample motor in laboratory, and the results are compared to each other. [ABSTRACT FROM PUBLISHER]

With advanced technology such as the Internet of Things, traditional consumer electronics are progressing not only toward personal devices but also sharable appliances that provide more coordinated management and intelligence. Under these conditions, the concept of consumer electronics as a service (CEaaS) has been introduced. In this paper, a framework for CEaaS is described. Specifically, an architecture and operational technology are suggested for a clustered energy storage system (ESS) that is shared among multiple households as a case of CEaaS. In multi-dwelling units that are common in urban areas, a clustered ESS installed in a shared space is considered because it is difficult to have sufficient space to install and operate an ESS at home. To operate a clustered ESS that is shared and virtually integrated among individual houses, the proposed architecture is designed in two phases: benefit maximization for all households, and benefit rebalancing for each household. Simulations show that clustered ESS operation based on the proposed architecture outperforms a conventional individual ESS operation in various conditions considering the characteristics of the household and appliances. [ABSTRACT FROM PUBLISHER]

Cooperative hybrid automatic retransmission request (C-HARQ) is a simple and effective method for wireless communication. Since its performance largely depends on the selection of a relevant relay, the design of an appropriate relay selection scheme is very important. Although various works have been performed on this topic, most of them did not take into account both the “not memoryless” and multiple retransmission characteristics of C-HARQ, resulting in the loss of achievable performance. In this paper, we propose a novel cooperative relay selection scheme in a distributed manner called transmission number relaying (TNR) to simultaneously reduce the outage probability and increase the energy efficiency by utilizing the features of C-HARQ. The TNR scheme determines a single relay based on the estimated number of transmissions, which is calculated from the channel condition. Using these numbers instead of just the channel condition improves the outage probability and reduces the total consumed energy. The exact probability of the number of transmissions at each relay and the outage probability are analyzed, and the numerical results show that the proposed scheme significantly improves the outage probability and reduces the total consumed energy. In addition, when the number of available relays is small, the proposed scheme achieves high energy efficiency. [ABSTRACT FROM AUTHOR]

Energy-efficient scientific applications require insight into how high-performance computing system features impact the applications' power and performance. This insight results from the development of performance and power models. When used with an earthquake simulation and an aerospace application, a proposed modeling framework reduces energy consumption by up to 48.65 percent and 30.67 percent, respectively. [ABSTRACT FROM PUBLISHER]

Power API--the result of collaboration among national laboratories, universities, and major vendors--provides a range of standardized power management functions, from application-level control and measurement to facility-level accounting, including real-time and historical statistics gathering. Support is already available for Intel and AMD CPUs and standalone measurement devices. [ABSTRACT FROM PUBLISHER]

This paper addresses the optimal battery charging schedule for a long trip. Starting with a fully charged battery, the electric vehicle (EV) must stop at least once for battery charging before reaching its destination. Since the battery lifespan is thoroughly related to its depth-of-discharge and since the charging time can be long, it is therefore useful to provide a feasible battery charging schedule during the long trip. Minimizing a cost function, which includes the charging energy cost, battery degradation, and the charging duration, an optimal charging schedule is proposed and successfully validated with small-pickup EV data. In addition, this method takes into account the possible mass change, as well as wind effect on the predicted energy consumption. A comparative study with the commonly used maximum depleting and charging schedule suggests that the proposed approach is efficient, and it contributes to reducing the overall trip duration while reducing, at the same time, the battery degradation. [ABSTRACT FROM AUTHOR]

Today's smartphones are also fundamentally transforming the traditional understanding of “crowdsourcing” to an emerging type of participatory task-oriented application. It aims to support the so-called “Citizen Science efforts” for knowledge discovery to understand human behavior and measure/evaluate their opinions. To facilitate these scenarios, in this paper, we propose a novel efficient network management framework for participatory crowdsourcing. Specifically, we first formulate the optimization problem and propose a closed-form optimal solution to meet the quality-of-information (QoI) requirements of the task while minimizing the energy consumption variance among participants. We then largely extend the traditional framework of Gur Game for distributed decision making to recommend different levels of information contribution for each participant by merging multiple automaton chains into a single chain with multiple steady states. By modeling the user bidding behaviors, we propose a few incentive-based participant selection schemes to maximize the platform's benefits and meet participants' expectations. We extensively evaluate the proposed schemes under the Massachusetts Institute of Technology (MIT) Social Evolution data set, where both QoI requirements of the request and credit saving are successfully achieved, with a satisfactory level of energy consumption fairness among participants. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to analyze the potential benefits of flywheel energy storage for dc light rail networks, primarily in terms of supply energy reduction, and to present the methods used. The method of analysis is based on train movement and electrical-network load-flow simulation. The results of the analysis indicate potential energy saving of up to 21.6% due to the introduction of the flywheel energy storage. The energy saving effects of receptivity (or energy transfer from one train to another) are also considered. Additional benefits of the flywheel energy storage in terms of voltage drop improvements of 29.8% and a reduction in peak substation power loading of 30.1% are demonstrated in a test case scenario. A novel traction electrical-network load-flow algorithm using modified nodal analysis (MNA) is described in detail. This allows an intuitive representation of network elements such as trains and substations and a direct solution of substation currents. [ABSTRACT FROM PUBLISHER]

