Search

This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion mode and then switch to the charge-sustaining mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the charge-sustaining mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average. [ABSTRACT FROM AUTHOR]

Abstract Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system. [ABSTRACT FROM AUTHOR]

Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub) . This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epochs. [ABSTRACT FROM AUTHOR]

Energy management systems are considered as appropriate instruments to identify and realize energy efficiency potentials in industrial plants. Studies point out a largely untapped potential for energy efficiency in industry through the adoption of systematic energy management. We suggest introducing qualitative and quantitative modelling tools into energy management to facilitate a focused process to set priorities and identify effective measures. This paper describes the case of voestalpine Austria Draht GmbH, an Austrian wire rolling mill. voestalpine Austria Draht GmbH at present is implementing a knowledge based energy management system. Within the work described in this paper, first an overview of energy flows using Sankey diagrams was elaborated. Then a qualitative systems analysis was developed for the plant and a gap analysis done for priority areas. Subsequently physical input–output models for several energy intensive units were prepared. The paper describes the models for the cooling water system and product cooling. This study revealed a significant potential to save electricity by optimizing the control of cooling water pumps along the milling train and to recover heat from the final products. 2 GWh of electricity can be saved annually. The biggest single energy loss in the mill is the heat from air cooling the products on hook conveyors. These heat flows contain about 30–50 % of the energy input for heating up the billets. Following a theoretical analysis of the potential, a demonstration plant was developed and pilot tests were run to characterize the realistic potential of heat recovery on the hook conveyor. Based on the test results, feasibility studies for large scale plants were carried out. As a result, 0.8 GWh of heat can be recovered annually. The method was found useful because it helped to structure the existing information with a reasonable effort mostly by the operators themselves in their work and to build a conceptual model of energy flows and energy efficiency of the total plant. [ABSTRACT FROM AUTHOR]

The regional energy management body has a large amount of regional industrial companies’ energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the characteristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems. [ABSTRACT FROM AUTHOR]

Fuel poverty is a recognized and increasing problem in several European countries. A growing body of literature covers this topic, but dedicated analysis for Portugal are scarce despite the high perception of this condition. This paper contributes to fill this knowledge gap focusing on a European southern city while bringing new datasets and analysis to the assessment of this topic; consumer groups identification and to policy discussion. Daily electricity smart meters' registries were combined with socio-economic data, collected from door-to-door surveys, to understand the extent and the determinants of energy consumption for two contrasting consumer groups (herein called fuel poverty and fuel obesity groups). The analysis is based on the amount and annual profile of electricity consumption and was complemented with building energy simulations for relevant building typologies in those groups, to identify heating and cooling thermal performance gaps. The existence of these gaps allowed confirming and/or discarding the initial hypothesis of the poverty or obesity conditions. Results disclose socio-economic variables, as income, and consumers' behavior as key determinants of electricity consumption. It was identified a severe lack of thermal comfort levels inside households of both groups, either in cooling (98% for fuel poverty and 87% for fuel obesity) and heating seasons (98% for fuel poverty and 94% for fuel obesity). Major conclusion refers that electricity consumption cannot be used alone to segment consumer groups. This assessment may serve to support energy policy measures and instruments targeted to different consumers' groups. For example, distinct campaigns and differentiated incentives may apply to achieve energy efficiency and reduction while keep or improve indoor comfort levels. [ABSTRACT FROM AUTHOR]

This paper provides a real option approach to analyze investment value for photovoltaic power generation under carbon market linkage. The primary purpose is to evaluate photovoltaic power generation under uncertainties from the perspective of power generation enterprises. Uncertainties in investment costs, electricity prices, carbon prices and subsidy payments are considered. In addition, the Market Co-movements of investment costs are considered in this study. Dunhuang is taken as a case study to evaluate the value of photovoltaic power generation through scenario analysis. The result shows that the investment value of photovoltaic power generation is negative. Enterprise should give up the investment or delay investment until mature conditions. Moreover, the investment value can be improved by technical improvement, increasing a certain range of subsidy payments, etc. The research presented would be useful for photovoltaic power generation project evaluation and related decision-marking. [ABSTRACT FROM AUTHOR]

• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [ABSTRACT FROM AUTHOR]

Engine idling of refrigerator trucks during loading and unloading contributes to greenhouse gas emissions due to their increased fuel consumption. This paper proposes a new anti-idling system that uses two sources of power, battery and engine-driven generator, to run the compressor of the refrigeration system. Therefore, idling can be eliminated because the engine is turned OFF and the battery supplies auxiliary power when the vehicle is stopped for loading or unloading. This system also takes advantage of regenerative braking for increased fuel savings. The power management of this system needs to satisfy two requirements: it must minimize fuel consumption in the whole cycle and must ensure that the battery has enough energy for powering the refrigeration system when the engine is OFF. To meet these objectives, a two-level controller is proposed. In the higher level of this controller, a fast dynamic programming technique that utilizes extracted statistical features of drive and duty cycles of a refrigerator truck is used to find suboptimal values of the initial and final SOC of any two consecutive loading/unloading stops. The lower level of the controller employs an adaptive equivalent fuel consumption minimization (A-ECMS) to determine the split ratio of auxiliary power between the generator and battery for each segment with initial and final SOC obtained by the high-level controller. The simulation results confirm that this new system can eliminate idling of refrigerator trucks and reduce their fuel consumption noticeably such that the cost of replacing components is recouped in a short period of time. [ABSTRACT FROM AUTHOR]

This paper shows development of a simple, cheap “Building Energy Management System” (BEMS) to monitor energy consumption, and an application to measure energy consumption, including electricity and gas consumption in buildings. The system uses current transformers to measure electric consumption at each distribution box. Web cameras monitor lighting in each room, as well as air conditioning status by recognizing the movement of a ribbon at the outlet. Web cameras can also monitor gas consumption at the meter through character recognition. The data is sent via network to a computer for monitoring and analysis. As an example, key components of this BEMS were tested to measure building energy consumption of Osaka City University Sugimoto Campus in Japan for a period of 2 years (2013, 2014). The electric consumption of campus as well as the electric and gas consumption for air conditioning use was measured. Web cameras were installed in several rooms in one section of the campus to record lighting and air conditioning use data. The data was analyzed to reveal a correlation between hourly outdoor temperature and hourly electric consumption, with a stronger correlation between hourly outdoor temperature and hourly electric consumption for air conditioning. [ABSTRACT FROM AUTHOR]

In-wheel motors (IWMs) substantially influence the output performance of electric vehicles (EVs). Size design and control issues are two critical factors to be explored. This paper proposes the use of hybridization for integrating dual motors to enhance EV system dynamics. A combined optimal sizing/control approach was developed using the global search method (GSM). The optimal control (torque ratio) of dual motors was first proposed using the nested-structure for-loop program to minimize the consumed power according to various power ratios. The next combined sizing/control developed using the GSM facilitated the search for the optimal control, power ratios, and T – N (Torque–Speed) ratios to minimize the accumulated energy consumed during ECE40 and UDDS driving cycles. The results from simulations of these two cycles indicated that the energy consumption during optimal control improved by 1.23% and 1.40%, 8.34% and 7.16% after optimal sizing, and the combined optimization improved total consumed energy by 15.31% and 15.16%, respectively. These results suggest that employing the novel hybrid IWM powertrains while using the combined sizing/control method can facilitate energy conservation. A real testing platform will be developed in the future. [ABSTRACT FROM AUTHOR]

In cognitive radio networks, it is well known that the cooperative spectrum sensing can overcome damaging effects of fading and shadowing. However, it also increases the amount of energy consumption which is a critical factor in low powered wireless communications. In this paper based on bi-threshold energy detection, we maximize the network throughput such that the energy consumption is below a predefined value and also sufficient protection of primary users against interference is guaranteed. Convex optimization analysis is presented to jointly obtain the optimal values of sensing time and detection thresholds. Simulation results show that the proposed method is very flexible such that a good tradeoff between achievable throughput and energy efficiency can be established, while it often outperforms the conventional sensing method significantly. [ABSTRACT FROM AUTHOR]