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[en] Although the meaning of energy efficiency is clear, different definitions exist and important issues relating to its implementation still need to be addressed. It is now recognised that complicating factors – such as complex industrial sites and energy flows, multiple products and fuels, and the influence of production rate on energy efficiency – render it necessary to adopt a structured framework to define and measure energy efficiency more precisely. In this paper, a methodology is proposed to build such a framework. The whole energy system of a site is represented using a single matrix equation, which expresses the relationship between imported energies and energy drivers. The elements of the matrix are the specific energy consumptions of each single process. Mathematical process modelling, through statistical analysis of energy consumption data, is used to quantify the specific energy consumption as a function of the output. The results of this structured approach are relevant for energy benchmarking, budgeting and targeting purposes. Furthermore, this approach is suitable for implementation in an energy management system standard (e.g. EN 16001, ISO 50001) or LCA standard (e.g. ISO 14044). Glass and cast iron melting processes are presented in order to illustrate the application of the method. -- Highlights: ► A structured framework for energy efficiency in industrial processes is proposed. ► Two energy efficiency indicators are revised to take into account a variable output. ► The whole energy system of a factory can be represented by a single matrix equation. ► Mathematical modelling is used to characterise the energy consumption of a process. ► The results are relevant for energy benchmarking, budgeting and energy targeting.
[en] In this paper a multi-objective genetic algorithm is used to solve a multi-objective model to optimize the time allocation of domestic loads within a planning period of 36 h, in a smart grid context. The management of controllable domestic loads is aimed at minimizing the electricity bill and the end-user’s dissatisfaction concerning two different aspects: the preferred time slots for load operation and the risk of interruption of the energy supply. The genetic algorithm is similar to the Elitist NSGA-II (Nondominated Sorting Genetic Algorithm II), in which some changes have been introduced to adapt it to the physical characteristics of the load scheduling problem and improve usability of results. The mathematical model explicitly considers economical, technical, quality of service and comfort aspects. Illustrative results are presented and the characteristics of different solutions are analyzed. - Highlights: • A genetic algorithm similar to the NSGA-II is used to solve a multi-objective model. • The optimized time allocation of domestic loads in a smart grid context is achieved. • A variable preference profile for the operation of the managed loads is included. • A safety margin is used to account for the quality of the energy services provided. • A non-dominated front with the solutions in the two-objective space is obtained
[en] This design is intended to revive and utilize fundamental principles of tall buildings into a modern design of an 80-story residential tower in Doha. The main goal in this design is to create an innovative and next generation sustainable tower design specifically for the Middle East by taking advantage of cutting-edge technologies while respecting the traditional way of living that reflects the cultural roots. The design utilizes a vertical exterior cable system which is used as a shading device and contributes significantly to the structural lateral load resistance. -- Highlights: → Linguistically paper has been checked and marked up with blue color as can be seen on marked-up manuscript. → All grammar corrections from reviewers have been made on manuscript with red colors. → On page 6 paragraphs has been added to explain e-QUEST basics. → References are being updated. → Fonts in have been changed.
[en] An energy management system for stand-alone microgrid composed of diesel generators, wind turbine generator, biomass generator and an ESS (energy storage system) is proposed in this paper. Different operation objectives are achieved by a hierarchical control structure with different time scales. Firstly, the optimal schedules of the diesel generators, wind turbine generator, biomass generator and ESS are determined fifteen minutes ahead according to the super short-term forecast of load and wind speed in the optimal scheduling layer. Comprehensive analysis which takes the uncertainty of load and wind speed into account is conducted in this layer to minimize the operation cost of the system and ensure a desirable range of the state of charge of the ESS. Secondly, the operation points of each unit are regulated dynamically to guarantee real-time power balance and safety range of diesel generation in the real-time control layer, based on which the response capability when suffering significant forecast deviation and other emergency issues, e.g. sudden load-up can be improved. Finally, the effectiveness of the proposed energy management strategy is verified on an RT-Lab based real-time simulation platform, and the economic performances with different types of ESS are analyzed as well. - Highlights: • A hierarchical control strategy is proposed for a stand-alone microgrid. • The uncertainties of load and wind speed have been considered. • Better economic performance and high reliability of the system can be achieved. • The influences of different energy storage systems have been analyzed.
[en] In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a multi-stage context. Moreover, it can reflect dynamic decisions for facility-capacity expansion during the planning horizon. The developed model is applied to a case of planning GHG-emission mitigation, demonstrating that IFSP is applicable to reflecting complexities of multi-uncertainty, dynamic and interactive energy management systems, and capable of addressing the problem of GHG-emission reduction. A number of scenarios corresponding to different GHG-emission mitigation levels are examined; the results suggest that reasonable solutions have been generated. They can be used for generating plans for energy resource/electricity allocation and capacity expansion and help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.