Last-level caches (LLCs) play a crucial role in reducing multicore system energy by filtering out expensive accesses to main memory. Cache compression can increase effective LLC capacity and reduce misses. However, previous designs limit compression benefits caused by internal fragmentation, limited tags, and energy-expensive recompaction when a block's size changes. The authors propose decoupled compressed cache (DCC), which uses decoupled superblocks and noncontiguous sub-block allocation to decrease tag overhead without increasing internal fragmentation and to eliminate the need for energy-expensive recompaction. They also demonstrate a practical design based on a recent commercial LLC design. [ABSTRACT FROM AUTHOR]

For many enhanced computing designs, the energy efficiency is counterintuitive and should be viewed more critically. [ABSTRACT FROM PUBLISHER]

This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load data are used to stochastically model the wind generation, photovoltaic generation, and load. Using fuzzy C-Means (FCM) clustering, these data are grouped into 10 clusters of days with similar data points to account for seasonal variations. In order to minimize cost and increase efficiency, we use a GA-based optimization approach together with a two-point estimate method. Minimizing the cost function guarantees minimum PV and wind generation installation as well as storage capacity selection to supply the HVAC load. Different scenarios are examined to evaluate the efficiency of the system with different percentages of load shifting. The maximum capacity of the storage system and excess energy are calculated as the most important indices for energy efficiency assessment. The cumulative distribution functions of these indices are plotted and compared. A smart-grid strategy is developed for matching renewable energy generation (solar and wind) with the HVAC load. [ABSTRACT FROM AUTHOR]

According to the World Commission on Environment and Development, sustainable development allows the population to meet the needs of the present while preserving the environment for future generations to meet their own needs. The integration of renewable, low-polluting, distributed energy resources in the power grid is an important step toward sustainable development. In this article, we propose two architectures, clustered and distributed, for labeling the energy source provided to the smart grid. A label contains the energy amount provided to the smart grid and its sustainability index. Labels from multiple energy sources can be compounded and displayed to end users accordingly with the architecture used to inform how sustainable the production process of the energy being rendered to the consumers in a given area is. The comprehension of how sustainable the energy production is can entail a change of mind in consumers and energy distributors. We also evaluate both architectures according to the the sustainability index accuracy and generated network traffic. [ABSTRACT FROM PUBLISHER]

MapReduce is a distributed computing paradigm widely used for building large-scale data processing applications. When used in cloud environments, MapReduce clusters are dynamically created using virtual machines (VMs) and managed by the cloud provider. In this paper, we study the energy efficiency problem for such MapReduce clouds. We describe a unique spatio-temporal tradeoff that includes efficient spatial fitting of VMs on servers to achieve high utilization of machine resources, as well as balanced temporal fitting of servers with VMs having similar runtimes to ensure a server runs at a high utilization throughout its uptime. We propose VM placement algorithms that explicitly incorporate these tradeoffs. Further, we propose techniques that dynamically scale MapReduce clusters to further improve energy consumption while ensuring that jobs meet or improve their expected runtimes. Our algorithms achieve energy savings over existing placement techniques, and an additional optimization technique further achieves savings while simultaneously improving job performance. [ABSTRACT FROM AUTHOR]

As technology is reaching physical limits, reducing power consumption is a key issue on our path to sustained performance. In this paper, we study fundamental tradeoffs and limits in efficiency (as measured in energy per operation) that can be achieved for an important class of kernels, namely the level-3 Basic Linear Algebra Subprograms (BLAS). It is well-accepted that specialization is the key to efficiency. This paper establishes a baseline by studying GEneral Matrix-matrix Multiplication (GEMM) on a variety of custom and general-purpose CPU and GPU architectures. Our analysis shows that orders of magnitude improvements in efficiency are possible with relatively simple customizations and fine-tuning of memory hierarchy configurations. We argue that these customizations can be generalized to perform other representative linear algebra operations. In addition to exposing the sources of inefficiencies in current CPUs and GPUs, our results show our prototype Linear Algebra Processor (LAP) implementing Double-precision GEMM (DGEMM) can achieve 600 GFLOPS while consuming less than 25 Watts in standard 45 nm technology, which is up to 50\times more energy efficient than cutting-edge CPUs. [ABSTRACT FROM AUTHOR]

