Search

Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]

Range-extended electric vehicles (REEVs) are becoming a development trend of new vehicles. Energy management is one of the core problems in REEVs. The structure and control method of the auxiliary power unit (APU) is determined based on the configuration analysis in this paper. An energy management optimization problem is proposed to solve the power distributions of APUs and batteries in the charge-sustaining (CS) stage of REEVs, which are determined by dynamic programming and pseudo-spectral optimal control, respectively. The results show that different limits of the APU power changing rate significantly influence fuel consumption. To obtain the power changing rate of APUs and to evaluate the energy management optimization method of REEVs, a model of the APU control system is built and verified by a platform test; the dynamic response characteristics and control parameters of the APU are obtained by step-changing conditions. Two types of strategies for tracking APU power are proposed for different power changing rates, and the fuel consumption of REEVs is analyzed in four types of driving cycles. The effect on fuel consumption caused by the power changing rate of the APU is verified. [ABSTRACT FROM AUTHOR]

The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters ( e . g . length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one. [ABSTRACT FROM AUTHOR]

This paper explores the impacts of active suspension with energy regeneration capability on the fuel economy of a power split Hybrid Electric Vehicle (HEV). Models of HEV powertrain and suspension are established and, based on their interactions, unified. The impacts of control parameters on the suspension performance are synthetically analyzed. The analyses form a basis for the design of suspension controller. High-level supervisory controller and low-level sub-controllers for HEV powertrain and suspension are designed. Low-level sub-controllers can ensure the performance of each system, whose control parameters are updated by the supervisory controller. The equivalent consumption minimization strategy (ECMS), superior in maintaining charge sustainability, is improved for better application in the proposed system. Simulation results under different conditions demonstrate that both HEV fuel economy and ride comfort are improved. Comparative analyses of various scenarios validate the positive effect of suspension energy regeneration on the energy conservation performance of power split HEV. Meanwhile, the improvement of HEV fuel economy can be highlighted by applying ECMS. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable. [ABSTRACT FROM AUTHOR]

Abstract: Thermal energy storage (TES) is widely recognized as a means to integrate renewable energies into the electricity production mix on the generation side, but its applicability to the demand side is also possible. In recent decades, TES systems have demonstrated a capability to shift electrical loads from high-peak to off-peak hours, so they have the potential to become a powerful instrument in demand-side management programs (DSM). Thermal storage is a technology that ensures energy security, efficiency and environmental quality. Of particular interest are applications where TES systems help manage the mismatch between availability of renewable electricity and the demand for electricity in buildings where hot water, heating and cooling are delivered by heat pumps and air conditioning for example. Thus this paper demonstrates the state of the art of present applications of thermal storage for demand-side management. A particular focus of this work is the attention paid to the characteristics of DSM and their relationship to different thermal storage systems. If TES effectiveness for the abovementioned applications is demonstrated, TES devices have a small percentage of the potential market. Therefore challenges and guidelines for a development plan are suggested. [Copyright &y& Elsevier]

[Display omitted] • LSTM integrated with Robust Local Mean Decomposition for half-hour solar prediction. • RLMD-LSTM benchmarked against RLMD-MARS, RLMD-SVR, RLMD- Persistence. • RLMD-LSTM evaluated at four solar-rich cities in Vietnam. • RLMD-LSTM outperforms comparative models at Vietnam's studied sites. Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments. Traditional forecasting methods find it relatively difficult to obtain a reliable solar energy monitoring system because of the inherent nonlinearities in solar radiation and the related atmospheric input variables to any forecasting system. This paper proposes a new artificial intelligence-based hybrid model by employing the robust version of local mean decomposition (RLMD) and Long Short-term Memory (LSTM) network denoted as RLMD-LSTM. The objective model (i.e., RLMD-LSTM) is built near real-time, half-hourly ground-based solar radiation dataset for the solar rich, metropolitan study sites in Vietnam with all of the forecasting results being benchmarked through classical modelling approaches (i.e., Support Vector Regression SVR, Long Short-term Memory LSTM, Multivariate Adaptive Regression Spline MARS, Persistence) as well as the other alternative hybrid methods (i.e., RLMD-MARS, RLMD-Persistence and RLMD-SVR). Verified by statistical metrics and visual infographics, the present results demonstrate that the proposed model can generate satisfactory predictions, outperforming several counterpart methods. The predictive performance is stable for all study sites that the root-mean-square error remained profoundly lower for RLMD-LSTM (19–20%) compared with RLMD-MARS (20–21%), RLMD-SVR (29–35%), RLMD- Persistence (29–51%), LSTM (25–48%), MARS (21–51%) and SVR (23–85%), Persistence (29–51%). The Legates and McCabe's Index, yielding a value of approximately 0.7988–0.9256 for RLMD-LSTM compared with 0.765–0.8142, 0.4917–0.5711, 0.6900–0.7482, 0.6914–0.7646, 0.4349–0.7170 respectively, for the RLMD-MARS, RLMD-SVR, RLMD-Persistence, LSTM, MARS, SVR, Persistence models, also confirms the outstanding performance of RLMD-LSTM model. Accordingly, the study ascertains that the newly designed approach can be a potential candidate for real-time energy management, renewable energy integration into a power grid and other decisions to optimise the overall system's scheduling and performance. [ABSTRACT FROM AUTHOR]

• Different Energy Management Sytesm (EMS) modeling strategies are compared. • A graphic representation of the operational microgrid configurations is proposed. • Neural Networks, Fuzzy Logic and Support Vectors EMS based model are implemented. • A conventional Rolling Time Horizon EMS is used as benchmark solution. • EMSs operational time has been verified on a Raspberry Pi microcontroller. With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm. Such strategy, known as Rolling Time Horizon (RTH), demonstrated very effective when the supporting prediction system performs well. However, it is featured by high operational times. In this work, different lightweight EMS models synthesized through machine learning algorithms have been compared considering six different simulation scenarios. Results shows that an RTH-based EMS owns the best overall performances. However, in some case studies, also other EMSs show competitive results, especially those based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) trained by clustering, which in one case outperform RTH EMSs, and in other 3 cases (out of 6) yields performances close to RTH EMSs within 5 %. A second contribution concerns the RTH EMS implementation on a small micro-controller, highlighting the high computational effort which can range in the order of minutes. Conversely, the ANFIS EMS shows always almost negligible computational costs (less than one second) and therefore can be used in realistic scenarios on cheap devices at run time. The paper also proposed a novel graphic tool to better represent, observe and analyze microgrid energy flows in each time slot or along the overall considered dataset. [ABSTRACT FROM AUTHOR]