scholarly journals A Boom Energy Regeneration System of Hybrid Hydraulic Excavator Using Energy Conversion Components

Actuators ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Tri Cuong Do ◽  
Duc Giap Nguyen ◽  
Tri Dung Dang ◽  
Kyoung Kwan Ahn
Keyword(s):  
Management Strategy ◽  
Electrical Energy ◽  
Maximum Energy ◽  
Regeneration System ◽  
Hydraulic Pump ◽  
Energy Management Strategy ◽  
Energy Regeneration ◽  
System A ◽  
Time Optimal ◽  
Novel Design

In this paper, a novel design of an energy regeneration system was proposed for recovering as well as reusing potential energy in a boom cylinder. The proposed system included a hydraulic pump/motor and an electrical motor/generator. When the boom moved down, the energy regeneration components converted the hydraulic energy to electrical energy and stored in a battery. Then, the regenerated energy was reused at subsequent cycles. In addition, an energy management strategy has been designed based on discrete time-optimal control to guarantee position tracking performance and ensure component safety during the operation. To verify the effectiveness of the proposed system, a co-simulation (using MATLAB and AMESim) was carried out. Through the simulation results, the maximum energy regeneration efficiency could achieve up to 44%. Besides, the velocity and position of the boom cylinder achieved good performance with the proposed control strategy.

scholarly journals Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 537
Author(s):  
Rittichai Liemthong ◽  
Chitchai Srithapon ◽  
Prasanta K. Ghosh ◽  
Rongrit Chatthaworn
Keyword(s):  
Energy Management ◽  
Energy Cost ◽  
Management Strategy ◽  
Cost Minimization ◽  
Distribution Network ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Heuristic Optimization ◽  
Energy Management Strategy ◽  
Home Energy Management

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

scholarly journals SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Yuvaraja Teekaraman ◽  
K. A. Ramesh Kumar ◽  
Ramya Kuppusamy ◽  
Amruth Ramesh Thelkar
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Storage System ◽  
Research Work ◽  
Load Sharing ◽  
Renewable Energy Resources ◽  
Energy Management Strategy ◽  
System A ◽  
Load Demand ◽  
Pv Generation

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.

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