Moore's Law improvement in transistor density is driving a rapid increase in the number of cores per processor. DRAM device capacity and energy efficiency are increasing at a slower pace, so the importance of DRAM power is increasing. This problem presents system designers with two nominal options when designing future systems: 1) decrease off-chip memory capacity and bandwidth per core or 2) increase the fraction of system power allocated to main memory. Reducing capacity and bandwidth leads to imbalanced systems with poor processor utilization for noncache-resident applications, so designers have chosen to increase DRAM power budget. This choice has been viable to date, but is fast running into a memory power wall. To address the looming memory power wall problem, we propose a novel iso-power tiered memory architecture that supports 2-3X more memory capacity for the same power budget as traditional designs by aggressively exploiting low-power DRAM modes. We employ two "tiers” of DRAM, a "hot” tier with active DRAM and a "cold” tier in which DRAM is placed in self-refresh mode. The DRAM capacity of each tier is adjusted dynamically based on aggregate workload requirements and the most frequently accessed data are migrated to the "hot” tier. This design allows larger memory capacities at a fixed power budget while mitigating the performance impact of using low-power DRAM modes. We target our solution at server consolidation scenarios where physical memory capacity is typically the primary factor limiting the number of virtual machines a server can support. Using iso-power tiered memory, we can run 3 \times as many virtual machines, achieving a 250 percent improvement in average aggregate performance, compared to a conventional memory design with the same power budget. [ABSTRACT FROM AUTHOR]

In this paper, we define a new subclass of integer linear programming problems called the composition problem. We shall propose efficient algorithms for solving this problem and its variants. Moreover, as an application of the composition problem, those algorithms are applied to solve the P-constrained secure set problem, which is a variation of the secure set problem introduced in [CHECK END OF SENTENCE], on trees. A P-constrained secure set problem is to find a minimum secure set containing a set of \vert P\vert predetermined vertices. [ABSTRACT FROM AUTHOR]

The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture—an architecture customized for the domain of data parallel dense matrix style scientific application where power efficiency is the central focus. PEPSC utilizes a combination of a 2D single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2 W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs. [ABSTRACT FROM AUTHOR]

As we move to exascale machines, both peak power demand and total energy consumption have become prominent challenges. A significant portion of that power and energy consumption is devoted to cooling, which we strive to minimize in this work. We propose a scheme based on a combination of limiting processor temperatures using dynamic voltage and frequency scaling (DVFS) and frequency-aware load balancing that reduces cooling energy consumption and prevents hot spot formation. Our approach is particularly designed for parallel applications, which are typically tightly coupled, and tries to minimize the timing penalty associated with temperature control. This paper describes results from experiments using five different Charm++ and MPI applications with a range of power and utilization profiles. They were run on a 32-node (128-core) cluster with a dedicated air conditioning unit. The scheme is assessed based on three metrics: the ability to control processors' temperature and hence avoid hot spots, minimization of timing penalty, and cooling energy savings. Our results show cooling energy savings of up to 63 percent, with a timing penalty of only 2-23 percent. [ABSTRACT FROM AUTHOR]

Voltage and frequency island (VFI) was recently adopted as an effective energy management technique for multicore processors. For a set of periodic real-time tasks that access shared resources running on a VFI-based multicore system with dynamic voltage and frequency scaling (DVFS) capability, we study both static and dynamic synchronization-aware energy management schemes. First, based on the enhanced MSRP resource access protocol with a suspension mechanism, we devise a synchronization-aware task mapping heuristic for partitioned-EDF scheduling, which assigns tasks that access similar set of resources to the same core to reduce the synchronization overhead and thus improve schedulability. Then, static schemes that assign uniform and different scaled frequencies for tasks on different VFIs are studied. To further exploit dynamic slack, we propose an integrated synchronization-aware slack management framework to appropriately reclaim, preserve, release and steal slack at runtime to slow down the execution of tasks subject to the common voltage/frequency limitation of VFIs and timing/synchronization constraints of tasks. Taking the additional delay due to task synchronization into consideration, the new scheme allocates slack in a fair manner and scales down the execution of both noncritical and critical sections of tasks for more energy savings. Simulation results show that, the synchronization-aware mapping can significantly improve the schedulability of tasks. The energy savings obtained by the static scheme with different frequencies for tasks on different VFIs is close to that of an optimal Integer Nonlinear Programming (INLP) solution. Moreover, compared to the simple extension of existing solutions for uniprocessor systems, our schemes can obtain much better energy savings (up to 40 percent) with comparable DVFS overhead. [ABSTRACT FROM AUTHOR]

