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نتیجه جستجو - ذخیره انرژی ترکیبی

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
تجزیه و تحلیل جایگزین قدرت تطبیقی مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستم های ذخیره سازی انرژی ترکیبی مستقل با توجه به عدم اطمینان-2020
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint.
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
مقاله انگلیسی
2 Quantification of energy flexibility of residential net-zero-energy buildings involved with dynamic operations of hybrid energy storages and diversified energy conversion strategies
تعیین انعطاف پذیری انرژی ساختمانهای مسکونی خالص بدون انرژی درگیر با فعالیت های پویای ذخیره انرژی هیبریدی و استراتژی های تبدیل انرژی متنوع-2020
For immediate response and sufficient reaction to building energy demands by avoiding excessive production, increasing stability of energy networks, and minimising energy congestion, building energy flexibility has potentials to enhance the resilience of hybrid grids to fluctuations in energy demands of multi-energy systems. However, few studies focused on dealing with the complexity of energy flexibility quantification regarding diversified energy conversions and hybrid energy storages. Moreover, few studies focused on the exploitation of energy potential provided by building energy systems itself through flexible energy control strategy. In this study, a generic methodology was proposed to characterise energy flexibility of diversified energy systems. A series of new flexibility indicators are proposed such as flexible power, flexible electricity, on-site flexible electric load fraction, on-site flexible surplus renewable fraction ratio, and flexibility factors. Technical solutions are proposed to improve the energy flexibility using integrated solutions of energy conversion, energy storage, and rule-based control strategies. Case studies with different control strategies (i.e. ‘‘battery-todemand control strategy’’ and ‘‘renewable-to-demand control strategy’’) are studied for the technical viability assessment. This study provides a generic methodology to characterise the energy flexibility regarding sophisticated building energy systems, and a rule-based control strategy for the flexibility enhancement, which will be effective in the promotion of energy flexible buildings.
Keywords: Energy flexible building | Energy flexibility | Renewable energy | Building energy management | Hybrid energy storage
مقاله انگلیسی
3 A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity
یک استراتژی مدیریت انرژی سلسله مراتبی برای ذخیره انرژی هیبریدی از طریق اتصال وسیله نقلیه به ابر-2020
In order to enhance energy efficiency and improve system performance, the road mobility system requires more preview information and advanced methods. This paper proposes a novel hierarchical optimal energy management strategy for electric buses with a battery/ultracapacitor hybrid energy storage system, to optimal split the power and reduce the battery life degradation. This method is based on vehicle-to-cloud connectivity. In the cloud platform, an optimal energy management strategy is developed using dynamic programming, where the battery degradation cost and the electric cost are taken into consideration. In the vehicle level, a model predictive control is developed to deal with the uncertainties, reduce the energy losses, and handle the system constraints. The cost function of the model predictive control includes the ultracapacitor state of charge planning and energy losses. In order to evaluate the effectiveness of the proposed method, a rule-based energy management strategy is developed as the baseline approach. The China bus driving cycle and other six real bus driving cycles recorded in China are used to validate the robustness of the proposed method. To be more realistic, the random uncertainties up to 20% are included in all driving cycles. Furthermore, the time delay and packet losses in communication are also considered. Simulation results show that the proposed method significantly outperforms the rule-based method, and the average improvement could be over 40% in the studied driving cycles.
Keywords: Vehicle-to-cloud connectivity | Energy management | Model predictive control | Real-time optimization | Hybrid energy storage
مقاله انگلیسی
4 Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
تقویت قدرت مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستمهای ذخیره انرژی ترکیبی مستقل با توجه به عدم اطمینان-2020
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
مقاله انگلیسی
5 Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship
بهینه سازی اندازه و کنترل فرکانس در سیستم ذخیره انرژی هیبریدی باتری / ابررساننده برای حامل سوخت-2020
The fuel cell is generally coupled with the hybrid energy storage system (HESS) to improve power system dynamic performance and prolong the fuel cell lifetime. Therefore, the sizing of HESS and design of energy management strategy (EMS) have already become key research points. Based on support vector machine and frequency control, a novel EMS is proposed. As the sizing of HESS and the design of energy management strategy have a strong inner link, a multi-objective optimization method for the HESS and EMS is proposed. After that, simulations are used to compare the performance of the optimal hybrid power system. Compared with the different hybrid power system structures, the optimal HESS can meet power demand and reduce the cost of the energy storage device. Compared with the rule-based energy management strategy, the energy consumption of the optimal hybrid power system reduces 5.4%, and improves power quality and prolongs the device life. The results indicate that the proposed method can achieve excellent performance and is easily applied.
Keywords: Hybrid ship | Energy management strategy | Hybrid energy storage system | Whale optimization algorithm (WOA) | Support vector machine (SVM)
مقاله انگلیسی
6 Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition
استراتژی هوشمند مدیریت انرژی سیستم ذخیره انرژی هیبریدی برای وسایل نقلیه الکتریکی مبتنی بر شناخت الگوی رانندگی-2020
To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control energy management strategy based on driving pattern recognition (DPR) is proposed in view of the fact that driving cycle greatly affects the performance of EMS. The DPR uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns. After recognition results are obtained, an adaptive wavelet transform is employed to allocate the high frequency components of power demand to supercapacitor which contains transient power and rapid variations, while the low frequency components are distributed to battery accordingly. The use of fuzzy logic control is to maintain the SOC of supercapacitor within desired level. The simulation results indicate that the proposed control strategy can effectively decrease the maximum charge/discharge current of battery by 58.2%, and improve the battery lifetime by 6.16% and the vehicle endurance range by 11.06% compared with conventional control strategies. Further demonstrate the advantage of hybrid energy storage system and the presented energy management strategy.
Keywords: Hybrid energy storage system | Driving patterns recognition | Transient power | Adaptive wavelet transform | Fuzzy logic control
مقاله انگلیسی
7 A novel optimal energy management strategy for offshore wind/ marine current/battery/ultracapacitor hybrid renewable energy system
رویکرد استراتژی مدیریت انرژی بهینه برای بادی دریایی / دریایی فعلی / باتری / فراخازنی ترکیبی سیستم انرژی تجدید پذیر-2020
Climate change and high energy demand have significantly increased the need for renewable energy sources (RES). Marine and ocean energy sources draw attention through their high energy potential. Offshore wind and marine current energy is an attractive RES with great potential. The wind and current energy in the marine produces an intermittent and unstable power by nature. Energy storage systems are the most effective solution to minimize power fluctuations in the system and to ensure stable energy demand. This paper presents a novel optimal energy management strategy (NOEMS) for optimal power flow control of the offshore wind/marine current/battery/ultracapacitor hybrid power generation system and for the most efficient harvesting of hybrid renewable energy system (HRES). The proposed NOEMS algorithm calculates as real time the amount of power generated by the HRES and demanded by the load. In this study, nine different dynamic operation modes were considered. Experimental results have shown that the battery and ultracapacitor support to the HRES. In this study, the dynamic behavior of the NOEMS algorithm was investigated by performing a sudden load test from 18 W to 30 W. The NOEMS algorithm shows that the system can minimize power loss, voltage fluctuation, control the charge/ discharge status of the battery and ultracapacitor. The proposed algorithm continuously shifts the required power over the hybrid energy storage system to provide the load demand continuously.
Keywords: Offshore wind | Marine current | Battery | Ultracapacitor | Hybrid energy storage | Optimal energy management
مقاله انگلیسی
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