scholarly journals Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4672 ◽  
Author(s):  
Van-Hai Bui ◽  
Akhtar Hussain ◽  
Woon-Gyu Lee ◽  
Hak-Man Kim
Keyword(s):  
Energy Management ◽  
Management System ◽  
Wind Farm ◽  
Mixed Integer ◽  
Energy Management System ◽  
Optimization Strategy ◽  
Current Output ◽  
Hybrid Energy ◽  
Stage 1 ◽  
Optimal Set

In this paper, a hybrid energy management system is developed to optimize the operation of a wind farm (WF) by combining centralized and decentralized approaches. A two-stage optimization strategy, including distributed information sharing (stage 1); and centralized optimization (stage 2) is proposed to find out the optimal set-points of wind turbine generators (WTGs) considering grid-code constraints. In stage 1, cluster energy management systems (CEMSs) and transmission system operator (TSO) interact with their neighboring agents to share information using diffusion strategy and then determine the mismatch power amount between the current output power of WF and the required power from TSO. This amount of mismatch power is optimally allocated to all clusters through the CEMSs. In stage 2, a mixed-integer linear programming (MILP)-based optimization model is developed for each CEMS to find out the optimal set-points of WTGs in the corresponding cluster. The CEMSs are responsible for ensuring the operation of WF in accordance with the requirements of TSO (i.e., grid-code constraints) and also minimizing the power deviation for the set-points of WTGs in each cluster. The minimization of power deviation helps to reduce the internal power fluctuations inside each cluster. Finally, to evaluate the effectiveness of the proposed method, several case studies are analyzed in the simulations section for operation of a WF with 20 WTGs in four different clusters.

scholarly journals Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3268
Author(s):  
Mehdi Dhifli ◽  
Abderezak Lashab ◽  
Josep M. Guerrero ◽  
Abdullah Abusorrah ◽  
Yusuf A. Al-Turki ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Optimal Operation ◽  
Green Energy ◽  
Energy Management System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Ac Microgrid

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%.

An Online Energy Management System for a Grid-Connected Hybrid Energy Source

2018 ◽  
Vol 6 (4) ◽  
pp. 2015-2030 ◽  
Author(s):  
Mohamed S. Taha ◽  
Hussein Hassan Abdeltawab ◽  
Yasser Abdel-Rady I. Mohamed
Keyword(s):  
Energy Source ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Hybrid Energy

scholarly journals Mixed integer smart off-grid home energy management system

2021 ◽  
Vol 7 ◽  
pp. 9094-9107
Author(s):  
Rasha Elazab ◽  
Omar Saif ◽  
Amr M.A. Amin Metwally ◽  
Mohamed Daowd
Keyword(s):  
Energy Management ◽  
Management System ◽  
Mixed Integer ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

scholarly journals Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Tzu-Chia Chen ◽  
Fouad Jameel Ibrahim Alazzawi ◽  
John William Grimaldo Guerrero ◽  
Paitoon Chetthamrongchai ◽  
Aleksei Dorofeev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage Systems ◽  
Energy Management System ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Machine Learning Methods

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

Fuzzy Logic Energy Management System in Islanded Hybrid Energy Generation Microgrid

2018 ◽  
Author(s):  
Bruna Peixoto De Souza ◽  
Vetor Seger Zeni ◽  
Everthon Taghori Sica ◽  
Cesare Quinteiro Pica ◽  
Mauricio Varassim Hernandes
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Management System ◽  
Energy Generation ◽  
Energy Management System ◽  
Hybrid Energy

scholarly journals Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1096 ◽  
Author(s):  
Gi-Ho Lee ◽  
Jae-Young Park ◽  
Seung-Jun Ham ◽  
Young-Jin Kim
Keyword(s):  
Comparative Study ◽  
Energy Management ◽  
Management System ◽  
Economic Dispatch ◽  
Optimal Schedule ◽  
Mixed Integer ◽  
Energy Management System ◽  
Two Stage ◽  
Microgrid Energy Management ◽  
Optimization Solvers

A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization solver. This paper presents a comparative study of optimization solvers to investigate their suitability for ED implementation in the MEMS. Four optimization solvers, including commercial as well as open-source-based ones, were compared in terms of their computational capability and optimization results for ED. Two-stage scheduling was applied for the ED strategy, whereby a mixed-integer programming problem was solved to yield the optimal operation schedule of battery-based energy storage systems. In the first stage, the optimal schedule is identified one day before the operating day; in the second stage, the optimal schedule is updated every 5 min during actual operation to compensate for operational uncertainties. A modularized programming strategy was also introduced to allow for a comparison between the optimization solvers and efficient writing of codes. Comparative simulation case studies were conducted on three test-bed MGs to evaluate the optimization results and computation times of the compared optimization solvers.

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