Energy consumption of wireless data transmission, a significant part of the overall energy consumption on a mobile device, is context-dependent—it depends on both internal and external contexts, such as application workload and wireless signal strength. In this paper, we propose an event-driven framework that can be used for efficient power management on mobile devices. The framework adapts the behavior of a device component or an application to the changes in contexts, defined as events, according to developer-specified event-condition-action (ECA) rules that describe the power management mechanism. In contrast to previous work, our framework supports complex event processing. By correlating events, complex event processing helps to discover complex events that are relevant to power consumption. Using our framework developers can implement and configure power management applications by editing event specifications and ECA rules through XML-based interfaces. We evaluate this framework with two applications in which the data transmission is adapted to traffic patterns and wireless link quality. These applications can save roughly 12 percent more energy compared to normal operation. [ABSTRACT FROM AUTHOR]

Utility-directed transformations involve changing a design to optimize for given constraints while preserving behavior. These changes are often achieved by techniques such as linear programming or geometric programming. We present a systematic approach composing multiple utility-directed transformations for optimizing and mapping a sequential design onto a customizable parallel computing platform such as a Field-Programmable Gate Array (FPGA). Our aim is to enable automatic design optimization at compile time. Design goals specified by users drive the design transformations. Each utility-directed transformation achieves part of the overall goal, and multiple utility-directed transformations, connected by pattern-directed transformations, are composed to fulfill the overall design requirements. The utility-directed transformations in this work produce performance-optimized designs by exploiting data reuse, MapReduce, and pipelining for the target parallel computing platform. Moreover, it is shown that performing transformations in different orders allows users to trade speed for resources, and design performance for compile time. Several applications are used to evaluate this approach on FPGAs. The system performance of a 64-bit matrix multiplication is shown to improve up to 98 times compared to the original design, in the target hardware platform. [ABSTRACT FROM AUTHOR]

This paper presents low cost techniques for error detection and correction in Ternary Content Addressable Memories (TCAMs). The techniques exploit the inherent redundancy of TCAM cells to allow for protection at lower cost. A fault detection technique with the cost of parity but with about the half probability of silent data corruption is proposed. This technique is then applied at both horizontal and vertical dimensions of the TCAM array, and a low cost error correction scheme is derived. Last, another error correction scheme is proposed, which employs a SECDED ECC of the half complexity, by making use of the TCAM redundancy, without compromising single bit error correction. The proposed schemes come with minimal area, power, and critical path overheads, in comparison with standard schemes, and they are good alternatives for TCAM arrays protection. [ABSTRACT FROM AUTHOR]

Due to increasing circuit densities and data throughput rates, power consumption has become a significant concern in the design and operation of high-performance packet switches. We extend the idea of Dynamic Power Management (DPM) to input queued switches, allowing operators to tradeoff power and delay in a useful way. We frame the problem as a dynamic program and solve a relaxation using techniques from Linear Quadratic Regulation (LQR). This optimal policy is combined with existing, nonpower-aware switch controls to generate two novel scheduling algorithms: 1) LQR Power Aware Maximum Weight Matching (LQR PA MWM) and 2) LQR Power Aware Projective Cone Scheduling (LQR PA PCS). Simulation results suggest that our algorithms result in significant power savings compared to MWM and previous power control schemes with little performance degradation. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a two-layer energy-management system for a fuel cell hybrid electric vehicle (HEV). The first layer (upper layer) deals with the vehicle energy consumption, whereas the second layer (lower layer) deals with the power splitting between the fuel cell and the battery. The upper layer aims at providing the globally optimal energy consumption profile by considering the road-trip information and the vehicle dynamics. This energy profile is independent of the number and type of power sources on the vehicle. Therefore, it can be used to assist the real-time power splitting algorithm implemented into the lower layer. This layer design goal is mainly to share the vehicle power demand between the fuel cell and the battery while minimizing the hydrogen consumption. In addition, the splitting method takes into account the fuel cell efficiency map and the hydrogen/electricity relative pricing while imposing a smooth behavior on the fuel cell. This smooth behavior is desirable to preserve the fuel cell life and reduce the oxygen starvation phenomenon. The proposed energy-management system has been successfully implemented and validated on an HEV test bench. The experiments and simulations using several standard driving cycles suggest that the approach can reduce the hydrogen consumption up to 10% compared to a rule-based method and a depleting-sustaining method while preserving at the same time the battery pack from overdischarging. [ABSTRACT FROM PUBLISHER]

