An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm

2020 ◽  
Vol 29 ◽  
pp. 101488 ◽  
Author(s):  
L.F. Grisales-Noreña ◽  
Oscar Danilo Montoya ◽  
Carlos Andrés Ramos-Paja
Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3268
Author(s):  
Mehdi Dhifli ◽  
Abderezak Lashab ◽  
Josep M. Guerrero ◽  
Abdullah Abusorrah ◽  
Yusuf A. Al-Turki ◽  
...  

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%.


Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 512 ◽  
Author(s):  
Efe Isa Tezde ◽  
Halil Ibrahim Okumus ◽  
Ibrahim Savran

This study presents a new two-stage hybrid optimization algorithm for scheduling the power consumption of households that have distributed energy generation and storage. In the first stage, non-identical home energy management systems (HEMSs) are modeled. HEMS may contain distributed generation systems (DGS) such as PV and wind turbines, distributed storage systems (DSS) such as electric vehicle (EV), and batteries. HEMS organizes the controllable appliances considering user preferences, amount of energy generated/stored and electricity price. A group of optimum consumption schedules for each HEMS is calculated by a Genetic Algorithm (GA). In the second stage, a neighborhood energy management system (NEMS) is established based on Bayesian Game (BG). In this game, HEMSs are players and their pre-determined optimal schedules are their actions. NEMS regulates the total power fluctuations by allowing the energy transfer among households. In the proposed algorithm, HEMS decreases the electricity cost of the users, while NEMS flats the load curve of the neighborhood to prevent overloading of the distribution transformer. The proposed HEMS and NEMS models are implemented from scratch. A survey of 250 participants was conducted to determine user habits. The results of the survey and the proposed system were compared. In conclusion, the proposed hybrid energy management system saves power by up to 25% and decreases cost by 8.7% on average.


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