[en] Energy management systems are highly complicated with greenhouse-gas emission reduction issues and a variety of social, economic, political, environmental and technical factors. To address such complexities, municipal energy systems planning models are desired as they can take account of these factors and their interactions within municipal energy management systems. This research is to develop an interval-parameter two-stage stochastic municipal energy systems planning model (ITS-MEM) for supporting decisions of energy systems planning and GHG (greenhouse gases) emission management at a municipal level. ITS-MEM is then applied to a case study. The results indicated that the developed model was capable of supporting municipal energy systems planning and environmental management under uncertainty. Solutions of ITS-MEM would provide an effective linkage between the pre-regulated environmental policies (GHG-emission reduction targets) and the associated economic implications (GHG-emission credit trading).
[en] To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12%, and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning. - Highlights: • Therblig-embedded value stream mapping is proposed for energy management of machining process. • Current state map and future state map of TVSM are established for energy saving. • Machining more material with less energy without lowering the machining quality. • Total energy demand is reduced by 7.65% through implementing the proposed method. • Energy efficiency of value added activity/Therblig are improved by 4.95% and 1.58%, respectively.
[en] Microgrids facilitate optimum utilization of distributed renewable energy, provides better local energy supply, and reduces transmission loss and greenhouse gas emission. Because the uncertainty in energy demand affects the energy demand and supply system, the aim of this research is to develop a stochastic optimization and its market equilibrium for microgrids in the electricity market. Therefore, a two-stage stochastic programming model for microgrids and the market competition model are derived in this paper. In the stochastic model, energy demand and supply uncertainties are considered. Furthermore, a case study of the stochastic model is conducted to simulate the uncertainties on the INER microgrids in Taiwanese market. The optimal investment of the generators and batteries installation and operating strategies are determined under energy demand and supply uncertainties for the INER microgrids. The results show optimal investment and operating strategies for the current INER microgrids are also determined by the proposed two-stage stochastic model in the market. In addition, trade-off between the battery capacity and microgrids performance is investigated. Battery usage and power trading between the microgrids and main grid systems are the functions of battery capacity. - Highlights: • A two-stage stochastic programming model is developed for microgrids. • Market equilibrium analysis of microgrids is conducted. • A case study of the stochastic model is conducted for INER microgrids.
[en] This paper presents an advanced Real-Time Energy Management System (RT-EMS) for Microgrid (MG) systems. The proposed strategy of RT-EMS capitalizes on the power of Genetic Algorithms (GAs) to minimize the energy cost and carbon dioxide emissions while maximizing the power of the available renewable energy resources. MATLAB-dSPACE Real-Time Interface Libraries (MLIB/MTRACE) are used as new tools to run the optimization code in Real-Time Operation (RTO). The communication system is developed based on ZigBee communication network which is designed to work in harsh radio environment where the control system is developed based on Advanced Lead-Lag Compensator (ALLC) which its parameters are tuned online to achieve fast convergence and good tracking response. The proposed RT-EMS along with its control and communication systems is experimentally tested to validate the results obtained from the optimization algorithm in a real MG testbed. The simulation and experimental results using real-world data highlight the effectiveness of the proposed RT-EMS for MGs applications. - Highlights: • Real-time energy management system of a typical MG is developed, and analyzed. • RT-EMS considered the nonlinear cost function and emission constraints. • MLIB/MTRACE libraries in dSPACE are used as new tools to run the optimization code. • The communication system is developed based on a Zigbee communication network. • Control system parameters are tuned online to achieve good tracking response.
[en] Barriers to energy efficiency have been extensively discussed in the energy literature, but little is known about positive drivers. This paper investigates the role of top managers and more specifically of top operations managers on the adoption of energy-efficiency practices, based on 5779 energy efficiency recommendations made to 752 small and medium-sized manufacturing firms under the US Department of Energy's IACs (Industrial Assessment Centers) Program, through which teams of students and faculty from engineering schools provide free energy assessments. Top operations managers possess knowledge of production processes, for maximizing the effective manufacture and distribution of goods. We find that their involvement significantly increases the adoption of energy-efficiency initiatives, while involvement of general top managers without an operational role has little or no effect. Involvement of top operations managers increases the percentage of recommended energy savings that are implemented by 13.4% on average and increases the probability of adoption of more disruptive individual recommendations related to process and equipment change from 31% to 44%. Our findings imply that, in order to advance energy efficiency in SMEs (Small and Medium Enterprises), it may be advisable to target managers who are sufficiently senior but still in a clearly operationally-focused position. - Highlights: • We examine how top managers influence the adoption of energy-efficiency practices. • Top operations managers implement 13.4% more of recommended savings. • Involvement of top managers without an operational role has little or no effect. • Top operations managers enhance adoption of recommendations that are disruptive