A domestic energy management system provides effective positive behaviour change by offering end users direct and ambient feedback based on their monitored energy consumption and experiences. DEHEMS, as a wide scale domestic energy monitoring and managing system differs from others by enabling real-time and historical electricity monitoring and feedback. However, there is also a requirement to be able to monitor and report domestic gas consumption in order to reason and represent more complete energy feedback information to achieve effect positive behaviour changes. In this paper, we present the gas monitoring system in DEHEMS, that implements automatic retrieval of gas readings. We describe how the system is designed, integrated within the DEHEMS architecture, as well as its implementation and deployment. [ABSTRACT FROM PUBLISHER]

Sensor-censoring schemes have been widely applied to distributed detection to achieve the goal of energy saving in limited-energy wireless sensor networks (WSNs). In the traditional censoring scheme, the sensor transmits data to the fusion center (FC) only when the reliability is beyond a specified threshold, and hereby, energy saving is achieved. To further exploit the energy-efficiency capability of the censoring decision scheme, this paper proposes a new multilevel sensor-censoring scheme. As opposed to our earlier proposed three-region censoring scheme, the number of censoring levels in the proposed scheme is not restricted. A criterion on computing the reliability of the observation in each region is quantitatively determined, which controls the power of the signal transmitted to the FC. When the observation falls within the region with high reliability, the power of the transmitted signal is high, and vice versa. Both soft- and hard-decision fusion rules under the considered multilevel censoring strategy are investigated. For a given fusion rule, the main problem of this work is to minimize the error probability of the global decisions made by the FC by obtaining the best region allocation on the observation, which corresponds to the optimum multilevel censoring regions. The performance of the proposed multilevel censoring scheme is examined in terms of both energy saving and error performance. We compare the multilevel censoring scheme with the conventional scheme, which censors no sensor observations. The results show that the multilevel censoring scheme not only offers us a more flexible design of the censoring strategy but consumes much less energy as well, compared with the conventional scheme when the same error probability constraint is given. In addition, the obtained result shows that the superiority of the multilevel censoring scheme becomes more remarkable when the signal strength of the sensor observations is small. [ABSTRACT FROM PUBLISHER]

In this paper, we exploit the benefits of combining the diversity gains that arise from cooperation, multiple propagation paths, and opportunistic relaying (OR) of multiple users. Our goal is to improve the energy efficiency of the amplify-andforward (AF) single-relay-assisted single-carrier frequency-division multiple-access (SC-FDMA) uplink, where the single relay considered may support a single user or may be shared by multiple users who communicate over dispersive channels subject to large-scale fading. Based on the proposed amalgam of single-tap frequency-domain equalization (FDE) and a diversity-combining-aided receiver that relies on the minimum mean-square error (MMSE) criterion, three different relay selection schemes designed for either single-user or multiuser relaying scenarios are investigated, when combined with source/relay power sharing, which employ imperfect power control. Our results demonstrate that, at a bit error ratio (BER) of 10^-4, the proposed receiver can save 2 dB power by achieving a higher cooperative diversity gain than the conventional receiver. Moreover, a beneficial energy efficiency improvement may be achieved when the cooperative regime operates at Eb/N0 < 0. Most importantly, when the shadowing variance is increased from 4 dB to 8 dB, the energy consumption gain gleaned from our multiuser and multiaccess relay selection schemes may increase to 4 \sim 9, compared with the direct transmission in the absence of shadowing at Eb/N0 = -10 \ \dB. [ABSTRACT FROM PUBLISHER]

Energy efficient routing and power control techniques in wireless ad hoc networks have drawn considerable research interests recently. In this paper, we address the problem of energy efficient reliable routing for wireless ad hoc networks in the presence of unreliable communication links or devices or lossy wireless link layers by integrating the power control techniques into the energy efficient routing. We consider both the case when the link layer implements a perfect reliability and the case when the reliability is implemented through the transport layer, e.g., TCP. We study the energy efficient unicast and multicast when the links are unreliable. Subsequently, we study how to perform power control (thus, controlling the reliability of each communication link) such that the unicast routings use the least power when the communication links are unreliable, while the power used by multicast is close to optimum. Extensive simulations have been conducted to study the power consumption, the end-to-end delay, and the network throughput of our proposed protocols compared with existing protocols. [ABSTRACT FROM AUTHOR]